Adhesive dispensing system and method

Abstract

Apparatuses, systems (400), and methods for predicting parameters of a dispenser system, dispensing dispensable material, calibrating a dispenser system are described. The apparatuses and systems can use machine learning algorithms based on environmental variables and other factors in a system using an adhesive dispenser (100). Further, the apparatuses and systems can adjust operating parameters of the dispenser system based on at least one process parameter. Still further, the apparatuses and systems can provide one or more settings for one or more dispenser components based on a calibration model. The algorithms (412) can operate on remote software or local software and control systems, or as part of an edge computing system or an Internet of Things (IoT) system.

Description

Background Technology

[0001] Systems for dispensing adhesives typically include an inlet or internal area for holding the adhesive and an outlet or tip through which the adhesive is dispensed onto surfaces. The flow rate of the adhesive can be directly controlled using a metering system to meet the needs of downstream manufacturing processes. However, systems that actively control the flow rate can be too complex and expensive for most users. Indirect methods use calibration curves to provide or recommend settings based on various variables such as air pressure, pump settings, application time, total volume dispensed, or other variables. However, such calibration curves can become overly complex given the amount of adhesive, varying temperatures, and chemical reactions that can occur throughout the dispensing process. Other systems involving neural networks can learn or predict settings, but these require hundreds or thousands of calibration points to become useful. Therefore, there is generally a need for more accurate prediction of dispenser settings and other dispensing parameters in a timely and cost-effective manner. Summary of the Invention

[0002] In one aspect, this disclosure provides an apparatus comprising: a memory for storing data indicating at least one parameter of an adhesive dispensing system. The apparatus further comprises: a processor coupled to the memory. The processor is configured to: retrieve 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, the value of which will achieve an adhesive flow rate in 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 relate to the viscosity of the adhesive in the adhesive dispensing system. The operating parameter may include the driving force pressure used in the adhesive dispensing system. The processor may be configured to determine the driving force pressure based on the viscosity μ of the adhesive and parameters specific to the adhesive dispensing system, according to the relationship between pressure and viscosity. This relationship may include a constant determined based on a non-neural network machine learning algorithm. This relationship may also include a constant determined based on a hybrid algorithm consisting of a neural network component and a non-neural network machine learning algorithm. Similar methods and systems are also described.

[0004] In another aspect, this disclosure describes a dispenser system including one or more dispenser components; and a processor operatively 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 for dispensable material. The one or more sensors include a temperature sensor comprising a probe disposed in a fluid path of the dispensable material and configured to sense the temperature of the dispensable material. The processor is configured to: receive at least one parameter associated with the dispenser system; determine, based on the at least one parameter, a value of an operating parameter of the dispenser system that will result in a flow rate of the dispensable material in the dispenser system; and provide the operating parameter. The processor is further configured to: receive the temperature of the dispensable material from the temperature sensor; adjust the value of the operating parameter of the dispenser system based on the sensed temperature; and provide the adjusted operating parameter.

[0005] In another aspect, this disclosure describes a method for dispensing dispensable material using a dispenser system. The method includes: receiving at least one parameter associated with the dispenser system; determining, based on the at least one parameter, a value for an operating parameter of the dispenser system, the value of which will result in a flow rate of the dispensable material in the dispenser system; providing the operating parameter; receiving at least one process parameter including a sensed temperature of the dispensable material; adjusting the value of the operating parameter of the dispenser system based on the sensed temperature; and providing the adjusted operating parameter.

[0006] In another aspect, this disclosure describes a dispenser system comprising: one or more dispenser components configured to provide dispensable material; and a processor operatively 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 are based on a plurality of dispensing samples of the dispensable material. The processor is further configured to: select one or more predetermined models based on the one or more parameters of 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, this disclosure describes a method for calibrating a dispenser system. The method includes: receiving a plurality of calibration data points of the dispenser system and one or more parameters of dispenser components of the calibration system, the plurality of calibration data points being based on a plurality of dispensing samples of dispensable material; selecting one or more predetermined 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 predetermined models; and providing one or more settings for the dispenser system based on the calibration model.

[0008] The foregoing overview of this disclosure is not intended to describe every disclosed embodiment or every implementation of this disclosure. The following description provides more specific examples of exemplary embodiments. Guidance is provided at several points throughout this application by means of lists of examples, which may be used in various combinations. In each case, the cited lists are used only as representative groups and should not be construed as exclusive lists. Therefore, the scope of this disclosure should not be limited to the specific illustrative structures described herein, but should extend at least to the structures described by the language of the claims and their equivalents. Any element positively referenced as an alternative in this specification may be expressly included in or excluded from the claims in any combination as desired. While various theories and possible mechanisms may have been discussed herein, such discussions should in no way be used to limit the subject matter protected by the claims. Attached Figure Description

[0009] Figure 1 This is a block diagram of an adhesive dispenser that can implement an exemplary embodiment.

[0010] Figure 2 It is a linear calibration curve used to determine the flow rate of the adhesive dispenser.

[0011] Figure 3 It is a nonlinear calibration curve used to determine the flow rate of the adhesive dispenser.

[0012] Figure 4 It is a system based on some implementation schemes for predicting flow rate adjustments for multiple variables.

[0013] Figure 5A An exemplary graphical user interface (GUI) according to some implementation schemes is shown.

[0014] Figure 5B An exemplary GUI for manually entering calibration data is shown according to some implementation schemes.

[0015] Figure 6 This is a flowchart of a method for predicting adhesive dispenser pressure according to some implementation schemes.

[0016] Figure 7 A comparison of the performance between neural network-based models and learning models used in some implementation schemes is depicted.

[0017] Figure 8 The computation nodes are described according to some implementation schemes.

[0018] Figure 9 Further details regarding edge computing nodes according to some implementation schemes are described.

[0019] Figure 10 This is a schematic diagram of a feedback sensor based on some implementation schemes.

[0020] Figure 11 This is a flowchart of a method for calibrating a distributor system according to some implementation schemes.

[0021] Figure 12 It is a flowchart for allocating distributable materials according to some implementation schemes. Detailed Implementation

[0022] As part of the manufacturing process, the adhesive dispenser delivers liquid adhesive to a surface or substrate through a tip or nozzle. Figure 1 This is a block diagram of an adhesive dispenser 100. The adhesive dispenser is configured to deliver liquid adhesive from a liquid adhesive source 104 to a dispensing member 106 using a driving force 102, such that the liquid adhesive can be dispensed as needed using the dispensing member 106. The operator can use various settings or parameters of the adhesive dispenser, including using controllers 108 or 110 to dispense adhesive at a desired flow rate. These settings or parameters can vary depending on multiple factors and can be difficult to predict in terms of which settings will work for any particular combination of factors.

[0023] Given the viscosity or other identification information of the adhesive used, a calibration curve can be used to predict or recommend the settings for the adhesive dispenser 100. Figure 2 A linear calibration curve 200 is shown. In the exemplary curve 200, the mass of adhesive dispensed per unit time can be plotted based on the adhesive viscosity. The dispensed mass will vary with the driving force 102 pressure and the adhesive viscosity. The amount dispensed can be predicted by the linear curve. For example, as seen in curve 202, given a low pressure at the driving force 102 controller 108, less and less mass of adhesive is dispensed as the viscosity increases.

[0024] However, the actual distribution quality obtained may not accurately fit such curves. For example, as seen at curve 202, the actual distribution quality shown by measurement 204 is higher than curve 202. Another measurement 206 may be closer to the predicted linear curve 208. Therefore, linear fitting only provides a rough prediction.

[0025] Other neural network-based systems can be used to control instructions for applying adhesives. However, neural network-based systems require hundreds or even thousands of calibration points to be useful, and extensive learning must be performed before any control over adhesive dispensing can be achieved.

[0026] Systems and algorithms for adhesive dispensing

[0027] To address these and other complexities, systems, methods, and apparatuses, according to various implementations, use machine learning to predict dispenser settings or other parameters for accurate, adaptable adhesive dispensing that works well with downstream manufacturing processes as needed by the operator. Predictions can be based on any data related to the adhesive or the adhesive dispensing process. Figure 4 It is a system 400 for predicting flow rate adjustments for multiple variables, based on some implementation schemes.

[0028] refer to Figure 4 System 400 may include a data component 402. Data component 402 may include input received from human-machine interface 404, such as user input. Furthermore, data component 402 may include output provided for display on human-machine interface 404. Human-machine interface 404 may then provide input and receive output from distributor component 406. Additionally, human-machine interface 404 may provide data including visual and audio indicators to human operator 408 and receive input such as keyboard entries from human operator 408. Human operator 408 may interact with distributor component 406, for example, by changing settings or parameters of distributor component 406.

[0029] Data component 402 may include sensor data 410. Sensor data 410 may include, for example, rheological data about the calibration liquid and the adhesive to be dispensed; data about the adhesive manufacturing process, including in-plant measurements; environmental parameters or conditions (such as those provided by sensor 428); and batch information, canister information, or other information that identifies the adhesive batch. Data component 402 may include material properties 414 of the adhesive and dispenser properties 416. Sensor 418 may include data about the adhesive during the supply chain process, including the temperature or humidity the adhesive has been subjected to, and the amount of time that extreme temperature or humidity conditions have existed. Data component 402 may also include data 420 about the different modular components of the adhesive dispenser 100.

[0030] Data component 402 may also include algorithm 412, such as machine learning algorithms, curve fitting algorithms, minimization and optimization functions, etc. Algorithm 412, according to an embodiment, may generate predictions based on data associated with the calibration liquid. The calibration liquid may include a model of the liquid, an ideal liquid, or a special calibration liquid used only for the calibration process and not, for example, a production process. In any case, the calibration liquid should be dispensed using, for example, an adhesive dispenser 100. In the example, in addition to data input by a user or another system, input data may also include any of the aforementioned data, such as machine calibration data or dispenser calibration data. In some examples, temperature or humidity sensors or other sensors in the room or area where adhesive dispensing is being performed or will be performed may provide input to algorithm 412.

[0031] As mentioned earlier in this paper, the relationship between the quality, viscosity, and pressure of an adhesive can be nonlinear, thus introducing complexity when determining or predicting the appropriate pressure to distribute the adhesive under. Formulas or relationships can be developed to illustrate the nonlinear relationship between pressure and viscosity. An exemplary formula (1) can be used to summarize this nonlinear relationship:

[0032] m=G(F1(p),F2(μ)…) (1)

[0033] Where m is the mass of the dispensed adhesive, F1 is a function of pressure p, and F2 is a function of viscosity μ. Other functions and relationships may be included, and Equation (1) should not be construed as limiting the implementation to the relationship between pressure and viscosity. Constants within those relationships are solved using a machine learning algorithm as described herein. Constants may vary with environmental or other conditions, and in some implementations, different constants will be obtained in different iterations of the machine learning algorithm.

[0034] According to formula (1) or a similar formula, it can be seen that... Figure 3 Nonlinear fitting is provided. However, this increases the mathematical complexity of the prediction process. Furthermore, other variables can add dimensions to the prediction, further increasing complexity. For example, if temperature is considered, the prediction becomes a three-dimensional problem with three variables (e.g., temperature, mass, and pressure), making modeling using calibration curves increasingly difficult. Chemical reactions within the adhesive or between adhesive components, whether during storage or during the dispensing process, can further complicate the development of accurate calibration curves applicable to real-world conditions.

[0035] Algorithm 412 can generate predictions based on Equation (1). In some exemplary embodiments, referring to Equation (1), the constants associated with F1 and F2 can be solved using, for example, machine learning algorithms or by solving an optimization problem as described later herein. These constants may include dispenser-specific constants or adhesive-specific constants calculated for each adhesive dispenser 100 and that may change with time or temperature. Viscosity μ can be determined based on direct or indirect measurements, polynomial fitting, numerical regression, or formulas (such as the Andreid formula for liquid viscosity provided in Equation (2)):

[0036]

[0037] Where T is the temperature, and D and E are constants to be solved.

[0038] As described above, the constants associated with Equation (1), as well as constants D and E, can be calculated, for example, using machine learning algorithms or by solving an optimization problem. Such algorithms can estimate the viscosity (or receive a value indicating a viscosity estimate) based on the aging of the adhesive, and apply that estimate to the value of D or E. Some constants can be calculated based on machine learning algorithms that minimize a loss function. The loss function can have input parameters related to at least one of the pressure, mass, volume, time, and temperature associated with the adhesive, the adhesive dispenser, or the process associated with the adhesive application.

[0039] The quality of the model and any predictions made can be estimated using, for example, a root mean square error algorithm, which compares the predicted values ​​with measured values ​​of various parameters, including the mass to be dispensed, pressure, temperature, viscosity, etc. Based on the determined constants, the mass to be dispensed and the ambient temperature and humidity, pressure, or settings under which the adhesive should be dispensed are provided to the human-machine interface 404.

[0040] Still referencing Figure 4 The human-machine interface 404 may include a user interface 422, local storage and processing 424, a connection to a server 426, a sensor 428, and a connection to a dispenser 406 430. The sensor 428 may include an ambient temperature sensor and a humidity sensor. For example, the human-machine interface 404 may also include a two-dimensional (2D) barcode scanner or a QR barcode scanner to scan batch information or other identification information of the adhesive or adhesive container. Some components of the human-machine interface 404 are described herein with reference to... Figures 7 to 8 To describe in more detail.

[0041] Through connection 430, the human-machine interface 404 can communicate with the controller 432 of the dispenser component 406. The pressure or setting predicted by algorithm 412 can then 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., the same or similar to drive force 102) to dispense dispensable material 436 at a pressure predicted or indicated by algorithm 412. The dispenser component 406 may include additional sensors 438.

[0042] In addition to predicting the ideal pressure for distributing the adhesive, the aforementioned machine learning models can also predict or suggest other settings for adhesive flow rates to remedy the bonding process or provide for downstream process requirements. These suggestions can be provided through various human-machine interface elements as described later in this document.

[0043] feedback

[0044] Feedback can be used to incorporate new experiences into subsequent machine learning iterations. Data provided via feedback can indicate the quality of the dispensing process or processes associated with it. The data may also include qualitative data about, for example, the achieved flow rate. In some exemplary embodiments, data on whether adhesive dispensing is proceeding as intended can be provided by a human operator through visual inspection or other observation, via instructions, a human-machine interface 404, a smartphone, etc.

[0045] In other exemplary embodiments, machine sensors 438 (including, for example, machine vision sensors, mass sensors, etc.) or any device capable of detecting heat, humidity, weight, mass, adhesive quality, volume, bead profile, etc., can be used as a source of feedback information. Regardless of whether the feedback data is provided through a human interface or through machine-based sensors 438, algorithm 412 can use the provided data to refine the constants used in algorithm 412.

[0046] Recommended data capture

[0047] Predictions and suggestions can be provided on a user interface such as a graphical user interface (GUI) 500. Figure 5A An exemplary GUI 500 according to some implementation schemes is shown.

[0048] GUI 500 can be provided by a smartphone or other user device, or with a human-machine interface 404 or later in this article. Figure 7 and Figure 8 Any component of the described system is provided with associated standalone equipment. GUI 500 can display information about the adhesive being dispensed. Exemplary information may include product name, product color, an image of the product's tube or other container, batch number and other manufacturing information, and expiration date, etc.

[0049] The GUI 500 may display one or more parameters 502 indicating desired conditions for the dispensing function. For example, the GUI 500 may display the desired dispensing flow rate. In some embodiments, the parameter 502 is user-editable, allowing the user to suggest desired flow rate, quality, or dispensing time based on the needs of a process downstream of a controlled bonding process. The GUI 500 may display such information for any number of remote or local dispensing machines. In some embodiments, the user may use interface elements 504 such as dropdown menus, lists, or other elements to switch between different dispensing machines.

[0050] GUI 500 can be displayed as part of a web application to provide information for adhesive dispenser 100. Figure 1 Suggestions for pressure settings or other operational settings, in which algorithm 412 has been used. Figure 4 This type of operation setting is determined by algorithm 412. Figure 4 As an alternative or supplement to the periodic updates provided, the user can request pressure predictions or suggestions. For example, the user can press interface element 506, such as a button, and a smartphone or other user device can wirelessly transmit a request for algorithm 412 to execute in order to provide updated suggested pressure. Suggested values ​​for parameters (e.g., pressure) can be displayed on GUI 500. In some examples, the user can then manually use the suggested pressure with adhesive dispenser 100. In other examples, a controller 432 is included ( Figure 4 The distributor component 406 can automatically control the distributor hardware 434 based on the recommended pressure.

[0051] The machine learning model described above can also predict when current environmental conditions (such as temperature and humidity, but the implementation is not limited to these) will lead to lower quality predictions. The GUI 500 can then be used to request or suggest additional calibration points captured by the user and retrieve such calibration points from user input. This improves the overall machine learning model.

[0052] In other exemplary implementations, if a user requests a flow rate far beyond the previously requested rate, resulting in a significantly higher applied pressure than previously used, the user can be notified via GUI 500 or an audio alarm that an additional calibration point should be captured. For example, if a user requests a flow rate requiring 50 psi pressure, but the typical distribution range is 20-40 psi, the user will be asked to provide a calibration point associated with 50 psi. The calibration point can be as follows: Figure 5B As shown, for example, a calibration point may include batch number 512 information, pressure 514 information, allocated mass 516 information, allocation time 518 information, and temperature 520 information. Then, in the illustrated example of a 50 psi calibration point, the user will provide... Figure 5BThe information shown includes, for example, the temperature at 50 psi and the time of distribution. In the example, a timestamp may be added to the calibration point at storage by, for example, a human-machine interface 404 or associated processing circuitry. In some implementations, fewer or additional fields may be included in the calibration point.

[0053] The model described earlier in this document can identify opportunities to enhance predictive quality. In at least some exemplary embodiments, recommendations regarding such opportunities can be generated and displayed on a GUI 500 or other user interface. If an opportunity for predictive improvement is identified, the system and apparatus, depending on the embodiment, can instruct the user (whether via audio or visual alarms or instructions, text, voice messages, etc.) to capture adhesive data that could produce predictive improvements.

[0054] For example, if a machine learning model determines that flow rate data has not yet been captured at a specific temperature, the system according to an embodiment may request the user to capture distribution data at that specific temperature. Alternatively, the system according to an embodiment may control a sensor to capture such data at that temperature. In these and other embodiments, additional GUI screens may be provided, which the user can interact with to manually enter data related to data points that can enhance the machine learning algorithm. In at least these embodiments, the distributor component 406 ( Figure 4 It can automatically record some or all of the data via wireless near-field communication (NFC) or other methods. For example, users can... Figure 5B Manually enter calibration data in the GUI screen 510 shown.

[0055] Figure 5B An exemplary GUI for manually entering calibration data is shown according to some embodiments. In the example, the user can enter batch number 512, which identifies a manufacturing batch of the liquid adhesive or a manufacturing batch of a portion of the liquid adhesive. In other examples, batch number 512 may be provided automatically, for example, by reading an RFID chip or barcode associated with dispenser component 406 or via wireless communication from dispenser component 406. The pressure used may be provided at 514. The quality of dispensing may be provided by dispenser component 406, for example by sensor 438, for automatic or manual entry into segment 516. Sensor 438 may also provide feedback data for algorithm 412 (including machine learning algorithms). For example, the dispensing time amount 518 may be manually entered by the user, or such time values ​​may be provided by controller 432. Other parameters affecting pressure prediction, such as temperature 520, may be included or entered. The user can delete or add relevant data points using interface buttons 522 and 524, respectively.

[0056] Adjustments during the process

[0057] Feedback can also be used to adjust various settings during material dispensing to achieve or maintain operating parameters or performance setpoints. For example, data or process parameters related to changes in the viscosity of the dispensable material during dispensing can be used to adjust the driving force pressure to maintain a desired flow rate. Process parameters may include the temperature, conductivity, viscosity, weight, mass, or other parameters of the dispensable material 436 or dispenser component 406 during the dispensing process. Process parameters may be sensed by various sensors, such as sensor 438 of dispenser component 406, or determined by sensor data from these sensors. Sensor 438 may include, for example, a vision sensor, a mass sensor, a temperature sensor, a conductivity sensor, or any device capable of detecting heat, humidity, weight, mass, material quality, conductivity, or other parameters.

[0058] In one or more embodiments, one or more sensors (e.g., sensor 438) can provide at least one process parameter of the dispensable material. The at least one process parameter may include, for example, the temperature, conductivity, viscosity, weight, mass, or other parameters of the dispensable material (e.g., adhesive, sealant, hot paste, etc.). One or more sensors 438 may include sensors capable of sensing the temperature and / or conductivity of the dispensable material, such as… Figure 10 The sensor 910.

[0059] Figure 10 An example of one or more sensors 910 is shown, which can be used to dispensable material flow through a pipe fitting 900 (e.g., Figure 4 The dispenser component 406 or dispenser hardware 434 senses the temperature and / or conductivity of the dispenseable material. The pipe component 900 includes a pipe body 902 having a channel 904 extending from a pipe inlet 906 to a pipe outlet 908. The channel 904 provides a fluid path for the dispenseable material (e.g., dispensable material) to flow from the pipe inlet 906 to the pipe outlet 908 during the dispensing process.

[0060] As the dispensable material flows through channel 904, it can contact various means of one or more sensors 910 disposed within conduit 900 to allow the sensors 910 to sense process parameters of the dispensable material. For example, the sensors may include probes 912 arranged in the fluid path defined by channel 904. Probes 912 may be configured to respond to the temperature of the dispensable material in a manner detectable by the sensors 910. For example, probes 912 may include a thermistor, and the sensors 910 may be configured to determine the resistance of the thermistor and sense or determine the temperature of the dispensable material based on the thermistor's resistance. In one embodiment, probes 912 may be configured to directly contact the dispensable material in the fluid path. In another embodiment, a shield may separate the outer surface of probe 912 from the dispensable material, and the probe may be configured to sense the temperature of the dispensable material through the shield. The shield may include any suitable one or more materials such as, for example, plastic, metal, or other thermally conductive materials.

[0061] Furthermore, for example, one or more sensors 910 may include electrodes 914 arranged in a fluid path defined by channel 904. The one or more sensors 910 may be configured to provide a voltage across the electrodes 914 and measure the resulting current between the electrodes and through the dispensable material. Based on the measured current, the one or more sensors 910 may sense the conductivity of the dispensable material.

[0062] Sensor 438 may include additional sensors and sensor devices such as those described in PCT Publication No. 2022 / 013786A1 (Münstermann et al.), the disclosure of which is incorporated herein by reference.

[0063] Parts library

[0064] Systems, apparatus, and methods can generate predictive models based on the assumption that the dispenser is a "black box" or only has input and output terminals without understanding the internal components of the dispenser. However, other predictive models can be generated from mixing nozzles, dispensing tips, or adhesive dispensers 100 ( Figure 1Other components constitute the dispenser, including, for example, pipes, barrels, pressure valves, pumps, adapters, clamps, etc. In some embodiments, the predictive model may include a formula similar to Equation (1). In some embodiments, machine learning algorithms may be used to solve for similar or different constants. By creating a predictive model of the dispenser components, when a new dispenser is added to an operator's process, the need for calibration can be reduced or eliminated by using a setup based on a component model generated or stored from any process previously used in the above-described process. Furthermore, new dispenser components can be added to the dispenser without calibrating the setup. For example, new dispensing tips can be added, and these dispensing tips can be used to apply pressures that have already been generated and predicted, depending on factors such as viscosity, temperature, etc., as described earlier herein.

[0065] Exemplary methods

[0066] Figure 6 This is a flowchart of a method 600 for predicting adhesive dispenser pressure according to some embodiments. The operation of method 600 may be performed by, for example, algorithm 412 (…). Figure 4 ) processor 704 ( Figure 8 ) Execution. In the example, algorithm 412 can be partially or fully executed using machine learning and based on inputs automatically generated and provided by sensors or by user input, as well as other inputs.

[0067] Method 600 may begin with operation 602, in which processor 704 retrieves the adhesive dispenser 100 ( Figure 1 At least one parameter associated with the adhesive. In some embodiments, the at least one parameter relates to the viscosity of the adhesive in the adhesive dispenser 100. In some embodiments, operation 602 may include receiving at least one feedback parameter indicating the quality of the bonding process. The feedback parameter may be received from user input or a sensor. In some examples, the feedback parameter may include image data or may be based on image data or quality data. Quality data may indicate the amount of adhesive dispensed. In an exemplary embodiment, quality feedback data may be used to determine whether the amount of adhesive dispensed is within a target range for the desired amount of adhesive.

[0068] In an exemplary implementation, predictions and suggestions are provided on a user interface, and furthermore, processor 704 may request user input or capture calibration points. As previously described herein, calibration points may include those related to... Figure 5BThe information shown is similar. For example, the request might be based on the determination that the requested flow rate falls outside a threshold range of the normal range. In other examples, the request might respond to the determination that an environmental parameter falls outside a typical range or a threshold range of the typical range. For example, if the room temperature is -20 degrees Fahrenheit, processor 704 might request the user to manually enter the adhesive viscosity at that temperature. In at least these examples, the predictive power of the machine learning model may be limited due to the anomalies of environmental factors, and therefore manual data input is requested.

[0069] Please refer to the above. Figure 5A and Figure 5B The GUI provides suggestions or requests for data capture.

[0070] The machine learning model described above can also predict when current environmental conditions (such as temperature and humidity, but the implementation is not limited to these) will lead to lower quality predictions. The GUI 500 can then be used to request or suggest additional calibration points captured by the user and retrieve such calibration points from user input. This improves the overall machine learning model.

[0071] In other exemplary implementations, if a user requests a flow rate far beyond the previously requested rate, resulting in a significantly higher application pressure than previously used, the user can be notified via GUI 500 or an audio alarm that an additional calibration point should be captured. For example, if a user requests a flow rate that will require 50 psi pressure, but the typical distribution range is 20-40 psi, the user will be prompted to use, for example, a flow rate similar to... Figure 5B The interface shown provides calibration points associated with 50 psi.

[0072] The model described earlier in this document can identify opportunities to enhance predictive quality. In at least some exemplary embodiments, recommendations regarding such opportunities can be generated and displayed on a GUI 500 or other user interface. If an opportunity for predictive improvement is identified, the system and apparatus, depending on the embodiment, can instruct the user (whether via audio or visual alarms or instructions, text, voice messages, etc.) to capture adhesive data that could produce predictive improvements.

[0073] For example, if a machine learning model determines that flow rate data has not yet been captured at a specific temperature, the system according to an embodiment may request the user to capture distribution data at that specific temperature. Alternatively, the system according to an embodiment may control a sensor to capture such data at that temperature. In these and other embodiments, additional GUI screens may be provided, which the user can interact with to manually enter data related to data points that can enhance the machine learning algorithm. In at least these embodiments, the distributor component 406 ( Figure 4It can automatically record some or all of the data via wireless near-field communication (NFC) or other methods. For example, users can... Figure 5B Manually enter calibration data in the GUI screen 510 shown.

[0074] Figure 5B An exemplary GUI for manually entering calibration data is shown according to some embodiments. In the example, the user can enter batch number 512 to identify the manufacturing batch of the liquid adhesive. In other examples, batch number 512 may be provided automatically, for example, by reading an RFID chip associated with dispenser component 406 or via wireless communication from dispenser component 406. The pressure used may be provided at 514. The quality of dispensing may be provided by dispenser component 406, for example by sensor 438, for automatic or manual entry into segment 516. Sensor 438 may also provide feedback data to algorithm 412 (including machine learning algorithms). For example, the dispensing time amount 518 may be manually entered by the user, or such time values ​​may be provided by controller 432. Other parameters affecting pressure prediction, such as temperature 520, may be included or entered. The user can delete or add relevant data points using interface buttons 522 and 524, respectively.

[0075] Method 600 may proceed to operation 604, where processor 704 determines values ​​for operating parameters of adhesive dispenser 100. These values ​​may be based on at least one parameter received in operation 602. The values ​​of the operating parameters may result in a desired flow rate being achieved in the adhesive dispensing system.

[0076] Method 600 may proceed to operation 606, wherein processor 704 provides operating parameters to adhesive dispenser 100. In an embodiment, the operating parameters may be a driving force pressure for the adhesive dispensing system. In an embodiment, the driving force pressure is determined based on viscosity and parameters specific to adhesive dispenser 100. For example, the driving force pressure may be determined according to formula (1) previously described herein: m = G(F1(p), F2(μ)...), where m is the mass dispensed by the adhesive dispensing system, F1 is a function of pressure p, and F2 is a function of viscosity μ.

[0077] Viscosity μ can be determined based on direct or indirect measurements, polynomial fitting, numerical regression, or formulas (such as the Andreid formula for liquid viscosity provided in formula (2)).

[0078] As previously mentioned, the constants associated with equations (1) and (2) can be determined using machine learning algorithms. Machine learning algorithms can minimize a loss function associated with at least one of the driving forces: pressure, mass, viscosity, and temperature. Machine learning algorithms can be based on models other than neural network models (e.g., non-neural network models and machine learning algorithms), or on a mixture of neural network models and other models (e.g., hybrid algorithms).

[0079] As described earlier in this document, other systems based entirely on neural networks can be used to control instructions for applying adhesives. However, systems based solely on neural networks require hundreds or even thousands of calibration points to be useful, and extensive learning is necessary before any control over adhesive dispensing can be achieved. The exemplary implementations described earlier in this document may be based on other machine learning models, or on non-neural networks, or a combination of neural networks and other machine learning models.

[0080] Figure 7 A comparison of the performance between neural network-based models and learned models used in some exemplary embodiments is depicted. For this comparison, a dataset of laboratory-generated data was used. The dataset was sampled for a given number of data points and then split into two groups, with 70% of the data used to train the model and 30% reserved for testing the model's quality after training. The split data was fed to each model, causing the model to analyze the same experimental data in each case. The result is that the system using the model according to the embodiment has a lower mean root square error at each sampling level, as shown at curve 630 (in contrast to curve 632 showing the system using the neural network model), where the difference between curves 630 and 632 is particularly large at the lower sampling level 634. As will be understood, because the mean root square error is small even at low sample levels using the method according to the embodiment, cost savings can be achieved by reducing the need for large numbers of samples (which in turn reduces manufacturing, laboratory, and material costs). The ability to obtain useful predictions with relatively few data points is a significant advancement over existing neural network methods.

[0081] Figure 11 This is a flowchart of a method 1000 for calibrating a dispenser system according to some implementation schemes. The operation of method 1000 may be performed by, for example, a processor 704 (…). Figure 8 )implement.

[0082] Each dispenser system can differ in subtle and significant ways, making calibration data from one dispenser system unusable for calibrating another. To calibrate (e.g., set up) a dispenser system, the user loads dispensable material (e.g., dispensable material 436) into the system and configures all associated components, including plungers, air lines, controllers, static mixers, clamps or valves, tips, and other parts. When ready, the user dispenses the dispensable material into cups at different pressures and weighs the dispensed material. Each combination of dispensed mass and pressure settings forms a calibration data point. However, various conditions can lead to “bad” calibration data points. For example, if air bubbles are present in the system, if material adheres to the tip of the dispenser system, if material from a previous dispenser attempt is included in the dispensing mass of another attempt, if the pressure or mass is incorrectly set or recorded, or if other sources of error exist, the calibration data may not produce a useful model.

[0083] Method 1000 can evaluate the quality of the calibration data point set by requesting a set of calibration data points, remove “bad” or low-quality calibration data points, and request additional calibration data points as needed to correct the “bad” calibration data points.

[0084] Method 1000 may begin with operation 1002, in which processor 704 receives multiple calibration data points and one or more parameters from the dispenser components of the dispenser system. The user may be instructed to set a pressure gauge (e.g., dispenser pressure) to a specific pressure and record the dispensed mass. These instructions may also include the dispensing time for each calibration data point. Alternatively, the processor may automatically set the pressure, dispense the dispensable material, and record each calibration data point and associated parameters. Typical calibration methods may require hundreds or thousands of calibration data points. In contrast, the multiple calibration data points of method 1000 may include at least two calibration data points to no more than 100 calibration data points, or any suitable range between both. For example, multiple calibration data points may include a range from at least two, five, ten, or fifteen calibration data points to no more than 20, 40, 60, 80, or 100 calibration data points. Each of these calibration data points may include any suitable parameters. For example, each of the multiple calibration data points may include one or more of the following: manufacturing batch information of the dispensable material, dispenser pressure, dispensing mass, dispensing time, ambient temperature, temperature of the dispensable material, etc. In one embodiment, each of the multiple calibration points includes dispenser pressure, dispensing time, and the mass of dispensable material dispensed.

[0085] One or more parameters of the dispenser component may include any suitable parameters. For example, one or more parameters of the dispenser component may include manufacturing batch information of the dispensable material, the model of the dispenser component, the type of dispensable material (e.g., adhesive, sealant, hot paste, etc.), etc. One or more parameters may be received from the dispenser component, a database, a user, an image, etc.

[0086] Method 1000 may proceed to operation 1004, wherein the processor selects one or more predetermined models based on one or more parameters of the dispenser component. In one or more embodiments, the one or more parameters include the manufacturing batch of the dispenseable material. The one or more predetermined models may be retrieved based on the manufacturing batch of the dispenseable material. For example, each manufacturing batch or type of dispenseable material may correspond to a given predetermined set of models.

[0087] Method 1000 may proceed to operation 1006, wherein the processor determines a calibration model based on multiple calibration data points and one or more predetermined models. For example, the calibration model may include one or more settings corresponding to a predetermined model among one or more predetermined models, based on statistical methods and modeling, whereby a modified set of calibration data points best corresponds to that predetermined model. In some examples, the multiple calibration data points may fit or match one of the predetermined models, and the calibration model may be determined based on the multiple calibration data points and one or more predetermined models without modifying the multiple calibration data points. However, in some examples, due to one or more outliers or an insufficient number of calibration data points, the multiple calibration data points may not fit or match one of the predetermined models. Therefore, determining the calibration model may include generating a modified set of calibration data points, which may have removed outliers from the multiple calibration data points and / or added additional calibration data points.

[0088] Determining a calibration model may include identifying one or more outliers from a plurality of calibration data points based on one or more models. Identifying one or more outliers may include one or more suitable techniques. For example, identifying one or more outliers may include using one or more statistical models or methods. In one embodiment, identifying one or more outliers may include regression analysis based on a plurality of calibration points and one or more predetermined models. Alternatively or additionally, identifying one or more outliers may include identifying one or more calibration points from a plurality of calibration points that differ from the corresponding data points of one or more predetermined models by more than a threshold. The threshold may be a percentage difference. In other words, the difference between the allocation quality of an outlier calibration data point and the allocation quality of a data point corresponding to the same dispenser pressure and time in a predetermined model may have a difference exceeding a predetermined percentage of the allocation quality of the data points in the predetermined model. The threshold may be determined based on a predetermined model.

[0089] Determining the calibration model may further include removing one or more outliers from a plurality of calibration data points to produce a modified set of calibration data points. Method 1000 may optionally include an operation in which the processor determines that the modified set of calibration data points includes fewer than a threshold number of data points. If the modified set of calibration data points includes fewer than the threshold number of data points, the modified set of calibration data points may be insufficient to determine whether the calibration data points fit a particular predetermined model among one or more predetermined models.

[0090] Therefore, method 1000 may further include operations in which the processor determines one or more distributor pressures for one or more additional calibration data points based on a modified set of calibration data points and one or more predetermined models. The one or more additional calibration data points may include distributor pressures between two missing or removed data points. Additionally or alternatively, the one or more additional calibration data points may include distributor pressures identical to one or more outliers.

[0091] Method 1000 may further include the operation of the processor requesting one or more additional calibration data points using a display. Each of the one or more additional calibration data points may correspond to one of the one or more distributor pressures. Method 1000 may further include the operation of the processor receiving one or more additional calibration data points using a user interface and modifying a modified calibration data point set to include the one or more additional calibration data points.

[0092] Therefore, the calibration model can be determined based on a modified set of calibration data points and one or more pre-defined models. For example, the calibration model can be determined after one or more outliers have been removed or after one or more additional calibration data points have been added to the modified set of data points.

[0093] Method 1000 can proceed to operation 1008, wherein the processor provides one or more settings to the dispenser system based on a calibration model. In an embodiment, providing one or more settings may include adjusting one or more settings of the dispenser components of the dispenser system. In other words, the processor can adjust various settings of the dispenser system. Alternatively or concurrently, the processor may use a display such as a GUI 500 to present various calibration settings to a user.

[0094] Figure 12 This is a flowchart of a method 1100 for distributing distributable materials (e.g., distributable material 436) according to some embodiments. The operation of method 1100 can be performed by, for example, a processor 704 (…). Figure 8 )implement.

[0095] Method 1100 may begin with operation 1102, wherein processor 704 receives and dispenses material dispenser 100 ( Figure 1 At least one parameter associated with the dispensing material. The at least one parameter may be received from a dispenser component, a database, a user, an image, etc. In some embodiments, the at least one parameter relates to the viscosity of the dispensable material in the dispensable material dispenser 100. In some embodiments, the at least one parameter may relate to the density of the dispensable material in the dispensable material dispenser 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 user input or a sensor. In some examples, the feedback parameter may include image data or may be based on image data or on mass data. Mass data may indicate the amount of dispensable material being dispensed. For example, the mass data and density of the dispensable material may be used to determine the volume of the dispensable material. Therefore, the volumetric flow rate of the dispensable material may be determined and / or monitored based on the mass data and density of the dispensable material. In an exemplary embodiment, mass feedback data may be used to determine whether the amount of dispensable material being dispensed is within a target range for the desired amount of dispensable material.

[0096] Method 1100 may proceed to operation 1104, wherein processor 704 determines the values ​​of operating parameters of the dispensable material dispenser 100. These values ​​may be based on at least one parameter received in operation 1102. The values ​​of the operating parameters may enable a desired flow rate to be achieved in the dispensable material dispensing system.

[0097] Method 1100 can proceed to operation 1106, where processor 704 provides operating parameters. In one embodiment, the operating parameters may be provided to the dispensable material dispenser 100. In other embodiments, the operating parameters may be provided to human-machine interface 404. In one embodiment, the operating parameters may be a driving force pressure for the dispensable material dispensing system. In another embodiment, the driving force pressure is determined based on viscosity as previously described herein and parameters specific to the dispensable material dispenser 100.

[0098] Method 1100 may proceed to operation 1108, wherein the processor receives at least one process parameter, including the temperature of the dispensable material. The at least one process parameter may also include, for example, the conductivity, viscosity, weight, mass, or other parameters of the dispensable material (e.g., adhesive, sealant, hot paste, etc.). The at least one process parameter may be received from one or more sensors. For example, the temperature of the dispensable material may be received from... Figure 10 Temperature sensor 910 receives the data. Probe 912 of temperature sensor 910 can be positioned or arranged in the fluid path of the dispensable material, thereby allowing temperature sensor 910 to sense or measure the temperature of the dispensable material.

[0099] Method 1100 may continue with operation 1110, wherein the processor adjusts the values ​​of operating parameters of the dispensable material dispensing system based on the temperature of the dispensable material. When the temperature of the dispensable material changes, the viscosity of the dispensable material may change. Therefore, adjusting the values ​​of operating parameters (e.g., driving force pressure) can achieve a more consistent flow rate of the dispensable material. Additional process parameters can affect the viscosity and flow rate of the dispensable material. Such process parameters can be sensed using sensor 910 or any other suitable sensor or device. For example, sensor 910 may also include a conductivity sensor to sense the conductivity of the dispensable material. Method 1100 may also include the processor receiving the sensed conductivity of the dispensable material from the conductivity sensor. Furthermore, method 1100 may include determining the curing state of the dispensable material based on the sensed temperature and the sensed conductivity of the dispensable material; and adjusting the values ​​of operating parameters of the dispensable material dispensing system based on the sensed temperature and curing state.

[0100] Once the dispensable material has been mixed during the dispensing process, it can begin to cure before being dispensed. As the dispensable material cures, its viscosity can change. In addition to temperature and conductivity, the state of cure can also be determined based on time since initial mixing (e.g., residence time in a static mixer or longer). Using the known state of cure, the viscosity of the dispensable material can be predicted, and its impact on flow rate can be calculated. This information can be used to adjust operating parameters (e.g., driving force pressure) to maintain a more constant flow rate of the dispensable material 436. Additionally, method 1100 may include initiating cleaning if the viscosity of the dispensable material exceeds a threshold viscosity level. The threshold viscosity level can be set or determined based on operational limitations of the dispensable material dispensing system, parameters set by the user, or parameters of the current dispensing job or process.

[0101] Method 1100 can proceed to operation 1112, wherein the processor provides operating parameters. In one embodiment, the processor may provide operating parameters to one or more dispenser components. In other embodiments, the processor may provide operating parameters to a human-machine interface device. Thus, the flow rate of dispensable material can be maintained by continuously or periodically adjusting the operating parameters and providing the adjusted operating parameters to one or more dispenser components. For example, the driving force pressure can be continuously adjusted to account for changes in the curing, temperature, and / or viscosity of the dispensable material. Furthermore, the adjusted operating parameters can be provided to one or more dispenser components at any time during adjustment. The adjusted operating parameters can be provided using wired or wireless communication as described herein. Additionally, the processor may initiate cleaning by communicating with a motion controller configured to move or guide the dispenser to the cleaning container.

[0102] Method 1100 may further include operations for determining parameters for cleaning the dispensable material. Method 1100 may include operations in which a processor determines a maximum idle time or cleaning time for one or more dispenser components based on at least one parameter and at least one process parameter. Method 1100 may further include operations in which the processor provides the maximum idle time or cleaning time. In an embodiment, a maximum idle time or cleaning time may be provided to one or more dispenser components. Therefore, the dispenser components may initiate automatic cleaning based on the maximum idle time and / or cleaning time. In an embodiment, the maximum idle time or cleaning time may be provided to a human-machine interface. Therefore, the maximum idle time or cleaning time may be displayed to a user.

[0103] Many factors can affect the working life of a two-part dispensable material. These factors can include, for example, temperature, material aging, density, initial manufacturing viscosity, and other potential factors. These factors can help modify the curing rate kinetics and, consequently, the amount of time the dispensable material can remain in the dispenser before needing to be removed. An additional factor that can contribute to the maximum idle time is the dispensing rate. The period during which the dispenser component is determined to be idle can begin after the last dispensing of the dispensable material. If the active period (e.g., dispensing) includes high-flow-rate dispensing attempts with short intervals, the dispensable material may be relatively fresh when dispensing stops. However, if the flow rate is low and / or the dispensing attempts are spaced further apart, the dispensable material may be partially cured when dispensing stops. Therefore, the maximum idle time and / or cleaning time should differ for each of these cases. The maximum idle time and / or cleaning time can be customized based on the temperature, age, density, viscosity, dispensing rate, flow rate, time between dispensing points, etc., of the dispensable material by determining the maximum idle time and / or cleaning time based on at least one parameter and at least one process parameter.

[0104] Additionally, method 1100 may consider safety factors and process control factors when determining the maximum idle time and rinsing time. Method 1100 may also include operations in which a processor receives one or more user inputs, including safety factors, process control factors, and waste factors, and determines the maximum idle time or rinsing time based on one or more user inputs, at least one parameter, and at least one process parameter. User inputs may be received using a user interface (e.g., GUI 500). In one example, the user may select or provide a safety factor such that the dispensable material does not cure prematurely if variability exists during dispensing. In other words, when a safety factor is provided, the maximum idle time may be determined based on the curing time of the dispensable material. In another example, the user may want to strictly control the flow rate at the expense of waste of dispensable material. In other words, when a control factor is provided, the maximum idle time may be determined based on flow rate variability that may occur before the dispensable material cures. In yet another example, the user may select a waste factor indicating low waste, such that dispensable material is wasted as little as possible. When multiple factors are entered, specific values ​​of these factors may be balanced against each other to determine the maximum idle time and rinsing time.

[0105] The display also allows users to see how safety factors, process control factors, and waste factors affect maximum idle time and cleaning time, and vice versa. In some implementations, users can set safety factors, process control factors, and waste factors as parameter values. These parameter values ​​received from the user can be incorporated into the algorithm used to determine maximum idle time and cleaning time.

[0106] Computer devices

[0107] Systems, methods, and apparatuses can be implemented using processors in firmware or software, either remotely or locally for operator processes, or in cloud or edge computing devices, as described in more detail later herein. Machine learning can be distributed across several different devices and implemented entirely or partially within the adhesive dispensing device itself. For example, some recognition processes can be performed by the adhesive dispenser 100 ( Figure 1 The adhesive dispenser is executed, and input from the adhesive dispenser can be provided to local or remote devices to make predictions about the dispenser settings. Therefore, the adhesive dispenser 100, data unit 402, human-machine interface 404, and dispenser unit 406 ( Figure 4 The means and circuitry of other components may be executed or partially executed on the computing system (e.g., edge computing nodes). Figure 8 Edge computing nodes are depicted according to some implementation schemes.

[0108] exist Figure 8 In the simplified example depicted, the edge computing node 700 (e.g., the 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 communication circuitry subsystem 712, and optional one or more peripheral devices 714. In other examples, the corresponding computing device may include other or additional components, such as those commonly found in computers (e.g., displays, peripheral devices, etc.). Additionally, in some examples, one or more exemplary components may be incorporated into another component or otherwise formed as part of another component.

[0109] Computing node 700 may be embodied as any type of engine, device, or collection of devices capable of performing various computing functions. In some examples, computing node 700 may be embodied as a single device, such as an integrated circuit, embedded system, field-programmable gate array (FPGA), system-on-a-chip (SoC), or other integrated system or device. In exemplary examples, computing node 700 includes or is embodied as 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, microcontroller, or other processor or processing / control circuitry. In some examples, processor 704 may be embodied as, include, or coupled to an FPGA, application-specific integrated circuit (ASIC), reconfigurable hardware or hardware circuitry, or other special-purpose hardware to facilitate the execution of the functions described herein.

[0110] Memory 706 can be embodied as any type of volatile (e.g., dynamic random access memory (DRAM), etc.) or non-volatile memory or data storage device capable of performing the functions described herein. Volatile memory can be a storage medium that requires power to maintain the state of data stored by the medium. Non-limiting examples of volatile memory can include various types of random access memory (RAM), such as DRAM or static random access memory (SRAM). One particular type of DRAM that can be used in a memory module is synchronous dynamic random access memory (SDRAM).

[0111] In one example, the memory device is a block-addressable memory device, such as those based on NAND or NOR technology. In some examples, all or part of the memory 706 may be integrated into the processor 704. The memory 706 may store various software and data used during operation, such as one or more applications, data operated by the applications, libraries, and drivers.

[0112] Computational circuitry 702 is communicatively coupled to other components of computing node 700 via I / O subsystem 708, which may be embodied as circuitry or components for facilitating input / output operations with computing circuitry 702 (e.g., with processor 704 or main memory 706) and other components of computing circuitry 702. For example, I / O subsystem 708 may be embodied as or otherwise include a memory controller hub, input / output control hub, integrated sensor hub, firmware device, communication links (e.g., point-to-point links, bus links, wires, cables, light guides, printed circuit board traces, etc.), or other components and subsystems to facilitate input / output operations. In some examples, I / O subsystem 708 may form part of a system-on-a-chip (SoC) and be incorporated into computing circuitry 702 along with one or more of processor 704, memory 706, and other components of computing circuitry 702. I / O subsystem 708 can be accessed from... Figure 4 Other components (e.g., sensor 428, sensor 438, etc.) receive input data 707 and send it to... Figure 4 Other components (e.g., dispenser component 406) provide prediction and control 709.

[0113] One or more exemplary data storage devices 710 may be embodied as any type of device constructed for short-term or long-term data storage, such as memory devices and circuitry, memory cards, hard disk drives, solid-state drives, or other data storage devices. A separate data storage device 710 may include a system partition storing data and firmware code for the data storage device 710. A separate data storage device 710 may also include one or more operating system partitions, which, depending on the type of, for example, compute node 700, store operating system data files and executable files.

[0114] The communication circuit 712 can be embodied as any communication circuit, device, or collection thereof capable of enabling communication over a network between the computing circuit 702 and another computing device (e.g., an edge gateway implementing an edge computing system). The communication circuit 712 can be configured to use any one or more communication technologies (e.g., wired or wireless communication) and associated protocols (e.g., cellular networking protocols such as 3GPP 4G or 5G standards, wireless LAN protocols such as IEEE 802.11 / ...). Wireless WAN protocols, Ethernet, Bluetooth Low Energy, IoT protocols such as IEEE 802.15.4 or This type of communication can be achieved using protocols such as Low Power Wide Area Network (LPWAN), Ultra Broadband or Low Power Wide Area (LPWA).

[0115] The exemplary communication 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 chipsets, chipsets, or other devices that can be used by the computing node 700 to connect to another computing device (e.g., an edge gateway node). In some examples, the NIC 720 may be embodied as part of a system-on-a-chip (SoC) including one or more processors or included in a multi-chip package that also contains one or more processors. In some examples, the NIC 720 may include a local processor (not shown) or local memory (not shown), both local to the NIC 720. In such examples, the local processor of the NIC 720 may be able to perform one or more functions of the computing circuitry 702 described herein. Alternatively or additionally, in such examples, the local memory of the NIC 720 may be integrated into one or more components of the client computing node at the board level, socket level, chip level, or other level.

[0116] Additionally, in some examples, the corresponding compute node 700 may include one or more peripheral devices 714. Depending on the specific type of compute node 700, such peripheral devices 714 may include any type of peripheral device found in a compute device or server, such as audio input devices, displays, other input / output devices, interface devices, or other peripheral devices. In other examples, the compute node 700 may be embodied by a corresponding edge compute node (whether a client, gateway, or aggregation node) or a similar form of appliance, computer, subsystem, circuit system, or other component in an edge computing system.

[0117] In a more detailed example, Figure 9 A block diagram illustrating examples of components that may exist in edge computing node 850 for implementing the technologies described herein (e.g., operations, processes, methods, and methodologies). When implemented as a computing device (e.g., as a computer, mobile device, server, smart sensor, control system, etc.) or as part of it, edge computing node 850 provides a closer view of the corresponding components of node 700. Edge computing node 850 may include any combination of hardware or logic components referenced herein, and may include any device that can be used with or coupled to an edge communication network or a combination of such networks. Components may be implemented as integrated circuits (ICs), portions thereof, discrete electronic devices, or other modules, instruction sets, programmable logic or algorithms, hardware, hardware accelerators, software, firmware, or combinations thereof adapted in edge computing node 850, or implemented as components otherwise incorporated within the chassis of a larger system.

[0118] Edge computing node 850 may include processing circuitry in the form of processor 852, which may be a microprocessor, multi-core processor, multi-threaded processor, ultra-low voltage processor, embedded processor, or other known processing element. Processor 852 may be part of a system-on-a-chip (SoC), wherein processor 852 and other components are formed into a single integrated circuit or a single package. Processor 852 and accompanying circuitry may be single-slot form factor, multi-slot form factor, or various other formats (including those constructed with limited hardware or including more). Figure 9 The construction of all components shown is less complex than that of the components shown.

[0119] Processor 852 can communicate with system memory 854 via interconnect 856 (e.g., a bus). Any number of memory devices can be used to provide a fixed amount of system memory. As an example, memory 854 can be random access memory (RAM) designed according to the United Electron Device Engineering Council (JEDEC) specifications. In various implementations, individual memory devices can have any number of different package types, such as single die package (SDP), dual die package (DDP), or quad die package (Q17P). In some examples, these devices can be directly soldered to the motherboard to provide a low-profile solution, while in other examples, the devices are configured as one or more memory modules that are then coupled to the motherboard via a given connector. Any number of other memory implementations can be used, such as other types of memory modules, for example, different kinds of dual in-line memory modules (DIMMs), including but not limited to microDIMMs or miniDIMMs.

[0120] To provide persistent storage for information such as data, applications, and operating systems, storage device 858 may also be coupled to processor 852 via interconnect 856. In one example, storage device 858 may be implemented via a solid-state drive (SSDD). Other devices that can be used with storage device 858 include flash memory cards, such as Secure Digital (SD) cards, microSD cards, Extreme Digital (XD) picture cards, and Universal Serial Bus (USB) flash drives.

[0121] Components can communicate via interconnect 856. Interconnect 856 may include any number of technologies, including Industry Standard Architecture (ISA), Extended ISA (EISA), Peripheral Component Interconnect (PCI), Extended Peripheral Component Interconnect (PCIx), PCI Fast (PCIe), or any number of other technologies. Interconnect 856 may be a dedicated bus, for example, used in a SoC-based system. Other bus systems may be included, such as internal integrated circuit (I2C) interfaces, Serial Peripheral Interface (SPI) interfaces, point-to-point interfaces, dedicated buses, and power buses.

[0122] Interconnector 856 can couple processor 852 to transceiver 866 for communication with connected edge device 862. Connected edge device 862 may include... Figure 9 Other components or parts thereof depicted, or other components of the manufacturing system used by the operator (away from or locally within the automated system). The transceiver 866 can use any number of frequencies and protocols, such as 2.4 GHz transmission under the IEEE 802.15.4 standard, using as described by... Special interest groups are defined Low Energy Leakage (BLE) standard, or Standards, etc. Any number of radios configured for a specific wireless communication protocol can be used for connection to the connected edge device 862. For example, a wireless local area network (WLAN) unit can be used to implement the standard according to the Institute of Electrical and Electronics Engineers (IEEE) 802.11. Communication. In addition, wireless wide area communication, for example, according to cellular or other wireless wide area protocols, can be carried out via a wireless wide area network (WWAN) unit.

[0123] Wireless network transceivers 866 (or multiple transceivers) can use multiple standards or radio communications for communication at different ranges. For example, edge computing node 850 can use a Bluetooth Low Energy (BLE) based local transceiver or another low-power radio to communicate with nearby devices, for example, within approximately 10 meters, to save power. Edge devices 862 with more distant connections (e.g., within approximately 50 meters) can communicate via... Alternatively, it can be reached via other intermediate power radios. These two communication technologies can operate at different power levels on a single radio, or via separate transceivers (e.g., a local transceiver using BLE and a transceiver using...). It is performed on an independent mesh transceiver.

[0124] A wireless transceiver 866 (e.g., a radio transceiver) may be included to communicate with devices or services in the edge cloud 895 via LAN or WAN protocols. The wireless transceiver 866 may be a low-power wide-area (LPWA) transceiver conforming to standards such as IEEE 802.15.4 or IEEE 802.15.4g. The edge computing node 850 may use LoRaWAN, developed by Semtech and the LoRa Alliance. TM (Remote Wide Area Network) Communication over a wide area. The techniques described herein are not limited to these techniques, but can be used with any number of other cloud transceivers and other technologies that enable remote, low-bandwidth communication (such as Sigfox). Furthermore, other communication techniques described in the IEEE 802.15.4e specification, such as time-slot channel hopping, can be used.

[0125] In addition to the systems mentioned for wireless network transceiver 866, any number of other radio communications and protocols, as described herein, may be used. For example, transceiver 866 may include a cellular transceiver that uses spread spectrum (SPA / SAS) communication to achieve high-speed communication. Furthermore, any number of other protocols, such as those used for medium-speed communication and providing network communication, may be used. Network. Transceiver 866 may include a radio compatible with any number of 3GPP (3rd Generation Partnership Project) specifications, such as Long Term Evolution (LTE) and 5th Generation (5G) communication systems. A network interface controller (NIC) 768 may be included to provide wired communication to nodes of the edge cloud 895 or to other devices such as connected edge devices 862 (e.g., operating in a mesh). Wired communication may provide Ethernet connectivity 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. An additional NIC 768 may be included to enable connectivity to a second network; for example, a first NIC 768 provides communication to the cloud via Ethernet, and a second NIC 768 provides communication to other devices via another type of network. Ultra-wideband sensors and transmitters can be used to facilitate precise positioning relative to defined transmitter beacons, as well as communication such as data transmission.

[0126] In the case of various types of applicable communication from a device to another component or network, the applicable communication circuitry used by the device may include or be embodied in any or more of components 864, 866, 868, or 870. Thus, in various examples, applicable means for communication (e.g., receiving, transmitting, etc.) may be embodied in such communication circuitry.

[0127] 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, GPUs, data processing units (DPUs) or infrastructure processing units (IPUs), one or more SoCs, one or more CPUs, one or more digital signal processors, application-specific integrated circuits (ASICs), or other specialized processors or circuits designed to perform one or more specialized tasks. These tasks may include AI processing (including machine learning, training, inference, and classification operations), visual data processing, network data processing, object detection, rule analysis, and so on.

[0128] Interconnector 856 couples processor 852 to sensor hubs or external interfaces 870 for connecting additional devices or subsystems. Devices may include sensors 872, such as accelerometers, level sensors, flow sensors, optical sensors, camera sensors, temperature sensors or instruments, global navigation system (e.g., GPS) sensors, pressure sensors, atmospheric pressure sensors, and any sensors for detecting the condition of belts or other adhesives, primers, substrates, etc. These sensors may be directly connected to computing devices or remotely located as part of various manufacturing modules. Hub or interface 870 may also be used to connect edge computing nodes 850 to actuators 874, such as power switches, valve actuators, audible sound generators, visual warning devices, etc. These actuators may be directly connected to computing devices or remotely located as part of various manufacturing modules.

[0129] In some optional examples, various input / output (I / O) devices may be present within or connected to the edge computing node 850. For example, a display or other output device 884 may be included to show information such as sensor readings or actuator positions. Input devices 886, such as touchscreens or keypads, may be included to accept input. Output devices 884 may include any number of forms of audio or visual displays, 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 display screens (e.g., liquid crystal display (LCD) screens), where the output of characters, graphics, multimedia objects, etc., is generated or produced from the operation of the edge computing node 850. In the context of this system, display or console hardware may be used to provide output to and receive input from the edge computing system; manage components or services of the edge computing system; identify the status of edge computing components or services; or perform any other number of administrative or supervisory functions or service use cases. These various input / output devices may be directly connected to computing devices or remotely located as part of various manufacturing modules. In the example, notifications can be delivered to more than one device simultaneously; for instance, a user can view notifications on a separate module of system 400. Simultaneously or nearly simultaneously, notifications can be delivered to the user's smartphone or other devices (such as tablets or computers), or to standalone devices or devices separate from the user's personal equipment, based on proximity or other criteria.

[0130] Battery 876 can power edge computing node 850, but in an example where edge computing node 850 is installed in a fixed location, it may have a power source coupled to the power grid, or the battery may be used as a backup or for temporary capacity. Battery 876 can be a lithium-ion battery or a metal-air battery, such as a zinc-air battery, an aluminum-air battery, a lithium-air battery, etc.

[0131] A battery monitor / charger 878 may be included in an edge computing node 850 to track the state of charge (SoCh) of a battery 876 (if included). The battery monitor / charger 878 may be used to monitor other parameters of the battery 876 to provide fault prediction, such as the battery 876's state of health (SoH) and state of function (SoF). The battery monitor / charger 878 may transmit information about the battery 876 to a processor 852 via an interconnect 856. The battery monitor / charger 878 may also include an analog-to-digital converter (ADC) that enables the processor 852 to directly monitor the voltage of the battery 876 or the current from the battery 876.

[0132] Power block 880, or another power source coupled to the grid, can be coupled to battery monitor / charger 878 to charge battery 876. In some examples, power block 880 can be replaced with a wireless power receiver to obtain power wirelessly, for example, via a loop antenna in edge computing node 850. A specific charging circuit can be selected based on the size of battery 876 and the thus required current.

[0133] Storage device 858 may include instructions 882 in the form of software, firmware, or hardware commands to implement the techniques described herein. Although such instructions 882 are shown as blocks of code included in memory 854 and storage device 858, it is understood that either of the blocks of code may be replaced with hard-wired circuitry, for example, embedded in an application-specific integrated circuit (ASIC).

[0134] In one example, instructions 882 provided via memory 854, storage device 858, or processor 852 may be embodied as a non-transitory machine-readable medium 860, which includes code for instructing processor 852 to perform electronic operations in 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 device 858, or may include specific storage units such as optical discs, flash drives, or any number of other hardware devices. Non-transitory machine-readable medium 860 may include instructions for instructing processor 852 to perform specific sequences or processes of actions, for example, as described with respect to the flowcharts and block diagrams of operation and functionality depicted above. As used herein, the terms "machine-readable medium" and "computer-readable medium" are interchangeable.

[0135] Furthermore, in a specific example, instructions 882 on processor 852 (alone or in combination with instructions 882 on machine-readable medium 860) can construct the execution or operation of a trusted execution environment (TEE) 890. In one example, TEE 890 operates as a protected area accessible to processor 852 for secure execution of instructions and secure access to data.

[0136] In other examples, machine-readable media also includes any tangible medium capable of storing, encoding, or carrying instructions for machine execution and causing the machine to perform any one or more of the methods of this disclosure, or any tangible medium capable of storing, encoding, or carrying data structures utilized by or associated with such instructions. "Machine-readable media" can therefore include, but is not limited to, solid-state memory as well as optical and magnetic media. Specific examples of machine-readable media include non-volatile memory, including but not limited to, semiconductor memory devices (e.g., electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM)) and flash memory devices; magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. Instructions embodied in a machine-readable medium can also be transmitted or received over a communication network via a network interface device using a transmission medium, utilizing any of a plurality of transport protocols (e.g., Hypertext Transfer Protocol (HTTP)).

[0137] Machine-readable media may be provided by storage devices or other means capable of hosting data in a non-transitory format. In one example, information stored or otherwise set on a machine-readable medium may represent instructions, such as the instructions themselves or a format from which instructions can be derived. This format from which instructions can be derived may include source code, encoded instructions (e.g., in compressed or encrypted form), packaged instructions (e.g., divided into multiple packages), etc. The information representing instructions in the machine-readable medium may be processed by processing circuitry into instructions for implementing any of the operations discussed herein. For example, deriving instructions from information (e.g., processed by processing circuitry) may include: compiling (e.g., from source code, object code, etc.), interpreting, loading, organizing (e.g., dynamically or statically linking), encoding, decoding, encrypting, deencrypting, packaging, depackaging, or otherwise manipulating the information into instructions.

[0138] In one example, the derivation of instructions may include assembling, compiling, or interpreting information (e.g., by overprocessing circuitry) to create instructions from an intermediate or preprocessed format provided by a machine-readable medium. When provided in multiple parts, information may be combined, unpacked, and modified to create instructions. For example, information may be contained in multiple compressed source code packages (or object code, or binary executable code, etc.) on one or more remote servers. The source code packages may be encrypted during transmission over a network and, if necessary, decrypted, decompressed, assembled (e.g., linked), and compiled or interpreted at the local machine (e.g., into a library, standalone executable, etc.) and executed by the local machine.

[0139] In this document, the term "comprising" and its variations are not intended to be limiting wherever they appear in the specification and claims. Such terms are to be understood as implying the inclusion of the stated steps or elements or groups of steps or elements, but not excluding any other steps or elements or groups of steps or elements. The phrase "consisting of..." means including and limited to what follows the phrase "consisting of...". Thus, the phrase "consisting of..." indicates that the listed elements are required or mandatory, and that no other elements may be present. "Substantially consisting of..." means including any element listed following this phrase, and is limited to other elements that do not impede or contribute to the activity or effect specified for the listed elements in this disclosure. Thus, the phrase "substantially consisting of..." indicates that the listed elements are required or mandatory, but other elements are optional and may or may not be present depending on whether they substantially affect the activity or effect of the listed elements. Any element or combination of elements referenced in this specification in open language (e.g., including and its derivatives) is considered to be referenced in closed language (e.g., consisting of and its derivatives) and additionally in partially closed language (e.g., substantially consisting of and its derivatives).

[0140] The terms "preferred" and "ideally" refer to embodiments of this disclosure that provide certain beneficial effects under certain circumstances. However, other embodiments may also be preferred in the same or other circumstances. Furthermore, the description of one or more preferred embodiments does not imply that other claims are unavailable, nor is it intended to exclude other embodiments from the scope of this disclosure.

[0141] In this application, terms such as “a,” “an,” and “the” are not intended to refer only to a single entity, but to encompass general categories, with specific examples provided for illustration. The terms “a,” “an,” “the,” and “the” are used interchangeably with the term “at least one.” The phrases “at least one of…” and “containing at least one of…” followed by a list refer to any item in the list and any combination of two or more items in the list.

[0142] As used herein, the term “or” is generally used in its usual sense, including “and / or”, unless the context clearly indicates otherwise.

[0143] The term “and / or” means one or all of the listed elements, or any combination of two or more of the listed elements.

[0144] Furthermore, throughout this document, all numerical values ​​are assumed to be modified by the term “about,” and in some embodiments preferably by the term “precisely.” As used herein, with respect to the quantity measured, the term “about” refers to a deviation in the quantity measured that is commensurate with the target of the measurement and the accuracy of the measuring equipment used, as a technician who would expect to perform the measurement with a certain degree of care. Throughout this document, “at most” a number (e.g., at most 50) includes that number (e.g., 50).

[0145] Additionally, in this document, the numerical range expressed by endpoints includes all numbers contained within that range, as well as endpoint values ​​(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.).

[0146] As used in this article, the term "room temperature" refers to a temperature between 20°C and 25°C.

[0147] The terms “in the range” or “within the scope” (and similar expressions) include the endpoints of the range.

[0148] References throughout this specification to “one embodiment,” “implementation,” “certain embodiments,” or “some embodiments,” etc., refer to specific features, configurations, compositions, or characteristics described in connection with an embodiment, which are included in at least one embodiment of this disclosure. Therefore, such phrases appearing throughout this specification do not necessarily refer to the same embodiment as in this disclosure. Furthermore, features, configurations, compositions, or characteristics may be combined in any suitable manner in one or more embodiments.

[0149] Example

[0150] These embodiments are for illustrative purposes only and are not intended to unduly limit the scope of the appended claims. While the numerical ranges and parameters illustrating the broad scope of this disclosure are approximations, the values ​​shown in the specific examples are recorded as precisely as possible. However, any numerical value inherently contains some error, which is necessarily caused by the standard deviation present in the respective test measurements. At a minimum, and without attempting to limit the application of the doctrine of equivalence to the scope of the claims, at least the reported significant digits should be taken into account and each numerical parameter should be interpreted by applying conventional rounding.

[0151] Unless otherwise stated, all chemicals used in the examples are available from the mentioned suppliers. Adhesives used in the exemplary embodiments may include the adhesives described below.

[0152] adhesives

[0153] Structural adhesives are generally classified into two main categories: one-part adhesives and two-part adhesives. For one-part adhesives, a single composition contains all the materials necessary to obtain the final cured adhesive. These adhesives are typically applied to the substrate to be bonded and exposed to high temperatures (e.g., above 50°C) to cure. In contrast, two-part adhesives contain two components. The first component (often referred to as the "base resin component") contains a curable resin. The second component (often referred to as the "accelerator component") contains a curing agent and a catalyst. Various other additives may be included in one or both components.

[0154] Other adhesives used herein may include hot melt adhesives, such as one-component moisture-curing hot melt adhesives. This product group is characterized by extremely high heat resistance compared to conventional thermoplastic PO hot melts. In some examples, polyurethane (PUR) hot melts containing isocyanates for use in chemical crosslinking processes are used. In other examples, polyolefin (POR) hot melts using silanes as reactive components are used herein. Two-component adhesives are 100% solid systems that achieve their storage stability by separating the reactive components. These two-component adhesives are supplied as “resin” and “hardener” in separate containers. It is important to maintain the prescribed ratio of resin to hardener to obtain the desired curing and physical properties of the adhesive. The two components are mixed together only briefly before application to form the adhesive, which cures at room temperature. Because the reaction typically begins immediately after mixing the two components, the viscosity of the mixed adhesive increases over time until the adhesive can no longer be applied to the substrate or the bond strength decreases due to reduced wetting of the substrate. Formulations are available at various curing rates, thus providing a range of working times (working periods) and rates of strength gain after bonding. Final strength is reached from minutes to weeks after bonding, depending on the formulation. The adhesive must be removed from the mixing and application equipment before curing has progressed to the point where it is no longer soluble. Depending on the working period, two-component adhesives can be applied using a trowel, beads or tape, sprayer, or roller. Components are typically held in place until sufficient strength is achieved to allow for further processing. If a faster curing rate (strength gain) is desired, heat can be used to accelerate curing. This is particularly useful when parts require faster processing after bonding or when an additional working period is needed but a slower rate of strength gain cannot be accommodated. When cured, two-component adhesives are generally tough and rigid, with good temperature and chemical resistance.

[0155] For small applications, two-component adhesives can be mixed and applied by hand. However, this requires great care to ensure proper component ratios and thorough mixing for adequate curing and performance. Hand mixing also typically involves considerable waste. Therefore, adhesive suppliers have developed packaging that allows components to be stored separately and provides means for dispensing mixed adhesives, such as side-by-side syringes or concentric cartridges. The packaging is typically inserted into the applicator handle, and the adhesive is dispensed through a disposable mixing nozzle. The packaging design maintains proper component ratios, and proper mixing is ensured by using the mixing nozzle. Adhesive can be dispensed multiple times from these packagings, provided the time between uses does not exceed the adhesive's working life. If the working life is exceeded, a new mixing nozzle must be used. For larger applications, metering mixing equipment can be used to meter, mix, and dispense adhesives packaged in containers ranging from quarts to drums.

[0156] Two-component adhesives consist of a resin and a hardener component, which cure once mixed together. They remain stable during storage as long as the two components remain separate. Two-component adhesives are typically designed to be dispensed in a predetermined ratio to obtain desired properties from a specially formulated adhesive; common ratios include 10:1, 1:1, 2:1, etc. Once the two components are mixed, the reaction between them usually begins immediately, and the viscosity increases until both components are no longer usable. As described above, this can be referred to as the working life, open time, and shelf life. Once cured, two-component adhesives are tough and rigid, with good temperature and chemical resistance.

[0157] Epoxy resin adhesives

[0158] As described earlier herein, exemplary embodiments of epoxy adhesives may include single-component and two-component adhesives. Single-component epoxy adhesives may include a resin. Similar to their single-component counterparts, two-component epoxy adhesives are also formulated from epoxy resins. Two-component epoxy adhesives are widely used in structural applications and for bonding many materials, including, for example, metals, plastics, fiber-reinforced plastics (FRP), glass, and some rubbers. Two-component epoxy adhesives typically cure quickly and provide a relatively rigid bond. Some compositions can generally be brittle, but toughening agents and elastomers can be used to reduce this tendency.

[0159] Two-part epoxy adhesives consist of a resin (part A or part 1) and a hardener (part B or part 2). Accelerators or chemical catalysts can accelerate the reaction between the resin and the hardener.

[0160] Two-part epoxy resins cure at room temperature, so heat is not necessarily required when using them. Two-part epoxy resins typically reach treatment strength between five minutes and eight hours after mixing, depending on the hardener. Chemical catalysts or heat can be applied to accelerate the reaction between the resin and the hardener.

[0161] The resin upon which all epoxy resins are based is bisphenol A diglycidyl ether (DGEBA). Bisphenol A is prepared by reacting phenol with acetone under suitable conditions. "A" stands for acetone, "phenyl" refers to the phenolic group, and "bis" means two. Therefore, bisphenol A is a product obtained by chemically combining two phenols with one type of acetone. Unreacted acetone and phenol are separated from bisphenol A and then reacted with a material called epichlorohydrin. This reaction binds two ("bis") glycidyl groups to the ends of the bisphenol A molecule. The resulting product is bisphenol A diglycidyl ether, or basic epoxy resin. It is these glycidyl groups that react with the amine hydrogen atoms on the curing agent to produce the cured epoxy resin. Unmodified liquid epoxy resin is very viscous and, except as a very thick adhesive, is unsuitable for most applications.

[0162] The most common chemical raw materials used to manufacture curing agents or hardeners for room-temperature curing epoxy resins are polyamines. These are organic molecules containing two or more amine groups. The amine groups are structurally no different from ammonia; the difference is that these amine groups are attached to organic molecules. Similar to ammonia, amines are strongly basic. Due to this similarity, epoxy resin hardeners often have an ammonia-like odor, most noticeable in the air space inside the container immediately after opening. Epoxy resin hardeners are often referred to as "part B".

[0163] The reactive amine group consists of one or two hydrogen atoms attached to a nitrogen atom. These hydrogen atoms react with oxygen atoms from the glycidyl groups on the epoxy resin to form a cured resin—a highly cross-linked thermosetting plastic. Heat will soften but not melt the cured epoxy resin. The three-dimensional structure endows the cured resin with excellent physical properties.

[0164] Considering the various molecular weights and densities involved, the ratio of glycidyl oxygen to amine hydrogen determines the final resin-to-hardener ratio. An appropriate ratio produces a "fully cross-linked" thermosetting plastic. Changing the recommended ratio will leave unreacted oxygen or hydrogen atoms, depending on which one is in excess. The resulting cured resin will have lower strength because it is not fully cross-linked. Excess component B leads to increased moisture sensitivity in the cured epoxy resin and should generally be avoided.

[0165] Amine curing agents are not "catalysts." Catalysts promote reactions but are not chemically part of the final product. Amine curing agents work in conjunction with epoxy resins to greatly contribute to the final properties of the cured system. The curing time of an epoxy resin system depends on the reactivity of the amine hydrogen atoms. While the attached organic molecules do not directly participate in the chemical reaction, they do influence how easily the amine hydrogen atoms leave the nitrogen and react with the glycidyl group oxygen atoms. Therefore, the curing time is kinetically set by the specific amine used in the curing agent. The curing time of any given epoxy resin system can only be changed by adding an accelerator to a system that can accommodate an accelerator or by changing the temperature and mass of the resin / curing agent mixture. Adding more curing agent will not "speed things up," and adding less curing agent will not "slow things down."

[0166] The curing reaction of epoxy resin is exothermic. The curing rate of epoxy resin depends on the curing temperature. The higher the curing temperature, the faster the rate. For every 18℉ (10℃) change in temperature, the curing rate will change by approximately half or two times. For example, if an epoxy resin system takes 3 hours to become non-tack at 70℉, it will become non-tack in 1.5 hours at 88℉ or in 6 hours at 52℉. Everything related to reaction rate follows this general rule. The initial temperature of the mixed resin and hardener greatly affects the pot life and working time. For example, in hot weather, the two materials can be cooled before mixing to increase the working time.

[0167] The gel time of a resin is the time it takes for a given mass to harden while remaining in a compact volume. Gel time depends on the initial temperature of the material and follows the rules above. One hundred grams (approximately three fluid ounces) of silver-tipped laminated epoxy resin with a fast-curing agent (as an illustrative example) will harden within 25 minutes starting at 77℉; at 60℉, the gel time is approximately 50 minutes. If the same mass is spread over 4 square feet at 77℉, the gel time will be slightly over three hours. In addition to being temperature-sensitive, curing time is also sensitive to surface area / mass.

[0168] As the reaction proceeds, it is exothermic. If the heat generated is immediately dissipated into the environment (as occurs in a film), the temperature of the cured resin does not rise and the reaction proceeds at a constant rate. If the resin is confined (e.g., in a mixing tank), the exothermic reaction raises the temperature of the mixture, thereby accelerating the reaction.

[0169] The working time or working life (WL) of an epoxy formulation is approximately 75% of the gel time of the container's geometry. This working time or WL 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 container will increase in absolute viscosity (e.g., measured at 75℉) due to polymerization, but initially decrease in apparent viscosity due to heating. Material remaining in the container at 75% of its gel time may appear quite thin (due to heating), but will actually be quite thick when cooled to room temperature. Experienced users will mix batches that will be applied almost immediately, or increase the surface area to slow the reaction.

[0170] Although the curing rate of epoxy resin depends on temperature, the curing mechanism is independent of temperature. The reaction proceeds fastest in the liquid state. As curing progresses, the system changes from a liquid to a sticky, soft gel. After gelation, the reaction rate slows down as hardness increases. The chemical reaction proceeds more slowly in the solid state. From a soft, sticky gel, the system becomes harder, thus slowly losing its adhesive properties. Over time, the system becomes non-sticky and continues to become harder and stronger.

[0171] At normal temperatures, the system will reach approximately 60% to 80% of its ultimate strength after 24 hours. Curing then proceeds slowly over the next few weeks, eventually reaching a point where no further curing occurs without a significant increase in temperature. However, for most purposes, room temperature curing systems can be considered fully cured after 72 hours at 77℉. High-modulus systems (like two-phase epoxy resins) must be post-cured at elevated temperatures to achieve full cure.

[0172] If the specific system being used offers this option, working with the fastest possible cure time is generally more efficient for the current application. This allows the user to move on to the next stage without wasting time waiting for the epoxy to cure. Faster-curing films with shorter tack times will have a less chance of attracting flies, bedbugs, and other airborne contaminants.

[0173] Epoxy resin compositions typically comprise a first liquid portion containing epoxy resin and a second liquid portion containing a curing agent. Although both the first and second portions are liquids at ambient temperature, the liquid portions may contain solid components dissolved or dispersed within the liquid.

[0174] The first part of the two-part composition comprises at least one epoxy resin. The epoxy resin is a low molecular weight monomer or a high molecular weight polymer, which typically contains at least two epoxy groups. The epoxy group is a cyclic ether having three ring atoms, sometimes also referred to as a glycidyl group or an ethylene oxide group. Epoxy resins are typically liquid at ambient temperatures.

[0175] Various epoxy resins are known, including, for example, bisphenol A type epoxy resin, bisphenol F type epoxy resin, bisphenol S type epoxy resin, phenolic varnish type epoxy resin, alkylphenolic varnish type epoxy resin, cresolic varnish type epoxy resin, biphenyl type epoxy resin, aralkyl type epoxy resin, cyclopentadiene type epoxy resin, naphthalene type epoxy resin, naphthol type epoxy resin, epoxy resin of condensate of phenol and aromatic aldehydes with phenolic hydroxyl groups, biphenyl aralkyl type epoxy resin, fluorene type epoxy resin, xanthine type epoxy resin, triglycidyl isocyanate, rubber-modified epoxy resin, phosphorus-based epoxy resin, etc.

[0176] Blends of various epoxy-containing materials can also be used. Suitable blends may include two or more epoxy-containing compounds with different weight-average molecular weight distributions, such as low molecular weight epoxides (e.g., weight-average molecular weight less than 200 g / mol), medium molecular weight epoxides (e.g., weight-average molecular weight ranging from about 200 to 1000 g / mol), and high molecular weight epoxides (e.g., weight-average molecular weight greater than about 1000 g / mol). Alternatively or additionally, epoxy resins may contain blends of epoxy-containing materials with different chemical properties, such as aliphatic and aromatic, or different functionalities, such as polar and nonpolar.

[0177] 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 resin is formed by reacting epichlorohydrin with bisphenol A to form a diglycidyl ether of bisphenol A. The simplest resins of this type are formed by reacting two moles of epichlorohydrin with one mole of bisphenol A to form a diglycidyl ether of bisphenol A (commonly abbreviated as DGEBA or BADGE). DGEBA resin is a transparent, colorless to pale yellow liquid at ambient temperature, and its viscosity is typically in the range of 5 Pa·s to 15 Pa·s at 25°C. Industrial grades typically contain a specific molecular weight distribution because pure DGEBA exhibits a strong tendency to form crystalline solids when stored at ambient temperature. This same reaction can be carried out using other bisphenols (such as bisphenol F). The choice of epoxy resin used depends on its intended end use. If a greater degree of ductility is required in the adhesive layer, an epoxy with a softening backbone may be desired. Materials such as bisphenol A diglycidyl ether and bisphenol F diglycidyl ether can provide the desired structural adhesion properties achieved upon curing, while the hydrogenated products of these epoxides can be used to make them compatible with substrates with oily surfaces.

[0178] Aromatic epoxy resins can also be prepared by reacting aromatic alcohols such as biphenyl glycol, triphenyl glycol, and triphenyltriol with epichlorohydrin. These aromatic biphenyl and triphenyl epoxy resins are not bisphenol epoxy resins.

[0179] There are two main types of aliphatic epoxy resins: glycidyl epoxides and alicyclic epoxides. Glycidyl epoxides are typically formed by reacting epichlorohydrin with an aliphatic alcohol or polyol to obtain glycidyl ethers or with an aliphatic carboxylic acid to obtain glycidyl esters. The resulting resins can be monofunctional (e.g., dodecyl glycidyl ether), bifunctional (diglycidyl hexahydrophthalic acid), or have higher functionality (e.g., trimethylolpropane triglycidyl ether). Alicyclic epoxides contain one or more alicyclic rings (e.g., 3,4-epoxycyclohexylmethyl-3,4-epoxycyclohexane carboxylic acid ester) in the ethylene oxide ring and its fused molecule. They are formed by reacting cycloolefins with peracids (such as peracetic acid). These aliphatic epoxy resins typically exhibit low viscosity (10 mPa·s-200 mPa·s) at ambient temperature and are commonly used as reactive diluents. Therefore, these aliphatic epoxy resins are used to modify (reduce) the viscosity of other epoxy resins. This leads to the term "modified epoxy resin" referring to those containing reactive diluents that reduce viscosity. In some embodiments, the resin composition may also include reactive diluents. Examples of reactive diluents include: 1,4-butanediol diglycidyl ether, cyclohexane diglycidyl ether, resorcinol diglycidyl ether, p-tert-butylphenyl glycidyl ether, toluene glycidyl ether, neopentyl glycol diglycidyl ether, trimethylolethane triglycidyl ether, trimethylolpropane triglycidyl ether, triglycidyl p-aminophenol, N,N'-diglycidyl aniline, N,N,N',N'-tetraglycidyl m-xylenediamine, and vegetable oil polyglycidyl ether. The resin composition may contain at least 1 wt%, 2 wt%, 3 wt%, 4 wt%, or 5 wt%, and generally not more than 15 wt% or 20 wt% of such reactive diluents.

[0180] In some embodiments, the resin composition comprises (e.g., bisphenol A) epoxy resin in an amount of at least about 50% by weight of the total resin composition comprising a mixture of boron nitride particles and cellulose nanocrystals. In some embodiments, the amount of (e.g., bisphenol A) epoxy resin is not greater than 95%, 90%, 80%, 85%, 80%, 75%, 70%, or 65% by weight of the total resin composition.

[0181] Epoxides are typically cured using stoichiometric or near-stoichiometric amounts of curing agent. In the case of a two-part epoxy resin composition, the second part contains a curative, also referred to herein as a curing agent. The amount of co-reactant (hardener) to be used when curing the epoxy resin is calculated using equivalence weight or epoxide quantity. Epoxide quantity is the amount of epoxide equivalents in 1 kg of resin (equivalents / kg); while equivalence weight is the weight of resin containing 1 molar equivalent of epoxide, expressed in grams (g / mol). Equivalent weight (g / mol) = 1000 / epoxide quantity (equivalents / kg).

[0182] Common categories of curing agents used for epoxy resins include amines, amides, ureas, imidazoles, and thiols. In typical embodiments, the curing agent contains reactive -NH groups or reactive -NR groups. 1 R 2 Group, wherein R 1 and R 2 Independently, it is H or C1 to C4 alkyl, and most commonly H or methyl.

[0183] Curing agents typically react highly with epoxy groups at ambient temperature. Such curing agents are usually liquid at ambient temperature. However, the primary curing agent can also be a solid, provided it has an activation temperature equal to or lower than ambient temperature.

[0184] One class of curing agents is primary polyamine, secondary polyamine, and tertiary polyamine. Polyamine curing agents can be linear, branched, or cyclic. In some advantageous embodiments, the polyamine crosslinker is aliphatic. Alternatively, aromatic polyamines may be used.

[0185] Available polyamines have the general formula R 5 —(NR 1 R 2 ) x , where R 1 and R 2 Independently H or alkyl, R 5 It is a polyalkylene or arylene, and x is at least two. R 1 and R 2 The alkyl groups are typically C1 to C2. 18 Alkyl, more typically C1 to C4 alkyl, and most commonly methyl. R 1 and R 2 They can be combined to form a cyclic ether. In some embodiments, x is di (i.e., diamine). In other embodiments, x is 3 (i.e., triamine). In still other embodiments, x is 4.

[0186] Examples include hexamethylenediamine; 1,10-diaminodecane; 1,12-diaminododecane; 2-(4-aminophenyl)ethylamine; isophorone diamine; 4,4′-diaminodicyclohexylmethane; and 1,3-bis(aminomethyl)cyclohexane. Exemplary six-membered cyclohexanes include, for example, piperazine and 1,4-diazabicyclo[2.2.2]octane (“DABCO”).

[0187] Other available polyamines include those having at least three amino groups, wherein the three amino groups are primary amino groups, secondary amino groups, or combinations thereof. Examples include 3,3′-diaminobenzidine, hexamethylenetriamine, and triethylenetetramine.

[0188] The specific composition of the epoxy resin can be selected based on its intended end use. For example, in one embodiment, the resin composition can be used for insulation, as described in US2014 / 0080940, the disclosure of which is incorporated herein by reference.

[0189] The resin composition may optionally contain additives, including silane-treated or untreated fillers, anti-sagging additives, thixotropic agents, processing aids, waxes, and UV stabilizers. Examples of typical fillers include glass bulbs, pyrolytic silica, mica, feldspar, and wollastonite. In some embodiments, the resin composition also contains other thermally conductive fillers such as alumina, aluminum hydroxide, fused silica, zinc oxide, aluminum nitride, silicon nitride, magnesium oxide, beryllium oxide, diamond, and copper.

[0190] Methyl methacrylate (MMA) adhesive

[0191] Exemplary embodiments of methyl methacrylate (MMA) adhesives may include single-part MMA adhesives and two-part MMA adhesives. Single-part MMA adhesives may include resins. Two-part MMA adhesives exhibit faster strength enhancement compared to epoxides. MMA adhesives are commonly used for bonding plastics as well as for bonding metals to plastics. These MMA adhesives are also extremely effective in bonding solid surface materials together, and because they can be colored, they are widely used in the fabrication and installation of work surfaces.

[0192] MMA (methyl methacrylate) adhesives are structural acrylic adhesives made from a portion A (part 1) of resin and a portion B (part 2) of hardener. Most MMAs also contain rubber and additional reinforcing agents. MMAs cure rapidly at room temperature and have full bond strength shortly after application. The adhesive is resistant to shear, peel, and impact stress. Looking more technically at the bonding process, these adhesives work by generating an exothermic polymerization reaction. Polymerization is the process of reacting monomer molecules together in a chemical reaction to form polymer chains. This means the adhesive produces a strong bond while remaining flexible. These adhesives can form bonds between dissimilar materials with different flexibility, such as metals and plastics. Unlike some other structural adhesives (like bipartite epoxides), MMAs do not require heat to cure. A range of working times are available to suit specific needs for different MMAs.

[0193] MMA has higher peel strength and is more heat resistant. It develops strength faster, allowing parts to be used more quickly. It's also worth noting the different processing conditions used for MMA. For example, the two components of MMA can be applied separately to one of the materials being bonded together, and the MMA will not begin to cure until the joint is joined together, thus combining the components. This means that precise mixing ratios are not required to achieve good adhesion. It's important to remember that MMA does tend to have a strong odor, meaning good ventilation is necessary when applying it, and that MMA is flammable, so some caution is required.

[0194] MMA is formulated to have a working time between 5 minutes and 20 minutes.

[0195] All of these acrylic structural adhesive types offer excellent bond strength and durability—approaching that of epoxy adhesives—but with the advantages of faster curing, less sensitivity to surface preparation, and the ability to bond a wider variety of materials.

[0196] silicone adhesives

[0197] Exemplary embodiments of silicone adhesives may include single-part and two-part adhesives. Two-part silicone adhesives are typically used when there is a large bonding area or when there is insufficient relative humidity to complete curing. Common applications of these silicone adhesives are in electronics, including the manufacture of household appliances, automobiles, and windows.

[0198] Suitable silicone resins include moisture-curing silicone resins, condensation-curing silicone resins, and addition-curing silicone resins, such as hydroxyl-terminated silicone resins, silicone rubbers, and fluorosilicone resins. Examples of suitable commercially available silicone PSA compositions containing silicone resins include Dow Corning's 280A, 282, 7355, 7358, 7502, 7657, Q2-7406, Q2-7566, and Q2-7735; General Electric's PSA 590, PSA 600, PSA 595, PSA 610, PSA 518 (medium phenyl content), PSA 6574 (high phenyl content), and PSA 529, PSA 750-D1, PSA 825-D1, and PSA 800-C. An example of a commercially available bipartite silicone resin is a resin sold by Dow Chemical Company, Midland, Michigan, under the trade name "SILASTIC J".

[0199] Pressure-sensitive adhesives (PSA) may include: natural or synthetic rubbers, such as styrene block copolymers (styrene-butadiene; styrene-isoprene; styrene-ethylene / butene 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.

[0200] Other pressure-sensitive adhesives include poly(α-olefins), polychloroprene, and silicone elastomers. In some embodiments, polychloroprene and silicone elastomers are preferred because polychloroprene contains halogens, which can contribute to flame retardancy, and silicone elastomers are resistant to thermal degradation.

[0201] polyurethane adhesives

[0202] Exemplary polyurethane adhesives used in the embodiments may include both single-part polyurethane adhesives and two-part polyurethane adhesives. Two-part polyurethane adhesives can be formulated to have a wide range of properties and characteristics upon curing. For example, they are typically used when bonding dissimilar materials (such as glass) to metal or bonding aluminum to steel.

[0203] Most polyurethane adhesives are polyester or polyether-based. These adhesives are present in isocyanate prepolymers and in active hydrogen containing curing agents (polyols). These adhesives form the soft segments of the polyurethane, while the isocyanate groups form the hard segments. The soft segments typically constitute the larger portion of the elastic polyurethane adhesive and thus determine its physical properties. For example, polyester-based polyurethane adhesives have better oxidative and high-temperature stability than polyether-based adhesives, but they also have lower hydrolytic stability and lower-temperature flexibility. However, polyethers are generally more expensive than polyesters.

[0204] Many polyurethane adhesives are sold as two-component polyurethane adhesives. The first component contains diisocyanate and / or isocyanate prepolymers (component 1), and the second component consists of a polyol (and an amine / hydroxyl chain extender) (component 2). Catalysts (typically tin salts or tertiary amines) are often added to accelerate curing. Reactive components are often blended with additives and plasticizers to achieve desired processing and / or final properties and reduce costs.

[0205] For example, optionally in the presence of non-reactive components, polyurethane can be prepared by reacting one or more polyols and / or polyamines and / or amino alcohols with one or more polyisocyanates. For applications where weathering is likely, it is generally desirable that the polyols, polyamines and / or amino alcohols, and the polyisocyanates do not contain aromatic groups.

[0206] For example, suitable polyols include those commercially available under the trade name Desmophen from Bayer Corporation, Pittsburgh, PA. The polyols can be polyester polyols (e.g., Desmophen 631A, 650A, 651A, 670A, 680, 110, and 1150), polyether polyols (e.g., Desmophen 550U, 1600U, 1900U, and 1950U), or acrylic polyols (e.g., Demophen A160SN, A575, and A450BA / A).

[0207] Suitable polyamines include, for example: aliphatic polyamines such as ethylenediamine, 1,2-diaminopropane, 2,5-diamino-2,5-dimethylhexane, 1,11-diaminodecane, 1,12-diaminododecane, 2,4- and / or 2,6-hexahydrotoluenediamine, and 2,4′-diamino-dicyclohexylmethane; and aromatic polyamines such as 2,4- and / or 2,6-diaminotoluene, and 2,4′- and / or 4,4′-diaminodiphenylmethane; amine-terminated polymers, such as those available under the trade name JEFFAMINE polypropylene glycol diamine (e.g., Jeffamine XTJ-510) from Huntsman Chemical (Salt Lake City, Utah) and those available under the trade name Hycar ATBN (amine-terminated acrylonitrile-butadiene copolymer) from Noveon Chemical Company in Cleveland, Ohio. Those of Corp., Cleveland, Ohio, and those disclosed in U.S. Patent Nos. 3,436,359 (Hubin et al.) and 4,833,213 (Leir et al.) (amine-terminated polyethers and polytetrahydrofuran diamines); and combinations thereof.

[0208] Suitable amino alcohols include, for example, 2-aminoethanol, 3-aminopropion-1-ol, alkyl-substituted versions of the above compounds, and combinations thereof.

[0209] 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-chlorophenylene diisocyanate), methylene diphenylene-4,4′-diisocyanate; polycarbodiimide-modified methylene diphenylene diisocyanate, (4,4′-diisocyanate-3,3′,5,5′-tetraethyl)diphenylmethane, 4,4′-diisocyanate-3,3′-dimethoxybiphenyl (o-dimethoxyaniline diisocyanate); 5-chloro-2,4-toluene diisocyanate and 1-chloromethyl-2,4-diisocyanate phenylene, aromatic-aliphatic diisocyanates (e.g., m-phenylenedimethyl diisocyanate and tetramethyl-m-phenylenedimethyl diisocyanate). Esters), aliphatic diisocyanates (e.g., 1,4-diisocyanate butane, 1,6-diisocyanate hexane, 1,12-diisocyanate dodecane; and 2-methyl-1,5-diisocyanate pentane); alicyclic diisocyanates (e.g., methylene dicyclohexyl-4,4′-diisocyanate; 3-isocyanomethyl-3,5,5-trimethylcyclohexyl isocyanate (isophorone diisocyanate), 2,2,4-trimethylhexyl diisocyanate, and cyclohexyl-1,4-diisocyanate); polymers or oligomers terminated by two isocyanate functional groups (e.g., polyoxyethylene, polyester, polybutadiene, etc.) (e.g., toluene-2,4-diisocyanate-terminated polyoxypropylene glycol dicarboxylate); may be traded under the names MONDUR or DESMODUR (e.g., Desmodur). XP7100 and Desmodur N 3300A are polyisocyanates commercially available from Bayer Corporation (Pittsburgh, Pa.) in Pittsburgh, Pennsylvania; and combinations thereof.

[0210] In some embodiments, the polyurethane comprises a reaction product containing at least one polyisocyanate and at least one polyol. In some embodiments, the polyurethane comprises a reaction product containing 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.

[0211] Typically, polyurethane is stretchable and / or flexible. For example, the percentage elongation at break (under environmental conditions) of polyurethane or any layer containing polyurethane may be at least 10%, 20%, 40%, 60%, 80%, 100%, 125%, 150%, 175%, 200%, 225%, 250%, 275%, 300%, 350%, or even 400% or higher.

[0212] In some embodiments, the polyurethane has hard segments (typically segments corresponding to one or more polyisocyanates in any combination) in an amount ranging from 35 wt%, 40 wt%, or 45 wt% up to 50 wt%, 55 wt%, 60 wt%, or even 65 wt%.

[0213] As used in this article, wt% means a weight percentage based on the total weight of the material, and

[0214] Hard segment wt% = (Weight of short-chain diols and polyols + Weight of short-chain di or polyisocyanate) / Total weight of resin

[0215] in:

[0216] The equivalent weight of short-chain diols and polyols is ≤185 g / equivalent, and the functionality is ≥2; and

[0217] The short-chain isocyanates have an equivalent weight of ≤320 g / equivalent and a functionality of ≥2.

[0218] One or more catalysts are typically included with the bipartite urethane. Catalysts for bipartite urethanes are well known and include, for example, catalysts based on aluminum, bismuth, tin, vanadium, zinc, tin, and zirconium. Tin-based catalysts have been found to significantly reduce percolation during polyurethane formation. Examples of tin-based catalysts include dibutyltin compounds, such as dibutyltin diacetate, dibutyltin dilaurate, dibutyltin diacetylacetonate, dibutyltin dithiol, dibutyltin dioctanoate, dibutyltin dimaleate, dibutyltin diacetopyruvate, and dibutyltin oxide. If a catalyst is present, the content of any catalyst is typically at least 200 parts per million parts (200 ppm), 300 ppm, or higher by weight; however, this is not required.

[0219] Suitable descriptions of bipartite polyurethanes are found in U.S. Patent No. 6,258,918B1 (Ho et al.) and U.S. Patent No. 5,798,409 (Ho), the disclosures of which are incorporated herein by reference.

[0220] Typically, the amount of polyisocyanate in a two-component urethane is chosen based on approximate stoichiometric ratios to polyols, polyamines, and / or amino alcohols. However, in some cases, it may be desirable to adjust the relative amounts to other ratios. For example, a slight stoichiometric excess of polyisocyanate may be appropriate to ensure a high degree of binding between the polyol, polyamine, and / or amino alcohol. However, any excess isocyanate groups present after the polymerization reaction will generally react with materials containing reactive hydrogen (e.g., foreign water, alcohols, amines, etc.).

[0221] All patents, patent documents, and publications cited herein are incorporated herein by reference in their entirety as if each document were cited individually. In the event of any conflict or contradiction between this written specification and the disclosures in any document incorporated herein by reference, this written specification shall prevail. Various modifications and alterations to this disclosure will become apparent to those skilled in the art without departing from its scope and spirit. This disclosure is not intended to be unduly limited to the exemplary embodiments and examples shown herein, and such embodiments and examples are presented by way of example only. The scope of this disclosure is intended to be limited only by the claims set forth herein.

Claims

1. A distributor system, comprising: One or more dispenser components, said one or more dispenser components including one or more sensors for providing at least one process parameter for dispensable material, said one or more sensors including: (i) a temperature sensor, the temperature sensor comprising a probe disposed in the fluid path of the dispensable material and configured to sense the temperature of the dispensable material; and (ii) A conductivity sensor configured to sense the conductivity of the dispensable material. A processor, operably coupled to the one or more distributor components, is configured to: Receive at least one parameter associated with the distributor system; The value of the operating parameter of the dispenser system is determined based on the at least one parameter, and the value of the operating parameter will realize the flow rate of dispensable material in the dispenser system; Provide the aforementioned operating parameters; The temperature of the dispensable material is received from the temperature sensor; The conductivity of the dispensable material is sensed from the conductivity sensor; The curing state of the dispensable material is determined based on the sensed temperature and the sensed electrical conductivity of the dispensable material. The operating parameters of the dispenser system are adjusted based on the sensed temperature and curing state; and Provides the adjusted operating parameters.

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

3. The dispenser system of claim 1, wherein the temperature sensor further includes a shield separating the outer surface of the probe from the dispensable material, and the probe is configured to sense the temperature of the dispensable material through the shield.

4. The dispenser system of claim 1, wherein the probe is configured to directly contact the dispenseable material in the fluid path.

5. The dispenser system of claim 1, wherein the temperature sensor includes the conductivity sensor, and wherein the temperature sensor is further configured to apply a voltage to the dispensable material in the fluid path of the dispensable material.

6. The distributor system according to claim 1, further comprising: A user input device, wherein the at least one process parameter is received from user input at the user input device.

7. The dispenser system of claim 1, wherein the at least one parameter of the dispenser system includes the density of the dispenseable material.

8. The distributor system according to claim 7, wherein: The one or more sensors also include a mass measurement device configured to sense mass data of the dispensable material; The at least one process parameter includes the quality data; and The processor is further configured to determine the volumetric flow rate of the dispensable material based on the density and mass data of the dispensable material.

9. The distributor system according to claim 1, further comprising: Wireless communication interface.

10. The distributor system of claim 9, wherein the processor is further configured to communicate with the distributor system via the wireless communication interface.

11. The distributor system of claim 9, wherein the processor is further configured to communicate with a smartphone via the wireless communication interface.

12. The distributor system of claim 11, wherein the processor is configured to: provide the smartphone with a request for input data for determining the operating parameters via the wireless communication interface.

13. The distributor system of claim 1, wherein the processor is further configured to: The maximum idle time or cleaning time of the one or more dispenser components is determined based on the at least one parameter and the at least one process parameter; and Provide the maximum idle time or the cleaning time.

14. The dispenser system of claim 13, wherein the one or more sensors are further configured to provide at least one environmental parameter, and the processor is further configured to determine the maximum idle time or the cleaning time based on the at least one environmental parameter, the at least one parameter, and the at least one process parameter.

15. The distributor system of claim 13, further comprising: A user interface, operably coupled to the processor, wherein the processor is further configured to: Receives one or more user inputs including one or more of safety factors, process control factors, and waste factors; and The maximum idle time or the cleaning time is determined based on the one or more user inputs, the at least one parameter, and the at least one process parameter.

16. The distributor system of claim 1, wherein the processor is configured to provide the operating parameters and the adjusted operating parameters to the one or more distributor components.

17. The distributor system of claim 1, wherein the processor is configured to provide the operating parameters and the adjusted operating parameters to the human-machine interface.

18. The dispenser system of claim 1, wherein the dispenseable material comprises an adhesive.

19. A method for dispensing dispensable materials using a dispenser system, the method comprising: Receive at least one parameter associated with the distributor system; The value of the operating parameter of the dispenser system is determined based on the at least one parameter, and the value of the operating parameter will achieve the flow rate of the dispensable material in the dispenser system; Provide the aforementioned operating parameters; Receive at least one process parameter, including the sensed temperature of the dispensable material; Receive the sensed conductivity of the dispensable material from the conductivity sensor; The curing state of the dispensable material is determined based on the sensed temperature and the sensed electrical conductivity of the dispensable material. The values ​​of the operating parameters of the dispenser system are adjusted based on the sensed temperature and the curing state. as well as Provides the adjusted operating parameters.

20. The method of claim 19, wherein the operating parameter is the driving force pressure of the distributor system.

21. The method of claim 19, wherein the at least one parameter of the dispenser system includes the density of the dispenseable material.

22. The method of claim 21, wherein the at least one process parameter further includes mass data of the dispensable material, and wherein the method further includes: The volumetric flow rate of the dispensable material is determined based on the density and mass data of the dispensable material.

23. The method of claim 19, further comprising: The maximum idle time or cleaning time of one or more distributor components of the distributor system is determined based on the at least one parameter and the at least one process parameter. as well as Provide the maximum idle time or the cleaning time.

24. The method of claim 23, further comprising: Receive at least one environmental parameter provided by one or more sensors; as well as The maximum idle time or the cleaning time is determined based on the at least one environmental parameter, the at least one parameter, and the at least one process parameter.

25. The method of claim 23, further comprising: Receive one or more user inputs, including one or more of safety factors, process control factors, and waste factors; as well as The maximum idle time or the cleaning time is determined based on the one or more user inputs, the at least one parameter, and the at least one process parameter.

26. The method of claim 19, further comprising: The operating parameters and the adjusted operating parameters are provided to one or more distributor components of the distributor system.

27. The method of claim 19, further comprising: The operation parameters and the adjusted operation parameters are provided to the human-machine interface.

28. The method of claim 19, wherein the dispensable material comprises an adhesive.

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