Abstract:
A method and system for analyzing a plurality of coatings undergoing exposure testing is disclosed. Included in the system are a data acquisition system and a computer system. The data acquisition system acquires coating identification and attribute data through various input devices, while the computer system automatically receives, stores, analyzes and displays the coating attribute data. The analytical results relate to the durability of a coating composition under test, which may be used for predicting the performance of a coating characteristic and developing improved coating compositions.

Description:
CROSS REFERENCE TO RELATED PATENT APPLICATIONS 
   This is a non-provisional application of prior pending U.S. provisional application Ser. No. 60/497,083 filed on Aug. 22, 2003. 

   BACKGROUND 
   This invention relates to a method and system for analyzing coatings undergoing exposure testing, and more particularly, to a method and system for automatically acquiring, storing, analyzing, and displaying quality data relating to paint compositions undergoing outdoor exposure testing at various test sites. 
   Traditional analysis of indoor and outdoor paint test panels, referred to as exposure series testing, uses a manual and time-consuming process for generating data to judge paint durability. While color and gloss meter instruments may be used to generate some coating attribute data, often this data is manually acquired and manually recorded into a database for subsequent analysis. Other coating attribute data, such as cracking, flaking, and mildewing, for example, are subjective in nature, making them prone to variation depending on the tester&#39;s observation and interpretation. Yet other coating attribute data, such as reflectivity spectra, for example, is complex in nature, making it cumbersome for manual transcription. This traditional procedure, being dependent on the skill level of the tester, is not only time-consuming, but is also subject to systemic errors, including but not limited to, inaccurate data reading, incorrect data entry, and incorrect association of acquired data to test panel subject. Also, with a highly manual process, limited discrete data points may be acquired in a defined time window, which limits the quality of interpolated and extrapolated test data. In an effort to resolve some of these manual data entry issues in measuring painted test panels, an automated method and device has been described in U.S. Pat. No. 6,459,477, which involves measuring a coating property on a test panel by manipulating the panel before a measuring device, performing an instrument reading, and returning the test panel to its original location. However, such a painted test panel measuring method does not address all of the concerns and interests associated with multiple and different coating compositions undergoing exposure testing at various test sites in different geographic regions. 
   In an effort to advance materials analysis, well known image analysis techniques for characterizing materials have been applied to weathered materials. However, it is very difficult to analyze large numbers of materials efficiently because it is not practical to move large numbers of samples from an exterior placement to the laboratory for analysis, and then back out to their correct exterior placement. Such a process is logistically difficult and time consuming, and the handling may even cause damage to the materials undergoing testing. 
   Accordingly, there remains a need in the art for a paint exposure analysis system that provides for a greater degree of quantitative data entry and comparative analysis among and between multiple test samples undergoing exposure testing at widely dispersed test sites as well as a means to manage the physical inventory thereof. The system proposed herein provides such a system. 
   STATEMENT OF THE INVENTION 
   In a first aspect, there is provided a coating analysis system including a data acquisition system and a computer system. The data acquisition system is adapted to objectively acquire and store in digital form an identification code relating to a set of test samples of coating compositions undergoing an exposure test and coating attribute data relating to the set of test samples, the set of test samples being one of multiple sets of test samples. The computer system includes a computer and a storage device. The computer is programmed for receiving data from the data acquisition system, the data including the test sample identification code and the coating attribute data from the multiple sets of test samples, storing and retrieving the test sample identification code and the coating attribute data at a database at the storage device, analyzing the coating attribute data, and generating an output representative of the quality of the coating composition. 
   In another aspect, there is provided a method of analyzing a coating that includes objectively acquiring a test sample identification code relating to a set of coating compositions undergoing an exposure test, the set of coating compositions being one of a multiple of sets of coating compositions, objectively acquiring coating attribute data relating to a coating composition in the identified set and automatically storing the data in digital form, populating a database with temporal entries of the coating attribute data, analyzing the coating attribute data, and generating an output representative of the quality of the coating composition. 
   In a further aspect, there is provided a coating composition made using the method described above. 
   As herein disclosed, use of a robust coating analysis system and method that augments human subjective data with instrument read data promotes the development of coating compositions having high quality characteristics by providing mass quantities of high resolution temporal data that may be utilized for predictive analysis purposes, especially when used as herein disclosed and contemplated. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     Referring to the exemplary drawings wherein like elements are numbered alike in the accompanying Figures: 
       FIG. 1  depicts an exemplary coating analysis system in accordance with an embodiment of the invention; 
       FIG. 2  depicts an exemplary system architecture for managing the flow of data with the system of  FIG. 1 ; 
       FIGS. 3 and 4  depict exemplary graphical information resulting from an application of the system of  FIG. 1 . In  FIG. 3 , the x axis is the Pixel Intensity and the y axis is the Pixel Count. In  FIG. 4 , the x axis is the Crack Area (Pixel Count) and the y axis is the Crack Count; and 
       FIG. 5  depicts an inventory map for use in an embodiment of the invention. 
   

   DETAILED DESCRIPTION 
   As disclosed herein,  FIG. 1  depicts a schematic view of an exemplary coating analysis system  100  for acquiring, storing and analyzing objective data relating to a coating composition undergoing exposure testing, and  FIG. 2  depicts a schematic view of an exemplary system architecture  500  for managing the flow of data within system  100 . 
   In an embodiment and referring now to  FIG. 1 , coating analysis system  100  includes a data acquisition system  200  and a computer system  300 . In a broader sense, data acquisition system  200  is both a data acquisition system and a data management system, but will be referred to herein as a data acquisition system. Data acquisition system  200  is configured for objectively acquiring a test sample identification code  115 , such as a bar code for example, and for objectively acquiring and managing coating attribute data relating to a fixed test sample  105 , and computer system  300  is configured for storing and analyzing the acquired data in digital format. As used herein, the term “objectively acquiring” refers to the acquisition of data in the absence of human subjective interpretation. Each test sample  105  may be arranged in a set of test samples  110  having a test sample identification code  115 , such as a bar code (hereinafter “bar code”  115 ), or each test sample  105  may be arranged individually with its own bar code  115 . In this manner, the term “set of test samples” refers to one or more test samples. Each set of test samples  110  may be arranged on a board or a panel  125  in an array of test samples  120 . The panels  125  of test samples are typically arranged in rows and columns in a test area, such as an outdoor field for example, thereby providing a plurality of test samples for exposure testing with each test sample having an associated bar code  115 . An exemplary identification code includes a bar code, but may be any identification coding scheme suitable for the purpose of identifying each test sample  105  and the site at which each test sample  105  is undergoing exposure testing. In an embodiment, test sample set  110  includes 5 individual test samples  105 , but alternative embodiments may include any number of individual test samples  105 . The panels  125  of test samples may be arranged in either an open area exposed to the elements of nature, or in a closed area exposed to controlled elements. Multiple test sites may be employed at various geographic areas, thereby providing a variety of test data where the test samples are exposed to substantially different forces and elements of nature. The coating attributes that are viewed, acquired, analyzed and reported may include color reflectance, reflectance spectra, angular reflectance, color coordinates (L-lightness, a-red/green, and b-yellow/blue), color transmission, color absorption, color scattering, coating gloss, subjective attributes, and machine-derived objective attributes, for example. However, other coating attributes may be viewed, acquired, analyzed and reported as appropriate. The term color reflectance refers to measurements over the color reflectivity spectra, which includes, for example, 31 color reflectance measurements from 400 nanometers (nm) to 700 nm at 10 nm intervals. Drop-down menus, discussed below, control which color reflectance measurements are enabled or disabled. The term paint gloss refers to gloss readings at an angle relative to a plane parallel to the coated surface, such as at a 20-degree angle, a 60-degree angle, or an 85-degree angle, for example. However, other gloss reading angles may be employed as appropriate. The terms subjective and machine-derived objective attributes refer to qualitative representations of surface rust, chalking, checking, cracking, erosion, blistering, flaking, corrosion, dirt, mildew, adhesion, and scum, for example. While embodiments disclosed herein may refer to paint as an exemplary coating composition undergoing exposure testing, it will be appreciated that the teachings of the invention are also applicable to other coating compositions, such as a stain for example. 
   Data acquisition system  200  may include any number of the following input devices: a test sample identification reader  205 , such as a bar code scanner for example; a color meter  210 , such as the Miniscan XE Plus Color meter manufactured by Hunter for example; a gloss meter  215 , such as a QIP gloss meter manufactured by Quality Imaging Products, Inc., or a BYK-Gardener gloss meter manufactured by BYK-Gardner Gmbh, for example; a data entry device  220 , such as a portable computer  222  (such as a laptop computer, a notebook computer, or any other non-stationary computing device capable of data entry), a tablet computer  224 , and a personal desktop assistant (PDA)  226 , for example; and, a digital camera  225 . Data acquisition system  200  includes application software for acquiring and storing test sample bar code  115  and data, discrete or temporal, relating to various attributes, discussed above, of test sample  105  undergoing exposure testing. In an embodiment, data acquisition system  200  includes any number and combination of the aforementioned input devices arranged in signal communication with portable computer  222 , which is adapted to run application software, discussed further below, thereby enabling automated data entry and data transfer to a host computer  305 , discussed further below. 
   An exemplary data acquisition system  200  may be arranged as: a portable carrying device  230 , such as a backpack to be carried by a person  232  for example; a mobile device  235 , such as a push and/or pull cart, a self-powered device, or a motor vehicle (wheeled or having any other traction means), for example; a robot device  240 ; or, a stationary tower-mounted Lidar (light detection and ranging) device  245 . Mobile device  235  may be driven by a person on a road between and among test samples  105 , or may be self-directed along a track  250  or via underground self-guiding wire  255 . Robot device  240  may also follow track  250  or underground self-guiding wire  255 , or may alternatively be programmed to follow a predefined path between and among test samples  105 . Robot device  240  may be remote controlled or self-controlled via an onboard programmed controller. Tower-mounted device  245  may be operable to view many test samples  105  by employing a programmed controller and a multi-axis swivel head for mounting a Lidar transceiver. In an embodiment, each device employing data acquisition system  200  is adapted and operable to acquire and store data from a plurality of test samples  105 . 
   An exemplary computer system  300  includes a host computer  305 , a printer  310 , and a storage device  315 . Host computer  305  may include a storage medium (such as a memory)  306 , a processing circuit (such as a processor)  307 , an input/output device (such as a keyboard and a mouse for example)  308 , and a display  309 . Printer  310  is representative of a printer that is capable of printing an output such as a barcode, an alpha-numeric file, or a graphic file; however, other printers may be employed as appropriate. Other computers, such as a laptop computer  320  and a server  325 , for example, may be arranged in signal communication via wire or wireless with host computer  305 . Laptop computer  320  and server  325  may include a storage medium  321 ,  326  and a processing circuit  322 ,  327 , respectively, that includes application software, stored at memory  321 ,  326  and executed at processor  322 ,  327 , for communicating with host computer  305  and for storing and analyzing the acquired data. A second storage device  330  may be employed for backup or for global access purposes, and multiple devices may be interconnected via an Internet or other network connection  335 . While reference is made herein to a laptop computer  320 , it will be appreciated that any computing device serving the purpose of a portable computer may be substituted therefore. Similarly, while reference is made herein to a server  325 , it will be appreciated that any computing device capable of providing the service of a server may be substituted therefore. 
   Remotely acquired data may be communicated as a stream of data from data acquisition system  200  to host computer  305  directly via wire or wireless, or from data acquisition system  200  to a writeable media  340 , such as a floppy disk, a compact disc, or a PCMCIA (Personal Computer Memory Card International Association) card, for example, or from laptop computer  320  via writeable media  340  or network  335 , such as the Internet or Intranet for example, or from a remote acquisition communication system  345 , such as email for example. 
   In general, more than one data acquisition system may be connected to more than one computer, storage device, and database, through a plethora of different connections, including USB (Universal Serial Bus), serial, Firewire, wireless, Ethernet, or any other suitable communication arrangement. Exemplary software employs user interface techniques such as drop-down menus and point-and-click mechanisms. Data acquisition systems, including image acquisition systems, and image processing software, described further below, are interconnected to system computers and servers, thereby allowing a host of different analysis configurations and database services. An exemplary image analysis includes the automatic determination of surface cracking or flaking from an image. Other image analyses may include statistical analysis, chemometric analysis, correlation analysis, covariance analysis, or any other analysis suitable for image data evaluation. 
   More specifically, and in an exemplary embodiment, host computer  305  includes application software (the Application Software), stored at memory  306  and executed at processor  307 , for receiving, storing, analyzing, and displaying data acquired from data acquisition system  200 . In an exemplary embodiment, the Application Software includes a graphical user interface (GUI) employing GUI techniques, such as icons, drop-down menus, radio buttons, selection tabs, input boxes, scroll windows, action buttons, and check boxes, for example. In an alternative embodiment, host computer  305  includes application software for receiving the acquired data from data acquisition system  200 , communicating with server  325 , and displaying analysis results, and server  325  includes application software, stored at memory  326  and executed at processor  327 , for communicating with host computer  305  and for storing and analyzing the acquired data. In either embodiment, host computer  305  may include user interface software for enabling the user to manage the flow of data from data acquisition system  200  to and from databases at storage devices  315 ,  330 , where the acquired data is stored for subsequent analysis. In an embodiment, the Application Software includes image recognition software that is capable of processing image data acquired by digital camera  225  and providing an objective representation of a coating attribute. For example, a numerical count of contiguously arranged abnormally colored pixels may be representative of the degree of surface cracking or flaking. 
   The Application Software at host computer  305 , or alternatively at server  325 , is programmed for: periodically receiving the test sample bar code  115  and the coating attribute data from data acquisition system  200 , thereby providing temporal data entries; storing and retrieving the bar code and coating attribute data at a database at storage device  315 ; replicating all or selected portions of the coating attribute data at the first database at storage device  315  and storing it to the second database at storage device  330 ; managing the flow of data to and from the first and second databases at storage devices  315 ,  330 , respectively; viewing the contents of the first and second databases; analyzing the coating attribute data; and, generating a text file, a graphic file, or an exposure test report representative of the quality of the coating composition, which may include discrete data, temporal data, or both. As discussed above, the bar code and coating attribute data may be received at host computer  305  from a remotely operated data acquisition system  200 . Analysis of the coating attribute data may include a correlation analysis that relates a first coating attribute data to a second coating attribute data (such as paint gloss at a 20-degree angle as a function of chalking, or paint color reflectance as a function of erosion, for example), or a temporal analysis that tracks a set of coating attribute data (such as paint gloss at 20, 60 and 85-degree angles, for example) over time. 
   An exemplary system architecture  500  used in conjunction with the Application Software for managing the flow of data among and between the various elements of coating analysis system  100  will now be described with reference to  FIG. 2 , which depicts an arrangement of program modules that form the Application Software executed at processor  307 . Each program module is designated as a “double-walled-box” in  FIG. 2 , activated by a GUI icon, and discussed separately below. 
   The acquisition of coating attribute data is represented at block  510 , which in an embodiment includes inputs from a color meter  210 , such as a Hunter Color Meter for example, a gloss meter  215 , such as a QIP Gloss Meter for example, and other gloss meters  216 ,  217 , such as BYK-Gardner Gloss Meter #1 and BYK-Gardner Gloss Meter #2 for example. While an embodiment of the invention is depicted employing designated color and gloss meters, it will be appreciated that such designation is for exemplary purposes only, and that other meters for measuring color, gloss, or any other coating attribute, may be employed as desired. While only four input paths  511 ,  512 ,  513 ,  514  are depicted in  FIG. 2 , it will be appreciated that these inputs are exemplary only and that other input paths may be available as discussed above in reference to the input devices of data acquisition system  200 . In general, data acquisition software takes color, gloss, and other optical data and stores the data in a manner suitable for incorporation into a database. More specifically, and in an exemplary embodiment, a user launches a Data Acquisition Program (DAP)  515  by selecting an DAP launch icon on the display of portable computer  222 , which runs the color and gloss meters  210 ,  215 ,  216 ,  217  in an automated manner. Similar to DAP  515 , each program of system architecture  500  may be launched by using a GUI icon. Also providing input to DAP  515  is bar code scanner  205 , which is part of bar code system  520  along with a Bar Code Printer Program  525  and bar code printer  310 . The user uses bar code scanner  205  to read a bar code  115  into DAP  515 . Other user identification (ID) and/or password entries may be entered into DAP  515  prior to data acquisition for purposes of quality control and user tracking. The user then selects an appropriate data acquisition mode, such as, read color and gloss meters and digital camera, read color and gloss meters only, read color meter only, or read gloss meters only, for example, and then proceeds to acquire the desired coating attribute data sets, with each new data set being preceded by a bar code scan. Prior to data acquisition at color meter  210 , the user may run a calibration routine that uses a black and white standard. Statistical data entry  260  may be optionally selected at data acquisition system  200  by choosing an appropriate selection from a drop-down menu. While in statistical data entry mode  260 , three measurements are taken, for example, from which an average (x-bar) and a standard deviation (sigma) is calculated for each coating attribute under analysis. For subjective data entry, speech recognition software may be used to enter the subjective rating. All acquired data is temporarily stored at data acquisition system  200  for subsequent transfer to storage device  315 . 
   In an exemplary data acquisition system  200  that is in direct signal communication with host computer  305 , the Application Software automatically writes the acquired data into a database  531  (See Block  530 ) at storage device  315 , which may be verified by using a Database Visualizer Program  541 . Upon completion of the data set entries, the user may automatically replicate the data into a globally-accessible database  532  at storage device  330  by using a Replicator Program  533 . The contents of database  532  may be verified by using another Database Visualize Program  543 . As used herein, direct signal communication means that data acquisition system  200  is in wired or wireless communication with host computer  305  during data acquisition. 
   In an embodiment where data acquisition system  200  is not in direct signal communication with host computer  305 , acquired data may be stored at portable computer  222  and then downloaded to host computer  305  by copying to and reading from a compact disc (CD)  340 , by connection via a network  335 , by email communication  345 , or by any other suitable data transport device, such as a memory stick or flash memory, for example. The acquired data stored at portable computer  222  is automatically merged with the contents of database  531  by running a Merge-to-Database Program  551  (See Block  550 ), and then the Replicator Program  533  is run to automatically copy the data into database  532 . 
   In an embodiment where remotely acquired data is stored in spreadsheet form  561  (See Block  560 ). and communicated to host computer  305  via email communication  345 , coating analysis system  100  uses a Data Verification Program  562  to automatically verify the data for proper number placement, proper syntax, and proper lexicography prior to populating database  531  with data, and uses a Conversion Program  564  to automatically take the data out of the verified spreadsheet  563  and enter it into database  531 . As before, the user then runs the Replicator Program  533  to automatically copy the data into database  532 . Data Verification Program  562  flags illegitimate data and issues diagnostic routines, thereby automatically ensuring legitimate data at the entry point to database  531 . 
   An exemplary Data Mining Program  544  (See Block  540 ). takes data from database  532  and organizes the data for analysis, which may include correlation analysis given two or more types of tests or attributes. The analytical results may then be graphed. In an embodiment, Data Mining Program  544  finds all readings of a specified test series, grabs the attribute descriptors specified by the user, and arranges the data graphically. A correlation analysis graph may depict one coating attribute against another, or may depict the variation of one or more attributes over time. 
   An exemplary Mirror Program  534  is used in an embodiment to compare data between mirrored fields in database  531  and database  535 , which is used for quality assurance and data legitimacy. A Database Visualize Program  542  is used in an embodiment to obtain table information from database  532 . 
   Multiple databases  531 ,  532 ,  535  and multiple database visualization programs  541 ,  542 ,  543  are depicted for exemplary purposes only, which one skilled in the art will appreciate, may or may not be employed as depicted, and may be employed in alternative arrangements. 
   An exemplary Mirror Program  534  is used in an embodiment to compare data between mirrored fields in database  531  and database  535 , which is used for quality assurance and data legitimacy. A Database Visualize Program  542  is used in an embodiment to obtain table information from database  532 . Multiple databases  531 ,  532 ,  535  and multiple database visualization programs  541 ,  542 ,  543  are depicted for exemplary purposes only, which one skilled in the art will appreciate, may or may not be employed as depicted, and may be employed in alternative arrangements. 
   Databases  531 ,  532 ,  535  may be constructed to allow a multidimensional data warehouse to be constructed with automatic procedures in place to accomplish the warehousing during the evening on an off-line basis. In this manner, data mining may proceed automatically on the warehouse with automatic notification when there is a significant change in the analysis results. The integrity of the data in databases  531 ,  532 ,  535  may be preserved through the use of time stamping and high security log files. 
   An exemplary application of coating analysis system  100  involves the automated analysis of paint films undergoing either indoor or outdoor exposure testing, as discussed above. In an exemplary outdoor arrangement, panels  125  of test samples are arranged in rows in a field or other outdoor test area, with each panel  125  having a plurality of sets of test samples  110 . A weather resistant bar code  115  identifies each test sample  105  on each panel  125 . The user systematically inputs the coating attribute data by scanning the bar code  115  and each associated test sample  105  with one or more desired input device, and then repeats the process until the desired data on each test sample  105  has been taken. The Application Software installed on portable computer  222  enables the user to automatically insert the paint attribute data, such as color data, gloss data, reflectivity data, and digital images, for example, into a memory at portable computer  222 , and to subsequently automatically insert the saved attribute data into database  315 . The Application Software installed on host computer  305  enables the user to automatically receive the paint attribute data from portable computer  222 , to analyze the data, and to display the analysis results. The Application Software may also include speech-recognition software for registering a voice-commanded subjective data entry, such as a verbal rating representative of the degree of chalking for example, and image processing software for creating a machine-derived objective data entry, such as that discussed above in reference to abnormally colored pixels for example. Optical images taken by digital camera  225  may be stored at and retrieved from database  531 , and machine vision software may be employed in conjunction with the digital images to provide machine-estimated values that further characterize the mode and degree of paint degradation upon exposure to weathering conditions. Digital camera  225  is especially suited for taking high quality images of materials undergoing external exposure testing and may include an illumination scheme employing an auxiliary light source and baffles or shields to prevent shadows and to control light exposure. Exemplary auxiliary light sources may include an electronic flash, a light emitting diode, or any other suitable light source. Natural light, auxiliary light, baffles, diffusers and other optical elements, may be employed in combination to evenly illuminate the material being imaged. Furthermore, digital camera  225  is light weight and portable such that it may be hand held for the purposes of collecting digital images or it may be mechanically translated across the materials undergoing exposure testing using a motorized x-y stage, depicted generally at motor vehicle  235 . In alternative embodiments, data acquisition system  200  may be carried via a backpack  230  and operated from the backpack  230 , may be transported by a robot  240  and automatically operated via a programmable controller at the robot  240 , may be stationary at a tower  245  and operated via a programmable controller and Lidar device at the tower  245 , or may be operated from motor vehicle  235 , which is guided by a track  250  or underground self-guided wire  255 . Data acquired by data acquisition system  200  may be communicated to host computer  305  via wire, wireless, writeable media  340 , or any other suitable communication means. Data mining software included with the Application Software enables the user to extract, analyze and display quality data relating to the paint composition, such as two-variable correlation analysis and temporal variation analysis, for example. Predictive software enables both extrapolation-based parametric predictions and pattern recognition-based nonparametric predictions, and decision-based software using fuzzy logic enables contextual deduction of paint performance. 
   Reference is now made to  FIGS. 3 and 4 , which depict graphical results of exemplary attribute data analyses. The machine vision software referenced above may automatically interpret the image by identifying distinct features, classifying each feature, and then characterizing the features both individually and collectively. In  FIG. 3 , pixel count as a function of pixel intensity is depicted for a test sample  105 , where the pixel intensity threshold (depicted in  FIG. 3  as a pixel count in excess of 10,000 at a pixel intensity of approximately 0.69 (69%)) is determined from the distribution of pixel intensities provided by a digital image of sample  105 . A change in color intensity, which is related to pixel intensity, of sample  105  over time may be analytically determined and graphically displayed by comparing and charting the pixel intensity threshold as a function of time. In  FIG. 4 , the number of cracks (crack count) as a function of crack area (pixel count) is depicted for a test sample  105 . As depicted in  FIG. 4 , there are approximately 190 cracks with a crack area of about 50 pixels, approximately 40 cracks with a crack area of about 100 pixels, and approximately 15 cracks with a crack area of about 150 pixels, for example. A change in crack count or crack area of sample  105  over time may be analytically determined and graphically displayed to show the degree of paint degradation over time. In the analysis relating to  FIG. 4 , the optical image taken by digital camera  225  is converted to a black and white image and then inverted so that the cracks show up as white. The contiguous white pixels are then counted for each crack area and the results plotted in histogram form. By comparing large volumes of temporal data associated with a plurality of paint samples  105  that differ in coating composition, a user can efficiently determine those coating compositions that have superior quality characteristics. 
   Another aspect of the invention provides an inventory management system  516 , embodied in software and accessible via host computer  305  for example, for managing the physical inventory of test panels  125  undergoing exposure testing, with each test panel  125  having multiple test samples  105 . Test samples  105  are tracked physically over time by an inventory map  600 , best seen by now referring to  FIG. 5 . Map  600  is representative of the status of multiple test panels  125  undergoing exposure testing, where the test panels  125  may be closely distributed, distributed over a large area, or distributed over multiple locations. By employing map  600 , system  100  can objectively analyze sample performance and output performance as a function of time, location, and test conditions. 
   In an exemplary embodiment, numerical information provided by map  600  may be representative of the number of free space locations on test panel  125  or the number of actual test samples  105  on test panel  125 , and color-coded information may be representative of the orientation of test panel  125  to the sun, or the type of test sample  105  undergoing test. Other attributes of test panel  125  may be represented by map  600  as desired. 
   Referring now to  FIG. 5 , an exemplary map  600  includes multiple pairs of boxes  610  arranged to represent a test fence  605  with each pair of boxes  610  having a coordinate designator  620 , such as E41 for example. Each test fence  605  typically, but not necessarily, contains multiple test panels  125 . In an embodiment, the top box  602  of each pair represents the north facing side of a test fence  605  and the bottom box  604  of each pair represents the south facing side of a test fence  605 , with the contents of each box  602 ,  604  representing a particular attribute of an associated test panel  125 , as discussed above. In the exemplary map  600  of  FIG. 5 , the numerical entry in each box  602 ,  604  represents the “free space” status of that particular test panel  125 . For example, test panel  125  at coordinate location E34 has a numerical entry of 36, which represents 36 free space locations on that particular test panel  125 . Also, a blue colored box may depict a test rack orientation, such as south facing at a 45 degree angle to vertical for example, and a white colored box may depict a different test rack orientation, such as north facing vertical or south facing vertical for example. Additional coloring, such as a red border for example, may depict additional attributes of a test fence. For example, a general configuration of a test fence, such as a test rack configured to hold 5 inch by 36 inch test panels with multiple test areas therein, may be depicted by no borders in map  600 , while a test rack configured to hold individual test samples of dimension 4 inch by 12 inch, may be depicted by a red border in map  600 . Bar code  115  associated with each test sample  105  provides a pointer to the database location, at database  531 ,  532  or  535  for example, where the attribute data of test sample  105  resides, which includes a location attribute that identifies the particular test panel  125 , by coordinate location, that test sample  105  is a member of. If test sample  105  is moved from one test panel  125  to another, for reasons discussed below, the location attribute associated with test sample  105  is updated accordingly, thereby providing the user with up to date information regarding the location of each test sample  105 . 
   Parameters that may be tracked as part of the physical asset may include: the name of fence  605 ; the location of fence  605 , by coordinates for example; the location of panel  125 , such as coordinate designator  620  for example; the barcode  115  associated with fence  605 ; the type of fence  650 , such as standard panels, eves, aluminum panels, or tables, for example; the orientation of fence  605 , such as north-vertical, south-45-degrees, hung-up, or hung-down, for example; the capacity of fence  605 ; the capacity used of fence  605 ; and, the capacity available of fence  605 . Other parameters may be tracked as appropriate. 
   Parameters that may be tracked as part of the sample under test may include: a series name for a grouping of test panels  125 ; a panel descriptor for identifying a sub-set of a series of panels; a substrate identifier, such as pine (P), cedar (C), or aluminum (L), for example; a test area descriptor for identifying a test area on a panel; the orientation of test panel  125 , such as north-vertical, south-45-degrees, hung up, or hung-down, for example; a unique barcode relating to a series, a panel, or a test area; the test initiation date; the reading schedule for the samples; and, the test results. Other parameters may be tracked as appropriate. 
   Sometimes it is desirable to have larger areas of contiguous test space made available than is currently available, such as when a new series of test samples  105  need to be tested reasonably dose to each other, or when data acquisition needs to be more efficiently organized, for example. Other times it may be desirable to merely reorganize the test panels  125  to reduce the amount of fragmentation of the available free space. Hence, test samples  105  may be moved from one test panel  125  to another in order to make a larger contiguous vacant area from many smaller vacant areas. 
   In determining how to move test samples  105 , several factors may be considered, such as for example: the total number of contiguous free spaces needed; the amount of vacant area that currently exists or that is needed to form the large contiguous area; the amount of work the user is willing to expend in moving certain test samples; and, the distance that will be tolerated in moving a defined number of test samples for rearrangement. To assist in the decision making process, and in view of the distance-based coordinate system that map  600  is built on, optimization algorithms may be employed to make test sample moves via inventory management system  516  and to simulate moving test samples  105  under constraints to find an optimal number of moves to get the desired area. Such simulation constraints may include, for example: the maximum distance allotted for the total moves; the maximum number of moves; and, the number of test samples or test area that is allowed to move. 
   Algorithms for performing the test sample move simulations discussed above are similar to those commonly used for performing multidimensional optimization analyses. Such algorithms may include, for example: Linear Programning (LP) algorithms, which function under similar constraints to those listed above; Simplex methods; nonlinear least-squares methods; Karmarker&#39;s algorithm; simulated annealing algorithms; Genetic algorithms; and, Fuzzy optimization methods. Detailed descriptions of these algorithms and methods are available in various published text books and technical publications. 
   In an exemplary embodiment, inventory management system  516  utilizes the entire test sample location database, illustrated by map  600 , and is usually run as a batch process. Upon completion of a simulation, the results are reported, and if the moves are utilized, the acceptance of the move operation enables the location attribute of each effected test sample  105  to be updated in the database automatically by inventory management system  516 , without having to individually modify location information on each physical test sample  105 . A result of using coating analysis system  100  is the acquisition and entry of objective data into an analytical database in an automated fashion, thereby avoiding time-consuming manual data entry and inconsistent subjective data entry, and providing improved data quality. Use of system architecture  500  in conjunction with coating analysis system  100  enables large amounts of data to be collected and managed in a rapid and efficient manner, thereby enabling high temporal resolution and accurate data extrapolation for improved predictive analysis. By configuring coating analysis system  100  to accommodate multiple input devices, to communicate with multiple peripheral devices and databases, and to communicate via multiple communication schemes, coating analysis system  100  is well adapted for expansion or reconfiguration as needed.