Systems, methods, and interfaces for optimizing composition metrics of e-coat baths - Patents.com

Abstract

A computer-implemented method for maintaining key compositional metrics, such as percent solids and / or pigment to binder ratio, in an e-coat bath can include receiving sensor data directly from the e-coat bath, where the sensor data correlates with changes in the compositional metric. One or more computer algorithms can process the data in real time to quickly and accurately identify recommended adjustments to the e-coat bath.

Description

[Technical field]

[0001] The present disclosure relates to an apparatus, computer-implemented method, and system for optimizing key compositional metrics in an electrocoat environment. [Background technology]

[0002] Modern coatings serve several important functions in industry and society. Coatings can protect the coated material from corrosion (e.g., rust). Coatings can also serve aesthetic functions by imparting a particular color and / or texture to the object. For example, most assets, such as automobiles, are coated using paints and various other coatings to protect the automobile's metal body from the elements and also to provide aesthetic visual effects.

[0003] In some environments, coating operators apply coatings using an electrocoat ("e-coat" or "e-coating") process. The process of applying an e-coat can include several steps. In a first step ("pretreatment"), operators clean a substrate (e.g., a metal substrate) and apply a material such as a phosphate to prepare the surface for application of the e-coat. In a next step, operators can apply the coating to the pretreated substrate in an electrocoat bath using calibrated process control equipment. The e-coat bath can consist of certain compositional metrics (e.g., a certain percentage of solvent and a remaining percentage of paint solids), where the solvent can act as a carrier for the paint solids. It can be important to maintain key compositional metrics of the e-coat bath (including, without limitation, percentage of solids, as well as pigment-to-binder ratio), in some cases, within acceptable ranges.

[0004] Maintaining ideal ranges for such compositional metrics can be quite time and labor intensive. In one example, maintaining ideal ranges for, for example, solids percentage, or pigment-to-binder ratio, may require operators to continually take samples of the e-coat bath and analyze the samples, which can take hours or days to complete, potentially stalling production. Summary of the Invention

[0005] The present disclosure provides systems, methods, and computer program products for efficiently and accurately maintaining appropriate levels of composition metrics (including, without limitation, percentage of solids and / or pigment to binder ratio in the e-coat bath). For example, the system may include multiple sensors that may be applied within the e-coat bath (or may be connected directly / in-line to the e-coat bath). One or more sensors are selected to identify characteristics of the e-coat bath that correlate well with the composition metrics. A typical system may apply data from various inputs from the e-coat bath, such as by using one or more algorithms, including one or more machine learning algorithms, to identify whether a particular composition metric has changed. An operator, or the system itself, may then make appropriate real-time adjustments to the solution to ensure corresponding real-time optimization of the composition metrics in the e-coat bath.

[0006] For example, a computer-implemented method (and corresponding computer system configured to perform the same) for automatically maintaining compositional metric values ​​for an e-coat bath can include receiving initial data for the e-coat bath from a sensor. The initial data corresponds to compositional metric values ​​for the e-coat bath, the compositional metric values ​​including one or both of (i) percent solids and (ii) pigment to binder ratio. The method can also include processing the initial data using an algorithm to identify an initial compositional metric value for the e-coat bath. In addition, the method can include determining, by the computer system, from the initial compositional metric value, one or more amounts of materials to add to the e-coat bath. Furthermore, the method can include receiving subsequent data from the sensor and processing the received subsequent data with one or more algorithms to identify a subsequent compositional metric value for the e-coat bath.

[0007] Additional features and advantages will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice. The features and advantages may be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims and aspects. These and other features will become more fully apparent from the following description and appended claims, or may be learned by practice of the examples set forth hereinafter.

[0008] To describe how the foregoing and other advantages and features can be obtained, a more particular description of what has been briefly described above will be made by reference to specific examples thereof which are illustrated in the accompanying drawings, with the understanding that these drawings are merely illustrative and therefore should not be considered limiting of its scope, the present disclosure will be described and explained with additional specificity and detail through the use of the accompanying drawings, in which: [Brief description of the drawings]

[0009] [Figure 1] 1 shows a schematic diagram of a server computer interacting with sensor data to maintain key composition metrics of the e-coat bath. [Diagram 2] 1 shows a graph of one compositional metric (i.e., percent solids) in an e-coat bath in terms of monitoring and adjusting to sensor data. [Diagram 3] 4 shows a graph of another compositional metric (i.e., pigment to binder ratio) in an e-coat bath in terms of monitoring and adjusting to sensor data. [Figure 4] 1 shows a flowchart of a series of actions in a method for automatically maintaining percent solids in an e-coat bath. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] The present disclosure provides systems, methods, and computer program products for efficiently and accurately maintaining the proper percentage of material in the e-coat bath. For example, the system may include multiple sensors that may be applied to the e-coat bath (or may be connected directly / in-line to the e-coat bath). One or more sensors are selected to identify characteristics of the e-coat bath that correlate well with the percent solids in the bath. A typical system may apply data from various inputs from the e-coat bath, such as by using one or more algorithms, including one or more machine learning algorithms, to identify whether the percent solids has changed. An operator, or the system itself, may then make appropriate real-time adjustments to the solution to ensure a corresponding real-time optimization of the percent solids in the e-coat bath.

[0011] Thus, as will be more fully understood herein, the present disclosure can provide significant time and cost savings to the e-coat process. For example, the e-coat bath monitoring process, which would normally shut down production for hours or days to monitor and determine the percent solids in the e-coat bath, can now be done much faster than previously possible and can be done in real time without shutting down production. In this manner, the disclosed system can achieve increased speed without sacrificing accuracy and using a wide range of data available from readily available sensors.

[0012] As a preliminary matter, unless otherwise stated, all numbers (e.g., those expressing values, ranges, amounts, or percentages) may be read as if preceded by the term "about", even if the term does not explicitly appear. Any numerical ranges recited herein are intended to include all subranges subsumed therein. The plural encompasses the singular, and vice versa. For example, the present disclosure describes terms such as "a" component, "an" algorithm, "a" layer, "a" sensor, "a" pigment, etc., but two or more of these and other components, including mixtures, may be used. When ranges are given, any endpoints of those ranges and / or any numerical values ​​within those ranges may be combined with the ranges of the present disclosure. Similarly, the terms "including", "such as", "for example", etc., should be understood as open ended, meaning "including / such as / for example but not limited to".

[0013] Referring now to the figures, FIG. 1 shows a generalized schematic diagram of a server computer 120 interacting with sensor data to maintain composition metrics of an e-coat bath. In particular, FIG. 1 shows that the system 100 can include an e-coat bath 110 to which a series of sensors 115 (i.e., sensor A 115a, sensor B 115b, sensor C 115c, etc., as shown) can be connected. The e-coat bath 110 can include any number of compositional components (e.g., paint, base coat, or other protective coating / layer) used to coat a substrate. In one example, the e-coat bath includes approximately 80-90% deionized water and 10-20% paint solids. And, the substrate can include a metal substrate for use in, for example, an automotive manufacturing environment.

[0014] As previously mentioned, FIG. 1 further illustrates that the e-coat bath 110 can include any number of sensors A, B, C (i.e., 115a, 115b, 115c) directly connected thereto. For purposes of illustration, three sensors are shown in FIG. 1, however, the present disclosure contemplates the use of at least one of the illustrated sensors, and may further implement more than three sensors connected to the e-coat bath 110. In one example, the sensors 115 (e.g., a-c) can include any one or more sensors configured to measure any one or more of the following parameters, or "composition metrics": pigment vs. binder (or "P / B"), pH, conductivity, density, viscosity, turbidity, temperature, bath level, bath volume, product flow rate, biocide level, sulfuric acid, lactic acid, and / or bacterial growth.

[0015] By way of example and not limitation, a typical pH sensor can include a sensor model CPS61E manufactured by ENDRESS+HAUSER. In this or another example, the pH sensor (e.g., 115a) can include one or more ceramic reference junction pH probes with a range of 0-14 pH, installed in-line. In one example, the pH sensor can be installed in-line with an ENDRESS+HAUSER fitting (e.g., CPA871), and the probe can be removed for calibration / cleaning while the e-coating process continues to run. By way of example and not limitation, the conductivity sensor can include a sensor model CLS82E manufactured by ENDRESS+HAUSER. In one example, the conductivity sensor (e.g., 115b) can include a four-point conductivity probe, installed in-line with the aforementioned ENDRESS+HAUSER CPA871 fitting, and the probe can be removed for calibration / cleaning while the process continues to run.

[0016] By way of example and not limitation, the density and viscosity sensor can include an in-line Coriolis flowmeter, for example, manufactured by ENDRESS+HAUSER. In one example, the density and viscosity sensor (e.g., 115c) can include a model PROMASS Q 300, and / or a PROMASS I 300. By way of further example and not limitation, at least one embodiment of the turbidity sensor (e.g., 115d) is a model CUS52D manufactured by ENDRESS+HAUSER. As described for the previous sensor, the turbidity sensor can be installed in-line, thereby allowing it to be removed while the process continues to run. In addition to the above, although not shown, the server 120 can include or be otherwise operatively connected to one or more programmable logic controllers (PLCs) (e.g., model ILC2050BI PLC) that provide variable sample rates. In at least one example, the one or more PLCs can interface between the one or more sensors 115 (e.g., a-c) and the server 120. In one example, a pigment-to-binder ratio can be derived from the turbidity data. In another example, the pigment to binder ratio can be reasonably well estimated without turbidity (in this case using only density, viscosity, and conductivity). It will be understood upon consideration of the specification that both the percent solids and the pigment to binder ratio can be derived from one or more of the sensors listed herein, thereby allowing flexibility with the ability to mix and match sensors as needed.

[0017] 1 further illustrates that the server 120 can send and receive data to and from each of the given sensors 115(a-c) and / or ingredient dispensers 160, described more fully below, e.g., via a direct connection and / or via a network 170. The sent and received data can then be processed by the server 120 through one or more processing modules 125. The one or more processing modules 125 can interact with storage 130 and pass or retrieve information between a sensor data component 135 and an algorithm component 140.

[0018] For purposes of explanation, the term "module" as used herein means computer-executable code that, when executed by one or more processors in a given computer system (e.g., server 120), causes the given computer system to perform a particular function. In contrast, the term "component" as used herein means a passive set of instructions or data structures that store, manage, and / or otherwise provide information that is handled or otherwise processed through a given module. However, as will be appreciated by those skilled in the art, the distinction between different modules or components is at least partially arbitrary, and modules or components may be combined and / or divided in other ways while still being within the scope of the present disclosure. Thus, the description that a given element is a "module" or "component" is provided for clarity and explanation only and should not be construed as indicating that any particular structure of computer-executable code and / or computer hardware is required unless otherwise specified. In this description, the terms "component", "agent", "manager", "service", "engine", "virtual machine", and the like may be used as well.

[0019] 1, the one or more processing modules 125 of the server 120 can identify the percent solids level in the e-coat bath 110 by calculating the data received from the various sensors (115a, 115b, and 115c). In one example, the one or more processing modules 125 load one or more algorithms from the component 140. The one or more algorithms can include one or more machine learning algorithms. The one or more machine learning algorithms can further identify correlations between the sensor data and key compositional metrics (e.g., percent solids or pigment-to-binder ratio) using one or more mathematical formulations.

[0020] With respect to percent solids, for example, the computer system 120 can use a statistical modeling algorithm to identify whether the e-coat bath meets or does not meet expectations. An exemplary statistical modeling algorithm suitable for use with the present disclosure includes using a multiple linear regression algorithm represented by the following equation: β0+β1X1+...β n X n Here, β0 is the graph intercept value, and β1 to β n Each of contains a coefficient value, X1 to X n The values ​​of represent the raw values ​​obtained from each sensor.

number

[0021] In general, β1 to β n The coefficient values ​​essentially normalize the scale of the sensor 115(a-c) values ​​so that the values ​​can be summed in a meaningful way to achieve a meaningful Y result value (or reflect the percent solids in the e-coat bath 110 solution). Table I below provides an example set of coefficients (and intercepts) that may be used in accordance with the present disclosure. [Table 1] Thus, as an example, assuming β1X1 represents a subset relative to pH, and substituting 7.0 for pH, the equation may take the form: Y=-1.145718+(0.1349582)(7.0)+…β n X n

[0022] For purposes of illustration, the values ​​in Fig. 1 are examples of processed sensor data rather than raw sensor data, but may nevertheless be used directly in the model. For example, the raw data obtained from sensors 115(a-c) may in some cases be temperature compensated / adjusted to counteract slight process temperature variations before being applied within a given algorithm. This allows for the use of raw sensor 115(a-c) data in some cases and data adjusted for local conditions in other cases.

[0023] FIG. 1 further illustrates that upon determining the results of the calculations, the server 120 can send one or more messages 150 to the ingredient dispenser 160 to adjust the amount of material. In general, the thresholds for what constitutes in-range or out-of-range are recipe dependent. With respect to percent solids, some operators using a particular coating and substrate may desire a percent solids concentration of 1%-4% for various reasons, while others may desire the ideal composition metric (e.g., percent solids concentration) to be 15%-18% and 24%-50%. Similarly, specification ranges for pigment-to-binder ratios may vary based on the process, coating product, and desired coating properties. Thus, it is understood that the desired threshold ranges (or "thresholds") provided to the server 120 can depend on many different considerations (e.g., without limitation, customer preferences, end use, substrate to be coated, desired e-coat recipe ingredients), and can further include a variety of other considerations.

[0024] Thus, in one example, the message 150 may include a generalized value of percent solids (or other material adjustment) that the ingredient dispenser 160 (e.g., a dosing pump) can interpret to determine the appropriate amount of material to add or adjust in the solution. In such a case, the ingredient dispenser 160 may receive and interpret the message 150 and compare the adjustment instructions to customer-requested values ​​for the composition metrics (percent solids, P / B ratio) of the current solution in the e-coat bath 110. The ingredient dispenser 160 may then dispense the appropriate amount of material to adjust the solution.

[0025] For example, dispenser 160 may deliver an effective amount of "ingredient" defined as any one or more of e-coat paste, e-coat resin, and formic acid via a dosing pump. In another example, server 120 performs these same calculations and threshold evaluation steps via processor 125, so that message 150 includes specific instructions on which ingredients to add, how much, etc. In that case, ingredient dispenser 160 simply executes the instructions from message 150 and adjusts coat bath 110. In at least one example, server 120, e-coat bath 110, and ingredient dispenser 160 are in continuous communication, which allows for continuous monitoring and adjustment of e-coat bath 110 to optimize the percent solids within tolerances in real time.

[0026] A graph showing percent solids as a typical proof of concept is shown in Figure 2. The Y-axis considers the calculated values ​​measured from the e-coat bath 110, while the X-axis shows the manually measured values. As shown, the modeled values ​​(Y-axis) are substantially identical to the actual measured values ​​(X-values) for the same time points.

[0027] In contrast, FIG. 3 shows a graph depicting the pigment to binder (P / B) ratio in an e-coat bath. As with FIG. 2, the plot shows a similar relationship in that the P / B ratio is predicted using the same or similar multivariate approach as used above for the % solids in FIG. 2. In other words, multiple linear regression modeling of the P / B can be derived from sensors 115(a-c) sensing conductivity, density, and viscosity. The server 120 can estimate a P / B of + / - 0.41 for the specification range of 10-15 across all validation samples. In the case shown in FIG. 3, it shows that there were two outliers on the calibration set and one on the validation set, all of which appeared to occur near the beginning of the experiment. Once the experiment was in full swing and the process stabilized, FIG. 3 shows that the root mean square error was met with + / - 0.22 P / B for the specification range of 10-15.

[0028] Thus, Figures 1-3 show a number of components, modules, and schematics that can be used to accurately monitor and adjust key compositional metrics of an e-coat bath in real time. The present disclosure can also be described in terms of acts in a method to achieve a particular result. The method of Figure 4 is described below with respect to the elements, components, and modules of Figures 1-3.

[0029] For example, Figure 4 shows that a method 400 for automatically maintaining percent solids in an e-coat bath can include an act 410 of receiving (or identifying) e-coat bath sensor data, or other relevant characteristics. Act 410 includes receiving initial data for the e-coat bath from the sensor, the initial data corresponding to a composition metric of the e-coat bath, the composition metric including one or both of (i) percent solids, and (ii) pigment to binder ratio. For example, Figure 1 shows that server 120 measures characteristics of e-coat bath 110 via data from one or more of sensors 115a, 115b, 115c, and / or other sensors or combinations thereof, as described herein.

[0030] FIG. 4 also illustrates that the method 400 can include an act 420 of identifying key compositional metrics for the e-coat bath. Act 420 includes processing the initial data using an algorithm to identify initial compositional metric values ​​for the e-coat bath. For example, FIG. 1 illustrates the server 120 processing the sensor 115 data in conjunction with one or more algorithms 140. The one or more algorithms 140 can process the sensor data determined to correlate with percent solids using any number or type of statistical modeling algorithms. The present disclosure contemplates that one or more or all of the following sensor data points can be analyzed individually or in various combinations as correlating with the key compositional metrics: (i) pigment vs. binder, (ii) pH, (iii) conductivity, (iv) density, (v) viscosity, (vi) turbidity, (vii) temperature, (viii) bath level, (ix) bath volume, (x) product flow rate, (xi) biocide level, (xii) sulfuric acid, (xiii) lactic acid, (xiv) bacterial growth. In at least one example, temperature may be combined with a viscosity measurement. In yet another example, sensors used include pH, conductivity, density, and viscosity (which may be combined with temperature).

[0031] Additionally, FIG. 4 shows that the method 400 can include an act 430 of determining an amount of material to be adjusted / added. The act 430 includes determining, by a computer system, one or more amounts of material to be added to the e-coat bath from the initial composition metric. For example, FIG. 1 shows the server 120 sending one or more messages 150 to the ingredient dispenser 160. The one or more messages 150 may include general information regarding the calculated percent solids, which allows the ingredient dispenser 160 to separately determine an e-coat bath recipe-specific solution of material to be added. Alternatively, the message 150 can include specific instructions to dispense a particular material into the e-coat bath via the ingredient dispenser 160. In still other cases, the message 150 is displayed on a client device, such as a local or attached personal computer connected to the e-coat bath 110, or any number of other mobile devices (e.g., a laptop, cell phone, or tablet, etc.). The operator can then manually dispense the appropriate amount of material in light of the displayed information.

[0032] 4 further illustrates that method 400 can include act 440 of identifying updated composition metrics. Act 440 includes receiving subsequent data from the sensors and processing the received subsequent data with one or more algorithms to identify subsequent composition metrics for the e-coat bath. For example, as shown in FIG. 1, the connections and measurements between bath 110, sensor 115, server 120, and dispenser 160 can be continuous and ongoing, such that server 120 can continually calculate in real time the relevant materials to be adjusted (e.g., to adjust the percent solids or pigment to binder ratio) and can make the appropriate ongoing adjustments, all without stopping the ongoing e-coating process.

[0033] Thus, Figures 1-4 show a number of systems, components, and modules that can be used to significantly improve both the speed and accuracy of e-coating bath maintenance of percent solids. Part of this improvement can be obtained by using the monitoring of multiple data points that closely correlate with changes in percent solids in the solution. Thus, the principles outlined in this disclosure can give end users significantly more efficient and accurate tools for maintaining e-coat production lines without sacrificing quality.

[0034] The present disclosure may include or utilize a dedicated or general-purpose computer system including, for example, computer hardware such as one or more processors and system memory, as described in more detail below. The scope of the present disclosure also includes physical and other computer-readable media for holding or storing computer-executable instructions and / or data structures. Such computer-readable media may be any available media accessible by a general-purpose or special-purpose computer system. Computer-readable media that store computer-executable instructions and / or data structures are computer storage media. Computer-readable media that hold computer-executable instructions and / or data structures are transmission media. Thus, by way of example and not limitation, the present disclosure may include at least two distinctly different kinds of computer-readable media: computer storage media and transmission media.

[0035] A computer storage medium is a physical storage medium that stores computer-executable instructions and / or data structures. Physical storage media includes computer hardware, such as RAM, ROM, EEPROM, solid-state drive ("SSD"), flash memory, phase-change memory ("PCM"), optical disk storage, magnetic disk storage, or other magnetic storage, or any other hardware storage device(s) that can be used to store program code in the form of computer-executable instructions or data structures that can be accessed and executed by a general-purpose or special-purpose computer system to implement the disclosed functions of the present disclosure.

[0036] A transmission medium can include a network and / or data link that can be used to carry program code in the form of computer-executable instructions or data structures and that can be accessed by a general-purpose or special-purpose computer system. A "network" is defined as one or more data links that enable the transmission of electronic data between computer systems and / or modules and / or other electronic devices. When information is transferred or provided to a computer system over a network or another communications connection (either wired, wireless, or a combination of wired or wireless), the computer system can view the connection as a transmission medium. Combinations of the above should also be included within the scope of computer-readable media.

[0037] Furthermore, upon reaching the various computer system components, program code in the form of computer-executable instructions or data structures may be automatically transferred from transmission media to computer storage media (or vice versa). For example, computer-executable instructions or data structures received over a network or data link may be buffered in RAM in a network interface module (e.g., a "NIC") and eventually transferred to the computer system's RAM and / or to less volatile computer storage media in the computer system. Thus, it should be understood that computer storage media may be included within computer system components that also (or even primarily) utilize transmission media.

[0038] Computer-executable instructions include, for example, instructions and data that, when executed on one or more processors, cause a general-purpose computer system, special-purpose computer system, or special-purpose processing device to perform a certain function or group of functions. Computer-executable instructions may be, for example, binary, intermediate format instructions such as assembly language, or even source code.

[0039] As will be appreciated by those skilled in the art, the present disclosure may be practiced in a network computing environment having many types of computer system configurations (e.g., personal computers, desktop computers, laptop computers, message processors, handheld devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, cell phones, PDAs, tablets, pagers, routers, switches, etc.). The present disclosure may also be practiced in a distributed system environment in which both local and remote computer systems perform tasks that are linked through a network (either by wired data links, wireless data links, or a combination of wired and wireless data links). Thus, in a distributed system environment, a computer system may include multiple constituent computer systems. In a distributed system environment, program modules may be located in both local and remote memory storage devices.

[0040] As will also be appreciated by those skilled in the art, the present disclosure may be implemented in a cloud computing environment. The cloud computing environment may be distributed, but this is not required. If distributed, the cloud computing environment may be distributed internationally within an organization and / or may have components held across multiple organizations. In this description and in the claims that follow, "cloud computing" is defined as a model for enabling on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services). The definition of "cloud computing" is not limited to any of the many other advantages that are obtained when such a model is properly deployed.

[0041] Cloud computing models can consist of various characteristics (e.g., on-demand self-service, broad network access, resource pooling, rapid elasticity, metered services, etc.). Cloud computing models can also be offered in the form of various service models, such as, for example, software as a service ("SaaS"), platform as a service ("PaaS"), and infrastructure as a service ("IaaS")). Cloud computing models may also be deployed using various deployment models (e.g., private cloud, community cloud, public cloud, hybrid cloud, etc.).

[0042] A cloud computing environment (or cloud computing platform) may include a system including one or more hosts, each capable of running one or more virtual machines. In operation, the virtual machines emulate a running computing system supporting an operating system and possibly one or more other applications. Each host may include a hypervisor that emulates virtual resources to the virtual machines using physical resources that are abstracted from the virtual machines. The hypervisor also provides appropriate isolation between the virtual machines. Thus, from the perspective of any given virtual machine, the hypervisor provides the illusion that the virtual machine is working with physical resources, even though the virtual machine is only working with the appearance of physical resources (e.g., virtual resources). Examples of physical resources include processing power, memory, disk space, network bandwidth, media drives, and the like. In view of the foregoing, the present disclosure may be embodied in a number of different configurations, as outlined above and as exemplified by the following configurations or aspects.

[0043] For example, at least one exemplary configuration can include a computer-implemented method for use in a system including a computer system, an e-coat bath, a network, and one or more sensors connected thereto, for automatically maintaining a composition metric of the e-coat bath, the method including receiving initial data for the e-coat bath from the sensor, the initial data corresponding to a composition metric value for the e-coat bath, the composition metric including one or both of (i) percent solids and (ii) pigment to binder ratio; processing the initial data using an algorithm to identify an initial composition metric value for the e-coat bath; determining, by the computer system, from the initial composition metric value, one or more amounts of materials to add to the e-coat bath; receiving subsequent data from the sensor and processing the received subsequent data with one or more algorithms to identify a subsequent composition metric value for the e-coat bath.

[0044] In further or alternative configurations, the one or more sensors include a density sensor connected to the e-coat bath. In further or alternative configurations, the one or more sensors include a viscosity sensor connected to the e-coat bath. In further or alternative configurations, the one or more sensors include a pH sensor connected to the e-coat bath. In further or alternative configurations, the one or more sensors include a conductivity sensor connected to the e-coat bath. In further or alternative configurations, the sensor includes a turbidity sensor connected to the e-coat bath, and the computer system uses an algorithm to calculate the pigment to binder ratio from data obtained from the turbidity sensor. In further or alternative configurations, the one or more sensors include a temperature sensor connected to the e-coat bath. In further or alternative configurations, the one or more sensors include a bath level sensor connected to the e-coat bath. In further or alternative configurations, the one or more sensors include a bath volume sensor connected to the e-coat bath.

[0045] In yet further or alternative configurations, the one or more sensors include a product flow sensor connected to the e-coat bath. In further or alternative configurations, the initial composition metric indicates that the percent solids is outside a threshold range. In further or alternative configurations, the initial composition metric indicates that the pigment to binder ratio is outside a desired threshold range. In further or alternative configurations, the method may further include one or more of: displaying the determined amount of material to be added to the e-coat bath on a digital display and / or sending instructions via a computer system to a component dispenser to dispense the determined amount or amounts of material to the e-coat bath. In further or alternative configurations, the material dispensed by the component dispenser includes a make-up material including any one or more of an e-coat paste, an e-coat resin, and / or formic acid, and the component dispenser includes a dosing pump.

[0046] Still further, in a further or alternative configuration, the method further includes using a machine learning algorithm to process the received sensor data and determine an amount of material to add to the e-coat bath. In a further or alternative configuration, the processing step includes using a multiple linear regression algorithm having the following formula: Y=β_0+β_1X_1+...β_nX_n, where each of X_1 through X_n includes a value obtained from a sensor.

[0047] In addition to the foregoing, another configuration of the present disclosure can include a computer system including one or more processors and one or more computer readable storage media having computer executable instructions stored thereon that, when executed by the one or more processors, cause the computer system to: receive first sensor data from a sensor, the sensor configured to identify a characteristic for the e-coat bath that correlates with a composition metric value in the e-coat bath, the composition metric value including a measured solids percentage, or a pigment to binder ratio; process the received sensor data by calculating the received sensor data along one or more algorithms to identify an initial composition metric value in the e-coat bath; determine from the initial composition metric value an amount of material to adjust in the e-coat bath; receive second sensor data from the one or more sensors and process the received second sensor data with an algorithm to identify a subsequent composition metric value corresponding to an updated material in the e-coat bath. In further or alternative configurations, the sensor includes any one or more of a density sensor, a viscosity sensor, a pH sensor, a conductivity sensor, a turbidity sensor, a temperature sensor, a bath level sensor, a bath volume sensor, and a product flow rate sensor connected to the e-coat bath.

[0048] In a further or alternative configuration, the sensor is mounted in-line with a fitting, the fitting configured to allow the sensor to be removed for calibration and / or cleaning while the e-coat bath continues to operate. In a further or alternative configuration, the initial percent solids is outside of a desired threshold range. In a further or alternative configuration, the updated percent solids is within a desired threshold range. In a further or alternative configuration, the computer executable instructions, when executed by the one or more processors, further cause the computer system to perform one or more of: displaying the determined one or more amounts of material to be adjusted to the e-coat bath on a digital display and / or sending one or more instructions to an ingredient dispenser indicating the determined material to be added to the e-coat bath. In a further or alternative configuration, the method may further include one or more machine learning algorithms, when executed by the computer system, that perform the steps of processing the received sensor data and determining an amount of material needed to adjust the e-coat bath. In a further or alternative configuration, the step of processing the received sensor data by calculating the received sensor data further comprises using a multiple linear regression algorithm having the following formula: Y = β_0 + β_1X_1 + ... β_nX_n, where X_1 through X_n each contain values ​​obtained from one or more sensors.

[0049] The present disclosure may also be enumerated as exemplary embodiments, which may be combined or substituted with the above configurations in any variation or form.For example, in one embodiment, a computer-implemented method for automatically maintaining a percent solids in an e-coat bath may include receiving first sensor data from one or more sensors, the one or more sensors being configured to identify a characteristic for the e-coat bath that correlates with the percent solids in the e-coat bath; processing the received sensor data by calculating the received sensor data along one or more algorithms to identify an initial value corresponding to an initial percent solids in the e-coat bath; determining from the initial value one or more amounts of material that need to be added to the e-coat bath; receiving second sensor data from the one or more sensors; and processing the received second sensor data by one or more algorithms to identify a subsequent value corresponding to an updated percent solids in the e-coat bath.In a second embodiment, in the computer-implemented method according to the first embodiment, the one or more sensors include a density sensor connected to the e-coat bath.

[0050] In a third aspect, in the computer-implemented method of any of the preceding aspects, the one or more sensors include a viscosity sensor connected to the e-coat bath. In a fourth aspect, in the computer-implemented method of any of the preceding aspects, the one or more sensors include a pH sensor connected to the e-coat bath. In a fifth aspect, in the computer-implemented method of any of the preceding aspects, the one or more sensors include a conductivity sensor connected to the e-coat bath. In a sixth aspect, in the computer-implemented method of any of the preceding aspects, the one or more sensors include a turbidity sensor connected to the e-coat bath. In a seventh aspect, in the computer-implemented method of any of the preceding aspects, the one or more sensors include a temperature sensor connected to the e-coat bath. In an eighth aspect, in the computer-implemented method of any of the preceding aspects, the one or more sensors include a bath level sensor connected to the e-coat bath.

[0051] In a ninth aspect, the computer-implemented method of any of the preceding aspects, wherein the one or more sensors include a bath volume sensor connected to the e-coat bath. In a tenth aspect, the computer-implemented method of any of the preceding aspects, wherein the one or more sensors include a product flow sensor connected to the e-coat bath. In an eleventh aspect, the computer-implemented method of any of the preceding aspects, wherein an initial composition metric, such as percent solids (and / or pigment to binder ratio), is outside a threshold range. In a twelfth aspect, the computer-implemented method of any of the preceding aspects, wherein an updated composition metric, such as percent solids (and / or pigment to binder ratio), is within a desired threshold range. In a thirteenth aspect, the computer-implemented method of any of the preceding aspects, wherein a server computer receives and processes the initial and subsequent sensor data. In a fourteenth aspect, the computer-implemented method of any of the preceding aspects may further include one or more of: displaying the determined amount of one or more materials that need to be added to the e-coat bath on a digital display; and / or sending one or more messages to an ingredient dispenser indicating the determined amount of one or more materials that need to be added to the e-coat bath.

[0052] In a fifteenth aspect, the computer-implemented method of any of the preceding aspects, wherein the solids dispensed by the component dispenser include a make-up material including any one or more of an e-coat paste, an e-coat resin, and / or formic acid, and the component dispenser includes a dosing pump. In a sixteenth aspect, the computer-implemented method of any of the preceding aspects can further include performing the steps of processing the received sensor data using one or more machine learning algorithms and determining an amount of material to add to the e-coat bath. In a seventeenth aspect, the computer-implemented method of any of the preceding aspects, wherein the step of processing the received sensor data by calculating the received sensor data further includes using a multiple linear regression algorithm having the formula: Y = β0 + β1X1 + … β n X n X1~X n Each of β1 to β2 includes a value obtained from one or more sensors. n is X1~X n Contains coefficients (including normalization factors) that correspond to the values ​​of

[0053] In an eighteenth aspect, in a further or alternative configuration of the present disclosure, a computer system can include one or more processors and one or more computer-readable storage media having computer-executable instructions stored thereon, which when executed by the one or more processors, cause the computer system to: receive first sensor data from one or more sensors, the one or more sensors configured to identify a characteristic for the e-coat bath that correlates with a percent solids in the e-coat bath; process the received sensor data by calculating the received sensor data along one or more algorithms to identify an initial value corresponding to an initial percent solids in the e-coat bath; determine from the initial value one or more amounts of material that need to be added to the e-coat bath; receive second sensor data from the one or more sensors and process the received second sensor data according to one or more algorithms to identify a subsequent value corresponding to an updated percent solids in the e-coat bath.

[0054] In a nineteenth aspect, in the computer system according to the nineteenth aspect, the one or more sensors include a density sensor connected to the e-coat bath. In a twentieth aspect, in the computer system according to any of the eighteenth to nineteenth aspects, the one or more sensors include a viscosity sensor connected to the e-coat bath. In a twenty-first aspect, in the computer system according to any of the eighteenth to twentieth aspects, the one or more sensors include a pH sensor connected to the e-coat bath. In a twenty-second aspect, in the computer system according to any of the eighteenth to twenty-first aspects, the one or more sensors include a conductivity sensor connected to the e-coat bath. In a twenty-third aspect, in the computer system according to any of the eighteenth to twenty-second aspects, the one or more sensors include a turbidity sensor connected to the e-coat bath.

[0055] In a twenty-fourth embodiment, in the computer system according to any one of the eighteenth to twenty-third embodiments, the one or more sensors include a temperature sensor connected to the e-coat bath. In a twenty-fifth embodiment, in the computer system according to any one of the eighteenth to twenty-fourth embodiments, the one or more sensors include a bath level sensor connected to the e-coat bath. In a twenty-sixth embodiment, in the computer system according to any one of the eighteenth to twenty-seventh embodiments, the one or more sensors include a bath volume sensor connected to the e-coat bath. In a twenty-seventh embodiment, in the computer system according to any one of the eighteenth to twenty-sixth embodiments, the one or more sensors include a product flow sensor connected to the e-coat bath.

[0056] In a twenty-eighth aspect, the computer system of any of the eighteenth to twenty-seventh aspects, wherein the one or more sensors are installed in-line with fittings, and the fittings are calculated to allow the one or more sensors to be removed for calibration and / or cleaning while the e-coat bath continues to run. In a twenty-ninth aspect, the computer system of any of the eighteenth to twenty-eighth aspects, wherein the initial percent solids is outside of a desired threshold range. In a thirtieth aspect, the computer system of any of the eighteenth to twenty-ninth aspects, wherein the updated percent solids is within a desired threshold range.

[0057] In a thirty-first aspect, the computer system according to any of the eighteenth to thirty-first aspects, the computer executable instructions, when executed by the one or more processors, further cause the computer system to perform one or more of the following: displaying the determined amount or amounts of the material that needs to be added to the e-coat bath on a digital display; and / or sending one or more messages to the ingredient dispenser indicating the determined amount or amounts of the material that needs to be added to the e-coat bath. In a thirty-second aspect, the computer system according to any of the eighteenth to thirty-first aspects, the computer system further comprises one or more machine learning algorithms, which, when executed by the computer system, perform the steps of processing the received sensor data and determining the amount of material to be added to the e-coat bath. In a thirty-third aspect, the computer system according to any of the eighteenth to thirty-second aspects, the step of processing the received sensor data by calculating the received sensor data further comprises using a multiple linear regression algorithm having the following formula: Y = β0 + β1X1 + … β n X n X1~X n Each of β1 to β2 includes a value obtained from one or more sensors. n is X1~X n Contains coefficients (including normalization factors) that correspond to the values ​​of

[0058] In a thirty-second aspect, in the computer-implemented method of any of the first to seventeenth aspects, determining the amount of one or more materials to add to the e-coat bath takes into account that the (i) percent solids and / or (ii) pigment-to-binder ratio of the e-coat should be within a desired threshold range (e.g., within a desired threshold range of a desired e-coat recipe).

[0059] In a thirty-third aspect, in the computer-implemented method according to any of the first to seventeenth or thirty-third aspects, the method for automatically maintaining a compositional metric of an e-coat bath is performed continuously during the process of coating a plurality of substrates.

[0060] While the present subject matter has been described in language specific to structural features and / or methodological acts, and specific examples of the disclosure have been described above for purposes of illustration, it will be apparent to those skilled in the art that many variations in the details of the disclosure may be made without departing from the subject matter defined in the appended claims. In particular, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specifically described features or acts, or to the sequence of acts described above. Rather, the described features and acts are disclosed as exemplary forms of implementing the claims.

Claims

1. 1. A computer-implemented method for use in a system including a computer system, an e-coat bath, a network, and one or more sensors connected thereto, the method for automatically maintaining a compositional metric of the e-coat bath, comprising: receiving initial data for the e-coat bath from a sensor, the initial data corresponding to compositional metric values ​​for the e-coat bath, the compositional metric including one or both of (i) percent solids, and (ii) pigment to binder ratio; processing the initial data using an algorithm to identify an initial composition metric for the e-coat bath; determining, by the computer system, from the initial compositional metrics, an amount of one or more materials to be added to the e-coat bath; receiving subsequent data from the sensor and processing the received subsequent data with the one or more algorithms to identify subsequent compositional metrics of the e-coat bath.

2. The computer-implemented method of claim 1 , wherein the one or more sensors comprises a density sensor coupled to the e-coat bath.

3. 10. The computer-implemented method of any preceding claim, wherein the one or more sensors include a viscosity sensor connected to the e-coat bath.

4. 10. The computer-implemented method of any preceding claim, wherein the one or more sensors include a pH sensor connected to the e-coat bath.

5. 10. The computer-implemented method of any preceding claim, wherein the one or more sensors include a conductivity sensor connected to the e-coat bath.

6. the sensor includes a turbidity sensor connected to the e-coat bath; 10. A computer-implemented method according to any preceding claim, wherein the computer system uses the algorithm to calculate a pigment to binder ratio from the data obtained from the turbidity sensor.

7. 10. The computer-implemented method of any preceding claim, wherein the one or more sensors include a temperature sensor connected to the e-coat bath.

8. 10. The computer-implemented method of any preceding claim, wherein the one or more sensors include a bath level sensor connected to the e-coat bath.

9. 10. The computer-implemented method of any preceding claim, wherein the one or more sensors include a bath volume sensor connected to the e-coat bath.

10. 10. The computer-implemented method of any preceding claim, wherein the one or more sensors include a product flow sensor connected to the e-coat bath.

11. 20. The computer-implemented method of any preceding claim, wherein the initial compositional metric indicates a percent solids is outside a threshold range.

12. 20. The computer-implemented method of any preceding claim, wherein the initial compositional metric indicates that a pigment-to-binder ratio is outside a desired threshold range.

13. displaying on a digital display the determined amount of material to be added to the e-coat bath; and / or 4. The computer-implemented method of any of the preceding claims, further comprising one or more of the following: transmitting, via the computer system, instructions to a component dispenser to dispense the determined one or more amounts of material into the e-coat bath.

14. the material dispensed by the ingredient dispenser comprises a make-up material comprising any one or more of an e-coat paste, an e-coat resin, and / or formic acid; The computer-implemented method of claim 13 , wherein the component dispenser comprises a dosing pump.

15. Using machine learning algorithms, processing the received sensor data; 13. The computer-implemented method of any preceding claim, further comprising performing the steps of: determining the amount of material to add to the e-coat bath.

16. The processing step includes using a multiple linear regression algorithm having the following formula: Y=β ０ +b １ X １ +…b ｎ X ｎ X １ ~X ｎ 5. A computer-implemented method according to any preceding claim, wherein each of the first and second inputs comprises a value obtained from the sensor.

17. 1. A computer system comprising: one or more processors; One or more computer-readable storage media having computer-executable instructions stored thereon, the computer-executable instructions, when executed by the one or more processors, causing the computer system to: receiving initial data for the e-coat bath from a sensor, the initial data corresponding to compositional metric values ​​for the e-coat bath, the compositional metric including one or both of (i) percent solids, and (ii) pigment to binder ratio; processing the initial data using an algorithm to identify an initial composition metric for the e-coat bath; determining, by the computer system, from the initial compositional metrics, an amount of one or more materials to be added to the e-coat bath; and one or more computer readable storage media configured to receive subsequent data from the sensor and process the received subsequent data with the one or more algorithms to determine subsequent compositional metric values ​​for the e-coat bath.

18. 20. The computer system of claim 17, wherein the sensors include any one or more of a density sensor, a viscosity sensor, a pH sensor, a conductivity sensor, a turbidity sensor, a temperature sensor, a bath level sensor, a bath volume sensor, and a product flow rate sensor connected to the e-coat bath.

19. The sensor is mounted in-line with the fitting; The computer system of any of claims 17 to 18, wherein the fittings are configured to allow the sensor to be removed for calibration and / or cleaning while the e-coat bath continues to operate.

20. 20. The computer system of any of claims 17-19, wherein the initial percent solids is outside a desired threshold range.

21. The computer system of any of claims 17 to 20, wherein the updated percent solids is within a desired threshold range.

22. The computer-executable instructions, when executed by the one or more processors, further cause the computer system to: displaying on a digital display the determined amount or amounts of material to be added to the e-coat bath; and / or and transmitting one or more instructions to an ingredient dispenser indicating the determined ingredients to be added to the e-coat bath.

23. one or more machine learning algorithms, when executed by the computer system, processing the received sensor data; and determining an amount of material needed to condition the e-coat bath.

24. The step of processing the received sensor data by calculating the received sensor data further includes using a multiple linear regression algorithm having the following formula: Y=β ０ +b １ X １ +…b ｎ X ｎ X １ ~X ｎ 23. The computer system of claim 22, wherein each of comprises values ​​obtained from one or more sensors.

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