Computerized virtual paint manufacturing and application system

ES 2 219 829 T3

DESCRIPTION

Virtual computerized system for the manufacture and application of paints.

Field and background of the invention

The present invention relates generally to paint systems and more particularly to paint manufacturing, paint application, and paint product data acquisition and processing.

The operations that are part of automotive painting include many devices and process controllers that operate primarily independently to achieve their individual goals. In addition, the data is obtained from them individually without a structural framework to synthesize the data so that an overall systems analysis of the paint system can be carried out.

Information from automotive paint facilities is not only difficult to synthesize to gain a global system perspective, but information also comes out of the "hermetic" environment of these paint facilities with great difficulty. External sources, such as remote customer sites, need to access this synthesized information in order to make informed decisions about certain operating characteristics of auto paint shops. For example, customers want to know how their experimental paint products are performing in the car factory environment, as well as paint manufacturers and inventors. Also missing are the tools remote sites need for "fine tuning" operating parameters to set operations or paint chemistries that are out of tolerance with predetermined standards such as MSDS standards. Consequently, there is a need to overcome these and other disadvantages presented by previous proposals for the operation of paint-related facilities.

US 5,574,656 describes a computerized iterative process for generating chemical entities with predetermined physical, chemical and / or bioactive properties. During each iteration of the process, a directed diversity chemical library is robotically generated according to robotic synthesis instructions, the compounds of the directed diversity chemical library are analyzed to identify compounds with the desired properties, the structural property data is used to select compounds to be synthesized in the next iteration and new robotic synthesis instructions are automatically generated to control the synthesis of the directed diversity chemical library for the next iteration. Although paints are cited as one of the chemical compounds that could be generated by the process, paint-related process steps including resin fabrication, paint fabrication, and paint application are not explicitly mentioned.

Brief summary of the invention

In accordance with the present invention, the features of the independent claim define a computerized apparatus for coordinating paint-related process steps of at least one paint-related facility. Preferred embodiments are defined in the dependent claims.

Brief description of the figures

Other advantages and additional characteristics of the present invention appear from the subsequent description in the appended claims, together with the attached figures, in which:

Figure 1 is a process flow diagram that represents the steps included in the general painting system.

Figure 2 is a schematic network diagram showing the data interconnections of the components of the preferred embodiment of the present invention.

Figure 3 is a schematic network diagram depicting the data interconnections between a paint lab and the virtual paint manufacturing and application system.

Figure 4 is a functional data flow diagram depicting the flow of data between components of the present invention.

Figure 5a is a front view of a paint plate that can be analyzed by the paint analyzer device.

Figure 5b is an example of a contour graph produced by the paint analyzer device, depicting the lightness values ​​in relation to the positions on the plate of Figure 5a.

Figures 6a-6b are screens of the paint simulation computer program.

Figures 7a-7b are diagrams of the memory and data structures used in the present invention.

Figure 8 is an example of a computer screen for data acquisition and interrelation of resin manufacturing process control data.

Figure 9 is an example of a computer screen for data acquisition and interrelation of paint manufacturing process control data.

Figure 10 is an example of a computer screen for data acquisition and interrelation of vehicle assembly process control data.

Figure 11a is an example of a computer screen showing cross dependencies using the links between the data structures of the present invention.

Figure 11b is an example of a computer screen for authorization level for access to information according to the present invention.

Figure 12 is a flow chart depicting the use of the present invention for performing environmental tolerance checks.

Figure 13 is a flow chart showing the steps used in the system for troubleshooting and reporting cause and effect analysis.

Figure 14 is a flow chart showing the steps required to use the system to produce a weekly cause and effect analysis report.

Figure 15 and last is a computer output printout showing an example of a weekly report generated by the present invention.

Description of the preferred embodiment

Figure 1 shows the general steps in a paint manufacturing and application system. The ultimate goal of the overall process is to apply paint

ES 2 219 829 T3 manufactured on a vehicle (50) within predetermined tolerances. These tolerances include quality tolerances and ecological tolerances.

The raw material (54) enters the paint manufacturing and application system from an external raw material manufacturing source (62). In addition, the resin (66) enters the paint manufacturing and application system from an internal raw material manufacturing source (70). The terms "external" and "internal" refer to sources of the material that are internal or external to the company responsible for manufacturing the paint and supplying the manufactured paint to a vehicle assembly plant.

The raw material control process block (74) performs a data acquisition regarding the raw material (54). Similarly, the resin manufacturing process control block (78) performs a data acquisition with respect to the resin (66). The data acquired by blocks (74) and (78) are specially structured to provide system-like information regarding each process step that includes raw material (54) and resin (66). Data capture is preferably carried out by electronic sensors that detect paint-related characteristics and send the data electronically to the present invention for synthesis and storage. Furthermore, the present invention supports manual data entry as well as electronic retrieval of data required by the present invention directly from databases. This new data acquisition and its data structures as used by blocks 74 and 78 are described in more detail below.

The formulation guidelines (82) indicate how the raw material (54) and resin (66) are to be combined in the paint manufacturing process block (86) to produce paint material (90). These formulation guidelines (82) include guidelines such as the amount and temperature at which the raw material (54) and resin (66) are to be combined. The paint fabrication process control block (88) performs data acquisition regarding the paint fabrication.

Paint material (90) is supplied to a vehicle assembly plant for processing at said plant as indicated by block (94). Within the vehicle assembly plant processing block (94), the assembly plant process control block (98) performs data acquisition. The block (98) acquires data related to the paint material (90) and its application as the paint material (90) passes through each process step within the vehicle assembly plant processing block (94). The data acquired by the paint manufacturing process control block (88) and the assembly plant process control block (98) is used for purposes such as batch control (102). The batch control 102 is only intended to illustrate the use of the present invention and not to limit the scope of the present invention. Block control 102 includes analysis of data acquired by blocks 88 and 98 to determine if the paint material 90 is within predetermined tolerances.

All the data acquired by the blocks (74), (78), (88) and (98) reside within the virtual computerized paint manufacturing and application system (120), which structures the data in such a way that a global system perspective and provides an environment for the data captured by the present invention to be viewed by entities at a distance.

Figure 2 is a schematic network diagram of the interconnections between components of the virtual computerized paint manufacturing and application system (120) and the data sources, generally represented in (124), and the data receivers, represented generally in (128). The data sources (124) include data acquired from one or more paint labs (132), one or more paint factories (136), and one or more vehicle assembly plants (140).

Paint laboratories (132) provide technical data on the paint material such as mathematical models that relate paint application factors (for example, control settings for paint spraying equipment) with paint responses (for example, the shine of a painting). The models are stored within the virtual computerized paint manufacturing and application system (120) in the factor-response model database (144). In addition, paint labs (132) provide technical data to populate one or more of the following data structures contained within the virtual computerized paint manufacturing and application system (120): resin manufacturing data structure (148); paint manufacturing data structure (152); and paint application data structure (156).

The resin manufacturing data structure (148) refers to the data obtained from the resin material control process block (74) and the resin manufacturing process control block (78) (of Figure 1) . The paint manufacturing data structure 152 corresponds to the data acquired from the paint manufacturing process control block 88 (of FIG. 1). Furthermore, the paint application data structure (156) corresponds to the data acquired from the assembly plant process control block (98) (of FIG. 1).

Data structures 148, 152, and 156 are located on one or more computers, as generally depicted at 160. The data structures (148), (152) and (156) provide a new structure to aid the acquisition of data from the data sources (124) and the presentation and analysis of the data by the data receivers (128). .

Computers 160 are preferably arranged within the physical location of the data source. For example, data acquired from a paint factory (136) is preferably arranged on a computer located at the paint factory (136). Similarly, a computer containing the paint application data structure (156) is arranged in a vehicle assembly plant (140). The data structures (148), (152) and (156) and the computers (160) are collectively referred to as the process control coordinator (160). Computers (160) can enter and view data stored in databases located on networks (169), subject to a computerized security authorization.

The information structured by the data structures (148), (152) and (156) can be retrieved and ana3

ES 2 219 829 T3 lized by data receivers (128), such as paint manufacturers' remote sites (164) and customer remote sites (168). To allow data receivers 128 to analyze paint systems from a global system perspective, a technical database 172 provides additional data concerning paint, for example, but not exclusively, environmental standards and environmental standards. internal company quality.

Networks (169) connect the various components of the system so that data transmission can occur. The preferred embodiment of the networks (169) uses an Intranet network (173) to carry out the transmission of data between components within the virtual computerized paint manufacturing and application system (120), and within the data sources (124 ). In addition, remote paint manufacturer sites are connected to the Intranet (173). Remote customer sites (168) are connected to an Extranet network (175) so that there is greater security in accessing data from the virtual computerized paint manufacturing and application system (120). The security data is preferably located in the technical database (172) to ensure that only authorized users (whatever and wherever they may be) can see the parts of the information contained in the virtual computerized manufacturing and application system of paintings (120) for which they have authorization.

Figure 3 shows the preferred embodiment of the data interconnection between one of the paint laboratories (132) and the virtual computerized paint manufacturing and application system (120). Within the paint lab (132), the paint application equipment (242) is controlled by control settings (244) to spray paint onto vehicles. The sprayed paint is analyzed by a paint analyzer device (246). The paint analyzer device 246 examines the physical characteristics of the sprayed paint so that further analysis can reveal how the paint responds under various conditions and with various formulations. Paint analyzer device 246 examines physical characteristics such as, for example, color (e.g. L, a, b values ​​from different angles), leveling (in the form of wave scan values), gloss, turbidity and film thickness. In the preferred embodiment, the paint analyzer device 246 is a device known by the name "PROSIM" that is available from BASF.

The paint analyzer device (246) is preferably in data communication with a paint simulation computer program (248). Paint simulation computer program 248 models the interrelationship between automotive paint application equipment and spray paint in order to obtain the desired paint application characteristics. A database of factor-response models (144) is used to store mathematical models that interrelate paint application factors with paint responses. The paint application factors correspond to the control settings (244) of the paint application equipment (242). The paint responses correspond to characteristics of the paint such as can be obtained from the paint analyzer 246.

Paint simulation computer program 248 employs design experimentation techniques as well as optimization techniques to determine values ​​for paint responses based on desired paint tolerances. For a further understanding of paint simulation computer program 248, see US Serial No. 08 / 822,669 (entitled "Paint Equipment Setup Method and Apparatus"), filed March 24, 1997.

One of the goals of the paint simulation computer program (248) is the ability to identify regions within mathematical models that need further definition. For example, a range of paint application factor values ​​that results in relatively low R-squared values ​​for paint responses indicates regions within mathematical models that require refining. The paint lab specifically tests these regions within the mathematical models through an experimental design technique, and the paint analyzer device 246 collects data entry points. The design of the factor-response experimentation models are refined to incorporate this additional detail.

The computer (160) uses the paint manufacturing data structure (152) to acquire data from the paint analyzer device (246) and the paint simulation computer program (248). The acquired data is used for various purposes including conducting batch control (ie ensuring compliance with the quality standards included in the technical database). These characteristic paint data, such as the paint film thickness data, from the paint analyzer device (246) is interrelated with the type of paint material within the paint manufacturing data structure (152).

In another example, the paint simulation computer program (248) performs the design of experimentation calculations based on the data from the paint analyzer device (246) to identify which parameters and variables are key in the paint and assembly plant. vehicles. These identified key parameters are inserted into the process control data structures (such as the paint fabrication data structure and the vehicle assembly data structure).

Figure 4 shows the detailed information flow between the above-mentioned components of the present invention. In the preferred embodiment, the PROSIM device (246) provides characteristic paint data to the paint simulation computer program (248) in order to determine factor / control settings in order to produce specific appearance and application responses of the sprayed paint.

The process control coordinator (162) uses the data from the PROSIM device (246) to perform batch control. Within this capacity, the PROSIM device (246) allows the analysis of paint materials from a paint manufacturing plant to ensure compliance with predetermined quality standards. The batch control data from the PROSIM device (246) is used to populate the paint manufacturing data structure (152) (especially with respect to the quality forecast portion of the data structure).

ES 2 219 829 T3

As described above, the paint simulation computer program (248) uses the factor-response model database (144) to perform its design of experimentation calculations. In addition, the paint simulation computer program (248) updates the factor-response model database (144) based on the actual performance data of the paint spray system provided by the PROSIM device (246). The paint simulation computer program (248) together with the factor-response model database (144) makes it possible to check and control variable parameters through the technical database (172). The technical database (172), in its preferred embodiment, contains information such as paint product portfolio information (270), ecological information (272), communications information (274), and quality information (276) ( as a first-process capability). The technical database (172) also contains question and request for analysis forms (280) in order to capture the questions and problems and their subsequent analysis and resolution. The technical database (172) also includes the security data (277) on how an entity external to the virtual computerized paint manufacturing and application system (120) can access the information.

The process control coordinator (162) synthesizes and packages the data from the data sources so that remote systems can efficiently and effectively analyze the historical, current, and potential operating characteristics of the entire paint system (i.e., analysis history). The process control coordinator (162) synthesizes and packages the data into the resin manufacturing data structure (148), the paint manufacturing data structure (152), and the paint application data structure (156 ) corresponding to the type of data provided and the particular data source that provided them.

Additionally, the process control coordinator (162) provides troubleshooting and communicates information to the data destinations based on the information captured by the question and analysis request forms (280). The troubleshooting notification module (282) enables remote data destination sites to use previous solutions to similar problems to resolve existing problems. In addition, a weekly report module (284) of the process control coordinator (162) provides an automated ability to send information from the various components of the present invention to remote data destination sites.

The process control coordinator (246) provides a date and time stamp for each data received from the data sources. This not only creates a dynamic copy of historical baseline memory (285), but also allows the magnitude of change to be analyzed at different times throughout the paint manufacturing system.

Figure 5a shows how the PROSIM device captures characteristic paint data from the plate (290). The regions identified by way of example by reference numeral 291 show where the PROSIM device performs measurements. For this example, a variable amount of base coat (292) was applied to the plate (290). The upper part of the plate 290a included a thin base layer coating, while the lower part of the plate 290b included a greater amount of base layer. For this example, a consistent amount of clear coat (294) was applied to plate 290. The approximation of the variable amount with respect to the base layer (292) is possible with the PROSIM device since it captures characteristic data of the paint of the entire plate.

To illustrate the procedure for analyzing the entire plate of the PROSIM device, Figure 5b shows a sample of a contour plot (300) produced by the PROSIM device, which relates the lightness value of the paint to the position of the paint in the plate of figure 5a. The abscissa axis (302) shows the vertical position values ​​of the plate, while the ordinate axis (304) shows the horizontal position values ​​of the plate. The regions within the contour graph (300) reveal how the lightness values ​​vary as a function of position on the plate. For example, region 306 shows an area of ​​the plate that has a lightness value as indicated by reference bar 308.

Figure 6a is an example of a paint simulation computer program screen generally shown at 330 factor / control settings of paint spraying equipment. The factor settings are interrelated by mathematical models with certain spray paint responses, as shown generally in (334) and (338). The mathematical models have been generated through the design of experimentation techniques. In this example, the bell speed, shaping, and bell fluids factor / control settings (330) produce through mathematical models the paint application air and aspect response shown with reference numerals (334) and (338).

With respect to Figure 6b, an optimizer (342) is used to maintain one or more of the factor / control settings or response values ​​at a given level or range, while allowing other settings and / or responses to vary within of a predetermined interval. In this example, the shaping air and hood fluids factor / control settings were set at 36 pounds per square inch and 295 cubic centimeters / minute, respectively. Also, in this example, the average dry film thickness response was set by optimizer 342 in a range of 0.90 to 1.0 mils. Optimizer 342 preferably employs a simple algorithm such as that provided by the Microsoft Excel software product.^(TM) .

Figure 7a shows the process control data structure model (353) contained in the memory (360) of the computer (160). These components are part of the process control coordinator (162).

The process control data structure model (353) relates data relating to the paint to one or more relevant process steps of the paint spray system. The process steps (364) include the steps used within the process of a paint lab, a resin factory, a paint factory, or a vehicle assembly plant. For example, a process step within a vehicle assembly plant may include the process step when the paint is in stock.

ES 2 219 829 T3 or when the paint is in the mixing room, or the particular spray paint coating applied on a vehicle.

The paint application equipment data structure (368) relates relevant process steps (364) with data related to the paint application equipment such as equipment type, accessories, and equipment configuration.

The process data structure (370) relates relevant process steps (364) to process-related data such as environmental parameters, constant parameters, and variable parameters. Environmental parameters include such things as line speed, booth temperature, and humidity. Constant parameters include, but are not limited to, application parameters that are essentially constant for each paint (eg oven temperature / profile, target distance). Variable parameters include, but are not limited to, application parameters that are different for each paint (eg, fluid ratio, bell speed).

The material data structure (372) captures and stores material-related data such as material parameters, additions to the material mix, and consumption data. Specifically, consumption data refers to usage information, for example the consumption of resources or materials during a particular period (for example daily) or the consumption of resources and materials for a vehicle. Preferably, the material data structure 372 is not interrelated with the process steps, since typical material-related data is not acquired until the end of a complete process (for example, at the end of the manufacturing process of resin).

The quality forecast data structure (373) captures and stores quality forecast related data such as test data and evaluation data. The term "quality forecast" refers to a quality control of things such as defects in the material before the material is produced in a painting facility. Quality forecasting typically uses laboratory tests to make predictions about how a material should perform in production. To formulate these predictions for the quality forecast data structure (373) the paint lab setup in Figure is preferably used.

3. Preferably, the quality forecast data structure (373) is not interrelated with the process steps, since the data related to quality forecast is typically acquired before the process of a product begins (for example, at the beginning of the paint manufacturing process).

Regarding Figure 7b, the retrospective quality data structure (374) captures and stores data related to retrospective quality as first category retrospective quality data, second category retrospective quality data, and third category retrospective quality data. . The retrospective quality data structure (374) interrelates relevant process steps (364) with data related to retrospective quality such as typically the three categories of retrospective quality data. The first category corresponds to in-process batch control. The second category refers to the class / type, quantification and evaluation of defects. The third category includes: description of problems, interim mitigating measures, identification of potential cause (s), identification of the root cause, verification of corrective actions, permanent corrective actions and preventive actions. It should be noted that the present invention is not limited to three categories, but may include only one or two categories depending on the specific application. For example, the paint application data structure preferably includes only categories two and three for data related to retrospective quality, since in-process batch control is typically not performed within the paint application process.

The term “retrospective quality” refers to the adjustment of the process according to quality predictions, issues and resolutions identified in the “quality forecast” data structure. Within this capability, "look back quality" acts as a feedback loop for fine tuning of the process.

The people-related data structure (375) interrelates relevant process steps (364) with people-related data such as standard training program, title, and job descriptions.

The paint economics data structure (376) interrelates relevant process steps 364 with paint economics such as the kilogram / gallon amount of a particular paint type, the amount of spray painting a predetermined motor vehicle, and the cost of both internal and external quality.

Finally, an agreement data structure (384) is provided to interrelate contractual data, such as the contract identification number and the relevant parties and obligations of an agreement, with relevant process steps (364).

The process control coordinator (162) creates and maintains the process control data structure model (353) during the data acquisition steps of each data source.

Figure 8 shows the preferred embodiment of the process control data structure model for the resin manufacturing data structure (148). The process steps to be interrelated with the Equipment, Process, Retrospective Quality, People, Economics, and Agreements modules of the Resin Manufacturing Data Structure (148) are as follows: Material reception, material storage, reactor / vessel preparation, interim process, reactor / vessel loading, process, batch adjustment, material transfer, filtration, filling, equipment cleaning, product storage, and product supply. It is to be understood that the present invention is not limited by these process steps. The above enumeration is for example only and can be expanded or shortened depending on the specific application in question.

The primary input materials described by the resin manufacturing data structure (148) are the chemicals used to produce the resins. Chemicals and their properties are described within the raw materials module of the resin manufacturing data structure (148). The primary output product described by

ES 2 219 829 T3 the resin manufacturing data structure (148) consists of the resins produced with the chemicals.

Figure 9 shows the preferred embodiment of the process control data structure model for the paint manufacturing data structure (152). The process steps to be interrelated with the equipment, process, retrospective quality, people, economics and agreements modules of the paint manufacturing data structure (152) are as follows: material reception, material storage, material parking, equipment preparation, raw material transfer, intermediate process, batch mixing, batch adjustment, filling process, equipment cleaning process, product storage and product supply to the vehicle assembly plant. It is to be understood that the present invention is not limited by these process steps. The above enumeration is for example only and can be expanded or shortened depending on the specific application in question.

The primary input materials described by the paint manufacturing data structure (152) consist of the resins that are the products of the resin manufacturing data structure (148) and external raw materials (for example pigments; the raw materials External parts are indicated by reference numeral 62 in Figure 1). The resins, external raw materials, and the properties associated with them are described within the raw materials module of the paint manufacturing data structure (152). The primary output product described by the paint manufacturing data structure 152 consists of the paint materials produced with the external resins and raw materials.

Figure 10 shows the preferred embodiment of the process control data structure model for the paint application data structure (156). The process steps to be interrelated with the equipment, process, retrospective quality, people, economics and agreements modules of the paint application data structure (156) are as follows: storage-customer, mixing room storage-customer, mixing room, pre-cleaning, phosphate, electrocoating, manual cleaning, automatic cleaning, manual application, indoor / outdoor robots, rotating spray hoods, reciprocating air spray apparatus , flash evaporation, extraction, infrared, ovens, various automated applications, manual auxiliary operations, automatic auxiliary operations, no-apply zone, shutter, frame primer, wax, window glazing, transportation issues, and coagulation. It is to be understood that the present invention is not limited by these process steps. The above enumeration is for example only and can be expanded or shortened depending on the specific application in question.

The primary input materials described by the paint application data structure (156) consist of the paint materials that are the products of the paint manufacturing data structure (152). Paint materials and their properties are described within the materials module of the paint application data structure (156). The primary output product described by the paint application data structure (156) consists of paint coatings on vehicles.

Since there are variations within each painting process (i.e., resin fabrication, paint fabrication, and paint application process), data structures 148, 152, and 156 are structured such that Within each painting process, variations and cross-dependencies between materials and process steps can be analyzed. In addition, between data structures (148), (152) and (156) there is at least one common denominator / link such that variations and cross-dependencies between materials and process steps can be analyzed along the whole painting process. Preferably, the link between data structures 148, 152 and 156 consists of the output materials of one data structure that correspond to the input material of another data structure. For example, the resin material in the resin manufacturing data structure (148) is used as a link to the information contained in the paint manufacturing data structure (152), since the output of the data structure of Resin manufacturing (148) corresponds to the input of the paint manufacturing structure (152). Numeric identifiers are preferably used to uniquely identify the materials that link the data structures. Figure 11a is a computer screen showing an example of using the links between data structures to examine cross dependencies between one painting process and another. In this non-limiting example, the problem identified in the retrospective quality data structure is traced through materials and processes to the possible root cause, which is the use of an erroneous quality assurance test to certify that Resin n 419 is acceptable for use in production. Furthermore, it is to be understood that the present invention is not limited to linking only two data structures, but includes linking all three data structures to form a complete history view of the entire system, for example by providing a view of the history from the paint application data structure (156) to the resin manufacturing data structure (148), through the paint manufacturing data structure (152).

Figure 11b shows the preferred embodiment to ensure that data is viewed by remote data destination sites in a secure manner. For example, paint plant area managers identified by a computer system identifier could only view data within the present invention related to their own plant.

Figure 12 shows the steps in which remote data destination sites use information from the various components of the present invention to control the paint spray system. In the process block (400), a user, for example a customer, obtains from a remote site certain technical information regarding the paint spraying system. To obtain a product data sheet, a customer requests a process block (404) preferably by clicking an icon on the screen of the remote data destination site for a product related to a specific paint. The requested product data sheet is retrieved from the technical database and sent to the customer in process block (408). In process block (412), a

ES 2 219 829 T3 customer's industrial hygiene group reviews the information in the product data sheet and carries out all relevant emissions data in the process block (416). Preferably, the remote data destination site automatically calculates the emissions data for the group and generates a report that determines whether the volatile organic compounds (VOCs) and emissions data are in the specific range determined by the ecological threshold values. included in the technical database. This determination is made in decision block (420).

If decision block 420 determines that all VOCs and emissions data are within the specified range, process block 424 determines that the product is "useful". However, if decision block 420 determines that the specified intervals are not met, the remote data destination site uses the paint simulation computer program and data from the technical database to return the paint formulation. at the specified interval. This process is performed by the process block (428).

Figure 13 shows the steps to use the present invention in problem solving and cause and effect analysis report generation. The term VIS refers to the information presentation portions of the present invention to data receivers.

In process block 440, a customer detects a problem that occurs at a customer's assembly plant. The client accesses the process control coordinator data to determine at decision block 448 if a similar problem has occurred in the past. If the problem has not occurred in the past, the customer initiates in the process block (452) the problem analysis form included in the technical database of the present invention. Next, the process block (456) initiates a resolution procedure and updates the technical database with the way in which the problem has been faced and solved. In the block process (460), the client can access the technical database to check the status of any PR&R (ie Problem Resolution and Report Generation) open.

If the decision block (448) determines that the problem has occurred previously, the client identifies in the process block (464) the corrective actions stored in memory that were applied with respect to a similar problem. If decision block (468) determines that corrective actions still exist and are still used within the factory environment, process block (452) is executed.

However, if past corrective actions do not exist and are not being used, decision block 472 at the remote data destination site determines who has responsibility for taking corrective actions. Decision block 472 bases this information primarily on the agreement data structure of the process control coordinator. If the responsibility rests with the customer, the customer investigates the failure in the process block (476) and reapplies or modifies the corrective action plan. In process block 476, the customer uses the information contained in the technical database, as well as the information from the paint simulation computer program and the process control coordinator to investigate and correct the failure. If the agreement data structure determines that the responsibility for corrective action rests with the factory, the remote data destination site notifies the factory personnel in process block (480), so that the factory personnel The factory can investigate the fault and inform the customer of its resolution in process block (484).

Figure 14 shows the steps used in generating and using automatic weekly reports from the process control coordinator to analyze and control the operating parameters of the paint spraying system. In process block 500, factory technical service representatives enter batch-specific data into the data structures of the process control coordinator in real time. Lot-specific data includes product data, plant data, lot performance data, and defect presence and type of defect. The process control coordinator generates a product specific report that details the activities of the factory using information provided by the technical databases and data structures of the process control coordinator.

In process block (504), plant personnel access the weekly report from the process control coordinator, so that in process block (508) plant personnel can use the information to track performance and compliance. This information is used to troubleshoot and track product defects. Also, the weekly report is automatically produced for customers in process block (512). The customer uses the automatic weekly report for operational analysis such as: tracking batches at specific time periods for investigation of warranty claims; keep abreast of changes in the plant's products and processes; access information on test and experimentation products; access assembly plant product information; track factory performance at the assembly plant; and weekly view of the first process capacity of the assembly plant by product.

Figure 15 presents a sample weekly report provided by the present invention. The weekly report shows what specifically happened during what period of time with respect to a specific painting. This information can be used to detect problems with a batch over a particular week or over several weeks.