Adhesive dispensing system and method

Abstract

Apparatus, systems (400) and methods are described for predicting parameters of a dispensing device system, dispensing a dispenseable material, and calibrating the dispensing device system. The apparatus and systems can use machine learning algorithms based on environmental variables and other factors in systems in which the adhesive dispensing device (100) is used. Additionally, the apparatus and systems can adjust operating parameters of the dispensing device system based on at least one process parameter. Additionally, the apparatus and systems can provide one or more settings for one or more dispensing device components based on a calibration model. The algorithms (412) can operate on a remote or local software and control system, or as part of an edge computing system or Internet of Things (IoT) system.

Description

[Background technology]

[0001] A system for dispensing adhesives typically includes an inlet or internal area that holds the adhesive and an outlet or tip from which the adhesive is dispensed onto a surface. The adhesive flow rate can be directly controlled using a metering system to meet the needs of downstream manufacturing processes. However, systems that actively control the flow rate can be overly complex and expensive for most users. Indirectly, calibration curves can be used to provide or recommend settings based on various variables such as air pressure, pump settings, application time, total dispensing volume, or other variables. However, such calibration curves can become overly complex when considering the number of adhesives, fluctuating temperatures, and chemical reactions that may occur throughout the dispensing process. Other systems with neural networks can learn or predict settings, but these require hundreds or thousands of calibration points to be useful. Therefore, there is a general need to more accurately predict dispensing device settings and other dispensing parameters in a timely and cost-effective manner. [Overview of the Initiative]

[0002] In one embodiment, the disclosure provides an apparatus having a memory for storing data indicating at least one parameter of an adhesive dispensing system. The apparatus further comprises a processor coupled to the memory. The processor is configured to take in its at least one parameter. The processor is further configured to determine, based on its at least one parameter, a value for an operating parameter of the adhesive dispensing system to achieve a flow rate of adhesive in the adhesive dispensing system. The processor is further configured to provide its operating parameter to the adhesive dispensing system.

[0003] At least one of these parameters may relate to the viscosity of the adhesive in the adhesive dispensing system. The operating parameter may include the driving force pressure for the adhesive dispensing system. The processor can be configured to determine the driving force pressure according to a certain relationship between pressure and viscosity μ, based on the viscosity of the adhesive and parameters specific to the adhesive dispensing system. This relationship may include a constant determined based on a non-neural network machine learning algorithm. The relationship may also include a constant determined based on a hybrid algorithm consisting of a neural network component and a non-neural network machine learning algorithm. Similar methods and systems are also described.

[0004] In another embodiment, the disclosure describes a dispensing device system comprising one or more dispensing device components and a processor operably coupled to one or more of the dispensing device components. The one or more dispensing device components include one or more sensors for providing at least one process parameter of a dispensable material. The one or more sensors include a temperature sensor, which includes a probe positioned in the fluid path of the dispensable material and configured to sense the temperature of the dispensable material. The processor is configured to receive at least one parameter related to the dispensing device system, and based on that at least one parameter, to determine and provide an operating parameter of the dispensing device system that achieves a flow rate of the dispensable material in the dispensing device system. The processor is further configured to receive the temperature of the dispensable material from the temperature sensor, and to adjust the value of the operating parameter of the dispensing device system based on the sensed temperature and provide the adjusted operating parameter.

[0005] In another aspect, the disclosure describes a method for dispensing a dispensable material using a dispensing device system. The method includes receiving at least one parameter related to the dispensing device system, determining a value for an operating parameter of the dispensing device system to achieve a flow rate of the dispensable material in the dispensing device system based on that at least one parameter, and providing that operating parameter, receiving at least one process parameter including a sensed temperature of the dispensable material, adjusting a value for the operating parameter of the dispensing device system based on the sensed temperature, and providing the adjusted operating parameter.

[0006] In another aspect, the disclosure describes a dispensing device system comprising one or more dispensing device components configured to provide dispensable material, and a processor operably coupled to the one or more dispensing device components. The processor is configured to receive a plurality of calibration data points of the dispensing device system and one or more parameters of the one or more dispensing device components. The plurality of calibration data points are based on a plurality of dispensing samples of the dispensable material. The processor is further configured to select one or more predetermined models based on the one or more parameters of those dispensing device components, to determine a calibration model based on the plurality of calibration data points and the one or more models, and to adjust one or more settings of the one or more dispensing device components based on the calibration model.

[0007] In another aspect, the Disclosure describes a method for calibrating a dispensing device system. The method includes receiving a plurality of calibration data points of a dispensing device system, a plurality of configuration data points based on a plurality of dispensing samples of a dispensable material, and one or more parameters of the dispensing device components of the calibration system; selecting one or more predetermined models based on the one or more parameters of those dispensing device components; determining a calibration model based on the plurality of calibration data points and the one or more predetermined models; and providing one or more settings for the dispensing device system based on the calibration model.

[0008] The above summary of this disclosure is not intended to describe each of the disclosed embodiments or all implementations of this disclosure. The following description more specifically illustrates exemplary embodiments. Throughout this application, guidance is provided through the enumeration of examples, which can be used in various combinations. In each example, the enumerated items serve only as representative groups and should not be construed as an exclusive enumeration. Accordingly, the scope of this disclosure should not be limited to the specific exemplary structures described herein, but extends to at least the structures described by the language of the claims and their equivalents. Any of the elements explicitly listed as substitutes herein may be expressly included in or excluded from the claims in any combination as desired. Various theories and possible mechanisms may be considered herein, but in no case shall such considerations limit the subject matter of the claims. [Brief explanation of the drawing]

[0009] [Figure 1] This is a block diagram of an adhesive dispensing device that can implement one embodiment of the device. [Figure 2] This figure shows a linear calibration curve for determining the flow rate of an adhesive dispensing device. [Figure 3] This figure shows a nonlinear calibration curve used to determine the flow rate of an adhesive dispensing device. [Figure 4] This figure shows a system for predicting flow rate adjustment for multiple variables, according to several embodiments. [Figure 5A] This figure shows examples of graphical user interfaces (GUIs) according to several embodiments. [Figure 5B] This figure shows examples of GUIs for manual input of calibration data according to several embodiments. [Figure 6] This is a flowchart illustrating a method for predicting the pressure of an adhesive dispensing device according to several embodiments. [Figure 7] This figure shows a comparison of the performance of neural network-based models and machine learning models used in several embodiments. [Figure 8] This figure shows a computing node according to several embodiments. [Figure 9] This figure shows further details of edge computing nodes according to several embodiments. [Figure 10] This is a schematic diagram of a feedback sensor according to several embodiments. [Figure 11] This is a flowchart illustrating a method for calibrating a dispensing device system according to several embodiments. [Figure 12] This is a flowchart for dispensing dispensable material according to several embodiments. [Modes for carrying out the invention]

[0010] An adhesive dispenser, as part of a manufacturing process, delivers liquid adhesive to a surface or substrate through a tip or nozzle. Figure 1 is a block diagram of an adhesive dispenser 100. The adhesive dispenser is configured to use a drive force 102 to deliver liquid adhesive from a liquid adhesive source 104 to a dispensing component 106, allowing the liquid adhesive to be dispensed on demand using the dispensing component 106. The operator can use various settings or parameters in the adhesive dispenser, including dispensing the adhesive at a desired flow rate using a controller 108 or 110. These settings or parameters may vary depending on several factors, and it may be difficult to predict an effective setting for any particular combination of factors.

[0011] If the viscosity of the adhesive used or other identification information is provided, the calibration curve can be used to predict or recommend the settings of the adhesive dispensing device 100. FIG. 2 shows a linear calibration curve 200. In the curve example 200, the mass of the adhesive dispensed per unit time can be specified based on the viscosity of the adhesive. The dispensed mass varies depending on the pressure of the driving force 102 and the viscosity of the adhesive. The dispensed amount can be predicted by a linear curve. For example, as seen in curve 202, when a low pressure is applied by the controller 108 of the driving force 102, the mass of the adhesive dispensed decreases as the viscosity increases.

[0012] However, the measured value of the actually dispensed mass may not exactly match such a curve. For example, as seen in curve 202, the actual dispensed mass indicated by the measured value 204 is above curve 202. Another measured value 206 may be closer to the predicted linear curve 208. Therefore, what is obtained by a linear fit is only a rough prediction.

[0013] To control the commands for applying the adhesive, other systems based on neural networks can also be used. However, for a neural network-based system to be practical, it requires hundreds or thousands of calibration points and can only control adhesive dispensing after extensive learning.

[0014] Adhesive Dispensing Systems and Algorithms To address these and other complexities, systems, methods, and devices according to various embodiments use machine learning to predict dispensing device settings or other parameters for accurate and adaptive adhesive dispensing that functions well as needed by the operator in downstream manufacturing processes. The prediction can be based on any data related to the adhesive or the adhesive dispensing process. FIG. 4 shows a system 400 for predicting flow rate adjustment for multiple variables according to some embodiments.

[0015] Referring to FIG. 4, system 400 can include data component 402. Data component 402 can include inputs received from human machine interface 404, such as user input for example. Further, data component 402 can include outputs provided for display to human machine interface 404. Human machine interface 404 can provide inputs to and receive outputs from dispensing device component 406. In addition, human machine interface 404 can provide data including visual indicators and audio indicators to human operator 408 and receive inputs such as keyboard input from human operator 408. Human operator 408 can interact with dispensing device component 406, for example, by changing settings or parameters of dispensing device component 406.

[0016] Data component 402 can include sensor data 410. Sensor data 410 can include, for example, rheological data regarding calibration liquid and dispensed adhesive, data regarding production of adhesive including in-plant measurements, environmental parameters or conditions (provided by sensor 428), and lot information, cartridge information, or other information that can identify batches of adhesive. Data component 402 can include material properties 414 of the adhesive and dispensing device characteristics 416. Sensor 418 can include data regarding the adhesive during the supply chain process, including the temperature or humidity to which the adhesive was exposed and the amount of time extreme temperature or humidity conditions were present. Data component 402 can further include data 420 regarding various modular components of adhesive dispensing device 100.

[0017] The data component 402 may further include algorithms 412 such as machine learning algorithms, curve fitting algorithms, minimization, and optimization functions. Algorithms 412 according to embodiments can generate predictions based on data related to a calibration liquid. The calibration liquid may include a liquid model, an ideal liquid, or a special calibration liquid used only in the calibration process and not in the production process, for example. In any case, the calibration liquid is dispensed using, for example, an adhesive dispenser 100. In the example, the input data may include any of the above data in addition to data entered by the user or another system, such as machine calibration data or dispenser calibration data. In some examples, sensors such as temperature or humidity sensors in the room or area where adhesive dispensing is taking place or is planned to take place may provide input to algorithm 412.

[0018] As previously stated herein, the relationship between the mass, viscosity, and pressure of an adhesive can be nonlinear, creating complexity in determining or predicting the appropriate pressure for dispensing the adhesive. Equations or relationships can be developed to explain the nonlinear relationship between pressure and viscosity. This nonlinear relationship can be generalized using exemplary equation (1). m = G(F1(p), F2(μ)...) (1) Here, m is the mass of the dispensed adhesive, F1 is a function of pressure p, and F2 is a function of viscosity μ. Other functions and relationships may also be included, and equation (1) should not be understood as limiting embodiments to one relationship between pressure and viscosity. Solutions for the constants in those relationships are obtained using the machine learning algorithms described herein. The constants may vary with conditions such as environmental conditions, and in some embodiments, different constants will be obtained in different iterations of the machine learning algorithms.

[0019] From equation (1) or a similar equation, a nonlinear fit can be obtained as shown in Figure 3. However, this adds mathematical complexity to the prediction process. Furthermore, other variables can add dimensions to the prediction and increase complexity. For example, if temperature is considered, the prediction becomes three-dimensional with three variables (e.g., temperature, mass, and pressure), making it increasingly difficult to model using a calibration curve. Chemical reactions within or between adhesive components, whether during storage or the dispensing process, can further complicate the creation of accurate calibration curves applicable to real-world conditions.

[0020] Algorithm 412 can generate predictions based on equation (1). In some embodiments, solutions for constants related to F1 and F2 can be obtained by referring to equation (1), for example, using a machine learning algorithm or by solving an optimization problem described later herein. The constants may include dispenser-specific constants or adhesive-specific constants, which are calculated for each adhesive dispenser 100 and may change with time or temperature. Viscosity μ can be determined based on direct or indirect measurements, polynomial fit, numerical regression, or an equation such as Andrade's equation for the viscosity of a liquid given in equation (2).

number

[0021] As described above, the constants D and E related to equation (1) can be calculated, for example, using a machine learning algorithm or by solving an optimization problem. Such an algorithm can estimate the viscosity based on the age of the adhesive (or receive a value indicating an estimated viscosity) and apply this estimate to the value of D or E. Some constants can be calculated based on a machine learning algorithm that minimizes a loss function. The loss function may have input parameters related to at least one of pressure, mass, volume, time, and temperature of the adhesive, adhesive dispenser, or process related to adhesive application.

[0022] The quality of the model and any predictions made can be estimated using the root mean square error algorithm, which compares predicted values ​​with measured values ​​for various parameters, such as dispensing mass, pressure, temperature, and viscosity. Based on the determined constants, the dispensing mass, and the ambient temperature and humidity, the pressure or setting at which the adhesive should be dispensed is provided to the human-machine interface 404.

[0023] Referring further to Figure 4, the human-machine interface 404 may include a user interface 422, local storage and processing 424, connection to a server 426, sensors 428, and connection to a dispensing device 406 430. Sensors 428 may include an ambient temperature sensor and a humidity sensor. The human-machine interface 404 may further include a two-dimensional (2D) barcode scanner or a QR barcode scanner for scanning identification information, such as lot information on adhesives or adhesive containers. Some components of the human-machine interface 404 will be described in more detail later in this specification with reference to Figures 7 and 8.

[0024] Through connection 430, the human-machine interface 404 can communicate with the controller 432 among the dispensing device components 406. The controller 432 can use the pressure or setting predicted by algorithm 412 to control the dispensing device hardware 434. For example, the controller 432 can control the dispensing device hardware 434 (e.g., hardware identical or similar to the drive force 102) to dispense the dispensable material 436 at the pressure predicted or instructed by algorithm 412. The dispensing device components 406 may include additional sensors 438.

[0025] In addition to predicting the ideal pressure for dispensing adhesive, the machine learning model described above can predict or suggest other settings to improve the adhesive process or to provide adhesive flow rates that meet the needs of downstream processes. These suggestions can be provided through various human interface elements, as described later in this specification.

[0026] feedback Feedback can be used to incorporate new experiences into subsequent machine learning iterations. The data provided through feedback can indicate the quality of the dispensing process or processes related to the dispensing process. The data may further include qualitative data, for example, regarding the flow rate achieved. In some embodiments, the data can be provided by a human operator through observations such as visual inspection, and through indicators that show whether the adhesive dispensing proceeded as expected, via a human-machine interface 404, a smartphone, etc.

[0027] In other embodiments, the source of feedback information may be a mechanical sensor 438, including, for example, a machine vision sensor or a mass sensor, or any device capable of detecting heat, humidity, weight, mass, adhesive quality, volume, bead profile, etc. Whether the feedback data is provided through a human interface or through a mechanical sensor 438, the algorithm 412 can use the provided data to refine the constants used in the algorithm 412.

[0028] Acquisition of proposed data Predictions and suggestions can be provided to a user interface, such as a graphical user interface (GUI) 500. Figure 5A shows examples of GUI 500 according to several embodiments.

[0029] The GUI 500 may be provided by a user device such as a smartphone, or by a human-machine interface 404 of the system described later herein with respect to Figures 7 and 8, or by a standalone device associated with any of its components. The GUI 500 can display information about the dispensed adhesive. Examples of information may include, among other things, the product name, product color, an image of the container such as the product tube, lot number and other manufacturing information, and the expiration date.

[0030] The GUI 500 can display one or more parameters 502 that indicate desired conditions for the dispensing function. For example, the GUI 500 can display the desired flow rate for dispensing. In embodiments, the parameters 502 are editable by the user so that the user can suggest desired flow rates, masses, or dispensing times based, for example, on the needs of downstream processes in an adhesive process under control. The GUI 500 can display such information for any number of remote or local dispensing machines. In some embodiments, the user can switch between different dispensing machines using interface elements 504 such as drop boxes, lists, or other elements.

[0031] The GUI 500 can be displayed as part of a web application that provides suggestions regarding operational settings, such as pressure settings, for the adhesive dispensing device 100 (Figure 1), where such operational settings are determined using algorithm 412 (Figure 4). The user can request that, in addition to or instead of the periodic updates provided by algorithm 412 (Figure 4), pressure predictions or suggestions be provided. For example, the user can press an interface element 506, such as a button, and a user device, such as a smartphone, can wirelessly send a request asking algorithm 412 to run and provide updated suggested pressures. Suggested values ​​for parameters, such as pressure, can be displayed on the GUI 500. In some examples, the user can then manually apply the suggested pressure to the adhesive dispensing device 100. In other examples, the dispensing device components 406, including the controller 432 (Figure 4), can automatically control the dispensing device hardware 434 based on the suggested pressure.

[0032] The machine learning model described above can also predict when current environmental conditions (such as temperature and humidity, but not limited to these embodiments) will result in poor quality predictions. The GUI500 can then be used to request or suggest that the user obtain further calibration points, which can then be acquired from user input. This allows for improvement of the entire machine learning model.

[0033] In other embodiments, if a user requests a flow rate far exceeding a previously requested rate, such as one where the dispensing pressure is significantly higher than previously used, the user can be notified via the GUI 500 or an audio alarm that a further calibration point should be obtained. For example, if a user requests a flow rate requiring a pressure of 50 psi, but the normal dispensing range is 20-40 psi, the user is asked to provide a calibration point associated with 50 psi. The calibration point may be as shown in Figure 5B and may include, for example, information on lot number 512, pressure 514, dispensing mass 516, dispensing time 518, and temperature 520. In the illustrated example of a 50 psi calibration point, the user then provides the information illustrated in Figure 5B, such as the temperature and time for dispensing at 50 psi. In the example, a timestamp can be added to this calibration point when it is stored, for example, by the human-machine interface 404 or associated processing circuitry. In some embodiments, fewer fields may be included in the calibration point, or more fields may be added.

[0034] The models described earlier in this specification can identify opportunities to enhance predictive quality. In at least some embodiments, suggestions regarding such opportunities can be generated and displayed in a user interface such as GUI500. When opportunities for predictive improvement are identified, the systems and apparatus according to the embodiments can indicate to the user, through voice or visual alarms or instructions, text, voice messages, etc., that the user should acquire adhesive data that could lead to predictive improvement.

[0035] For example, if a machine learning model determines that flow rate data has not been acquired at a particular temperature, the system according to the embodiment can request the user to acquire dispensing data at that particular temperature. Alternatively, the system according to the embodiment can control a sensor to acquire such data at that temperature. In these and other embodiments, a further GUI screen may be provided, which the user can interact with to manually input data related to data points that can enhance the machine learning algorithm. In at least these embodiments, some or all of the data may be automatically input by the dispensing device component 406 (Figure 4) through one of the following methods: wireless communication, near-field communication (NFC), etc. For example, the user can manually input calibration data on the GUI screen 510 shown in Figure 5B.

[0036] Figure 5B shows examples of GUIs for manual input of calibration data according to several embodiments. In this example, the user can input a lot number 512 that identifies a manufacturing lot of the liquid adhesive or a manufacturing lot of one part of the liquid adhesive. In other examples, the lot number 512 may be provided automatically, for example, by reading an RFID chip or barcode associated with the dispensing device component 406, or by wireless communication from the dispensing device component 406. The pressure used can be provided in 514. The dispensing mass may be provided by the dispensing device component 406, for example, a sensor 438, for automatic or manual input into field 516. The sensor 438 may also provide feedback data for algorithm 412, including a machine learning algorithm. The dispensing time amount 518 can be entered manually by the user, or a controller 432 may provide such a time value, for example. Other parameters that affect pressure prediction, such as temperature 520, can be included or entered. The user can delete or add relevant data points using interface buttons 522 and 524, respectively.

[0037] Adjustments during the process Feedback can also be used to adjust various settings while dispensing material to achieve or maintain operating parameters or performance setpoints. For example, the driving force pressure can be adjusted using data or process parameters related to changes in the viscosity of such material as it is dispensed to maintain a desired flow rate. Process parameters may include parameters of the dispensable material 436 or dispensing device component 406 during the dispensing process, such as temperature, conductivity, viscosity, weight, and mass. Process parameters can be sensed or determined, for example, by sensor data from various sensors, such as sensor 438, among the dispensing device component 406. Sensor 438 may include, for example, a vision sensor, a mass sensor, a temperature sensor, a conductivity sensor, or any device capable of detecting parameters such as heat, humidity, weight, mass, material quality, and conductivity.

[0038] In one or more embodiments, one or more sensors (e.g., sensor 438) may provide at least one process parameter of the dispensable material. This at least one process parameter may include, for example, the temperature, conductivity, viscosity, weight, and mass of the dispensable material (e.g., adhesive, sealant, thermal paste, etc.). One or more sensors 438 may include a sensor capable of sensing the temperature and / or conductivity of the dispensable material, for example, sensor 910 in Figure 10.

[0039] Figure 10 shows an example of one or more sensors 910 that can sense the temperature and / or conductivity of the dispensable material as it flows through the duct piece 900 (e.g., the dispensable device component 406 or dispensable device hardware 434 in Figure 4). The duct piece 900 includes a duct body 902 having a channel 904 extending from a duct inlet 906 to a duct outlet 908. The channel 904 may provide a fluid path for the dispensable material (e.g., dispensable material) to flow from the duct inlet 906 to the duct outlet 908 during the dispensing process.

[0040] As the dispensable material flows through the channel 904, the dispensable material may come into contact with various devices of one or more sensors 910 disposed within the duct piece 900, allowing one or more sensors 910 to sense process parameters of the dispensable material. For example, one or more sensors may include a probe 912 positioned in the fluid path defined by the channel 904. The probe 912 may be configured to respond to the temperature of the dispensable material in a manner detectable by one or more sensors 910. For example, the probe 912 may include a thermistor, and one or more sensors 910 may be configured to determine the resistance of the thermistor and sense or determine the temperature of the dispensable material based on the resistance of the thermistor. In one embodiment, the probe 912 may be configured to directly contact the dispensable material in the fluid path. In another embodiment, the outer surface of the probe 912 may be separated from the dispensable material by a shield, and the probe may be configured to sense the temperature of the dispensable material through the shield. The shield may include any suitable material, such as plastic, metal, or other thermally conductive material.

[0041] Furthermore, for example, one or more sensors 910 may include electrodes 914 positioned within a fluid path defined by the channel 904. One or more sensors 910 may be configured to provide a voltage across the electrodes 914 and measure the resulting currents between the electrodes and through the dispensable material. Based on the measured currents, one or more sensors 910 can sense the conductivity of the dispensable material.

[0042] Sensor 438 may include additional sensors and sensor devices described in PCT Publication 2022 / 013786A1 (Munstermann et al.), the disclosure of which is incorporated herein by reference.

[0043] Component Library The systems, apparatus, and methods can generate predictive models without understanding the internal components of a dispensing device, under the assumption that the dispensing device is a “black box” or simply a component with inputs and outputs. However, other predictive models may be made from components of the adhesive dispensing device 100 (Figure 1), including, for example, mixing nozzles, dispensing tips, or components such as tubes, cartridges, pressure valves, pumps, adapters, and pinch tubes. In some embodiments, the predictive models may include equations similar to equation (1). In some embodiments, machine learning algorithms can be used to find solutions for similar or different constants. By creating predictive models of dispensing device components, the need for calibration can be reduced or eliminated when adding a new dispensing device to an operator’s process by using settings based on component models previously generated or stored using one of the processes described above. Furthermore, new dispensing device components can be added to the dispensing device without requiring calibration of the settings. For example, new dispensing tips can be added, and pressures that have already been generated and predicted can be applied to these tips depending on factors such as viscosity and temperature, as described herein.

[0044] Example of method Figure 6 is a flowchart of a method 600 for predicting adhesive dispensing device pressure according to several embodiments. The operation of method 600 can be carried out, for example, by a processor 704 (Figure 8) capable of executing algorithm 412 (Figure 4). In the example, algorithm 412 can be made to be executed partially or fully using machine learning and based on inputs automatically generated and provided by sensors or user inputs, among other inputs.

[0045] Method 600 can begin in operation 602, in which the processor 704 takes in at least one parameter related to the adhesive dispenser 100 (Figure 1). In some embodiments, the at least one parameter relates to the viscosity of the adhesive in the adhesive dispenser 100. In some embodiments, operation 602 may include receiving at least one feedback parameter, the at least one of which indicates the quality of the bonding process. The feedback parameter may be received from user input or a sensor. In some examples, the feedback parameter may include or be based on image data or mass data. The mass data may indicate the amount of adhesive dispensed. In some embodiments, the mass feedback data can be used to determine whether the amount of adhesive dispensed is within a target range for a desired amount of adhesive.

[0046] In the embodiment, predictions and suggestions are provided to the user interface, and the processor 704 can further request the user to input or obtain calibration points. As previously described herein, the calibration points may include information similar to that shown in Figure 5B. This request may be based, for example, on the determination that the requested flow rate is outside the threshold range of a normal range. In other examples, the request may be in response to the determination that environmental parameters are outside the typical range or the threshold range of a typical range. For example, if the room temperature is minus 20 degrees Fahrenheit, the processor 704 may request the user to manually input the adhesive viscosity at that temperature. In at least these examples, the predictive ability of the machine learning model may be limited due to the unusual nature of environmental factors, and therefore manual data input is required.

[0047] Proposals or requests for data acquisition can be provided on the GUI, as described above with reference to Figures 5A and 5B.

[0048] The machine learning model described above can also predict when current environmental conditions (such as temperature and humidity, but the embodiment is not limited to these) will result in low-quality predictions. The GUI500 can then be used to request or suggest that the user obtain further calibration points, which can then be obtained from user input. This allows for improvement of the entire machine learning model.

[0049] In other embodiments, if the user requests a flow rate far exceeding a previously requested rate, resulting in a significantly higher dispensing pressure than previously used, the user can be notified via the GUI 500 or an audio alarm that further calibration points should be obtained. For example, if the user requests a flow rate requiring a pressure of 50 psi, but the normal dispensing range is 20-40 psi, the user may be prompted to provide calibration points related to 50 psi using an interface similar to, for example, the one shown in Figure 5B.

[0050] The models described earlier in this specification can identify opportunities to enhance predictive quality. In at least some embodiments, suggestions regarding such opportunities can be generated and displayed in a user interface such as GUI500. When opportunities for predictive improvement are identified, the systems and apparatus according to the embodiments can indicate to the user, through voice or visual alarms or instructions, text, voice messages, etc., that the user should acquire adhesive data that could lead to predictive improvement.

[0051] For example, if a machine learning model determines that flow rate data has not been acquired at a particular temperature, the system according to the embodiment can request the user to acquire dispensing data at that particular temperature. Alternatively, the system according to the embodiment can control a sensor to acquire such data at that temperature. In these and other embodiments, a further GUI screen may be provided, which the user can interact with to manually input data related to data points that can enhance the machine learning algorithm. In at least these embodiments, some or all of the data may be automatically input by the dispensing device component 406 (Figure 4) through one of the following methods: wireless communication, near-field communication (NFC), etc. For example, the user can manually input calibration data on the GUI screen 510 shown in Figure 5B.

[0052] Figure 5B shows examples of GUIs for manual input of calibration data according to several embodiments. In this example, the user can input a lot number 512 that identifies the manufacturing lot of the liquid adhesive. In other examples, the lot number 512 may be provided automatically, for example, by reading an RFID chip associated with the dispensing device component 406, or by wireless communication from the dispensing device component 406. The pressure used can be provided in 514. The dispensed mass may be provided by the dispensing device component 406, for example, a sensor 438, for automatic or manual input into field 516. The sensor 438 may also provide feedback data for algorithm 412, including a machine learning algorithm. The dispensing time amount 518 can be entered manually by the user, or a controller 432 may provide such a time value, for example. Other parameters that affect pressure prediction, such as temperature 520, can be included or entered. The user can delete or add relevant data points using interface buttons 522 and 524, respectively.

[0053] Method 600 can continue in operation 604, in which the processor 704 determines a value for an operating parameter of the adhesive dispenser 100. This value may be based on at least one parameter received in operation 602. The value of this operating parameter may be such that a desired flow rate is achieved in the adhesive dispenser system.

[0054] Method 600 can continue with operation 606, in which the processor 704 provides this operation parameter to the adhesive dispensing device 100. In embodiments, this operation parameter may be the driving force pressure of the adhesive dispensing system. In embodiments, the driving force pressure is determined based on viscosity and parameters specific to the adhesive dispensing device 100. For example, the determination of the driving force pressure may follow equation (1) previously described herein: m = G(F1(p), F2(μ)...), where m is the mass dispensed by the adhesive dispensing system, F1 is a function of pressure p, and F2 is a function of viscosity μ.

[0055] Viscosity μ can be determined by direct or indirect measurement, polynomial fitting, numerical regression, or by equations such as Andrade's equation for the viscosity of a liquid given by equation (2).

[0056] As previously stated herein, the constants related to equations (1) and (2) can be determined using machine learning algorithms. These machine learning algorithms can minimize a loss function related to at least one of the driving force pressure, mass, viscosity, and temperature. The machine learning algorithms may be based on models other than neural network models (e.g., non-neural network models and machine learning algorithms), or on hybrids of neural network models and other models (e.g., hybrid algorithms).

[0057] As previously stated herein, other systems based purely on neural networks can be used to control instructions for applying adhesive. However, systems based solely on neural networks require hundreds or thousands of calibration points to be practical, and only after extensive training can control adhesive dispensing. The embodiments described earlier herein may be based on other machine learning models, or non-neural network-based systems, or combinations of neural networks and other machine learning models.

[0058] Figure 7 shows a comparison of the performance of neural network-based models and trained models used in several embodiments. A dataset of laboratory-generated data was used for this comparison. The dataset was sampled for a given number of data points and then split into two groups: 70% of the data to be used to train the models and 30% of the data reserved to test the quality of the models after they had been trained. The split data was passed to each model so that each model analyzed the same experimental data in all cases. As a result, the systems using the models according to the embodiments had lower mean square root error at all sampling levels (compared to curve 632, which shows the systems using the neural network models), as shown by curve 630, and the difference between curves 630 and 632 was particularly large at the lower sampling level 634. As can be seen, using the methods according to the embodiments, cost reductions can be achieved by reducing the need for a large number of samples, as the mean square root error is small even at low sample levels (which in turn reduces manufacturing, laboratory, and material costs). The ability to obtain useful predictions with a relatively small amount of data represents a significant advancement over existing neural network methods.

[0059] Figure 11 is a flowchart of method 1000 for calibrating a dispensing device system according to several embodiments. The operation of method 1000 can be performed, for example, by a processor 704 (Figure 8).

[0060] Each dispensing system is subtly or significantly different, and calibration data obtained from one dispensing system cannot be used to calibrate another. To calibrate (e.g., set up) a dispensing system, the user may fill the dispensing system with dispensable material (e.g., dispensable material 436) and configure all relevant components of the dispensing system, including components such as plungers, air lines, controllers, stationary mixers, pinch tubes or valves, and tips. Once ready, the user dispenses the dispensable material into cups at several different pressures and weighs the dispensed material. Each combination of dispensed mass and pressure setting can form a calibration data point. However, various conditions can result in "bad" calibration data points. For example, if there are air bubbles in the system, if material adheres to the tip of the dispensing device system, if material from the previous dispensing device shot is included in the dispensing mass of the next shot, if the pressure or mass is not accurately set or recorded, or if other sources of error exist, the data obtained from calibration may not provide a useful model.

[0061] Method 1000 can correct "bad" calibration data points by requesting a set of calibration data points, evaluating the quality of the set of calibration data points, removing "bad" or low-quality calibration data points, and requesting additional calibration data points as necessary.

[0062] Method 1000 can be initiated in operation 1002 by the processor 704 receiving a plurality of calibration data points and one or more parameters of the dispensing device components of the dispensing device system. The user may be instructed to set a pressure gauge (e.g., dispensing device pressure) to a specific pressure and record the dispensed mass. This instruction may also include the dispensing time for each calibration data point. Alternatively, the processor may automatically set the pressure, dispense the dispensable material, and record each calibration data point and associated parameters. Typical calibration methods may require hundreds or thousands of calibration data points. In contrast, the plurality of calibration data points in Method 1000 may include at least two calibration data points to 100 or fewer calibration data points, or any suitable range in between. For example, the multiple calibration data points may include several calibration data points ranging from at least two, five, ten, or fifteen calibration data points to 20 or fewer calibration data points, 40 or fewer calibration data points, 60 or fewer calibration data points, 80 or fewer calibration data points, or 100 or fewer calibration data points. Each calibration data point may include any appropriate parameter. For example, each of the multiple calibration data points may include one or more of the following: manufacturing lot information of the dispensable material, dispensing device pressure, dispensing mass, dispensing time, ambient temperature, temperature of the dispensable material, etc. In one embodiment, each of the multiple calibration points includes dispensing device pressure, dispensing time, and the mass of the dispensable material being dispensed.

[0063] One or more parameters of a dispensing device component may include any appropriate parameters. For example, one or more parameters of a dispensing device component may include manufacturing lot information of the dispensable material, the model number of the dispensing device component, the type of dispensable material (e.g., adhesive, sealant, heat paste, etc.). One or more parameters may be obtained from the dispensing device component, a database, a user, an image, etc.

[0064] Method 1000 can continue in operation 1004 by the processor selecting one or more predetermined models based on one or more parameters of the dispensing device components. In one or more embodiments, the one or more parameters include the manufacturing lot of the dispensable material. The one or more predetermined models may be taken based on the manufacturing lot of the dispensable material. For example, each manufacturing lot or type of the dispensable material may correspond to a given set of predetermined models.

[0065] Method 1000 can continue in operation 1006 by the processor determining a calibration model based on a plurality of calibration data points and one or more predetermined models thereof. For example, the calibration model may include one or more settings of the one or more predetermined models, where the modified set of calibration data points best corresponds to one predetermined model based on statistical methods and modeling. In some examples, a plurality of calibration data points may fit or match one of the predetermined models, and the calibration model can be determined based on the plurality of calibration data points and one or more predetermined models without any modifications to the plurality of calibration data points. However, in some examples, a plurality of calibration data points may not fit or match one of the predetermined models due to one or more outliers or an insufficient number of calibration data points. Therefore, determining the calibration model may include generating a modified set of calibration data points from which outliers of the plurality of calibration data points have been removed and / or to which additional calibration data points may be added.

[0066] Determining a calibration model may include identifying one or more outliers among a plurality of calibration data points based on one or more models. Identifying one or more outliers may include one or more appropriate techniques. For example, identifying one or more outliers may include using one or more statistical models or methods. In one embodiment, identifying one or more outliers may include regression analysis based on a plurality of calibration points and one or more predetermined models. In addition, or separately, identifying one or more outliers may include identifying one or more calibration points among a plurality of calibration points whose difference from a corresponding data point of one or more predetermined models exceeds a threshold. The threshold may be a percentage difference. In other words, the difference between the dispensing mass of an outlier calibration data point and the dispensing mass of a data point corresponding to the same dispensing device pressure and time in one of the predetermined models may exceed a predetermined percentage of the dispensing mass of the data point in the predetermined model. The threshold may be determined based on the predetermined model.

[0067] Determining a calibration model may further involve removing one or more outliers from a set of calibration data points in order to generate a modified set of calibration data points. Optionally, method 1000 may include the operation in which the processor determines that the modified set of calibration data points contains fewer data points than a threshold number. If the modified set of calibration data points contains fewer data points than a threshold number, this modified set of calibration data points may not be sufficient to determine whether the calibration data points fit into one or more given models.

[0068] Therefore, method 1000 may also include an operation in which the processor determines one or more dispenser pressures for one or more additional calibration data points based on the modified set of calibration data points and one or more predetermined models. The one or more additional calibration data points may include a dispenser pressure between the dispenser pressures of two missing or removed data points. In addition, or alternatively, the one or more additional calibration data points may include the same dispenser pressure as one or more outliers.

[0069] Method 1000 may also include an operation in which the processor requests one or more additional calibration data points using a display. Each of the one or more additional calibration data points may correspond to one or more dispensing device pressures. Method 1000 may also include an operation in which the processor receives one or more additional calibration data points using a user interface and modifies the modified set of calibration data points to include the one or more additional calibration data points.

[0070] Therefore, determining the calibration model may be based on the modified set of calibration data points and one or more predetermined models. For example, the calibration model may be determined after one or more outliers have been removed, or after one or more additional calibration data points have been added to the set of modified data points.

[0071] Method 1000 can continue in operation 1008 by the processor providing one or more settings for the dispensing device system based on a calibration model. In embodiments, providing one or more settings may include adjusting one or more settings for the dispensing device components of the dispensing device system. In other words, the processor may adjust various settings for the dispensing device system. In addition, or separately, the processor may present various calibration settings to the user using a display such as GUI 500.

[0072] Figure 12 is a flowchart of method 1100 for dispensing a dispensable material (e.g., dispensable material 436) according to several embodiments. The operation of method 1100 can be performed, for example, by a processor 704 (Figure 8).

[0073] Method 1100 can be initiated in operation 1102 by the processor 704 receiving at least one parameter associated with a dispenseable material dispenser 100 (Figure 1). This at least one parameter may be received from a dispenser component, a database, a user, an image, etc. In some embodiments, the at least one parameter relates to the viscosity of the dispenseable material in the dispenseable material dispenser 100. In some embodiments, the at least one parameter may also relate to the density of the dispenseable material in the dispenseable material dispenser 100. In some embodiments, operation 1102 may include receiving at least one feedback parameter of the dispenseable material. Process parameters may be received from user input or sensors. In some examples, the feedback parameter may include or be based on image data or mass data. Mass data may indicate the amount of dispenseable material dispensed. For example, the mass data and density of the dispenseable material may be used to determine the volume of dispenseable material to be dispensed. Thus, based on the mass data and density of the dispenseable material, the volumetric flow rate of the dispenseable material can be determined and / or monitored. In one embodiment, mass feedback data can be used to determine whether the amount of dispensed material is within a target range for the desired amount of material.

[0074] Method 1100 can continue in operation 1104, in which the processor 704 determines the value of the operating parameter of the dispenseable material dispenser 100. This value may be based on at least one parameter received in operation 1102. The value of the operating parameter may be such that a desired flow rate is achieved in the dispenseable material dispenser system.

[0075] Method 1100 can continue in operation 1106, with the processor 704 providing an operating parameter. In an embodiment, the operating parameter may be provided to the dispenseable material dispenser 100. In another embodiment, the operating parameter may be provided to the human-machine interface 404. In an embodiment, the operating parameter may be the driving force pressure of the dispenseable material dispenser system. In an embodiment, the driving force pressure is determined based on viscosity and parameters specific to the dispenseable material dispenser 100 described earlier herein.

[0076] Method 1100 can proceed in operation 1108 by the processor receiving at least one process parameter, including the temperature of the dispensable material. The at least one process parameter may also include, for example, parameters of the dispensable material (e.g., adhesive, sealant, thermal paste, etc.), such as conductivity, viscosity, weight, and mass. The at least one process parameter may be received from one or more sensors. For example, the temperature of the dispensable material may be received from the temperature sensor 910 in Figure 10. The probe 912 of the temperature sensor 910 may be placed or positioned in the fluid path of the dispensable material so that the temperature sensor 910 can sense or measure the temperature of the dispensable material.

[0077] Method 1100 can continue in operation 1110 by the processor adjusting the values ​​of the operating parameters of the dispenseable material dispensing system based on the temperature of the dispenseable material. When the temperature of the dispenseable material changes, the viscosity of the dispenseable material may change. Therefore, by adjusting the values ​​of the operating parameters (e.g., driving force pressure), a more consistent flow rate of the dispenseable material can be achieved. Additional process parameters may affect the viscosity and flow rate of the dispenseable material. Such process parameters may be sensed using sensor 910 or any other suitable sensor or device. For example, sensor 910 may further include a conductivity sensor that senses the conductivity of the dispenseable material. Method 1100 may further include the processor receiving the sensed conductivity of the dispenseable material from the conductivity sensor. Furthermore, Method 1100 may also include determining the curing state of the dispenseable material based on the sensed temperature and the sensed conductivity of the dispenseable material, and adjusting the values ​​of the operating parameters of the dispenseable material dispensing system based on the sensed temperature and curing state.

[0078] Once the dispenseable material is mixed during the dispensing process, it may begin to harden before it is dispensed. As the dispenseable material hardens, its viscosity may change. In addition to temperature and conductivity, the hardening state can also be determined based on the time after initial mixing (e.g., residence time in the stationary mixer and subsequent equipment). The viscosity of the dispenseable material can be predicted using the known hardening state, and its effect on the flow rate can be calculated. This information can be used to adjust operating parameters (e.g., driving force pressure) to maintain a more constant flow rate of the dispenseable material 436. In addition, method 1100 may include initiating purging if the viscosity of the dispenseable material exceeds a threshold viscosity level. The threshold viscosity level may be set or determined based on the operating limits of the dispenseable material dispensing system, user-set parameters, or parameters of the current dispensing job or process.

[0079] Method 1100 can be continued in operation 1112 by the processor providing operating parameters. In embodiments, the processor may provide operating parameters to one or more dispensing device components. In other embodiments, the processor may provide operating parameters to a human interface device. Thus, the flow rate of the dispensable material can be maintained by continuously or periodically adjusting the operating parameters and providing the adjusted operating parameters to one or more dispensing device components. For example, the driving force pressure can be continuously adjusted to account for hardening, temperature, and / or viscosity changes of the dispensable material. Furthermore, whenever the operating parameters are adjusted, the adjusted operating parameters can be provided to one or more dispensing device components. The adjusted operating parameters can be provided using wired or wireless communication as described herein. In addition, the processor may initiate purging by communicating with a motion controller configured to move or guide the dispensing device to a purge container.

[0080] Method 1100 may also include an operation to determine the parameters for purging the dispensable material. Method 1100 may include an operation in which the processor determines the maximum idle time or purge time for one or more dispensing device components based on at least one parameter and at least one process parameter. Method 1100 may also include an operation in which the processor provides the maximum idle time or purge time. In embodiments, the maximum idle time or purge time may be provided to one or more dispensing device components. Thus, the dispensing device components can initiate automatic purging based on the maximum idle time and / or purge time. In embodiments, the maximum idle time or purge time may be provided to a human-machine interface. Thus, the maximum idle time or purge time may be displayed to the user.

[0081] Many factors can influence the pot life of a two-component dispensable material. Such factors may include, for example, temperature, material aging, density, initial manufacturing viscosity, and other potential factors. These factors can alter the curing kinetics, and consequently, the amount of time the dispensable material can remain in the dispensing apparatus before purging becomes necessary. An additional factor that may contribute to the maximum idle time is the dispensing rate. The period during which the dispensing apparatus components are considered idle may begin after the last dispensing of the dispensable material. If the active period (e.g., during dispensing) involves high-flow dispensing shots with little time interval between them, the dispensable material may be relatively fresh when dispensing is complete. However, if the flow rate is low and / or the time interval between dispensing shots is long, the dispensable material may be partially cured when dispensing is complete. Therefore, the maximum idle time and / or purge time should differ for each of these cases. By determining the maximum idle time and / or purge time based on at least one parameter and at least one process parameter, the maximum idle time and / or purge time can be adjusted to suit the temperature of the dispenseable material, the age of the dispenseable material, the density of the dispenseable material, the viscosity of the dispenseable material, the dispensing rate, the flow rate, the time between dispensing shots, etc.

[0082] In addition, Method 1100 may consider safety factors and process control factors when determining the maximum idle time and purge time. Method 1100 may also include an operation in which the processor receives one or more user inputs, including safety factors, process control factors, and waste factors, and determines the maximum idle time or purge time based on one or more user inputs, at least one parameter, and at least one process parameter. The user inputs may be received using a user interface (e.g., GUI 500). In one example, the user may select or provide a safety factor to prevent premature hardening of the dispensable material when there is variability in the dispensing process. In other words, the maximum idle time may be determined based on the hardening time of the dispensable material, if a safety factor is provided. In another example, the user may want strict control over the flow rate, even at the expense of wasted dispensable material. In other words, the maximum idle time may be determined based on the flow rate fluctuations that may occur before the dispensable material hardens, if a control factor is provided. In yet another example, the user may select a waste factor that indicates a low amount of waste in order to minimize the amount of dispensable material wasted. If multiple factors are input, the specific values ​​of the factors may be balanced against each other in order to determine the maximum idle time and purge time.

[0083] The display allows the user to see how safety factors, process control factors, and discard factors affect the maximum idle time and purge time, and vice versa. In some embodiments, the user can set the safety factors, process control factors, and discard factors as parameter values. The parameter values ​​received from the user can be incorporated into the algorithm used to determine the maximum idle time and purge time.

[0084] Computer device The systems, methods, and apparatus can be implemented using a processor, as will be described in detail later herein, within firmware or software, remotely or locally to an operator process, or within a cloud or edge computing device. Machine learning can be distributed across several different devices and performed entirely or partially within the adhesive dispensing device itself. For example, several identification processes can be performed by the adhesive dispensing device 100 (Figure 1), and inputs from there can be provided to local or remote devices to formulate predictions regarding dispensing device settings. Thus, the apparatus and circuits of the adhesive dispensing device 100, data components 402, human-machine interface 404 and dispensing device components 406 (Figure 4), and other components can be run, or partially run, on a computing system such as an edge computing node. Figure 8 shows an edge computing node according to several embodiments.

[0085] In the simplified example shown in Figure 8, the edge compute node 700 (e.g., device) includes a compute engine (also referred to herein as “Compute Circuits”) 702, an input / output (I / O) subsystem 708, a data storage device 710, a communication circuit subsystem 712, and optionally one or more peripheral devices 714. In other embodiments, each compute device may include other or additional components (e.g., displays, peripheral devices, etc.) that are typically found in a computer. Furthermore, in some embodiments, one or more examples of components may be incorporated into another component or form part of another component.

[0086] The compute node 700 may be embodied as any kind of engine, device, or collection of devices capable of performing various computing functions. In some embodiments, the compute node 700 may be embodied as a single device such as an integrated system or device, including an integrated circuit, an embedded system, a field-programmable gate array (FPGA), or a system-on-a-chip (SOC). In a descriptive example, the compute node 700 includes, or is embodied as, a processor 704 and a memory 706. The processor 704 may be embodied as any kind of processor capable of performing the functions described herein (e.g., running an application). For example, the processor 704 may be embodied as a multicore processor (one or more), a microcontroller, or other processor or processing / control circuit. In some embodiments, the processor 704 may be embodied as, include, or be coupled to, an FPGA, an application-specific integrated circuit (ASIC), reconfigurable hardware or hardware circuitry, or other dedicated hardware that facilitates the performance of the functions described herein.

[0087] Memory 706 may be embodied as any kind of volatile (e.g., dynamic random access memory (DRAM)) or non-volatile memory or data storage device capable of performing the functions described herein. Volatile memory can be a storage medium that requires power to maintain the state of data stored in the medium. Non-limiting examples of volatile memory may include various types of random access memory (RAM), such as DRAM or static random access memory (SRAM). One particular type of DRAM that may be used in a memory module is synchronous dynamic random access memory (SDRAM).

[0088] In one embodiment, the memory device is a block-addressable memory device, such as one based on NAND or NOR technology. In some embodiments, all or part of the memory 706 may be integrated into the processor 704. The memory 706 may store various software and data, such as data used during the operation of one or more applications, data manipulated by those applications, libraries, and drivers.

[0089] The computing circuit 702 is communicably connected to other components of the computing node 700 via an I / O subsystem 708, which may be embodied as circuits or components to facilitate input / output operations with the computing circuit 702 (e.g., with the processor 704 or main memory 706) and with other components of the computing circuit 702. For example, the I / O subsystem 708 may be embodied as, or include, a memory controller hub, an input / output control hub, an integrated sensor hub, a firmware device, a communication link (e.g., a point-to-point link, a bus link, wires, cables, light guides, printed circuit board traces, etc.), or other components and subsystems to facilitate input / output operations. In some embodiments, the I / O subsystem 708 may form part of a system-on-a-chip (SoC) and may be integrated with the computing circuit 702 together with the processor 704, memory 706, and one or more other components of the computing circuit 702. The I / O subsystem 708 can receive input data 707 from other components in Figure 4, such as sensors 428 and 438, and provide prediction and control 709 to other components in Figure 4, such as the dispensing device component 406.

[0090] One or more exemplary data storage devices 710 may be embodied as any type of device configured for short-term or long-term storage of data, such as memory devices and circuits, memory cards, hard disk drives, solid-state drives, etc. Each data storage device 710 may include a system partition for storing data and firmware code for the data storage device 710. Each data storage device 710 may also include one or more operating system partitions for storing data files and executable files for the operating system, depending on the type of compute node 700, for example.

[0091] The communication circuit 712 may be embodied as any communication circuit, device, or set thereof capable of enabling communication over a network between the computing circuit 702 and another computing device (e.g., an edge gateway of the edge computing system being implemented). The communication circuit 712 may be configured to perform such communication using one or more communication technologies (e.g., wired or wireless) and associated protocols (e.g., cellular networking protocols such as 3GPP 4G or 5G standards, wireless local area network protocols such as IEEE 802.11 / Wi-Fi®, wireless wide area network protocols, IoT protocols such as Ethernet®, Bluetooth®, Bluetooth Low Energy, IEEE 802.15.4 or ZigBee®, low-power wide area network (LPWAN), ultra-wideband or low-power wide area (LPWA) protocols, etc.).

[0092] An exemplary communication circuit 712 includes a network interface controller (NIC) 720. The NIC 720 may be embodied as one or more add-in boards, daughter cards, network interface cards, controller chips, chipsets, etc., which can be used by a compute node 700 to connect to another computing device (e.g., an edge gateway node). In some embodiments, the NIC 720 may be embodied as part of a system-on-a-chip (SoC) including one or more processors, or it may be contained on a multi-chip package also including one or more processors. In some embodiments, the NIC 720 may include a local processor (not shown) or local memory (not shown), both of which are local to the NIC 720. In such embodiments, the local processor of the NIC 720 may be capable of performing one or more of the functions of the compute circuit 702 described herein. In addition, or / or, in such embodiments, the local memory of the NIC 720 may be integrated into one or more components of a client compute node at a board-level, socket-level, chip-level, etc.

[0093] Furthermore, in some embodiments, each compute node 700 may include one or more peripheral devices 714. Such peripheral devices 714 may include any type of peripheral device found in a compute device or server, such as audio input devices, displays, other input / output devices, interface devices, etc., depending on the particular type of compute node 700. In further embodiments, the compute node 700 may be embodied by each edge compute node (either a client, gateway, or aggregation node) within an edge computing system or a similar form of appliance, computer, subsystem, circuit, or other component.

[0094] In a more detailed embodiment, Figure 9 shows a block diagram of an example of components that may be present within an edge computing node 850 to implement the technologies described herein (e.g., operations, processes, methods, and methodologies). This edge computing node 850 provides a more detailed view of each component when node 700 is implemented as a computing device or as part of a computing device (e.g., as a computer, mobile device, server, smart sensor, control system, etc.). The edge computing node 850 may include any combination of hardware or logical components referred to herein, and may include or be linked with any device usable with an edge communication network or a combination of such networks. Components may be implemented as integrated circuits (ICs) employed in the edge computing node 850, parts thereof, independent electronic devices, or other modules, instruction sets, programmable logic or algorithms, hardware, hardware accelerators, software, firmware, or combinations thereof, or as components otherwise incorporated into the chassis of a larger system.

[0095] The edge computing node 850 may include processing circuitry in the form of a processor 852, which may be a known processing element such as a microprocessor, multicore processor, multithreaded processor, ultra-low voltage processor, or embedded processor. The processor 852 may be part of a system-on-a-chip (SoC) in which the processor 852 and other components are formed on a single integrated circuit or a single package. The processor 852 and associated circuitry may be provided in a single-socket form factor, a multi-socket form factor, or a variety of other forms, including limited hardware configurations or configurations containing fewer elements than all elements shown in Figure 9.

[0096] The processor 852 may communicate with system memory 854 via an interconnect 856 (e.g., a bus). Any number of memory devices may be used to provide a given amount of system memory. In one embodiment, memory 854 may be random access memory (RAM) according to the Joint Electron Devices Engineering Council (JEDEC) design. In various implementations, individual memory devices may be any number of different package types, such as single-die packages (SDP), dual-die packages (DDP), or quad-die packages (Q17P). In some embodiments, these devices may be soldered directly onto the motherboard to provide a low-profile solution, while in other embodiments, the devices are configured as one or more memory modules connected to the motherboard by a given connector. Any number of other memory implementations may be used, including, but not limited to, different types of dual in-line memory modules (DIMMs), such as microDIMMs or miniDIMMs.

[0097] To provide persistent storage of information such as data, applications, and operating systems, the storage device 858 may also be coupled to the processor 852 via an interconnect 856. In one embodiment, the storage device 858 may be implemented via a solid-state disk drive (SSDD). Other devices that may be used for the storage device 858 include flash memory cards such as Secure Digital (SD) cards, microSD cards, and Extreme Digital (XD) picture cards, and Universal Serial Bus (USB) flash drives.

[0098] The components may communicate via interconnect 856. Interconnect 856 may include any number of technologies, including industry standard architectures (ISA), extended ISAs (EISA), peripheral component interconnects (PCI), extended peripheral component interconnects (PCIx), PCI Express (PCIe), or any number of other technologies. Interconnect 856 may also be a custom-developed bus used, for example, in SoC-based systems. Other bus systems may include, in particular, inter-integrated circuit (I2C) interfaces, serial peripheral interface (SPI) interfaces, point-to-point interfaces, custom-developed buses, and power buses.

[0099] The interconnect 856 may link the processor 852 to the transceiver 866 in order to communicate with the connected edge device 862. The connected edge device 862 may include other elements or parts of other elements shown in Figure 9, or other elements of the manufacturing system used by the operator, either remotely or locally, to the tape automation system. The transceiver 866 may use any number of frequencies and protocols, such as 2.4 gigahertz (GHz) transmission under the IEEE 802.15.4 standard, using, among other things, the Bluetooth® Low Energy (BLE) standard or the ZigBee® standard as defined by the Bluetooth® Special Interest Group. Any number of radios configured for a particular radio protocol may be used for connection to the connected edge device 862. For example, a wireless local area network (WLAN) unit may be used to perform Wi-Fi® communication in accordance with the IEEE 802.11 standard. In addition, for example, wireless wide-area communications that follow cellular or other wireless wide-area protocols may be conducted via a wireless wide-area network (WWAN) unit.

[0100] A wireless network transceiver 866 (or more transceivers) may communicate using multiple standards or radios for communication at different ranges. For example, an edge computing node 850 may communicate with nearby devices, for example within about 10 meters, using a Bluetooth Low Energy (BLE) based local transceiver or another low-power radio to conserve power. Edge devices 862 connected further away, for example within about 50 meters, may be reached via ZigBee® or other intermediate-power radios. Both communication technologies may be carried out over a single radio at different power levels, or over separate transceivers, for example, a local transceiver using BLE and separate mesh transceivers using ZigBee®.

[0101] A wireless network transceiver 866 (e.g., a wireless transceiver) may be included to communicate with devices or services within the edge cloud 895 via a local or wide-area network protocol. The wireless network transceiver 866 may, among other things, be a low-power wide-area (LPWA) transceiver conforming to the IEEE 802.15.4 or IEEE 802.15.4g standard. The edge computing node 850 may communicate over a wide area using LoRaWAN® (Long Range Wide Area Network), developed by Semtech and the LoRa Alliance. The technologies described herein are not limited to these technologies and may be used in conjunction with any number of other cloud transceivers implementing long-range low-bandwidth communication and other technologies such as Sigfox. Furthermore, other communication technologies, such as time-slot channel hopping as described in the IEEE 802.15.4e specification, may be used.

[0102] As described herein, in addition to the systems mentioned for the wireless network transceiver 866, any number of other wireless communications and protocols may be used. For example, transceiver 866 may include a cellular transceiver that uses spread spectrum (SPA / SAS) communication to perform high-speed communication. Furthermore, any number of other protocols may be used, such as Wi-Fi® networks for providing medium-speed and network communications. Transceiver 866 may include any number of radios compatible with 3GPP (Third Generation Partnership Project) specifications, such as Long-Term Evolution (LTE) and fifth-generation (5G) communication systems. A network interface controller (NIC) 768 may be included to provide wired communication to nodes of edge cloud 895 or other devices such as connected edge devices 862 (e.g., operating within a mesh). Wired communication may provide an Ethernet® connection, or may be based on other types of networks, particularly Controller Area Network (CAN), Local Interconnection Network (LIN), DeviceNet, ControlNet, Data Highway+, PROFIBUS, or PROFINET. Additional NIC768s may be included to enable connectivity to a second network, for example, a first NIC768 providing communication to the cloud via Ethernet®, and a second NIC768 providing communication to other devices via another type of network. Ultra-wideband sensors and emitters can be used to facilitate communication such as precise tape positioning relative to defined emitter beacons, as well as data transfer.

[0103] Given the diversity of applicable communications from a device to other components or networks, an applicable communication circuit used by a device may include, or be embodied by, one or more of components 864, 866, 868, or 870. Thus, in various embodiments, applicable means for communicating (e.g., receiving, transmitting, etc.) may be embodied by such communication circuits.

[0104] The edge computing node 850 may include or be connected to an accelerator 864, which may be embodied by one or more artificial intelligence (AI) accelerators, neural compute sticks, neuromorphic hardware, FPGAs, a set of GPUs, a set of data processing units (DPUs) or infrastructure processing units (IPUs), one or more SoCs, one or more CPUs, one or more digital signal processors, a dedicated ASIC, or other forms of specialized processors or circuits designed to accomplish one or more specialized tasks. These tasks may include AI processing (including machine learning, training, inference, and classification operations), visual data processing, network data processing, object detection, rule analysis, and the like.

[0105] The interconnect 856 may connect the processor 852 to a sensor hub or external interface 870 used to connect additional devices or subsystems. The devices may include sensors 872, which may be accelerometers, level sensors, flow sensors, optical sensors, camera sensors, temperature sensors or gauges, global navigation system (e.g., GPS) sensors, pressure sensors, barometric pressure sensors, or any sensor for detecting the condition of tape or other adhesives, primers, substrates, etc. These sensors may be directly connected to the computing device or located remotely as part of various manufacturing modules. The hub or interface 870 may further be used to connect the edge computing node 850 to actuators 874, such as power switches, valve actuators, audible sound generators, and visual warning devices. These actuators may be directly connected to the computing device or located remotely as part of various manufacturing modules.

[0106] In some optional examples, various input / output (I / O) devices may reside within or be connected to the edge computing node 850. For example, a display or other output device 884 may be included to show information such as sensor readings or actuator positions. An input device 886, such as a touchscreen or keypad, may be included to receive input. Output device 884 may include several forms of audio or visual displays, including simple visual outputs such as binary status indicators (e.g., light-emitting diodes (LEDs)) and multi-character visual outputs, or more complex outputs such as display screens (e.g., liquid crystal display (LCD) screens), where outputs such as characters, graphics, and multimedia objects are generated or produced from the operation of the edge computing node 850. In the context of this system, the display or console hardware may also be used to provide outputs and receive inputs for the edge computing system, to manage components or services of the edge computing system, to identify the state of edge computing components or services, or to perform any other management or administration functions or service use cases. These various input / output devices may be directly connected to the computing device or may be located remotely as part of various manufacturing modules. In this example, notifications can be provided to multiple devices simultaneously; for example, an operator can view notifications on individual modules of system 400. Notifications can be provided simultaneously or nearly simultaneously to other devices such as the user's smartphone or tablet or computer, or to standalone devices or devices separate from the user's personal equipment, based on criteria such as proximity.

[0107] The edge computing node 850 may be powered by battery 876, but in examples where the edge computing node 850 is mounted in a fixed location, it may have a power supply connected to the power grid, or the battery may be used as a backup or for temporary capacity. Battery 876 may be a lithium-ion battery, or a metal-air battery such as a zinc-air battery, aluminum-air battery, or lithium-air battery.

[0108] If a battery 876 is included, a battery monitor / charger 878 may be included in the edge computing node 850 to track its charge state (SoCh). The battery monitor / charger 878 may also be used to monitor other parameters of the battery 876 to provide failure predictions such as the battery health (SoH) and functional state (SoF) of the battery 876. The battery monitor / charger 878 may transmit information about the battery 876 to the processor 852 via the interconnect 856. The battery monitor / charger 878 may also include an analog-to-digital (ADC) converter that allows the processor 852 to directly monitor the voltage of the battery 876 or the current from the battery 876.

[0109] To charge the battery 876, a power block 880, or another power source connected to the power grid, may be connected to the battery monitor / charger 878. In some examples, the power block 880 may be replaced with a wireless power receiver, which may obtain power wirelessly, for example, through a loop antenna in an edge computing node 850. A specific charging circuit may be selected based on the size of the battery 876 and therefore the current required.

[0110] The storage device 858 may contain instructions 882 in the form of software commands, firmware commands, or hardware commands for carrying out the techniques described herein. Although such instructions 882 are shown as code blocks contained in memory 854 and storage device 858, it can be understood that any of these code blocks may be replaced by hardwired circuits, for example, incorporated in an application-specific integrated circuit (ASIC).

[0111] In one embodiment, instructions 882 provided via memory 854, storage device 858, or processor 852 may be embodied as non-temporary machine-readable medium 860 containing code that instructs processor 852 to perform electronic operations within edge computing node 850. Processor 852 may access non-temporary machine-readable medium 860 via interconnect 856. For example, non-temporary machine-readable medium 860 may be embodied by the devices described for storage device 858, or it may include specific storage units, which may be optical discs, flash drives, or any number of other hardware devices. Non-temporary machine-readable medium 860 may include instructions that instruct processor 852 to perform a specific sequence or flow of actions, as described, for example, with respect to the flowcharts and block diagrams of operations and functions shown above. As used herein, the terms “machine-readable medium” and “computer-readable medium” are interchangeable.

[0112] In addition, in certain embodiments, instructions 882 on the processor 852 (separately or in combination with instructions 882 on the machine-readable medium 860) may constitute the execution or operation of a trusted execution environment (TEE) 890. In one embodiment, the TEE 890 operates as a protected area accessible to the processor 852 for the secure execution of instructions and secure access to data.

[0113] In further embodiments, the machine-readable medium also includes any tangible medium capable of storing, encoding, or holding instructions for machine execution that cause a machine to perform one or more of the methods of the present disclosure, or any tangible medium capable of storing, encoding, or holding data structures used by or associated with such instructions. Thus, “machine-readable medium” may include, but is not limited to, solid-state memory, as well as optical and magnetic media. Specific examples of machine-readable mediums include, but are not limited to, semiconductor memory devices (e.g., electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM)), and flash memory devices, magnetic disks such as internal hard disks and removable disks, magneto-optical disks, and non-volatile memories including, but not limited to, CD-ROMs and DVD-ROMs. Instructions embodied by the machine-readable medium may be further transmitted or received over a communication network using a transmission medium via a network interface device using one of several transfer protocols (e.g., Hypertext Transfer Protocol (HTTP)).

[0114] The machine-readable medium may be provided by a storage device or other apparatus capable of hosting data in a non-temporary format. In one embodiment, information stored on or otherwise provided on the machine-readable medium may represent an instruction itself, or an instruction in a format from which an instruction can be derived. This format from which an instruction can be derived may include source code, encoded instructions (e.g., in a compressed or encrypted form), packaged instructions (e.g., divided into multiple packages), and the like. Information representing an instruction in the machine-readable medium may be processed into an instruction by a processing circuit to perform any of the operations described herein. For example, deriving an instruction from information (e.g., processing by a processing circuit) may include compiling, interpreting, loading, organizing (e.g., dynamically or statically linking), encoding, decrypting, encrypting, decrypting, packaging, unpackaging, or otherwise manipulating the information (e.g., from source code, object code, etc.) into an instruction.

[0115] In one embodiment, instruction derivation may involve assembling, compiling, or interpreting information (e.g., by a processing circuit) for creating instructions from some intermediate or pre-processed format provided by a machine-readable medium. If the information is provided in multiple parts, it may be combined, unpacked, and modified to create instructions. For example, the information may be in multiple compressed source code packages (or object code, or binary executable code, etc.) on one or more remote servers. The source code packages may be encrypted as they are transferred over a network, decrypted as needed, decompressed, assembled (e.g., linked), compiled or interpreted on the local machine (e.g., into a library, a standalone executable, etc.), and executed by the local machine.

[0116] In this specification, the terms “comprises” and their variations are not limited in meaning when they appear in the specification and claims. Such terms are understood to suggest that they include one or more processes or elements described, but not that any other one or more processes or elements are excluded. “Consisting of” means including and limiting to everything that precedes the phrase “consisting of.” Thus, the phrase “consisting of” indicates that the enumerated elements are necessary or essential, and no other elements may be present. “Consisting essentially of” means including all elements that follow the phrase, and limiting to other elements that do not interfere with or contribute to the function or role specified in this disclosure with respect to the enumerated elements. Thus, the phrase “consisting essentially of” indicates that the enumerated elements are necessary or essential, but other elements are included at the discretion of the enumerator and may or may not be present, depending on whether they substantially affect the function or role of the enumerated elements. Any element or combination of elements listed in this specification in open-ended language (e.g., "contains" and its derivatives) shall be deemed to be further listed in closed-ended language (e.g., "consists of" and its derivatives) and partially closed-ended language (e.g., "essentially consists of" and its derivatives).

[0117] The terms “preferred” and “preferably” refer to embodiments of the disclosure that can provide a particular benefit under certain circumstances. However, other embodiments may also be preferred under the same or other circumstances. Furthermore, the description of one or more preferred embodiments does not imply that other claims are unhelpful, nor is it intended to exclude other embodiments from the scope of the disclosure.

[0118] In this application, terms such as “a,” “an,” and “the” are not intended to refer to only one entity, but include a general category for which specific examples may be used to illustrate. The terms “a,” “an,” and “the” are used interchangeably with the term “at least one.” The phrases “at least one of” and “including at least one of” following an enumeration refer to any one item in the enumeration, and any combination of two or more items in the enumeration.

[0119] As used herein, the term "or" is used in its ordinary sense, generally including "and / or," unless otherwise specified.

[0120] The term "and / or" means one or all of the enumerated elements, or any combination of two or more of the enumerated elements.

[0121] Furthermore, in this specification, all numbers are considered to be modified by the term “about,” and in certain embodiments, preferably by the term “exactly.” As used herein, in relation to a measured quantity, the term “about” refers to the variation of the measured quantity that can be predicted by a person skilled in the art who performs the measurement and exercises a level of care commensurate with the purpose of the measurement and the precision of the measuring instrument used. In this specification, the “maximum” number (e.g., maximum 50) includes that number (e.g., 50).

[0122] Furthermore, the notation of a numerical range by endpoints includes all numbers contained within that range, as well as its endpoints (for example, 1-5 includes 1, 1.5, 2, 2.75, 3, 3.80, 4, 5, etc.) and any subranges (for example, 1-5 includes 1-4, 1-3, 2-4, etc.).

[0123] As used herein, the term "room temperature" refers to a temperature between 20°C and 25°C.

[0124] The terms “in the range” or “within a range” (and similar phrases) include the endpoints of the range that is described.

[0125] Throughout this specification, references to “one embodiment,” “a certain embodiment,” “a particular embodiment,” or “several embodiments” mean that a particular feature, configuration, composition, or characteristic described in relation to an embodiment is included in at least one embodiment of this disclosure. Therefore, the appearance of such phrases in various places throughout this specification does not necessarily refer to the same embodiment of this disclosure. Furthermore, features, configurations, compositions, or characteristics may be combined in any preferred manner in one or more embodiments. [Examples]

[0126] These examples are for illustrative purposes only and are not intended to unduly limit the scope of the attached claims. While the broad ranges and parameters of this disclosure are approximations, the values ​​shown in the specific examples are reported as accurately as possible. However, each value inherently contains certain errors that inevitably arise from the standard deviation found in the respective test measurements. At a minimum, each numerical parameter should be interpreted by applying common rounding techniques in light of the number of significant figures reported, but this is not intended to limit the application of the doctrine of equivalents to the claims.

[0127] Unless otherwise specified, all chemicals used in the examples can be obtained from the specified suppliers. The adhesives used in the examples may include those listed below.

[0128] glue Structural adhesives can generally be classified into two broad categories: one-component adhesives and two-component adhesives. One-component adhesives contain all the materials necessary to obtain the final cured adhesive in a single composition. Such adhesives are typically applied to the substrates to be bonded and exposed to high temperatures (e.g., temperatures above 50°C) to cure. In contrast, two-component adhesives contain two components. The first component is typically called the “main component” and contains a curable resin. The second component is called the “accelerator component” and contains a curing agent(s) and a catalyst. Various other additives may be included in one or both components.

[0129] Other adhesives used herein include hot melt adhesives, such as one-component moisture-curing hot melt adhesives. This product family is characterized by extremely high heat resistance compared to conventional thermoplastic PO hot melts. In some examples, polyurethane (PUR) hot melts containing isocyanates for chemical crosslinking processes are used. In other examples, polyolefin (POR) hot melts using silane as a reactive component are used herein. Two-component adhesives are 100% solid systems that achieve storage stability by separating the reactive components. These components are supplied in separate containers as "resin" and "curing agent". Maintaining a predetermined ratio of resin to curing agent is important to obtain the desired curing and physical properties of the adhesive. The two components are first mixed shortly before application to form the adhesive, and curing occurs at room temperature. Typically, the reaction begins as soon as the two components are mixed, so the viscosity of the mixed adhesive increases over time until the adhesive can no longer be applied to the substrate or the adhesive strength decreases due to reduced "wetting" of the substrate. Because various curing rate formulations are available, a wide range of working time (worklife) and strength rise rates after bonding can be obtained. Depending on the formulation, final strength can be reached in minutes to weeks after bonding. The adhesive must be removed from the mixing and application equipment before curing progresses to the point where the adhesive is no longer soluble. Two-part adhesives can be applied by trowel, bead or ribbon, spray, or roller, depending on the working time. The assembled object is usually fixed in place until sufficient strength is achieved for further processing. If a faster curing rate (strength rise) is desired, heat can be used to accelerate curing. This is particularly useful when the parts need to be processed more quickly after bonding, or when additional working time is required but a slow strength rise rate is unacceptable. Typically, once cured, two-part adhesives are tough and rigid with good heat and chemical resistance.

[0130] For small-volume applications, two-part adhesives can be mixed and applied manually. However, in this case, considerable care is required to ensure the correct ratio of components and sufficient mixing to guarantee proper curing and performance. Even when mixing by hand, a considerable amount is usually wasted. As a result, adhesive suppliers have developed packaging methods that allow the components to remain separated for storage and provide means for dispensing the mixed adhesive, such as parallel syringes or concentric cartridges. The package is typically inserted into an applicator handle, and the adhesive is dispensed through a disposable mixing nozzle. The package design maintains the correct ratio of components, and the use of a mixing nozzle ensures proper mixing. The adhesive can be dispensed multiple times from these packages, as long as the time between uses does not exceed the adhesive's pot life. If the pot life is exceeded, a new mixing nozzle must be used. For large-volume applications, measuring and mixing devices are available for measuring, mixing, and dispensing adhesives packaged in containers ranging from quarts to drums.

[0131] Two-part adhesives consist of a resin and a curing agent component, and harden when the two components are mixed. Two-part adhesives remain stable during storage as long as the two components are kept separate from each other. Two-part adhesives are typically designed to be dispensed in ratios set to obtain the desired properties from a specific formulation of adhesive, with common ratios being 10:1, 1:1, 2:1, etc. The reaction between the two components usually begins immediately after mixing, and the viscosity increases until it is no longer potable. This can be described as the pot life, open time, or working time, as mentioned above. Once cured, two-part adhesives are tough and rigid and have good heat and chemical resistance.

[0132] epoxy adhesive As previously described herein, the epoxy adhesives of the embodiments may include one-component and two-component adhesives. One-component epoxy adhesives may include resins. Two-component epoxies, like the one-component types of the same type, are formulated from epoxy resins. Two-component epoxies are widely used in structural applications and are used to bond many materials, for example, metals, plastics, fiber-reinforced plastics (FRP), glass, and some rubbers. They generally cure quickly and provide relatively strong adhesion. Some compositions are often brittle, but this tendency can be reduced by utilizing toughening agents and elastomers.

[0133] Two-component structural epoxy adhesives consist of a resin (Part A or Part 1) and a curing agent (Part B or Part 2). The reaction between the resin and the curing agent can be accelerated by an accelerator or chemical catalyst.

[0134] Two-part epoxy can cure at room temperature, so heat is not always necessary when using it. Generally, two-part epoxy reaches practical strength sometime between 5 minutes and 8 hours after mixing, depending on the curing agent. The reaction between the resin and curing agent can be accelerated by applying a chemical catalyst or heat.

[0135] The basic resin of all epoxies is bisphenol A diglycidyl ether (DGEBA). Bisphenol A is produced by reacting phenol with acetone under appropriate conditions. "A" stands for acetone, "phenyl" means phenol group, and "bis" means two. Therefore, bisphenol A is a product made by chemically bonding two phenols with one acetone. Unreacted acetone and phenol are extracted from bisphenol A and then reacted with a substance called epichlorohydrin. This reaction causes two ("di")glycidyl groups to attach to the ends of the bisphenol A molecule. The resulting product is bisphenol A diglycidyl ether, the basic epoxy resin. These glycidyl groups react with amine hydrogen atoms on the curing agent to produce a cured epoxy resin. Unmodified liquid epoxy resin is very viscous and unsuitable for most applications other than as a very thick adhesive.

[0136] The most common chemical raw materials used to manufacture curing agents (or hardeners) for epoxy resins that cure at room temperature are polyamines. These are organic molecules containing two or more amine groups. Structurally, amine groups are not significantly different from ammonia, except that they are bonded to an organic molecule. Like ammonia, amines are strongly alkaline. For this similarity, epoxy resin curing agents often have an ammonia-like odor, most noticeable in the voids inside the container immediately after opening. Epoxy curing agents are commonly referred to as "Part B."

[0137] The reactive amine group is a nitrogen atom having one or two hydrogen atoms bonded to it. These hydrogen atoms react with oxygen atoms from the glycidyl group on the epoxy to form a cured resin, i.e., a highly crosslinked thermosetting plastic. The cured epoxy softens with heat but does not melt. The three-dimensional structure provides the cured resin with excellent physical properties.

[0138] The ratio of glycidyl oxygen to amine hydrogen determines the final ratio of resin to curing agent, taking into account the various molecular weights and densities involved. At the appropriate ratio, a "fully crosslinked" thermosetting plastic is produced. Changing the recommended ratio leaves either unreacted oxygen or hydrogen atoms, depending on which is in excess. The resulting cured resin is not fully crosslinked and therefore has lower strength. Excess part B should generally be avoided as it leads to increased moisture sensitivity in the cured epoxy.

[0139] Amine curing agents are not "catalysts." Catalysts accelerate reactions but do not chemically become part of the final product. Amine curing agents bond with epoxy resins and significantly contribute to the final properties of the cured system. The curing time of an epoxy system depends on the reactivity of the amine hydrogen atoms. The bonded organic molecules do not directly participate in the chemical reaction, but they influence how easily the amine hydrogen atoms detach from nitrogen and react with the glycidyl oxygen atoms. Therefore, the curing time is determined by the kinetics of the specific amine used as the curing agent. The curing time of any given epoxy system can only be changed by adding an accelerator to a system that can accept that accelerator, or by changing the temperature and mass of the resin / curing agent mixture. Increasing the amount of curing agent added does not "speed things up," nor does decreasing the amount of curing agent added "slow things down."

[0140] The epoxy curing reaction is exothermic. The rate at which epoxy resins cure depends on the curing temperature; the warmer the temperature, the faster the curing. The curing rate changes by approximately half or double for every 18°F (10°C) change in temperature. For example, if an epoxy system takes 3 hours to become non-tacky at 70°F, it will become non-tacky in 1.5 hours at 88°F and in 6 hours at 52°F. All matters related to the reaction rate follow this general rule. Pot life and working time are greatly influenced by the initial temperature of the mixed resin and curing agent. For example, on a hot day, the two materials can be cooled before mixing to extend the working time.

[0141] The gel time of a resin is the time it takes for a given mass of resin, held in a dense volume, to solidify. Gel time depends on the initial temperature of the mass and follows the rules described above. For example, 100 grams (approximately 3 fluid ounces) of Silver Tip Laminating Epoxy and Fast Hardener (as a descriptive example) will solidify in 25 minutes if started at 77°F, and the gel time at 60°F will be approximately 50 minutes. If the same mass were spread over 4 square feet at 77°F, the gel time would be slightly over 3 hours. In addition to being temperature-sensitive, curing time is also surface area / mass-sensitive.

[0142] As the reaction progresses, heat is released. If the generated heat is immediately dissipated into the surroundings (as happens with films), the temperature of the curing resin does not rise, and the reaction proceeds at a uniform pace. If the resin is contained (as in a mixing pot), the exothermic reaction raises the temperature of the mixture and accelerates the reaction.

[0143] The working time / work life (WL) of epoxy formulations is approximately 75% of the gel time in the pot configuration. Working time can be extended by increasing the surface area, working with smaller masses, or cooling the resin and curing agent before mixing. The material remaining in the pot increases in absolute viscosity due to polymerization (e.g., measured at 75°F), although its apparent viscosity initially decreases due to heating. The material remaining in the pot at 75% of the gel time appears very thin (due to heating), but is actually very thick once cooled to room temperature. Experienced users will either mix batches intended for almost immediate application or increase the surface area to slow down the reaction.

[0144] The curing rate of epoxy depends on temperature, but the curing mechanism is temperature-independent. The reaction proceeds most rapidly in the liquid state. As curing progresses, the system changes from a liquid to a sticky, viscous, soft gel. After gelling, the reaction rate slows down as the hardness increases. Chemical reactions proceed more slowly in the solid state. The system hardens from a soft, sticky gel and slowly loses its stickiness. The system becomes non-sticky and continues to harden and strengthen over time.

[0145] At room temperature, the system reaches approximately 60-80% of its final strength after 24 hours. Curing then proceeds slowly over the next few weeks, eventually reaching a point where further curing will not occur without a significant increase in temperature. However, for most purposes, a system cured at room temperature can be considered fully cured after 72 hours at 77°F. Highly elastic systems, such as Phase Two epoxy, require post-curing at high temperatures to reach complete curing.

[0146] Generally, if possible with the specific system being used, it is more efficient to work with a curing time that is practical for manual application. This allows the user to proceed to the next step without wasting time waiting for the epoxy to cure. Films with short adhesion times and fast curing times are less likely to pick up airborne contaminants such as fly footprints and insects.

[0147] An epoxy resin composition generally comprises a first liquid portion containing epoxy resin and a second liquid portion containing a curing agent. The first and second portions are liquid at ambient temperature, but the liquid portions may contain solid components dissolved or dispersed within the liquid.

[0148] The first component of the two-component composition contains at least one epoxy resin. The epoxy resin is typically a low molecular weight monomer or high molecular weight polymer containing at least two epoxy groups. The epoxy group is a cyclic ether having three ring atoms and is sometimes called a glycidyl group or oxirane group. The epoxy resin is typically liquid at ambient temperature.

[0149] Various epoxy resins are known, including, for example, bisphenol A type epoxy resin, bisphenol F type epoxy resin, bisphenol S type epoxy resin, phenol novolac type epoxy resin, alkylphenol novolac type epoxy resin, cresol novolac type epoxy resin, biphenyl type epoxy resin, aralkyl type epoxy resin, cyclopentadiene type epoxy resin, naphthalene type epoxy resin, naphthol type epoxy resin, epoxy resins of condensates of phenol and aromatic aldehydes having phenolic hydroxyl groups, biphenyl aralkyl type epoxy resin, fluorene type epoxy resin, xanthene type epoxy resin, triglycidyl isocyanurate, rubber-modified epoxy resin, and phosphorus-based epoxy resin.

[0150] Furthermore, blends of various epoxy-containing materials can be used. Suitable blends may include two or more weight-average molecular weight distributions of epoxy-containing compounds, such as low molecular weight epoxides (e.g., those with a weight-average molecular weight of less than 200 g / mole), intermediate molecular weight epoxides (e.g., those with a weight-average molecular weight of about 200 to 1000 g / mole), and high molecular weight epoxides (e.g., those with a weight-average molecular weight greater than about 1000 g / mole). Alternatively, or in addition, the epoxy resin may include blends of epoxy-containing materials having different chemical properties (e.g., aliphatic and aromatic) or functional properties (e.g., polar and nonpolar).

[0151] In one embodiment, the first component of a two-component composition comprises at least one bisphenol (e.g., A) epoxy resin. Bisphenol (e.g., A) epoxy resins are formed by reacting epichlorohydrin with bisphenol A to form a diglycidyl ether of bisphenol A. The simplest resins in this class are formed by reacting 2 moles of epichlorohydrin with 1 mole of bisphenol A to form a bisphenol A diglycidyl ether (commonly abbreviated as DGEBA or BADGE). DGEBA resins are colorless to pale yellow transparent liquids at ambient temperature, typically with a viscosity in the range of 5–15 Pa·s at 25°C. Because pure DGEBA tends to form a crystalline solid when stored at ambient temperature, industrial grades usually have some molecular weight variation. The same reaction can be carried out using other bisphenols, such as bisphenol F. The choice of epoxy resin to be used depends on the intended end application. Epoxides with a flexible skeleton may be desirable if greater ductility is required at the bonding line. Materials such as bisphenol A diglycidyl ether and bisphenol F diglycidyl ether provide desirable structural adhesive properties, which are achieved through curing. On the other hand, hydrogenated versions of these epoxies may be useful in terms of compatibility with substrates having oily surfaces.

[0152] Aromatic epoxy resins can also be prepared by the reaction of aromatic alcohols such as biphenyldiols, triphenyldiols, and triols with epichlorohydrins. Such aromatic biphenyl epoxy resins and aromatic triphenyl epoxy resins are not bisphenol epoxy resins.

[0153] There are two main types of aliphatic epoxy resins: glycidyl epoxy resins and alicyclic epoxides. Glycidyl epoxy resins are typically formed by reacting epichlorohydrin with an aliphatic alcohol or polyol to obtain a glycidyl ether, or with an aliphatic carboxylic acid to obtain a glycidyl ester. The resulting resins can be monofunctional (e.g., dodecanol glycidyl ether), difunctional (diglycidyl ester of hexahydrophthalic acid), or highly functional (e.g., trimethylolpropane triglycidyl ether). Alicyclic epoxides contain one or more alicyclic rings in the molecule to which an oxirane ring is condensed (e.g., 3,4-epoxycyclohexylmethyl-3,4-epoxycyclohexanecarboxylate). They are formed by the reaction of cycloolefins with peracids, such as peracetic acid. These aliphatic epoxy resins typically exhibit low viscosity (10-200 mPa·s) at ambient temperature and are often used as reactive diluents. As such, they are used to modify (reduce) the viscosity of other epoxy resins. Thus, the term “modified epoxy resin” refers to an epoxy resin containing a reactive diluent that reduces viscosity. In some embodiments, the resin composition may further contain a reactive diluent. Examples of reactive diluents include diglycidyl ether of 1,4-butanediol, diglycidyl ether of cyclohexanedimethanol, diglycidyl ether of resorcinol, p-tert-butylphenyl glycidyl ether, cresyl glycidyl ether, diglycidyl ether of neopentyl glycol, triglycidyl ether of trimethylolethane, triglycidyl ether of trimethylolpropane, triglycidyl p-aminophenol, N,N'-diglycidylaniline, N,N,N',N'-tetraglycidylmeth-xylylenediamine, and polyglycidyl ether of vegetable oil. The resin composition may contain at least 1, 2, 3, 4, or 5% by weight, typically 15 or 20% by weight or less, of such reactive diluents.

[0154] In some embodiments, the resin composition contains (e.g., bisphenol A) epoxy resin in an amount of at least about 50% by weight of the total resin composition, which includes a mixture of boron nitride particles and cellulose nanocrystals. In some embodiments, the amount of (e.g., bisphenol A) epoxy resin does not exceed 95, 90, 80, 85, 80, 75, 70, or 65% by weight of the total resin composition.

[0155] Epoxys are typically cured using a stoichiometric or nearly stoichiometric amount of curing agent. In the case of two-component epoxy compositions, the second component contains a curing agent, also referred to herein as the curing agent. Equivalents or epoxide value are used to calculate the amount of co-reactants (curing agent) used when curing epoxy resins. The epoxide value is the number of epoxide equivalents (eq / kg) in 1 kg of resin, where equivalent is the weight in grams (g / mol) of resin containing 1 molar equivalent of epoxide. Equivalents (g / mol) = 1000 / epoxide value (eq / kg).

[0156] Common curing agents for epoxy resins include amines, amides, ureas, imidazoles, and thiols. In typical embodiments, the curing agent is a reactive-NH group or a reactive-NR group. 1 R 2 Includes the base, where R 1 and R 2 These are independently H or C1-C4 alkyl groups, most typically H or methyl groups.

[0157] The curing agent is typically highly reactive with epoxy groups at ambient temperature. Such curing agents are typically liquid at ambient temperature. However, the first curing agent may be solid, provided that it has an activation temperature below ambient temperature.

[0158] Some curing agents are primary, secondary, and tertiary polyamines. Polyamine curing agents may be linear, branched, or cyclic. In some preferred embodiments, the polyamine crosslinking agent is aliphatic. Alternatively, aromatic polyamines can be used.

[0159] Useful polyamines are polyamines of the general formula R 5 -(NR 1 R 2 ) x , wherein R 1 and R 2 are independently H or alkyl, R 5 is a polyvalent alkylene or arylene, and x is at least 2. The alkyl groups of R 1 and R 2 are typically C1-C 18 alkyl, more typically C1-C4 alkyl, and most typically methyl. R 1 and R 2 can be combined to form a cyclic amine. In some embodiments, x is 2 (i.e., a diamine). In other embodiments, x is 3 (i.e., a triamine). In still other embodiments, x is 4.

[0160] Examples include hexamethylenediamine; 1,10-diaminodecane; 1,12-diaminododecane; 2-(4-aminophenyl)ethylamine; isophoronediamine; 4,4'-diaminodicyclohexylmethane; and 1,3-bis(aminomethyl)cyclohexane. Examples of 6-membered ring diamines include, for example, piperazine and 1,4-diazabicyclo[2.2.2]octane ("DABCO").

[0161] Other useful polyamines include polyamines having at least three amino groups, where the three amino groups are primary, secondary, or a combination thereof. Examples include 3,3'-diaminobenzidine, hexamethylenetriamine, and triethylenetetramine.

[0162] The specific composition of the epoxy resin can be selected based on the intended end use. For example, in one embodiment, the resin composition may be for insulation, as described in U.S. Patent Application Publication No. 2014 / 0080940, which is incorporated herein by reference.

[0163] The resin composition may optionally further contain additives including fillers (e.g., silane-treated or untreated), anti-sagging additives, thickeners / anti-sagging agents, processing aids, waxes, and UV stabilizers. Typical examples of fillers include glass bubbles, fumed silica, mica, feldspar, and wollastonite. In some embodiments, the resin composition further contains other thermally conductive fillers such as aluminum oxide, aluminum hydroxide, fused silica, zinc oxide, aluminum nitride, silicon nitride, magnesium oxide, beryllium oxide, diamond, and copper.

[0164] Methyl methacrylate (MMA) adhesive The methyl methacrylate (MMA) adhesives in the embodiments may include one-component and two-component MMA adhesives. One-component MMA adhesives may contain resin. Two-component methyl methacrylate (MMA) adhesives have a faster rise in strength than epoxy. MMA adhesives are commonly used for bonding plastics and metals to plastics. They are also very effective for bonding solid surface materials and can be colored, so they are widely used in the manufacture and installation of countertops.

[0165] Methyl methacrylate adhesives are structural acrylic adhesives made from Part A (Part 1) resin and Part B (Part 2) curing agent. Most MMAs also contain rubber and additional reinforcing agents. MMAs cure rapidly at room temperature and have maximum adhesive strength immediately after application. This adhesive is resistant to shear, peel, and impact stress. Looking at the bonding process more technically, these adhesives work by producing an exothermic polymerization reaction. Polymerization is the process of reacting multiple monomer molecules together in a chemical reaction to form polymer chains. This means that the adhesive produces a strong bond while maintaining flexibility. These adhesives can form bonds between dissimilar materials with different flexibility, such as metals and plastics. Unlike some other structural adhesives, such as two-part epoxy, MMAs do not require heat for curing. Several MMAs with a wide pot life are available so that they can be tailored to specific needs.

[0166] MMA has relatively high peel strength and relatively high heat resistance. Because MMA develops strength relatively quickly, parts can be used relatively soon. It is also worth noting the various different processing conditions that can be used with MMA. For example, the two components of MMA can be applied separately to each of the materials to be bonded, and MMA will not begin to harden until the joint is brought together and the components bond. This means that good adhesion can be achieved without having to deal with precise mixing ratios. It is important to remember that MMA tends to have a strong odor, so good ventilation should be ensured when applying it, and that MMA is flammable, requiring some precautions.

[0167] MMA is formulated to have a pot life of 5 to 20 minutes.

[0168] All types of these acrylic structural adhesives offer exceptional bonding strength and durability, which is close to that of epoxy adhesives, but with the advantages of faster curing speed, lower sensitivity to surface pretreatment, and the ability to bond a wider variety of materials.

[0169] Silicone adhesive The silicone adhesives in the embodiments may include one-component and two-component silicone adhesives. Two-component silicone adhesives are generally used when the bonding area is large or when there is insufficient relative humidity to complete curing. Common applications of these include electronics applications, including the manufacture of home appliances, in the manufacture of automobiles and windows.

[0170] Suitable silicone resins include moisture-curing silicones such as hydroxyl-terminated silicones, silicone rubbers, and fluorosilicones, as well as condensation-curing silicones and addition-curing silicones. Examples of suitable commercially available silicone PSA compositions containing silicone resins include Dow Corning's 280A, 282, 7355, 7358, 7502, 7657, Q2-7406, Q2-7566, and Q2-7735; and General Electric's PSA 590, PSA 600, PSA 595, PSA 610, PSA 518 (medium phenyl content), PSA 6574 (high phenyl content), PSA 529, PSA 750-D1, PSA 825-D1, and PSA 800-C. An example of a commercially available two-component silicone resin is sold by Dow Chemical Company (Midland, Michigan) under the trade name "SILASTIC J".

[0171] Examples of pressure-sensitive adhesives (PSAs) include natural or synthetic rubbers such as styrene block copolymers (styrene-butadiene; styrene-isoprene; styrene-ethylene / butylene block copolymers); nitrile rubber, synthetic polyisoprene, ethylene-propylene rubber, ethylene-propylene-diene monomer rubber (EPDM), polybutadiene, polyisobutylene, butyl rubber, styrene-butadiene random copolymer, and combinations thereof.

[0172] Additional pressure-sensitive adhesives include poly(alpha-olefin) elastomers, polychloroprene elastomers, and silicone elastomers. Polychloroprene contains halogens that can contribute to flame retardancy, and silicone elastomers are resistant to thermal degradation; therefore, in some embodiments, polychloroprene elastomers and silicone elastomers may be preferred.

[0173] Urethane adhesive Examples of urethane adhesives used in the embodiments may include both one-component and two-component urethane adhesives. Two-component urethane adhesives can be formulated to have a wide range of properties and characteristics upon curing. Two-component urethane adhesives are often used, for example, to bond glass to metal or aluminum to steel.

[0174] Most polyurethane adhesives are either polyester-based or polyether-based. They exist within the isocyanate prepolymer and the active hydrogen-containing curing agent component (polyol). These form multiple flexible parts composed of polyurethane, while the isocyanate groups form multiple rigid parts. The flexible parts typically constitute the majority of the elastic polyurethane adhesive and therefore determine its physical properties. For example, polyester-based polyurethane adhesives have better oxidation and high-temperature stability than polyether-based polyurethane adhesives, but inferior hydrolysis stability and low-temperature flexibility. However, polyethers are generally more expensive than polyesters.

[0175] Many polyurethane adhesives are sold as two-component polyurethane adhesives. The first component contains a diisocyanate and / or isocyanate prepolymer (Part 1), and the second component consists of a polyol (and amine / hydroxyl chain extender) (Part 2). Catalysts are often added to accelerate curing, and these catalysts are typically tin salts or tertiary amines. The reactive raw materials are often blended with additives and plasticizers to achieve desired processing and / or final properties, and to reduce costs.

[0176] Polyurethanes may be prepared by the reaction of, for example, one or more polyols and / or polyamines and / or amino alcohols with one or more polyisocyanates in the presence of one or more non-reactive components (optional). In applications where weathering is likely, it is typically desirable that the polyols, polyamines and / or amino alcohols, and polyisocyanates do not contain aromatic groups.

[0177] Suitable polyols include, for example, materials commercially available under the trademark name DESMOPHEN from Bayer Corporation in Pittsburgh, Pennsylvania. The polyols may be polyester polyols (e.g., Desmophen 631A, 650A, 651A, 670A, 680, 110, and 1150); polyether polyols (e.g., Desmophen 550U, 1600U, 1900U, and 1950U); or acrylic polyols (e.g., Demophen A160SN, A575, and A450BA / A).

[0178] Suitable polyamines include, for example, aliphatic polyamines such as ethylenediamine, 1,2-diaminopropane, 2,5-diamino-2,5-dimethylhexane, 1,11-diaminoundecane, 1,12-diaminododecane, 2,4- and / or 2,6-hexahydroxytoluenediamine, and 2,4'-diaminodicyclohexylmethane; aromatic polyamines such as 2,4- and / or 2,6-diaminotoluene and 2,4'- and / or 4,4'-diaminodiphenylmethane; for example, those available from Huntsman Chemical (Salt Lake City, Utah) under the trade name JEFFAMINE polypropylene glycol diamine (e.g., Jeffamine XTJ-510), and from Noveon Corp. (Cleveland, Ohio) under the trade name Hycar Examples include amine-terminated polymers available as ATBN (amine-terminated acrylonitrile butadiene copolymers), as well as those disclosed in U.S. Patent No. 3,436,359 (Hubin et al.) and U.S. Patent No. 4,833,213 (Leir et al.) (amine-terminated polyethers and polytetrahydrofrangimine); and combinations thereof.

[0179] Suitable amino alcohols include, for example, 2-aminoethanol, 3-aminopropan-1-ol, alkyl-substituted derivatives thereof, and combinations thereof.

[0180] Suitable polyisocyanate compounds include, for example, aromatic diisocyanates (e.g., 2,6-toluene diisocyanate; 2,5-toluene diisocyanate; 2,4-toluene diisocyanate; m-phenylenediisocyanate; p-phenylenediisocyanate; methylenebis(o-chlorophenyl diisocyanate); methylenediphenylene-4,4'-diisocyanate; polycarbodiimide-modified methylenediphenylenediisocyanate; (4,4'-diisocyanato-3,3',5,5'-tetraethyl)diphenylmethane; 4,4'-diisocyanato-3,3'-dimethoxybiphenyl(o-dianisidine diisocyanate); 5-chloro-2,4-toluene diisocyanate; and 1-chloromethyl-2,4-diisocyanatobenzene), aromatic-aliphatic diisocyanates (e.g., m-xylylene diisocyanate and Tetramethyl-m-xylylene diisocyanate); aliphatic diisocyanates (e.g., 1,4-diisocyanatobutane; 1,6-diisocyanatohexane; 1,12-diisocyanatododecane; and 2-methyl-1,5-diisocyanatopentane); alicyclic diisocyanates (e.g., methylenedicyclohexylene-4,4'-diisocyanate; 3-isocyanatomethyl-3,5,5-trimethylcyclohexyl isocyanate (isophorone diisocyanate); 2,2,4-trimethylhexyl diisocyanate; and cyclohexylene-1,4-diisocyanate); polymers or oligomer compounds terminated by two isocyanate functional groups (e.g., polyoxyalkylenes, polyesters, polybutadienyl, etc.) (e.g., toluene-2,4-diisocyanate-terminated polypropylene oxide glycol); Bayer Examples include polyisocyanates marketed by Corporation (Pittsburgh, PA) under the trademark names MONDUR or DESMODUR (e.g., Desmodur XP7100 and Desmodur N 3300A), and combinations thereof.

[0181] In some embodiments, the polyurethane comprises a reaction product consisting of components comprising at least one polyisocyanate and at least one polyol. In some embodiments, the polyurethane comprises a reaction product consisting of components comprising at least one polyisocyanate and at least one polyol. In some embodiments, the at least one polyisocyanate comprises an aliphatic polyisocyanate. In some embodiments, the at least one polyol comprises an aliphatic polyol. In some embodiments, the at least one polyol comprises a polyester polyol or a polycarbonate polyol.

[0182] Typically, polyurethane(s) are stretchable and / or flexible. For example, polyurethane(s), or any layer containing polyurethane, may have a breaking elongation of at least 10, 20, 40, 60, 80, 100, 125, 150, 175, 200, 225, 250, 275, 300, 350, or even at least 400 percent (under atmospheric conditions).

[0183] In certain embodiments, the polyurethane has multiple rigid parts, which are typically parts in amounts ranging from 35, 40, or 45 percent by weight to 50, 55, 60, or even 65 percent by weight, corresponding to one or any combination of polyisocyanates.

[0184] As used herein, weight percent means weight percent based on the total weight of the material. Weight of hard portion % = (Weight of short-chain diols and polyhydric alcohols + Weight of short-chain di- or polyisocyanates) / Total weight of resin Here, Short-chain diols and polyols have an equivalent weight of ≤185 g / eq and a functionality of ≥2, while short-chain isocionates have an equivalent weight of ≤320 g / eq and a functionality of ≥2.

[0185] Typically, two-component polyurethanes contain one or more catalysts. Well-known catalysts for two-component polyurethanes include, for example, aluminum-based, bismuth-based, tin-based, vanadium-based, zinc-based, and zirconium-based catalysts. Tin-based catalysts have been shown to significantly reduce gas emissions during polyurethane formation. Examples of tin-based catalysts include dibutyltin compounds such as dibutyltin diacetate, dibutyltin dilaurate, dibutyltin diacetylacetonate, dibutyltin dimercaptide, dibutyltin dioctoate, dibutyltin dimaleate, dibutyltin acetonyl acetonate, and dibutyltin oxide. If present, each catalyst is typically included at a level of at least 200 parts per million by weight (ppm), 300 ppm, or higher, but this is not a requirement.

[0186] Further preferred two-component urethanes are described in U.S. Patent No. 6,258,918B1 (Ho et al.) and U.S. Patent No. 5,798,409 (Ho), which are incorporated herein by reference.

[0187] Generally, the amount of polyisocyanate relative to polyols, polyamines, and / or amino alcohols in two-component urethanes is selected in approximately stoichiometric equivalents, but in some cases, it may be desirable to adjust the relative amount to other ratios. For example, a stoichiometrically slightly excess of polyisocyanate may be useful for ensuring a high degree of incorporation of polyols, polyamines, and / or amino alcohols, but if excess isocyanate groups are present after polymerization, they will typically react with materials containing reactive hydrogen (e.g., foreign water, alcohols, amines, etc.).

[0188] All disclosures of patents, patent documents, and publications referenced herein are incorporated by reference as if each were incorporated individually. To the extent that there is any inconsistency or contradiction between this Specified Version and any disclosure of any document incorporated herein by reference, this Specified Version shall prevail. Those skilled in the art will see various modifications and changes to this Disclosure that do not deviate from the scope and intent of this Disclosure. This Disclosure is not intended to be unduly limited by the embodiments and examples described herein, which are presented only as examples within the scope of this Disclosure, intended to be limited only by the claims described herein as follows: The following are exemplary embodiments. [Item 1] A dispensing device system, One or more dispensing device components comprising one or more sensors for providing at least one process parameter of a dispensable material, wherein one or more sensors include a temperature sensor having a probe positioned in a fluid path of the dispensable material and configured to sense the temperature of the dispensable material, The system comprises a processor operably connected to one or more dispensing device components, and the processor The system receives at least one parameter related to the dispensing device system, Based on the above at least one parameter, the values ​​of the operating parameters of the dispensing device system are determined to achieve the flow rate of the dispensable material within the dispensing device system. The aforementioned operating parameters are provided, The temperature of the dispensable material is received from the temperature sensor. Based on the perceived temperature, the values ​​of the operating parameters of the dispensing device system are adjusted. The system is configured to provide the adjusted operating parameters. Dispensing device system. [Item 2] The dispensing device system according to item 1, wherein the operating parameter is the driving force pressure of the dispensing device system. [Item 3] The dispensing apparatus system according to item 1, wherein the temperature sensor further comprises a shield that separates the outer surface of the probe from the dispensable material, and the probe is configured to sense the temperature of the dispensable material through the shield. [Item 4] The dispensing apparatus system according to item 1, wherein the probe is configured to directly contact the dispensable material in the fluid path. [Item 5] The one or more sensors further comprises conductivity sensors configured to sense the conductivity of the dispensable material, and the processor, The conductivity of the dispenseable material is received from the conductivity sensor. Based on the perceived temperature and the perceived conductivity of the dispenseable material, the hardening state of the dispenseable material is determined. The system is further configured to adjust the values ​​of the operating parameters of the dispensing device system based on the sensed temperature and the curing state. The dispensing device system described in item 1. [Item 6] The dispensing apparatus system according to item 5, wherein the temperature sensor comprises the conductivity sensor, and the temperature sensor is further configured to apply a voltage to the dispensable material in the fluid path of the dispensable material. [Item 7] The dispensing apparatus system according to item 1, further comprising a user input device, wherein at least one process parameter is received from user input via the user input device. [Item 8] The dispensing device system according to item 1, wherein at least one parameter of the dispensing device system includes the density of the dispensable material. [Item 9] The mass measuring device further comprises one or more sensors configured to sense the mass data of the dispensable material, The at least one process parameter includes the mass data, The processor is further configured to determine the volumetric flow rate of the dispensable material based on the density and mass data of the dispensable material. The dispensing device system described in item 8. [Item 10] The dispensing apparatus system described in item 1, further comprising a wireless communication interface. [Item 11] The dispensing apparatus system according to item 10, wherein the processor is further configured to communicate with the dispensing apparatus system via the wireless communication interface. [Item 12] The dispensing device system according to item 10, wherein the processor is further configured to communicate with a smartphone via the wireless communication interface. [Item 13] The dispensing device system according to item 12, wherein the processor is configured to provide the smartphone via the wireless communication interface with a request for input data to determine the operating parameters. [Item 14] The aforementioned processor, Based on the at least one parameter and the at least one process parameter, the maximum idle time or purge time of one or more dispensing device components is determined. Further configured to provide the maximum idle time or the purge time, The dispensing device system described in item 1. [Item 15] The dispensing apparatus system according to item 14, wherein the one or more sensors are further configured to provide at least one environmental parameter, and the processor is further configured to determine the maximum idle time or the purge time based on the at least one environmental parameter, the at least one parameter, and the at least one process parameter. [Item 16] The processor further comprises a user interface operably connected to the processor, and the processor It receives one or more user inputs that include one or more of the following: safety factors, process control factors, and discard factors. The system is further configured to determine the maximum idle time or the purge time based on the one or more user inputs, the at least one parameter, and the at least one process parameter. The dispensing device system described in item 14. [Item 17] The dispensing apparatus system according to item 1, wherein the processor is configured to provide the operating parameters and the adjusted operating parameters to one or more dispensing apparatus components. [Item 18] The dispensing device system according to item 1, wherein the processor is configured to provide the operating parameters and the adjusted operating parameters to a human-machine interface. [Item 19] The dispensing apparatus system according to item 1, wherein the dispenseable material includes an adhesive. [Item 20] A method for dispensing dispensable material using a dispensing device system, The system receives at least one parameter related to the aforementioned dispensing device system, Based on the aforementioned at least one parameter, the values ​​of the operating parameters of the dispensing device system are determined to achieve the flow rate of the dispensable material within the dispensing device system. To provide the aforementioned operating parameters, The process receives at least one process parameter, including the sensed temperature of the dispenseable material, Based on the sensed temperature, the values ​​of the operating parameters of the dispensing device system are adjusted. To provide the adjusted operating parameters, Methods that include... [Item 21] The method according to item 20, wherein the operating parameter is the driving force pressure of the dispensing device system. [Item 22] Receiving the sensed conductivity of the dispensable material from the conductivity sensor, Based on the sensed temperature and the sensed conductivity of the dispenseable material, the curing state of the dispenseable material is determined. Based on the sensed temperature and the curing state, the values ​​of the operating parameters of the dispensing device system are adjusted. The method described in item 20, further including the method described in item 20. [Item 23] The method according to item 20, wherein the at least one parameter of the dispensing device system includes the density of the dispensable material. [Item 24] The method according to item 23, wherein the at least one process parameter further includes mass data of the dispensable material, and the method further includes determining the volumetric flow rate of the dispensable material based on the density of the dispensable material and the mass data of the dispensable material. [Item 25] Based on the at least one parameter and the at least one process parameter, the maximum idle time or purge time of one or more dispensing device components of the dispensing device system is determined. To provide the aforementioned maximum idle time or the aforementioned purge time, The method described in item 20, further including the method described in item 20. [Item 26] It receives at least one environmental parameter provided by one or more sensors, Determining the maximum idle time or the purge time based on the at least one environmental parameter, the at least one parameter, and the at least one process parameter, The method described in item 25, further including the method described in item 25. [Item 27] Receiving one or more user inputs that include one or more of the following: safety factors, process control factors, and discard factors, Determining the maximum idle time or the purge time based on the one or more user inputs, the at least one parameter, and the at least one process parameter, The method described in item 25, further including the method described in item 25. [Item 28] The method according to item 20, further comprising providing the aforementioned operating parameters and the adjusted operating parameters to one or more components of the dispensing device system. [Item 29] The method according to item 20, further comprising providing the aforementioned operating parameters and the adjusted operating parameters to a human-machine interface. [Item 30] The method according to item 20, wherein the dispenseable material includes an adhesive. [Item 31] A dispensing device system, One or more dispensing device components configured to provide dispensable material, The system comprises a processor operably connected to one or more of the aforementioned dispensing device components, and the processor is Multiple calibration data points of the dispensing device system, which receive multiple calibration data points based on multiple dispensing samples of the dispensable material and one or more parameters of one or more components of the dispensing device, Select one or more predetermined models based on one or more parameters of the dispensing device components, A calibration model is determined based on the plurality of calibration data points and one or more models. Based on the calibration model, it is configured to adjust one or more settings of one or more of the one or more dispensing device components. Dispensing device system. [Item 32] The dispensing device system according to item 31, wherein the plurality of calibration data points include at least 5 calibration data points and 15 or fewer calibration data points. [Item 33] The dispensing device system according to item 31, wherein one or more parameters of one or more dispensing device components include a parameter indicating the manufacturing lot of the dispensable material, and the processor is configured to select one or more predetermined models based on the manufacturing lot of the dispensable material. [Item 34] In order to determine the calibration model, the processor, Based on the one or more models, determine one or more outliers among the plurality of calibration data points. To generate a corrected set of calibration data points, remove one or more outliers from the plurality of calibration data points. The system is further configured to determine the calibration model based on the modified set of calibration data points and one or more predetermined models. The dispensing device system described in item 31. [Item 35] The dispensing device system according to item 34, wherein the processor is further configured to perform a regression analysis based on the plurality of calibration points and the one or more predetermined models in order to determine the one or more outliers. [Item 36] The dispensing device system according to item 34, wherein the processor is further configured to determine one or more calibration points among the plurality of calibration points in which the difference from the corresponding data points of one or more predetermined models exceeds a threshold, in order to determine the one or more outliers. [Item 37] The dispensing device system according to item 36, wherein the threshold is a percentage difference. [Item 38] The system further comprises a display for displaying information and a user interface for receiving user input, and the processor is: If it is determined that the aforementioned corrected set of calibration data points contains fewer data points than the threshold number, Based on the modified set of calibration data points and the one or more predetermined models, one or more dispensing device pressures for one or more additional calibration data points are determined. Using the display, request one or more additional calibration data points, each of which corresponds to one or more of the one or more dispensing device pressures. Using the user interface described above, receive one or more additional calibration data points. The dispensing device system according to item 34, further configured to modify the modified set of calibration data points to include one or more additional calibration data points. [Item 39] The dispensing apparatus system according to item 31, wherein each of the plurality of calibration data points includes the dispensing apparatus pressure, the dispensing time, and the mass of the dispensable material to be dispensed. [Item 40] A method for calibrating a dispensing device system, The dispensing device system has multiple calibration data points, which receive multiple calibration data points based on multiple dispensing samples of dispensable material, and one or more parameters of the dispensing device components of the calibration system. Selecting one or more predetermined models based on one or more parameters of the dispensing device components, Determining a calibration model based on the plurality of calibration data points and one or more predetermined models, This includes providing one or more settings for the dispensing device system based on the calibration model, method. [Item 41] The method according to item 40, wherein the plurality of calibration data points include at least two calibration data points and 100 or fewer calibration data points. [Item 42] The method according to item 40, wherein one or more parameters of the dispensing device components include a parameter indicating the manufacturing lot of the dispensable material, and one or more predetermined models are incorporated based on the manufacturing lot of the dispensable material. [Item 43] Determining the aforementioned model Determining one or more outliers among the plurality of calibration data points based on the one or more models, To generate a corrected set of calibration data points, remove one or more outliers from the plurality of calibration data points, The process includes determining the calibration model based on the modified set of calibration data points and one or more predetermined models, The method described in item 40. [Item 44] The method according to item 43, wherein determining the one or more outliers includes a regression analysis based on the plurality of calibration points and the one or more predetermined models. [Item 45] The method according to item 43, wherein determining the one or more outliers includes determining one or more calibration points among the plurality of calibration points in which the difference from the corresponding data points of the one or more predetermined models exceeds a threshold. [Item 46] The method according to item 45, wherein the threshold is a percentage difference. [Item 47] It is determined that the aforementioned set of corrected calibration data points contains fewer data points than the threshold number, Based on the modified set of calibration data points and the one or more predetermined models, determine one or more dispensing device pressures for one or more additional calibration data points, The requirement is one or more additional calibration data points, each corresponding to one or more of the one or more dispensing device pressures, Receiving one or more additional calibration data points, Modifying the modified set of calibration data points to include one or more additional calibration data points, The method described in item 43, further including the method described in item 43. [Item 48] The method according to item 40, wherein each of the plurality of calibration data points includes the pressure of the dispensing device, the dispensing time, and the mass of the dispensable material to be dispensed.

Claims

1. A dispensing device system, One or more dispensing device components comprising one or more sensors for providing at least one process parameter of a dispensable material, wherein the one or more sensors include (i) a temperature sensor having a probe positioned in a fluid path of the dispensable material and configured to sense the temperature of the dispensable material, and (ii) a conductivity sensor configured to sense the conductivity of the dispensable material, The system comprises a processor operably connected to one or more of the aforementioned dispensing device components, and the processor is The system receives at least one parameter related to the dispensing device system, Based on the at least one parameter, the values ​​of the operating parameters of the dispensing device system are determined to achieve the flow rate of the dispensable material within the dispensing device system. The aforementioned operating parameters are provided, The temperature of the dispensable material is received from the temperature sensor. The conductivity of the dispenseable material is received from the conductivity sensor. Based on the perceived temperature and the perceived conductivity of the dispenseable material, the hardening state of the dispenseable material is determined. Based on the perceived temperature and the curing state, the values ​​of the operating parameters of the dispensing device system are adjusted. The system is configured to provide the adjusted operating parameters. Dispensing device system.

2. The dispensing device system according to claim 1, wherein the operating parameter is the driving force pressure of the dispensing device system.

3. (i) the probe is configured to be in direct contact with the dispensable material in the fluid path, or (ii) the temperature sensor further comprises a shield that separates the outer surface of the probe from the dispensable material. The dispensing apparatus system according to claim 1, wherein the probe is configured to sense the temperature of the dispensable material through the shield.

4. The dispensing apparatus system according to claim 1, wherein the temperature sensor comprises the conductivity sensor, and the temperature sensor is further configured to apply a voltage to the dispensable material in the fluid path of the dispensable material.

5. The at least one parameter relating to the dispensing device system includes the density of the dispensable material, The mass measuring device further comprises one or more sensors configured to sense the mass data of the dispensable material, The at least one process parameter includes the mass data, The processor is further configured to determine the volumetric flow rate of the dispensable material based on the density and mass data of the dispensable material. The dispensing apparatus system according to claim 1.

6. The aforementioned processor, Based on the at least one parameter and the at least one process parameter, the maximum idle time or purge time of one or more dispensing device components is determined. Further configured to provide the maximum idle time or the purge time, The dispensing apparatus system according to claim 1.

7. The dispensing apparatus system according to claim 6, wherein the one or more sensors are further configured to provide at least one environmental parameter, and the processor is further configured to determine the maximum idle time or the purge time based on the at least one environmental parameter, the at least one parameter, and the at least one process parameter.

8. The processor further comprises a user interface operably connected to the processor, and the processor It receives one or more user inputs that include one or more of the following: safety factors, process control factors, and discard factors. The system is further configured to determine the maximum idle time or the purge time based on the one or more user inputs, the at least one parameter, and the at least one process parameter. The dispensing apparatus system according to claim 6.

9. The dispensing device system according to claim 1, wherein the processor is configured to provide the operating parameters and the adjusted operating parameters to one or more dispensing device components.

10. The dispensing apparatus system according to claim 1, wherein the dispenseable material includes an adhesive.

11. A method for dispensing dispensable material using a dispensing device system, The system receives at least one parameter related to the aforementioned dispensing device system, Based on the aforementioned at least one parameter, the values ​​of the operating parameters of the dispensing device system are determined to achieve the flow rate of the dispensable material within the dispensing device system. To provide the aforementioned operating parameters, The process receives at least one process parameter, including the sensed temperature of the dispenseable material, Receiving the sensed conductivity of the dispensable material from the conductivity sensor, Based on the sensed temperature and the sensed conductivity of the dispenseable material, the curing state of the dispenseable material is determined. Based on the perceived temperature and the curing state, the values ​​of the operating parameters of the dispensing device system are adjusted. To provide the adjusted operating parameters, Methods that include...

12. The method according to claim 11, wherein the operating parameter is the driving force pressure of the dispensing device system.

13. The method according to claim 11, wherein the at least one parameter relating to the dispensing apparatus system includes the density of the dispensable material, the at least one process parameter further includes mass data of the dispensable material, and the method further comprises determining the volumetric flow rate of the dispensable material based on the density of the dispensable material and the mass data of the dispensable material.

14. Based on the at least one parameter and the at least one process parameter, the maximum idle time or purge time of one or more components of the dispensing device system is determined. To provide the aforementioned maximum idle time or the aforementioned purge time, The method according to claim 11, further comprising:

15. Receiving at least one environmental parameter provided by one or more sensors, Determining the maximum idle time or the purge time based on the at least one environmental parameter, the at least one parameter, and the at least one process parameter, The method according to claim 14, further comprising:

16. Receiving one or more user inputs that include one or more of the following: safety factors, process control factors, and discard factors, Determining the maximum idle time or the purge time based on the one or more user inputs, the at least one parameter, and the at least one process parameter, The method according to claim 14, further comprising:

17. The method according to claim 11, further comprising providing the operating parameters and the adjusted operating parameters to one or more components of the dispensing device system.

18. The method according to claim 11, wherein the dispenseable material includes an adhesive.

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