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 adhesive typically includes an inlet or internal area that holds the adhesive and an outlet or tip where the adhesive is dispensed onto a surface. The flow rate of the adhesive can be directly controlled using a metering system to meet the needs of the downstream manufacturing process. However, systems that actively control the flow rate can be overly complex and expensive for most users. Indirect methods can use calibration curves to provide or recommend settings based on various variables such as air pressure, pump settings, application time, total dispense volume, or other variables. However, such calibration curves can become overly complex when considering the number of adhesives, varying temperatures, and chemical reactions that may occur throughout the dispensing process. Other systems involving neural networks can learn or predict settings, but they require hundreds or thousands of calibration points to be useful. Thus, there is a general need to more accurately predict dispensing device settings and other dispensing parameters in a timely and cost-effective manner. Summary of the Invention

[0002] In one aspect, the present disclosure provides an apparatus comprising a memory storing data indicative of at least one parameter of an adhesive dispensing system. The apparatus further comprises a processor coupled to the memory. The processor is configured to capture the at least one parameter. The processor is further configured to determine, based on the at least one parameter, a value of an operating parameter of the adhesive dispensing system that achieves a flow rate of adhesive within the adhesive dispensing system. The processor is further configured to provide the operating parameter to the adhesive dispensing system.

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

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

[0005] In another aspect, the present disclosure describes a method of dispensing a dispenser material using a dispenser system, the method including receiving at least one parameter associated with the dispenser system, determining a value of an operating parameter of the dispenser system that achieves a flow rate of the dispenser material in the dispenser system based on the at least one parameter, providing the operating parameter, receiving at least one process parameter including a sensed temperature of the dispenser material, adjusting a value of the operating parameter of the dispenser system based on the sensed temperature, and providing the adjusted operating parameter.

[0006] In another aspect, the present disclosure describes a dispenser system including one or more dispenser components configured to provide a dispenseable material and a processor operably coupled to the one or more dispenser components. The processor is configured to receive a plurality of calibration data points of the dispenser system and one or more parameters of the one or more dispenser components. The plurality of calibration data points is based on a plurality of dispensed samples of the dispenser components. The processor is further configured to select one or more predefined models based on the one or more parameters of the dispenser components, determine a calibration model based on the plurality of calibration data points and the one or more models, and adjust one or more settings of the one or more dispenser components based on the calibration model.

[0007] In another aspect, the present disclosure describes a method for calibrating a dispenser system, the method including receiving a plurality of calibration data points of the dispenser system, the plurality of configuration data points being based on a plurality of dispensed samples of a dispenseable material and one or more parameters of dispenser components of the calibration system, selecting one or more predefined models based on the one or more parameters of the dispenser components, determining a calibration model based on the plurality of calibration data points and the one or more predefined models, and providing one or more settings of the dispenser system based on the calibration model.

[0008] The above summary of the disclosure is not intended to describe each disclosed embodiment or every implementation of the disclosure. The following description more particularly illustrates exemplary embodiments. In several places throughout this application, guidance is provided through the enumeration of examples, which can be used in various combinations. In each instance, the recited items serve only as a representative group and should not be construed as an exclusive enumeration. Thus, the scope of the 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 equivalents of these structures. Any of the elements expressly enumerated herein as alternatives can also be expressly included or excluded from the claims in any combination as desired. Although various theories and possible mechanisms may be discussed herein, in no event should such discussion be construed as limiting the claimed subject matter. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a block diagram of an adhesive dispensing device in which an example embodiment can be implemented. [Diagram 2] FIG. 1 illustrates a linear calibration curve for determining the flow rate of an adhesive dispensing device. [Diagram 3] FIG. 1 illustrates a non-linear calibration curve for determining the flow rate of an adhesive dispensing device. [Figure 4] FIG. 1 illustrates a system for predicting flow regulation for multiple variables, according to some embodiments. [Figure 5A] FIG. 2 illustrates an example graphical user interface (GUI) according to some embodiments. [Figure 5B] FIG. 13 illustrates an example GUI for manual entry of calibration data, according to some embodiments. [Figure 6] FIG. 13 is a flow diagram of a method for predicting adhesive dispensing device pressure, according to some embodiments. [Figure 7] FIG. 1 illustrates a comparison of the performance of a neural network-based model and a learning model used in some embodiments. [Figure 8] FIG. 2 illustrates a computing node according to some embodiments. [Figure 9] FIG. 2 illustrates further details of an edge computing node, according to some embodiments. [Figure 10] FIG. 1 is a schematic diagram of a feedback sensor according to some embodiments. [Figure 11] FIG. 1 is a flow diagram of a method for calibrating a pipetting device system, according to some embodiments. [Figure 12] FIG. 1 is a flow diagram for dispensing a dispenseable material according to some embodiments. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] An adhesive dispensing apparatus provides liquid adhesive through a tip or nozzle to a surface or substrate as part of a manufacturing process. FIG. 1 is a block diagram of an adhesive dispensing apparatus 100. The adhesive dispensing apparatus is configured to use a driving force 102 to deliver liquid adhesive from a source of liquid adhesive 104 to a dispensing component 106 so that the liquid adhesive can be dispensed on demand using the dispensing component 106. An operator can use various settings or parameters on the adhesive dispensing apparatus, including using a controller 108 or 110 to dispense adhesive at a desired flow rate. These settings or parameters can vary depending on a number of factors, and it can be difficult to predict which settings will be effective for any particular combination of factors.

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

[0012] However, measurements of the actual dispensed mass may not exactly fit such a curve. For example, as seen in curve 202, the actual dispensed mass indicated by measurement 204 is above curve 202. Another measurement 206 may be closer to the predicted linear curve 208. Thus, a linear fit provides only a rough estimate.

[0013] Other neural network-based systems can also be used to control the instructions for applying the adhesive, although to be practical, neural network-based systems require hundreds or thousands of calibration points and extensive learning before they are capable of controlling adhesive dispensing.

[0014] Adhesive dispensing system and algorithm 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 performs well with operator needs in downstream manufacturing processes. The predictions can be based on any data related to the adhesive or adhesive dispensing process. Figure 4 is a system 400 for predicting flow rate adjustments for multiple variables, according to some embodiments.

[0015] 4, the system 400 can include a data component 402. The data component 402 can include inputs received from a human machine interface 404, such as, for example, user inputs. Additionally, the data component 402 can include outputs provided for display to the human machine interface 404. The human machine interface 404 can provide inputs to and receive outputs from a pipetting device component 406. Additionally, the human machine interface 404 can provide data including visual and audio indicators to a human operator 408 and receive inputs, such as keyboard inputs, from the human operator 408. The human operator 408 can interact with the pipetting device component 406, for example, by changing settings or parameters of the pipetting device component 406.

[0016] The data component 402 can include sensor data 410. The sensor data 410 can include, for example, rheological data about the calibration liquid and the adhesive dispensed, data about the manufacturing of the adhesive including in-factory measurements, environmental parameters or conditions (provided by sensors 428), and lot information, cartridge information, or other information that can identify a batch of adhesive. The data component 402 can include adhesive material properties 414 and dispensing device properties 416. The sensors 418 can include data about the adhesive during the supply chain process, including the temperature or humidity to which the adhesive was exposed and the amount of time that extreme temperature or humidity conditions were present. The data component 402 can further include data 420 about various modular components of the 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. According to an embodiment, the algorithm 412 may generate a prediction based on data related to the calibration liquid. The calibration liquid may include a model of the liquid, an ideal liquid, or a special calibration liquid that is used only for the calibration process, e.g., not used in the production process. In either case, the calibration liquid is assumed to be dispensed using, e.g., the adhesive dispensing device 100. In an example, the input data may include any of the data described above, in addition to data entered by a user or another system, such as machine calibration data or dispensing device calibration data. In some examples, sensors, such as temperature or humidity sensors, in a room or area where adhesive dispensing is occurring or will occur may provide input to the algorithm 412.

[0018] As previously described herein, the relationship between adhesive mass, viscosity, and pressure can be non-linear, creating complications in determining or predicting the appropriate pressure to dispense the adhesive. An equation or relationship can be developed to account for the non-linear relationship between pressure and viscosity. The exemplary equation (1) can be used to generalize this non-linear relationship. m = G(F1(p),F2(μ)...) (1) where m is the mass of adhesive dispensed, F1 is a function of pressure p, and F2 is a function of viscosity μ. Other functions and relationships can be included, and equation (1) should not be understood as limiting embodiments to one relationship between pressure and viscosity. The constants in those relationships are solved for using the machine learning algorithms described herein. The constants can vary with conditions, such as environmental conditions, and in some embodiments, different constants will be obtained in different iterations of the machine learning algorithm.

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

[0020] The algorithm 412 may generate a prediction based on Equation (1). In some example embodiments, with reference to Equation (1), a solution for constants related to F1 and F2 may be obtained, for example, using a machine learning algorithm or by solving an optimization problem as described below in this specification. The constants may include dispensing device specific constants or adhesive specific constants, which are calculated for each adhesive dispensing device 100 and may vary with time or temperature. The viscosity μ may be determined based on direct or indirect measurements, polynomial fits, numerical regression, or an equation such as the Andrade equation for the viscosity of a liquid given in Equation (2).

number

[0021] As discussed above, the constants associated with Equation (1) as well as the constants D and E can be calculated, for example, using a machine learning algorithm or by solving an optimization problem. Such an algorithm can estimate the viscosity (or receive a value indicative of an estimate of the viscosity) based on, for example, the age of the adhesive 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 can have input parameters related to at least one of pressure, mass, volume, time, and temperature of the adhesive, the adhesive dispensing device, or a process associated with adhesive application.

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

[0023] 4, the human machine interface 404 may include a user interface 422, local storage and processing 424, a connection to a server 426, sensors 428, and a connection 430 to the dispensing device 406. The 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 example, to scan identifying information, such as lot information of the adhesive or adhesive container. Some components of the human machine interface 404 are described in more detail later in this specification with respect to FIGS. 7-8.

[0024] Through connection 430, the human machine interface 404 can communicate with a controller 432 of the dispenser component 406. The pressure or setting predicted by the algorithm 412 can be used by the controller 432 to control the dispenser hardware 434. For example, the controller 432 can control the dispenser hardware 434 (e.g., hardware the same as or similar to the driving force 102) to dispense the dispenser material 436 at a pressure predicted or prescribed by the algorithm 412. The dispenser component 406 can include additional sensors 438.

[0025] In addition to predicting the ideal pressure to dispense the adhesive, the machine learning models described above can predict or suggest other settings to improve the adhesive process or to provide an adhesive flow rate that meets the needs of downstream processes. These suggestions can be provided via various human interface elements, as described later in this specification.

[0026] feedback The feedback can be used to incorporate new experience into subsequent machine learning iterations. The data provided via the feedback can be indicative of the quality of the dispensing process or processes associated with the dispensing process. The data can further include qualitative data regarding the flow rate achieved, for example. In some example embodiments, the data can be provided by a human operator through observations, such as visual inspection, through indicators that the adhesive dispensing proceeded as expected, via a human machine interface 404, a smartphone, or the like.

[0027] Other example embodiments may use mechanical sensors 438, including, for example, machine vision sensors, mass sensors, 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 machine-based sensors 438, the algorithm 412 may use the provided data to refine the constants used in the algorithm 412.

[0028] Get proposal data The predictions and suggestions can be provided in a user interface, such as a graphical user interface (GUI) 500. Figure 5A illustrates an example GUI 500, according to some embodiments.

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

[0030] The GUI 500 may display one or more parameters 502 indicative of desired conditions for the dispense function. For example, the GUI 500 may display a desired flow rate for dispensing. In embodiments, the parameters 502 are user editable so that the user may suggest a desired flow rate, mass, or dispense time, for example, based on the needs of processes downstream of the adhesive process under control. The GUI 500 may display such information for any number of dispensers, remote or local. In some embodiments, the user may use an interface element 504, such as a drop box, list, or other element, to switch between different dispensers.

[0031] The GUI 500 can be displayed as part of a web application that provides suggestions regarding operational settings, such as pressure settings, of the adhesive dispensing apparatus 100 (FIG. 1), which were determined using the algorithm 412 (FIG. 4). A user can request to be provided with a pressure prediction or suggestion instead of or in addition to the periodic updates provided by the algorithm 412 (FIG. 4). For example, a user can press an interface element 506, such as a button, and a user device, such as a smartphone, can wirelessly send a request for the algorithm 412 to run and provide an updated suggested pressure. A suggested value for a parameter, 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 apparatus 100. In other examples, the dispensing apparatus components 406, including the controller 432 (FIG. 4), can automatically control the dispensing apparatus hardware 434 based on the suggested pressure.

[0032] The machine learning model described above can also predict when current environmental conditions (such as, but not limited to, temperature and humidity) will result in poor quality predictions. The GUI 500 can then be used to request or suggest that further calibration points be obtained by the user, and such calibration points can be obtained from user input, thereby improving the overall machine learning model.

[0033] In other example embodiments, if the user requests a flow rate that is much higher than previously requested, such that the dispense pressure is significantly higher than previously used, the user may be notified, such as through the GUI 500 or an audio alarm, that an additional calibration point should be obtained. For example, if the user requests a flow rate that requires a pressure of 50 psi, but the normal dispense range is 20-40 psi, the user is requested to provide a calibration point associated with 50 psi. A calibration point may be as shown in FIG. 5B, for example, the calibration point may include lot number 512 information, pressure 514 information, dispense mass 516 information, dispense time 518 information, and temperature 520 information. In the illustrated 50 psi calibration point example, the user would then provide the information illustrated in FIG. 5B, such as the temperature and time to dispense at 50 psi. In an example, a timestamp may be added to this calibration point upon storage, such as by the human machine interface 404 or associated processing circuitry. In some embodiments, the calibration point may include fewer or additional fields.

[0034] The models described herein above can identify opportunities to enhance prediction quality. In at least some example embodiments, suggestions can be generated regarding such opportunities and displayed in a user interface, such as GUI 500. When an opportunity for prediction improvement is identified, systems and devices according to embodiments can indicate to a user through an audio or visual alarm or prompt, text, audio message, etc., that the user should obtain adhesive data that may lead to prediction improvement.

[0035] For example, if the machine learning model determines that flow data has not been acquired at a particular temperature, a system according to an embodiment may request that the user acquire dispense data at that particular temperature. Alternatively, a system according to an embodiment may control a sensor to acquire such data at that temperature. In these and other embodiments, additional GUI screens may be provided with which the user may interact to manually input data related to data points that may enhance the machine learning algorithm. In at least these embodiments, some or all of the data may be entered automatically by the dispenser component 406 (FIG. 4), either through wireless communication, near field communication (NFC), or other methods. For example, a user may manually enter calibration data into a GUI screen 510 shown in FIG. 5B.

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

[0037] In-process adjustments Feedback can also be used to adjust various settings while dispensing material to achieve or maintain operating parameters or performance set points. For example, data or process parameters related to changes in viscosity of a dispenseable material as such material is dispensed can be used to adjust the driving force pressure to maintain a desired flow rate. Process parameters can include parameters such as temperature, conductivity, viscosity, weight, mass, etc. of the dispenseable material 436 or the dispenser component 406 during the dispensing process. The process parameters can be sensed or determined, for example, by sensor data from various sensors, such as sensor 438, of the dispenser component 406. Sensor 438 can include, for example, visual sensors, mass sensors, temperature sensors, conductivity sensors, or any device capable of detecting parameters such as heat, humidity, weight, mass, material quality, conductivity, etc.

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

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

[0040] As the dispenseable material flows through the channel 904, it may contact various devices of one or more sensors 910 disposed within the duct piece 900, enabling the one or more sensors 910 to sense a process parameter of the dispenseable material. For example, the one or more sensors may include a probe 912 disposed within a fluid path defined by the channel 904. The probe 912 may be configured to respond to a temperature of the dispenseable material in a manner that can be detected by the one or more sensors 910. For example, the probe 912 may include a thermistor, and the one or more sensors 910 may be configured to determine a resistance of the thermistor and sense or determine a temperature of the dispenseable material based on the resistance of the thermistor. In one embodiment, the probe 912 may be configured to directly contact the dispenseable material in the fluid path. In another embodiment, an exterior surface of the probe 912 may be separated from the dispenseable material by a shield, and the probe may be configured to sense a temperature of the dispenseable material through the shield. The shield may comprise any suitable material, such as, for example, plastic, metal, or other thermally conductive material.

[0041] Further, for example, the one or more sensors 910 may include electrodes 914 disposed in a fluid path defined by the channel 904. The one or more sensors 910 may be configured to provide a voltage across the electrodes 914 and measure a resulting current flow between the electrodes and through the dispenseable material. Based on the measured current, the one or more sensors 910 can sense the conductivity of the dispenseable material.

[0042] Sensor 438 may include additional sensors and sensor devices described in PCT Publication No. 2022 / 013786 A1 (Munstermann et al.), the disclosure of which is incorporated herein by reference.

[0043] Building Block Library The systems, devices, and methods can generate predictive models without understanding the internal components of the 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 could be created from components of the adhesive dispensing device 100 (FIG. 1), including mixing nozzles, dispensing tips, or components of the adhesive dispensing device 100 (FIG. 1), including, for example, tubing, cartridges, pressure valves, pumps, adapters, pinch tubes, etc. The predictive model, in some embodiments, can include an equation similar to equation (1). In some embodiments, machine learning algorithms can be used to solve for similar or different constants. By creating predictive models of the dispensing device components, the need for calibration can be reduced or eliminated when adding new dispensing devices to an operator's process using settings based on component models previously generated or stored using any of the above processes. Furthermore, new dispensing device components can be added to the dispensing device without requiring calibration of settings. For example, new dispensing tips can be added, and these can apply pressures that have already been generated and predicted depending on factors such as viscosity, temperature, etc., as described above in this specification.

[0044] Example method 6 is a flow diagram of a method 600 for predicting adhesive dispensing device pressure, according to some embodiments. The operations of method 600 may be performed by a processor 704 (FIG. 8), which may execute, for example, algorithm 412 (FIG. 4). In examples, algorithm 412 may be performed partially or fully using machine learning and based on inputs automatically generated and provided by sensors or inputs by a user, among other inputs.

[0045] The method 600 may begin at operation 602 with the processor 704 capturing at least one parameter associated with the adhesive dispensing apparatus 100 ( FIG. 1 ). In some embodiments, the at least one parameter relates to a viscosity of the adhesive in the adhesive dispensing apparatus 100. In some embodiments, operation 602 may include receiving at least one feedback parameter, the at least one feedback parameter indicative of a quality of the bonding process. The feedback parameter may be received from a 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 be indicative of an amount of adhesive dispensed. In example embodiments, the mass feedback data may be used to determine if the amount of adhesive dispensed is within a target range for a desired amount of adhesive.

[0046] In example embodiments, predictions and suggestions are provided in a user interface, and further, the processor 704 may request the user to input or obtain calibration points. As described previously herein, the calibration points may include information similar to that shown in FIG. 5B. The request may be based, for example, on a determination that the requested flow rate is outside a threshold range of a normal range. In other examples, the request may be in response to a determination that an environmental parameter is outside a typical range or a 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 capabilities of the machine learning model may be limited due to the unconventionality of the environmental factors, and thus manual data entry is required.

[0047] The suggestions or requests for data acquisition may be provided on a 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, but not limited to, temperature and humidity) will result in a poor quality prediction. The GUI 500 can then be used to request or suggest that further calibration points be obtained by the user, and such calibration points can be obtained from user input, thereby improving the overall machine learning model.

[0049] In other example embodiments, if a user requests a flow rate significantly greater than previously requested, such that the dispense pressure is significantly higher than previously used, the user may be notified, such as through the GUI 500 or an audio alarm, that an additional calibration point should be obtained. For example, if a user is requesting a flow rate that would require a pressure of 50 psi, but the normal dispense range is 20-40 psi, the user may be requested to provide a calibration point associated with 50 psi, for example, using an interface similar to that shown in FIG. 5B.

[0050] The models described herein above can identify opportunities to enhance prediction quality. In at least some example embodiments, suggestions can be generated regarding such opportunities and displayed in a user interface, such as GUI 500. When an opportunity for prediction improvement is identified, systems and devices according to embodiments can indicate to a user through an audio or visual alarm or prompt, text, audio message, etc., that the user should obtain adhesive data that may lead to prediction improvement.

[0051] For example, if the machine learning model determines that flow data has not been acquired at a particular temperature, a system according to an embodiment may request that the user acquire dispense data at that particular temperature. Alternatively, a system according to an embodiment may control a sensor to acquire such data at that temperature. In these and other embodiments, additional GUI screens may be provided with which the user may interact to manually input data related to data points that may enhance the machine learning algorithm. In at least these embodiments, some or all of the data may be entered automatically by the dispenser component 406 (FIG. 4), either through wireless communication, near field communication (NFC), or other methods. For example, a user may manually enter calibration data into a GUI screen 510 shown in FIG. 5B.

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

[0053] The method 600 may continue at operation 604 with the processor 704 determining a value for an operating parameter of the adhesive dispensing apparatus 100. The value may be based on at least one parameter received at operation 602. The value of the operating parameter may be such that a desired flow rate is achieved in the adhesive apparatus system.

[0054] The method 600 may continue at operation 606 with the processor 704 providing the operating parameter to the adhesive dispensing apparatus 100. In an embodiment, the operating parameter may be a driving force pressure of the adhesive dispensing system. In an embodiment, the driving force pressure is determined based on the viscosity and parameters specific to the adhesive dispensing apparatus 100. For example, the determination of the driving force pressure may follow equation (1) described earlier herein: m=G(F1(p),F2(μ)...), where m is the mass dispensed by the adhesive dispensing system, F1 is a function of the pressure p, and F2 is a function of the viscosity μ.

[0055] The viscosity μ can be determined based on direct or indirect measurements, polynomial fits, numerical regression, or equations such as the Andrade equation for the viscosity of a liquid given in equation (2).

[0056] As previously described herein, the constants associated with equations (1) and (2) can be determined using a machine learning algorithm. The machine learning algorithm can minimize a loss function associated with at least one of the driving force pressure, mass, viscosity, and temperature. The machine learning algorithm can be based on a model other than a neural network model (e.g., a non-neural network model and a machine learning algorithm), or can be based on a hybrid between a neural network model and other models (e.g., a hybrid algorithm).

[0057] As previously described herein, other systems based purely on neural networks can be used to control the instructions for applying the adhesive, although systems based solely on neural networks require hundreds or thousands of calibration points to be practical and undergo extensive training before they are capable of controlling adhesive dispensing. The example embodiments previously described herein can be based on other machine learning models, or non-neural network based, or a combination of neural networks and other machine learning models.

[0058] FIG. 7 shows a comparison of the performance of the neural network-based model and the learning model used in some example embodiments. A data set of laboratory-generated data was used for this comparison. The data set was sampled for a given number of data points and then split into two groups: 70% of the data used to train the model and 30% of the data reserved for testing the quality of the model after it was 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 system using the model according to the embodiment had a lower average root-mean-square error (compared to curve 632, which shows the system using the neural network model) at all sampling levels, as shown by curve 630, and especially at the lower sampling level 634, the difference between curves 630 and 632 was large. As can be seen, using the method according to the embodiment, the average root-mean-square error is small even at low sample levels, so cost reduction can be achieved by reducing the need for a large number of samples (which in turn reduces manufacturing, laboratory, and material costs). The ability to obtain useful predictions with relatively few data points is a major advancement over existing neural network methods.

[0059] 11 is a flow diagram of a method 1000 of calibrating a pipetting device system, according to some embodiments. The operations of method 1000 can be performed, for example, by processor 704 (FIG. 8).

[0060] Every dispenser system is subtly or significantly different, and calibration data from one dispenser system cannot be used to calibrate another. To calibrate (e.g., set up) a dispenser system, a user may load the dispenser system with a dispensable material (e.g., dispensable material 436) and configure all associated parts of the dispenser system, including components such as plungers, air lines, controllers, static mixers, pinch tubes or valves, tips, etc. Once ready, the user dispenses the dispensable material into a cup at multiple different pressures and weighs the dispensed dispensable material. Each combination of dispensed mass and pressure setting may form a calibration data point. However, various conditions may result in a "bad" calibration data point. For example, if there are air bubbles in the system, if material is stuck to the tip of the dispenser system, if material from a previous dispenser shot is included in the dispensed mass of the next shot, if pressure or mass is not set or recorded accurately, or other sources of error are present, the data obtained from the calibration may not yield 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 needed.

[0062] Method 1000 may begin at operation 1002 with processor 704 receiving a plurality of calibration data points and one or more parameters of a dispenser component of a dispenser system. A user may be instructed to set a pressure gauge (e.g., dispenser pressure) to a particular pressure and record the dispensed mass. The instructions may also include the dispense time for each calibration data point. Alternatively, the processor may automatically set the pressure, dispense the dispenseable material, and record each calibration data point and associated parameters. A typical calibration method may require hundreds or thousands of calibration data points. In contrast, the plurality of calibration data points of method 1000 may include at least two calibration data points to up to 100 calibration data points, or any suitable range therebetween. For example, the plurality of calibration data points may include a number of calibration data points in a range from at least 2 calibration data points, 5 calibration data points, 10 calibration data points, or 15 calibration data points to no more than 20 calibration data points, no more than 40 calibration data points, no more than 60 calibration data points, no more than 80 calibration data points, or no more than 100 calibration data points. Each of the calibration data points may include any suitable parameter. For example, each of the plurality of calibration data points may include one or more of manufacturing lot information for the dispenseable material, a dispenser pressure, a dispense mass, a dispense time, an ambient temperature, a temperature of the dispenseable material, etc. In one embodiment, each of the plurality of calibration points includes a dispenser pressure, a dispense time, and a mass of the dispensed dispensable material.

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

[0064] The method 1000 may continue at operation 1004 with the processor selecting one or more pre-defined models based on one or more parameters of the dispensing device components. In one or more embodiments, the one or more parameters include a production lot of the dispensable material. The one or more pre-defined models may be populated based on the production lot of the dispensable material. For example, each production lot or type of dispensable material may correspond to a given set of pre-defined models.

[0065] Method 1000 may continue at operation 1006 with the processor determining a calibration model based on the calibration data points and the one or more predefined models. For example, the calibration model may include one or more configurations of the one or more predefined models that correspond to one of the predefined models to which the set of modified calibration data points best corresponds based on statistical methods and modeling. In some examples, the calibration data points may fit or match one of the predefined models, and the calibration model may be determined based on the calibration data points and the one or more predefined models without modification to the calibration data points. However, in some examples, the calibration data points may not fit or match one of the predefined models due to one or more outliers or an insufficient number of calibration data points. Thus, determining the calibration model may include generating a modified set of calibration data points where outliers of the calibration data points may be removed and / or additional calibration data points may be added.

[0066] Determining the calibration model may include determining one or more outliers of the plurality of calibration data points based on the one or more models. Determining the one or more outliers may include one or more suitable techniques. For example, determining the one or more outliers may include using one or more statistical models or methods. In one embodiment, determining the one or more outliers may include a regression analysis based on the plurality of calibration points and one or more predefined models. Additionally or alternatively, determining the one or more outliers may include determining one or more calibration points of the plurality of calibration points that differ from a corresponding data point of the one or more predefined models by more than a threshold value. The threshold value may be a percentage difference. In other words, the difference between the dispensed mass of the outlier calibration data point and the dispensed mass of a data point corresponding to the same dispenser pressure and time in one of the predefined models may exceed a predefined percentage of the dispensed mass of the data point of the predefined model. The threshold value may be determined based on the predefined model.

[0067] Determining the calibration model may further include removing one or more outliers from the plurality of calibration data points to generate a set of modified calibration data points. Method 1000 may optionally include an operation of the processor determining that the set of modified calibration data points includes fewer than a threshold number of data points. If the set of modified calibration data points includes fewer than a threshold number of data points, the set of modified calibration data points may not be sufficient to determine whether the calibration data points fit a particular one of the one or more predetermined models.

[0068] Thus, the method 1000 may also include an operation of the processor determining one or more dispenser pressures for one or more additional calibration data points based on the revised set of calibration data points and the one or more predefined models. The one or more additional calibration data points may include a dispenser pressure between the dispenser pressures of the two missing or removed data points. Additionally or alternatively, the one or more additional calibration data points may include a dispenser pressure that is the same as one or more outliers.

[0069] Method 1000 may also include an act of the processor requesting one or more additional calibration data points using the display. Each of the one or more additional calibration data points may correspond to one of the one or more dispenser pressures. Method 1000 may also include an act of the processor receiving the one or more additional calibration data points using a user interface and modifying the revised set of calibration data points to include the one or more additional calibration data points.

[0070] Thus, determining the calibration model may be based on the set of corrected calibration data points and one or more predefined 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 corrected data points.

[0071] Method 1000 may continue at operation 1008 with the processor providing one or more settings for the pipetting device system based on the calibration model. In an embodiment, providing the one or more settings may include adjusting one or more settings of a pipetting device component of the pipetting device system. In other words, the processor may adjust various settings of the pipetting device system. Additionally or alternatively, the processor may present the various calibration settings to a user using a display, such as the GUI 500.

[0072] 12 is a flow diagram of a method 1100 of dispensing a dispenseable material (e.g., the dispenseable material 436) according to some embodiments. The operations of the method 1100 may be performed, for example, by the processor 704 (FIG. 8).

[0073] The method 1100 may begin at operation 1102 with the processor 704 receiving at least one parameter associated with the dispensable material dispensing device 100 ( FIG. 1 ). The at least one parameter may be received from a dispensing device component, a database, a user, an image, etc. In some embodiments, the at least one parameter relates to a viscosity of the dispensable material in the dispensable material dispensing device 100. In some embodiments, the at least one parameter may relate to a density of the dispensable material in the dispensable material dispensing device 100. In some embodiments, operation 1102 may include receiving at least one feedback parameter of the dispensable material. The process parameter may be received from a 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 be indicative of an amount of the dispensable material dispensed. For example, the mass data and the density of the dispensable material may be used to determine a volume of the dispensable material to be dispensed. Thus, based on the mass data and the density of the dispensable material, a volumetric flow rate of the dispensable material may be determined and / or monitored. In example embodiments, mass feedback data may be used to determine if the amount of dispensed dispensable material is within a target range for the desired amount of dispensable material.

[0074] The method 1100 may continue at operation 1104 with the processor 704 determining a value for an operating parameter of the dispenseable material dispensing device 100. The value may be based on the at least one parameter received at operation 1102. The value of the operating parameter may be one at which a desired flow rate is achieved in the dispenseable material dispensing system.

[0075] The method 1100 may continue at operation 1106 with the processor 704 providing operational parameters. In an embodiment, the operational parameters may be provided to the dispenseable material dispensing device 100. In other embodiments, the operational parameters may be provided to the human machine interface 404. In an embodiment, the operational parameter may be a driving force pressure of the dispenseable material dispensing system. In an embodiment, the driving force pressure is determined based on the viscosity and parameters specific to the dispenseable material dispensing device 100 as previously described herein.

[0076] The method 1100 may continue at operation 1108 with the processor receiving at least one process parameter, including a temperature of the dispenseable material. The at least one process parameter may also include, for example, parameters such as conductivity, viscosity, weight, mass, etc. of the dispenseable material (e.g., adhesive, sealant, thermal paste, etc.). The at least one process parameter may be received from one or more sensors. For example, the temperature of the dispenseable material may be received from temperature sensor 910 of FIG. 10. A probe 912 of temperature sensor 910 may be disposed or positioned in a fluid path of the dispenseable material, enabling temperature sensor 910 to sense or measure a temperature of the dispenseable material.

[0077] The method 1100 may continue at operation 1110 with the processor adjusting a value of an operational parameter of the dispenseable material dispensing system based on the temperature of the dispenseable material. As the temperature of the dispenseable material changes, the viscosity of the dispenseable material may change. Thus, by adjusting the value of the operational parameter (e.g., driving force pressure), a more consistent flow rate of the dispenseable material may be achieved. Additional process parameters may affect the viscosity and flow rate of the dispenseable material. Such process parameters may be sensed using the sensor 910 or any other suitable sensor or device. For example, the sensor 910 may further include a conductivity sensor that senses the conductivity of the dispenseable material. The method 1100 may further include the processor receiving the sensed conductivity of the dispenseable material from the conductivity sensor. Still further, the method 1100 may include determining a cure state of the dispenseable material based on the sensed temperature of the dispenseable material and the sensed conductivity of the dispenseable material, and adjusting a value of the operational parameter of the dispenseable material dispensing system based on the sensed temperature and the cure 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, the viscosity of the dispenseable material may change. In addition to temperature and conductivity, the state of hardening may also be determined based on time after initial mixing (e.g., residence time in the static mixer and subsequent devices). The known state of hardening may be used to predict the viscosity of the dispenseable material, and the effect on flow rate may be calculated. This information may be used to adjust operating parameters (e.g., driving force pressure) to maintain a more constant flow rate of the dispenseable material 436. Additionally, the method 1100 may include initiating a purge 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, parameters set by the user, or parameters of the current dispense job or process.

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

[0080] Method 1100 may also include an act of determining a parameter for purging of the dispenseable material. Method 1100 may include an act of a processor determining a maximum idle time or purge time for one or more dispenser components based on the at least one parameter and the at least one process parameter. Method 1100 may also include an act of a processor providing a maximum idle time or purge time. In an embodiment, the maximum idle time or purge time may be provided to one or more dispenser components. Thus, the dispenser components may initiate an automatic purge based on the maximum idle time and / or purge time. In an embodiment, 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 a user.

[0081] Many factors can affect the pot life of a two-component dispenseable material. Such factors may include, for example, temperature, aging of the material, density, initial manufacturing viscosity, and other potential factors. Such factors may contribute to modify the curing kinetics, which in turn may change the amount of time the dispenseable material can reside in the dispensing device before purging may be required. An additional factor that may contribute to the maximum idle time is the dispense speed. The period during which a dispensing device component is determined to be idle may begin after the last dispense of dispenseable material. If the active period (e.g., during dispensing) involves high flow rate dispense shots with little time between them, the dispenseable material may be relatively fresh when dispensing ends. However, if the flow rate is low and / or the time between dispense shots is long, the dispenseable material may be partially cured when dispensing ends. Thus, the maximum idle time and / or purge time should be different 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 tailored to 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 dispense speed, the flow rate, the time between dispense shots, etc.

[0082] Additionally, the method 1100 may consider safety factors and process control factors in determining the maximum idle time and purge time. The method 1100 may also include an operation of a processor receiving one or more user inputs including a safety factor, a process control factor, and a waste factor, and determining the maximum idle time or purge time based on the one or more user inputs, the at least one parameter, and the at least one process parameter. The user input may be received using a user interface (e.g., GUI 500). In one example, a user may select or provide a safety factor so that the dispenseable material does not prematurely harden if there is variability in the dispensing process. In other words, the maximum idle time may be determined based on the hardening time of the dispenseable material if a safety factor is provided. In another example, a user may desire tight control over the flow rate even at the expense of wasting the dispenseable material. In other words, the maximum idle time may be determined based on the flow rate fluctuations that may occur before the dispenseable material hardens if a control factor is provided. In yet another example, a user may select a waste factor that indicates a low amount of waste so that as little of the dispenseable material is wasted as possible. If multiple factors are entered, the particular values ​​of the factors may be balanced against each other to determine the maximum idle and purge times.

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

[0084] Computer equipment The systems, methods, and devices may use a processor to implement embodiments in firmware or software, remotely or locally to an operator process, or in a cloud or edge computing device, as described in detail later herein. Machine learning may be distributed among several different devices and performed in whole or in part within the adhesive dispensing device itself. For example, several identification processes may be performed by the adhesive dispensing device 100 (FIG. 1), and inputs therefrom may be provided to a local or remote device to formulate predictions regarding dispensing device settings. Thus, the devices and circuitry of the adhesive dispensing device 100, data component 402, human machine interface 404, and dispensing device component 406 (FIG. 4), as well as other components, may execute or be partially executed in a computing system, such as, for example, an edge computing node. FIG. 8 illustrates an edge computing node according to some embodiments.

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

[0086] Computational node 700 may be embodied as any type of engine, device, or collection of devices capable of performing various computational functions. In some embodiments, computational node 700 may be embodied as a single device, such as an integrated circuit, embedded system, field programmable gate array (FPGA), system on chip (SOC), or other integrated system or device. In an illustrative example, computational node 700 includes or is embodied as a processor 704 and memory 706. Processor 704 may be embodied as any type of processor capable of performing the functions described herein (e.g., executing an application). For example, processor 704 may be embodied as a multi-core processor(s), a microcontroller, or other processor or processing / control circuitry. In some embodiments, 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 performing the functions described herein.

[0087] The memory 706 may be embodied as any type 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 may be a storage medium that requires power to maintain the state of data stored by 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 the 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 a portion 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 operation of one or more applications, data manipulated by the applications, libraries, and drivers.

[0089] The computational circuit 702 is communicatively coupled to other components of the computational node 700 via an I / O subsystem 708, which may be embodied as circuits or components for facilitating input / output operations with the computational circuit 702 (e.g., with the processor 704 or main memory 706) and with other components of the computational 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, firmware devices, communications links (e.g., point-to-point links, bus links, wires, cables, light guides, printed circuit board traces, etc.), or other components and subsystems for facilitating input / output operations. In some embodiments, the I / O subsystem 708 may form part of a system-on-chip (SoC) or may be incorporated in the computational circuit 702 along with one or more of the processor 704, memory 706, and other components of the computational circuit 702. The I / O subsystem 708 can receive input data 707 from other components of FIG. 4, such as sensor 428, sensor 438, and can provide prediction and control 709 to other components of FIG. 4, such as pipetting device component 406.

[0090] The 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, for example, data storage devices 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 that stores data and firmware code for the data storage device 710. Each data storage device 710 may also include one or more operating system partitions that store data files and executable files for an operating system, depending on, for example, the type of compute node 700.

[0091] The communications circuitry 712 may be embodied as any communications circuitry, device, or collection thereof capable of enabling communications over a network between the computing circuitry 702 and another computing device (e.g., an edge gateway of an implementing edge computing system). The communications circuitry 712 may be configured to perform such communications using any one or more communications technologies (e.g., wired or wireless communications) 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] The exemplary communications circuitry 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, or other devices that may be used by the computing node 700 to connect with another computing device (e.g., an edge gateway node). In some embodiments, the NIC 720 may be embodied as part of a system-on-chip (SoC) that includes one or more processors, or may be included on a multi-chip package that also includes 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 computing circuitry 702 described herein. Additionally or alternatively, in such embodiments, the local memory of the NIC 720 may be integrated into one or more components of the client computing node at a board level, socket level, chip level, or other level.

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

[0094] In a more detailed embodiment, FIG. 9 illustrates a block diagram of example components that may be present in an edge computing node 850 to implement the techniques (e.g., operations, processes, methods, and methodologies) described herein. This edge computing node 850 provides a more detailed view of each component when the 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 mentioned herein and may include or be coupled with any device usable with an edge communications network or combination of such networks. The components may be implemented as integrated circuits (ICs), parts thereof, separate electronic devices, or other modules, instruction sets, programmable logic or algorithms, hardware, hardware accelerators, software, firmware, or combinations thereof employed in the edge computing node 850, or as components otherwise incorporated within 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 microprocessor, a multi-core processor, a multi-threaded processor, an ultra-low voltage processor, an embedded processor, or other known processing elements. 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 in a single package. The processor 852 and associated circuitry may be provided in a single socket form factor, a multiple socket form factor, or a variety of other formats, including limited hardware configurations or configurations that include less than all of the elements shown in FIG. 9.

[0096] The processor 852 may communicate with the 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. As an example, the memory 854 may be a random access memory (RAM) following the Joint Electron Devices Engineering Council (JEDEC) design. In various implementations, the individual memory devices may be in any number of different package types, such as a single die package (SDP), a dual die package (DDP), or a quad die package (Q17P). These devices may be soldered directly onto a motherboard in some embodiments to provide a low profile solution, while in other embodiments, the devices are configured as one or more memory modules that couple to a motherboard by a given connector. Any number of other memory implementations may be used, such as other types of memory modules, such as different types of dual in-line memory modules (DIMMs), including but not limited to micro-DIMMs or mini-DIMMs.

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

[0098] The components may communicate through an interconnect 856. The interconnect 856 may include any number of technologies, including Industry Standard Architecture (ISA), Extended ISA (EISA), Peripheral Component Interconnect (PCI), Enhanced Peripheral Component Interconnect (PCIx), PCI Express (PCIe), or any number of other technologies. The interconnect 856 may be a proprietary bus, such as used in SoC-based systems. Other bus systems may be included, such as an Inter-Integrated Circuit (I2C) interface, a Serial Peripheral Interface (SPI) interface, a point-to-point interface, a proprietary bus, and a power bus, among others.

[0099] The interconnect 856 may couple the processor 852 to a transceiver 866 for communicating with a connected edge device 862. The connected edge device 862 may include other elements or parts of other elements shown in FIG. 9, or other elements of a manufacturing system used by an 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) transmissions under the IEEE 802.15.4 standard, using the Bluetooth® Low Energy (BLE) standard as defined by the Bluetooth® Special Interest Group, or the ZigBee® standard, among others. Any number of radios configured for a particular wireless 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® communications according to the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standard. Additionally, wireless wide area communications, for example according to cellular or other wireless wide area protocols, may occur via Wireless Wide Area Network (WWAN) units.

[0100] The wireless network transceiver 866 (or multiple transceivers) may communicate using multiple standards or radios for communication at different ranges. For example, the edge computing node 850 may communicate with nearby devices, for example within about 10 meters, using a local transceiver based on Bluetooth Low Energy (BLE) 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 medium power radio. Both communication techniques may be done via a single radio at different power levels or via separate transceivers, for example a local transceiver using BLE and a separate mesh transceiver using ZigBee.

[0101] A wireless network transceiver 866 (e.g., a radio transceiver) may be included to communicate with devices or services in the edge cloud 895 via local or wide area network protocols. The wireless network transceiver 866 may be a low power wide area (LPWA) transceiver conforming to the IEEE 802.15.4 or IEEE 802.15.4g standard, among others. 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 techniques described herein are not limited to these techniques and may be used with any number of other cloud transceivers implementing long range, low bandwidth communications such as Sigfox and other techniques. Additionally, other communication techniques may be used, such as time slotted channel hopping as described in the IEEE 802.15.4e specification.

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

[0103] Given the variety of applicable communications from the device to another component or network, applicable communications circuitry used by the device may include or be embodied by any one or more of components 864, 866, 868, or 870. Thus, in various embodiments, any applicable means for communicating (e.g., receiving, transmitting, etc.) may be embodied by such communications circuitry.

[0104] The edge computing node 850 may include or be coupled to acceleration circuitry 864, which may be embodied by one or more artificial intelligence (AI) accelerators, neural compute sticks, neuromorphic hardware, FPGAs, a bank of GPUs, a bank of data processing units (DPUs) or infrastructure processing units (IPUs), one or more SoCs, one or more CPUs, one or more digital signal processors, special purpose ASICs, or other forms of specialized processors or circuitry designed to accomplish one or more specialized tasks. These tasks may include AI processing (including machine learning, training, inference, and classification operations), vision data processing, network data processing, object detection, rules analysis, etc.

[0105] The interconnect 856 may link the processor 852 to a sensor hub or external interface 870 used to connect additional devices or subsystems. The device may include sensors 872, which may be accelerometers, level sensors, flow sensors, optical light 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 a tape or other adhesive, primer, substrate, or the like. These sensors may be directly connected to the computing device or may be 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, visual warning devices, and the like. These actuators may be directly connected to the computing device or may be located remotely as part of various manufacturing modules.

[0106] In some optional examples, various input / output (I / O) devices may be present in or connected to the edge computing node 850. 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 touch screen or keypad may be included to accept input. The output device 884 may include any number of forms of audio or visual display, including simple visual output such as binary status indicators (e.g., light emitting diodes (LEDs)) and multi-character visual output, or more complex output such as a display screen (e.g., a liquid crystal display (LCD) screen), where output such as text, graphics, multimedia objects, etc. are generated or produced from the operation of the edge computing node 850. The display or console hardware may be used in the context of the present system to provide edge computing system output and receive input, to manage edge computing system components or services, to identify the status of edge computing components or services, or to perform any number of 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 the various manufacturing modules. In examples, notifications can be provided to multiple devices simultaneously, e.g., an operator can view notifications on individual modules of system 400. Notifications can be provided simultaneously or near simultaneously based on criteria such as proximity 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.

[0107] The edge computing node 850 may be powered by a battery 876, although in instances where the edge computing node 850 is installed at a fixed location it may have a power source tied to the utility grid, or the battery may be used as a backup or for temporary capacity. The battery 876 may be a lithium ion battery or a metal air battery, such as a zinc air battery, an aluminum air battery, or a lithium air battery.

[0108] A battery monitor / charger 878 may be included in the edge computing node 850 to track the state of charge (SoCh) of a battery 876, if one is included. The battery monitor / charger 878 may be used to monitor other parameters of the battery 876 to provide fault predictions, such as a state of health (SoH) and state of function (SoF) of the battery 876. The battery monitor / charger 878 may communicate 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] A power block 880, or other power source coupled to a power grid, may be coupled to the battery monitor / charger 878 to charge the battery 876. In some examples, the power block 880 may be replaced with a wireless power receiver to obtain power wirelessly, for example through a loop antenna in the edge computing node 850. The particular charging circuit may be selected based on the size of the battery 876 and therefore the current required.

[0110] Storage 858 may include instructions 882 in the form of software, firmware, or hardware commands for implementing the techniques described herein. Although such instructions 882 are illustrated as code blocks contained in memory 854 and storage 858, it may be understood that any code block may be replaced by hardwired circuitry, such as incorporated in an application specific integrated circuit (ASIC).

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

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

[0113] In further embodiments, machine-readable media also include any tangible medium capable of storing, encoding, or retaining instructions for execution by a machine that cause the machine to perform any one or more of the methods of the present disclosure, or any tangible medium capable of storing, encoding, or retaining data structures used by or associated with such instructions. Thus, a "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 media include, but are not limited to, non-volatile memory, including, by way of example, 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 CD-ROM and DVD-ROM disks. Instructions embodied by the machine-readable medium may further be transmitted or received over a communications network using a transmission medium via a network interface device using any one of a number of 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-transitory format. In one embodiment, information stored or otherwise provided on the machine-readable medium may represent instructions, such as the instructions themselves or a format from which instructions may be derived. This format from which instructions may be derived may include source code, encoded instructions (e.g., compressed or encrypted format), packaged instructions (e.g., split into multiple packages), and the like. Information representing instructions in the machine-readable medium may be processed by a processing circuit into instructions to perform any of the operations described herein. For example, deriving instructions from information (e.g., processing by a processing circuit) may include compiling (e.g., from source code, object code, etc.), interpreting, loading, organizing (e.g., dynamically or statically linking), encoding, decrypting, encrypting, decrypting, packaging, unpackaging, or otherwise manipulating information into instructions.

[0115] In one embodiment, deriving the instructions may include assembling, compiling, or interpreting the information (e.g., by a processing circuit) to create the instructions from some intermediate or preprocessed format provided by a machine-readable medium. If the information is provided in multiple parts, it may be combined, unpacked, modified, etc. to create the instructions. For example, the information may be in multiple compressed source code packages (or object code, or binary executable code, etc.) on one or several remote servers. The source code packages may be encrypted when transferred over a network, decrypted as necessary, 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] As used herein, the term "comprises" and variations thereof do not have a limiting meaning when these terms appear in the specification and claims. Such terms are understood to imply the inclusion of the described step or element, or group of steps or elements, but not the exclusion of any other step or element, or group of steps or elements. "Consisting of" means to include and be limited to everything before the phrase "consisting of". Thus, the phrase "consisting of" indicates that the recited elements are necessary or mandatory, and that no other elements may be present. "Consisting essentially of" means to include any elements recited after the phrase, and is limited to other elements that do not interfere with or contribute to the action or function specified in this disclosure with respect to those recited elements. Thus, the phrase "consisting essentially of" indicates that the recited elements are necessary or mandatory, but that other elements are optionally included and may or may not be present depending on whether they materially affect the action or function of the recited elements. Any element or combination of elements recited herein with open-ended language (e.g., "comprising" and its derivatives) shall be deemed to be further recited with closed-ended language (e.g., "consisting of" and its derivatives) and partially closed-ended language (e.g., "consisting essentially of" and its derivatives).

[0117] The words "preferred" and "preferably" refer to embodiments of the present disclosure that may provide certain benefits, under particular circumstances, although other embodiments may also be preferred, under the same or other circumstances. Moreover, the recitation of one or more preferred embodiments does not imply that other claims are not useful, and is not intended to exclude other embodiments from the scope of the present disclosure.

[0118] In this application, terms such as "a," "an," and "the" are not intended to refer to only one entity, but include general categories for which specific examples may be used for illustration. 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 a list refer to any one of the items in the list, as well as any combination of two or more items in the list.

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

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

[0121] Further, all numbers herein are deemed to be modified by the term "about," and in certain embodiments, preferably, by the term "exactly." As used herein, in the context of a measured quantity, the term "about" refers to the variation in the measured quantity as would be expected by one of ordinary skill in the art making the measurement and exercising a level of care commensurate with the purpose of the measurement and the precision of the measuring device used. As used herein, a "up to" number (e.g., up to 50) is inclusive of that number (e.g., 50).

[0122] Additionally, the recitation of numerical ranges by endpoints includes all numbers subsumed within that range, as well as the endpoints thereof (e.g., 1 to 5 includes 1, 1.5, 2, 2.75, 3, 3.80, 4, 5, etc.) and any subranges (e.g., 1 to 5 includes 1 to 4, 1 to 3, 2 to 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 descriptions) include the endpoints of the stated range.

[0125] References throughout this specification to "one embodiment," "an embodiment," "particular embodiment," or "some embodiments" mean that the particular feature, configuration, composition, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosure. Thus, the appearances of such phrases in various places throughout this specification do not necessarily refer to the same embodiment of the disclosure. Furthermore, the features, configurations, compositions, or characteristics may be combined in any suitable manner in one or more embodiments. EXAMPLES

[0126] These examples are merely illustrative and are not intended to unduly limit the scope of the appended claims. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the present disclosure are approximations, the numerical values ​​set forth in the specific examples are reported as precisely as possible. However, any numerical value inherently contains certain errors necessarily resulting from the standard deviation found in their respective testing measurements. At the very least, each numerical parameter should be construed by at least applying ordinary rounding techniques in light of the number of reported significant digits, but this is not intended to limit the application of the doctrine of equivalents to the scope of the claims.

[0127] Unless otherwise stated, all chemicals used in the examples can be obtained from the suppliers stated. Adhesives used in the example embodiments can include the adhesives described below.

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

[0129] Other adhesives used herein may include hot melt adhesives, such as one-part moisture-curing hot melt adhesives. This product family is characterized by very high heat resistance compared to traditional thermoplastic PO hot melts. In some examples, polyurethane (PUR) hot melts containing isocyanates for the chemical crosslinking process are used. In other examples, polyolefin (POR) hot melts using silanes as reactive components are used herein. Two-part adhesives are 100% solids systems that obtain storage stability by separating the reactive components. The components are supplied in separate containers as "resin" and "hardener". It is important to maintain a certain ratio of resin and hardener 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 hardening occurs at room temperature. Typically, the reaction begins immediately upon mixing the two components, so the viscosity of the mixed adhesive increases with time until the adhesive cannot be applied to the substrate or the adhesive strength decreases due to reduced "wetting" of the substrate. Formulations with various cure rates are available, resulting in a wide range of working times after mixing and strength build-up rates after bonding. Depending on the formulation, final strength is reached in minutes to weeks after bonding. The adhesive must be removed from the mixing and application equipment before the cure has progressed 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 work-life. The assembled objects are usually clamped until sufficient strength is achieved to allow further processing. If a faster cure rate (strength build-up) is desired, heat can be used to accelerate the cure. This is particularly useful when parts need to be processed more quickly after bonding, or when additional work-life is required but a slow strength build-up rate is unacceptable. Typically, two-part adhesives are strong and rigid when cured, with good heat and chemical resistance.

[0130] Two-part adhesives can be mixed and applied by hand for small applications, but this requires considerable care to ensure proper ratios and thorough mixing of the components to ensure proper cure and performance. Even hand mixing typically results in significant waste. As a result, adhesive suppliers have developed packaging methods that allow the components to remain separated for storage and provide a means for dispensing the mixed adhesive, e.g., side-by-side syringes, concentric cartridges, etc. The packages are typically inserted into an applicator handle and the adhesive is dispensed through a disposable mixing nozzle. The design of the package maintains the proper ratios of the components, and the use of the 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, at which point a new mixing nozzle must be used. For larger applications, metered mix devices are available to measure, mix, and dispense adhesives packaged in containers ranging from quarts to drums.

[0131] Two-part adhesives consist of a resin and a hardener component and cure 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 a set ratio to obtain the desired properties from a particular formulation of adhesive; common ratios include 10:1, 1:1, 2:1, etc. The reaction between the two components usually begins as soon as they are mixed, and the viscosity increases until it is no longer usable. This can be described as the pot life, open time, and pot life, as discussed above. Once cured, two-part adhesives are tough and rigid, with good heat and chemical resistance.

[0132] Epoxy adhesive As previously described herein, example embodiment epoxy adhesives can include one-part and two-part adhesives. One-part epoxy adhesives can include a resin. Two-part epoxies, like their one-part cousins, are also formulated from epoxy resins. Two-part epoxies are widely used in structural applications and are used to bond many materials including, for example, metals, plastics, fiber reinforced plastics (FRP), glass, and some rubbers. They generally cure quickly and provide a relatively tough bond. Some compositions are often brittle, although tougheners and elastomers can be utilized to reduce this tendency.

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

[0134] Heat is not necessarily required when using two-part epoxies, as they can be cured at room temperature. Two-part epoxies generally reach working strength anywhere between 5 minutes and 8 hours after mixing, depending on the hardener. A chemical catalyst or heat can be applied to speed up the reaction between the resin and hardener.

[0135] The resin that is the basis of all epoxies is bisphenol A diglycidyl ether (DGEBA). Bisphenol A is made by reacting phenol with acetone under the right conditions. The "A" stands for acetone, "phenyl" means the phenol group, and "bis" means two. Thus, bisphenol A is the product made from chemically combining two phenols with one acetone. The unreacted acetone and phenol are stripped from the bisphenol A, which is then reacted with a substance called epichlorohydrin. This reaction attaches two ("di") glycidyl groups to the end of the bisphenol A molecule. The resulting product is bisphenol A diglycidyl ether, the basic epoxy resin. It is these glycidyl groups that react with the amine hydrogen atoms on the hardener to produce the cured epoxy resin. Unmodified liquid epoxy resins are very viscous and unsuitable for most applications except as very thick adhesives.

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

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

[0138] The ratio of glycidyl oxygen to amine hydrogen determines the final ratio of resin to hardener, taking into account the various molecular weights and densities involved. The proper ratio produces a "fully crosslinked" thermoset plastic. Varying the recommended ratio leaves either unreacted oxygen or hydrogen atoms, depending on which is in excess. The resulting cured resin will be less strong because it is not fully crosslinked. Excess Part B should generally be avoided, as it will result in increased moisture sensitivity in the cured epoxy.

[0139] Amine hardeners are not "catalysts." Catalysts accelerate the reaction but do not become chemically part of the final product. Amine hardeners interlock with the epoxy resin and contribute significantly to the final properties of the cured system. The cure time of an epoxy system depends on the reactivity of the amine hydrogen atoms. The bound organic molecules do not directly participate in the chemical reaction, but they do affect how easily the amine hydrogen atoms leave the nitrogen to react with the glycidyl oxygen atoms. Thus, the cure time is set by the kinetics of the particular amine used in the hardener. The cure time of any given epoxy system can only be altered by adding accelerators to a system that can accept them, or by changing the temperature and mass of the resin / hardener mixture. Adding more hardener does not "speed things up," and adding less hardener does not "slow things down."

[0140] The epoxy curing reaction is exothermic. The rate at which an epoxy resin cures depends on the cure temperature; the warmer it is, the faster it is. The cure rate varies 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 tack-free at 70°F, it will be tack-free in 1.5 hours at 88°F and 6 hours at 52°F. Anything that has to do with reaction rate follows this general rule. Pot life and working time are greatly affected by the initial temperature of the mixed resin and hardener. 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 held in a dense volume to solidify. Gel time depends on the initial temperature of that mass, following the rules above. 100 grams (about 3 fluid ounces) of Silver Tip Laminating Epoxy and Fast Hardener together (as an illustrative example) will set in 25 minutes when started at 77°F, and at 60°F the gel time is about 50 minutes. If the same mass were spread over 4 square feet at 77°F, the gel time would be a little over 3 hours. In addition to being temperature sensitive, cure time is surface area / mass sensitive.

[0142] As the reaction proceeds, heat is released. If the generated heat is immediately dissipated to the surroundings (as occurs with films), the temperature of the cured resin does not increase and the reaction rate proceeds at a steady pace. If the resin is contained (as in a mixing pot), the exothermic reaction increases the temperature of the mixture, accelerating the reaction.

[0143] The working time (WL) of an epoxy formulation, in pot form, is approximately 75% of the gel time. The working time can be extended by increasing the surface area, working with a smaller mass, or cooling the resin and hardener before mixing. The material remaining in the pot increases in absolute viscosity due to polymerization (e.g., measured at 75°F), but initially decreases in apparent viscosity upon heating. The material remaining in the pot until 75% of the gel time appears very thin (due to heating), but is actually very thick when cooled to room temperature. Experienced users either mix a batch that is to be applied almost immediately, or increase the surface area to slow the reaction.

[0144] The cure rate of epoxies is temperature dependent, but the mechanism of cure is temperature independent. The reaction proceeds most quickly in the liquid state. As cure progresses, the system changes from a liquid to a sticky, viscous soft gel. After gelation, the reaction rate slows as hardness increases. Chemical reactions proceed slower in the solid state. As the system hardens from a soft, viscous gel, it slowly loses its tackiness. The system becomes tack-free and continues to get harder and stronger over time.

[0145] At room temperature, the system reaches approximately 60-80% of its final strength after 24 hours. Cure then progresses slowly over the next few weeks, eventually reaching a point where no further cure will 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, for example, require post-curing at elevated temperatures to reach full cure.

[0146] It is usually more efficient to work with a cure time that is practical for manual application, if that is possible for the particular system being used. This allows the user to move on to the next step without wasting time waiting for the epoxy to cure. A film with a short tack time and fast setting has less opportunity to pick up fly tracks, insects, and other airborne contaminants.

[0147] Epoxy resin compositions generally include a first liquid part that includes an epoxy resin and a second liquid part that includes a hardener. The first and second parts are liquid at ambient temperature, but the liquid part may include solid components dissolved or dispersed within the liquid.

[0148] The first part of the two-part composition comprises at least one epoxy resin. Epoxy resins are low molecular weight monomers or high molecular weight polymers that typically contain at least two epoxy groups. Epoxy groups are cyclic ethers with three ring atoms, sometimes called glycidyl or oxirane groups. Epoxy resins are typically liquid at ambient temperature.

[0149] Various epoxy resins are known, and examples thereof include bisphenol A type epoxy resins, bisphenol F type epoxy resins, bisphenol S type epoxy resins, phenol novolac type epoxy resins, alkylphenol novolac type epoxy resins, cresol novolac type epoxy resins, biphenyl type epoxy resins, aralkyl type epoxy resins, cyclopentadiene type epoxy resins, naphthalene type epoxy resins, naphthol type epoxy resins, epoxy resins that are condensates of phenol and aromatic aldehydes having a phenolic hydroxyl group, biphenyl aralkyl type epoxy resins, fluorene type epoxy resins, xanthene type epoxy resins, triglycidyl isocyanurate, rubber modified epoxy resins, and phosphorus-based epoxy resins.

[0150] Blends of various epoxy-containing materials can also be utilized. Suitable blends can include two or more weight average molecular weight distributions of epoxy-containing compounds, such as low molecular weight epoxides (e.g., those having a weight average molecular weight of less than 200 g / mole), medium molecular weight epoxides (e.g., those having a weight average molecular weight of about 200 to 1000 g / mole), and high molecular weight epoxides (e.g., those having a weight average molecular weight of more than about 1000 g / mole). Alternatively, or in addition, the epoxy resin can include a blend of epoxy-containing materials having different chemical properties (e.g., aliphatic and aromatic) or functionality (e.g., polar and non-polar).

[0151] In one embodiment, the first part of the two-part 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 the diglycidyl ether of bisphenol A. The simplest resin of this class is formed by reacting two moles of epichlorohydrin with one mole of bisphenol A to form bisphenol A diglycidyl ether (commonly abbreviated as DGEBA or BADGE). DGEBA resins are colorless to pale yellow transparent liquids at ambient temperature, typically with viscosities ranging from 5 to 15 Pa·s at 25°C. Pure DGEBA has a strong tendency to form crystalline solids when stored at ambient temperature, so technical grades usually have some molecular weight variation. Other bisphenols, such as bisphenol F, can be used to carry out the same reaction. The choice of epoxy resin to be used depends on the intended end use. Epoxides with flexibilized backbones may be desirable when greater ductility is required at the bond line. Materials such as bisphenol A diglycidyl ether and bisphenol F diglycidyl ether provide desirable structural adhesive properties, which they achieve upon curing, while hydrogenated versions of these epoxies can be useful for compatibility with substrates having oily surfaces.

[0152] Aromatic epoxy resins can also be prepared by the reaction of aromatic alcohols, such as biphenyl diols and triphenyl diols and triols, with epichlorohydrin. 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 cycloaliphatic epoxides. Glycidyl epoxy resins are typically formed by reacting epichlorohydrin with an aliphatic alcohol or polyol to give a glycidyl ether or with an aliphatic carboxylic acid to give a glycidyl ester. The resulting resins can be monofunctional (e.g., dodecanol glycidyl ether), difunctional (diglycidyl ester of hexahydrophthalic acid), or of higher functionality (e.g., trimethylolpropane triglycidyl ether). Cycloaliphatic epoxides contain one or more cycloaliphatic rings in the molecule to which oxirane rings are fused (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 viscosities (10-200 mPa·s) at ambient temperatures and are often used as reactive diluents. As such, they are used to modify (reduce) the viscosity of other epoxy resins. Hence, the term "modified epoxy resin" is meant to denote an epoxy resin containing a reactive diluent that reduces the viscosity. In some embodiments, the resin composition may further comprise 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'-diglycidyl aniline, N,N,N',N'-tetraglycidyl meta-xylylenediamine, and polyglycidyl ethers of vegetable oils. The resin composition may comprise at least 1, 2, 3, 4, or 5% by weight, typically no more than 15 or 20% by weight, of such reactive diluents.

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

[0155] Epoxies are typically cured using a stoichiometric or near stoichiometric amount of hardener. In the case of two-part epoxy compositions, the second part contains a hardener, also referred to herein as the curing agent. To calculate the amount of co-reactant (hardener) used when curing an epoxy resin, the equivalent weight or epoxide value is used. The epoxide value is the number of epoxide equivalents in 1 kg of resin (eq / kg), and the equivalent weight is the weight in grams of resin containing 1 molar equivalent of epoxide (g / mol). Equivalent weight (g / mol) = 1000 / epoxide value (eq / kg).

[0156] Common classes of curing agents for epoxy resins include amines, amides, ureas, imidazoles, and thiols. In an exemplary embodiment, the curing agent has a reactive -NH group or a reactive -NR 1 R 2 group, where R 1 and R 2 is independently H or C1-C4 alkyl, most typically H or methyl.

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

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

[0159] Useful polyamines have the general formula R 5 -(NR 1 R 2 ) x where R 1 and R 2 are independently H or alkyl; R 5 is a polyvalent alkylene or arylene, and x is at least 2. 1 and R 2 The alkyl group is typically C1-C 18 R is alkyl, more typically C1-C4 alkyl, and most typically methyl. 1 and R 2 may 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 yet 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 six-membered ring diamines include, for example, piperazine and 1,4-diazabicyclo[2.2.2]octane ("DABCO").

[0161] Other useful polyamines include those having at least three amino groups, which may be 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 comprise additives including fillers (e.g., silanized or untreated), anti-sagging additives, thickening / anti-sagging agents, processing aids, waxes, and UV stabilizers. Examples of typical fillers include glass bubbles, fumed silica, mica, feldspar, and wollastonite. In some embodiments, the resin composition further comprises 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 Example embodiment methyl methacrylate (MMA) adhesives can include one-part and two-part MMA adhesives. One-part MMA adhesives can include resin. Two-part MMA adhesives build up strength faster than epoxies. MMA adhesives are commonly used to bond plastics and metal to plastic. They are also very effective at joining solid surface materials and can be colored, making them widely used in countertop manufacturing and installation.

[0165] Methyl methacrylate adhesives are structural acrylic adhesives made from a part A (part 1) resin and a part B (part 2) hardener. Most MMAs also contain rubber and additional enhancers. MMA cures rapidly at room temperature and has maximum bond strength soon after application. This adhesive is resistant to shear, peel, and impact stresses. Looking at the bonding process more technically, these adhesives work by creating 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 creates a strong bond while remaining flexible. These adhesives can form bonds between dissimilar materials with different flexibilities such as metals and plastics. Unlike some other structural adhesives such as two-part epoxies, MMA does not require heat to cure. Several MMAs with a wide range of pot lives are available so they can be tailored to your specific needs.

[0166] MMA has a relatively high peel strength and a relatively high heat resistance. It develops strength relatively quickly, so parts can be used relatively soon. It is also worth noting the several different processing conditions used with MMA. For example, the two components of MMA can be applied separately to each side of the materials being bonded, and the MMA does not begin to cure until the joint is brought together to bond the components. This means that you do not have to deal with precise mix ratios to get a good bond. It is important to remember that MMA tends to have a strong odor, so good ventilation should be provided when applying MMA, and that MMA is flammable, so some care should be taken.

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

[0168] All of these acrylic structural adhesive types offer exceptional bond strength and durability, approaching the bond strength of epoxy adhesives, but with the advantages of faster cure speeds, less sensitivity to surface pretreatment, and a greater variety of materials that can be bonded.

[0169] Silicone Adhesive Example embodiment silicone adhesives can include one-part and two-part silicone adhesives. Two-part silicone adhesives are typically used when the bonding area is large or when there is not enough relative humidity to complete the cure. Common applications of these are in electronics applications, including in automobile and window manufacturing, and in home appliance manufacturing.

[0170] Suitable silicone resins include moisture curable silicones, condensation curable silicones, and addition curable silicones, such as hydroxyl-terminated silicones, silicone rubbers, and fluoro-silicones.Suitable examples of commercially available silicone PSA compositions that contain silicone resins include 280A, 282, 7355, 7358, 7502, 7657, Q2-7406, Q2-7566, and Q2-7735 from Dow Corning; 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 from General Electric.One example of a commercially available two-part silicone resin is sold under the trade name "SILASTIC J" by Dow Chemical Company (Midland, Michigan).

[0171] Pressure sensitive adhesives (PSA) can 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 copolymers, and combinations thereof.

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

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

[0174] Most polyurethane adhesives are either polyester-based or polyether-based. They are present in an isocyanate prepolymer and in an active hydrogen-containing hardener component (polyol). They form the soft segments made of urethane, and the isocyanate groups form the hard segments. The soft segments usually constitute the majority of the elastic urethane adhesive, and therefore determine its physical properties. For example, polyester-based urethane adhesives have better oxidative and high-temperature stability than polyether-based urethane adhesives, but poorer hydrolytic stability and low-temperature flexibility. However, polyethers are usually more expensive than polyesters.

[0175] Many urethane adhesives are sold as two-component urethane 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). A catalyst is often added to accelerate the cure, usually a tin salt or a tertiary amine. The reactive ingredients are often blended with additives and plasticizers to achieve desired processing and / or final properties, and to reduce costs.

[0176] Polyurethanes may be prepared, for example, by reaction of one or more polyols and / or polyamines and / or aminoalcohols with one or more polyisocyanates, optionally in the presence of non-reactive component(s). In applications where weathering is likely to occur, it is typically desirable for the polyols, polyamines and / or aminoalcohols, and polyisocyanates to be free of aromatic groups.

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

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

[0179] Suitable aminoalcohols include, for example, 2-aminoethanol, 3-aminopropan-1-ol, alkyl substituted versions 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-phenylene diisocyanate; p-phenylene diisocyanate; methylene bis(o-chlorophenyl diisocyanate); methylene diphenylene-4,4'-diisocyanate; polycarbodiimide-modified methylene diphenylene diisocyanate; (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 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), polymeric or oligomeric compounds terminated with two isocyanate functional groups (e.g., polyoxyalkylenes, polyesters, polybutadienyls, etc.) (e.g., toluene-2,4-diisocyanate terminated polypropylene oxide glycol); Bayer Polyisocyanates commercially available from MONDUR Corporation, Pittsburgh, PA under the tradename DESMODUR (e.g., Desmodur XP7100 and Desmodur N 3300A); and combinations thereof.

[0181] In some embodiments, the polyurethane comprises a reaction product of components including at least one polyisocyanate and at least one polyol. In some embodiments, the polyurethane comprises a reaction product of components including 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, the polyurethane(s) are extensible and / or flexible. For example, the polyurethane(s), or any layer containing a polyurethane, may have an elongation at break of at least 10, 20, 40, 60, 80, 100, 125, 150, 175, 200, 225, 250, 275, 300, 350, or even at least 400 percent or more (at atmospheric conditions).

[0183] In certain embodiments, the polyurethane has multiple hard segments, typically in amounts from 35, 40, or 45 weight percent to 50, 55, 60, or even 65 weight percent of the segments corresponding to any combination of one or more polyisocyanates.

[0184] As used herein, wt % means the weight percent based on the total weight of the material. Weight percent of hard segments = (weight of short chain diols and polyhydric alcohols + weight of short chain di- or polyisocyanates) / total weight of resin Where: The short chain diols and polyols have an equivalent weight ≦185 g / eq and a functionality ≧2, and the short chain isocyanates have an equivalent weight ≦320 g / eq and a functionality ≧2.

[0185] Typically, one or more catalysts are included with two-part urethanes. Catalysts for two-part urethanes are well known, and include, for example, aluminum-, bismuth-, tin-, vanadium-, zinc-, tin-, and zirconium-based catalysts. Tin-based catalysts have been found to significantly reduce the amount of gassing during the formation of polyurethanes. Examples of tin-based catalysts include dibutyltin compounds such as dibutyltin diacetate, dibutyltin dilaurate, dibutyltin diacetylacetonate, dibutyltin dimercaptide, dibutyltin dioctoate, dibutyltin dimaleate, dibutyltin acetonylacetonate, and dibutyltin oxide. When present, any catalyst is typically included at a level of at least 200 parts per million (ppm), 300 ppm, or more, although this is not a requirement.

[0186] Additional suitable two-part urethanes are described in US Pat. No. 6,258,918 B1 (Ho et al.) and US Pat. No. 5,798,409 (Ho), which are incorporated herein by reference.

[0187] Generally, the amount of polyisocyanate to polyol, polyamine, and / or aminoalcohol in a two-part urethane is selected at approximately stoichiometric equivalence, although in some cases it may be desirable to adjust the relative amounts to other ratios. For example, a slight stoichiometric excess of polyisocyanate can be useful to ensure a high degree of incorporation of the polyol, polyamine, and / or aminoalcohol, but after polymerization, any excess isocyanate groups present will typically react with materials having reactive hydrogens (e.g., adventitious moisture, alcohols, amines, etc.).

[0188] The entire disclosures of patents, patent documents, and publications cited herein are incorporated by reference in their entirety as if each were individually incorporated. To the extent that any conflict or inconsistency exists between the written specification and the disclosure of any document incorporated herein by reference, the written specification shall control. Various modifications and alterations to the present disclosure will become apparent to those skilled in the art without departing from the scope and spirit of the present disclosure. The present disclosure is not intended to be unduly limited by the exemplary embodiments and examples described herein, which are presented only as examples within the scope of the present disclosure, which is intended to be limited only by the claims set forth herein as follows.

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 material to be dispensed, wherein the one or more sensors comprise: (i) a temperature sensor comprising a probe configured to be disposed within a fluid path of the material to be dispensed and sense the temperature of the material to be dispensed; and (ii) a conductivity sensor configured to sense the conductivity of the material to be dispensed. A processor operably coupled to the one or more dispensing device components, the processor being configured to: Receive at least one parameter related to the dispensing device system, Based on the at least one parameter, determine a value of an operating parameter of the dispensing device system to achieve a flow rate of the material to be dispensed within the dispensing device system, Provide the operating parameter, Receive the temperature of the material to be dispensed from the temperature sensor, Receive the sensed conductivity of the material to be dispensed from the conductivity sensor, Based on the sensed temperature of the material to be dispensed and the sensed conductivity of the material to be dispensed, determine a state of curing of the material to be dispensed, Based on the sensed temperature and the state of curing, adjust the value of the operating parameter of the dispensing device system, And provide the adjusted operating parameter. A dispensing device system.

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

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

4. The dispensing device 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 within the fluid path of the dispensable material.

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

6. The processor is configured to determine a maximum idle time or purge time of the one or more dispensing device components based on the at least one parameter and the at least one process parameter, and is further configured to provide the maximum idle time or the purge time. The dispensing device system according to claim 1.

7. 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. The dispensing device system according to claim 6.

8. The processor further comprises a user interface operably coupled thereto, and the processor is Receiving one or more user inputs including one or more of a safety factor, a process control factor, and a discard factor, and 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 according to claim 6.

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

10. The dispensing device system according to claim 1, wherein the dispensable agent contains an adhesive.

11. A method of dispensing a dispensable material using a dispensing device system, comprising: receiving at least one parameter associated with the dispensing device system; determining a value of an operation parameter of the dispensing device system to achieve a flow rate of the dispensable material within the dispensing device system based on the at least one parameter; providing the operation parameter; receiving at least one process parameter including a sensed temperature of the dispensable material; receiving a sensed conductivity of the dispensable material from a conductivity sensor; determining a state of curing of the dispensable material based on the sensed temperature of the dispensable material and the sensed conductivity of the dispensable material; adjusting the value of the operation parameter of the dispensing device system based on the sensed temperature and the state of curing; providing the adjusted operation parameter; A method comprising.

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

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

14. Determining a maximum idle time or purge time of one or more dispensing device components of the dispensing device system based on the at least one parameter and the at least one process parameter; Providing the maximum idle time or the 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 including one or more of a safety factor, a process control factor, and a discard factor; 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 operation parameter and the adjusted operation parameter to one or more dispensing device components of the dispensing device system.

18. The method according to claim 11, wherein the dispensable material contains an adhesive.

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