MXPA06007470A - Maximation of yield for web-based articles - Google Patents

Abstract

Techniques are described for inspecting a web and controlling subsequent conversion of the web into one or more products. A system, for example, comprises an imaging device, an analysis computer and a conversion control system. The imaging device images the web to provide digital information. The analysis computer processes the digital information to identify regions on the web containing anomalies. The conversion control system subsequently analyzes the digital information to determine which anomalies represent actual defects for a plurality of different products. The conversion control system determines a value for at least one product selection parameter for each of the products, and selects one of the products for conversion of the web based on the respective determined value. Exemplary product selection parameters include web utilization, unit product produced, estimated revenue or profit, process time, machine capacity and demand for the different products.

Description

PERFORMANCE MAXIMIZATION FOR NETWORK-BASED ITEMS FIELD OF THE INVENTION A * ~: The present invention relates to the automated inspection of systems, and more particularly, to the optical inspection of networks. BACKGROUND OF THE INVENTION Inspection systems for the analysis of mobile network materials have proven to be critical for modern manufacturing operations. Industries as varied as the manufacture of metal, paper, nonwovens and films rely on this system of inspection ** products and online process monitoring. A major difficulty in the industry is related to the extremely high data processing speeds required to deal with the current manufacturing processes. With the commercially-available speeds and width networks that are typically used, and the pixel sizes that are typically required, data acquisition rates of tens or even hundreds of megabytes per second are required for inspection systems. It is a continuous challenge to process images and perform accurate detection of defects at these data rates. The technique has responded to this dilemma by limiting image processing to very simple algorithms, Ref.:173987 limiting the scope and complexity of detection algorithms, and by using custom dispersion system architectures that incorporate customary electronic hardware or dedicated processors, each working in part on the data stream. While such systems are capable of achieving the data rates required for the inspection of mobile networks, it is very difficult to adapt the system for a new production process and network materials. Also, the processing algorithms are limited to the capabilities of dedicated processing modules. Finally, as the image processing algorithms become more complex, the hardware required to implement the required processing quickly becomes unmanageable. The manufacturing industry has recognized the importance of being able to produce the product "just in time" with obvious advantages in reduced inventory. However, the achievement of this goal often causes manufacturers to work on developing systems and devices that allow a rapid change between the various products. The rapid exchange between the products is inconsistent with the specialized hardware of signal processing that now requires the technique of optical inspection of mobile networks. Other dilemmas occur more in situations when a given product can be subsequently used for multiple applications, with each of the multiple applications requiring different levels of quality. The difficulties that during the time of manufacture, it is not known what level of quality will be required. Therefore, the current technique attempts to graduate the quality level after defect detection, by using various defect classification techniques based on spatial characteristics. of the defects extracted. While this is sometimes appropriate when there are new differences in defect levels for different quality requirements, it is not suitable for more demanding situations in which more subtle differences between defects require different algorithms for image processing and defect extraction. . In this way, if someone waits until after the extraction of the defect for classification, the information is lost and classification is impossible. BRIEF DESCRIPTION OF THE INVENTION The invention relates to the techniques for the automated inspection of mobile networks. An inspection system, for example, acquiring anomaly information from a network using an optical acquisition device, and performs a preliminary examination with a first, less sophisticated algorithm. The image information regarding regions of the network containing anomalies is stored for subsequent processing, accepting the probability that although some of the anomalies will be defective, many could be "false positives", for example, anomalies that are not defective. In fact, some anomaly areas can ultimately be classified as defective if the network is used in a particular product application, but not defective if the network is used in another. The original anomaly information can be reconsidered and fully analyzed at a convenient time, even after the inspection network has been dominated on a roll and is not available. As a result, the speed of the motion network during inspection can be much greater than what is possible when the entire surface of the network is subjected to sophisticated analysis. In addition, offline conversion decisions can be made, and can be based on many factors. A conversion control system subsequently reconsiders the information of the original image, and subjects the image information to at least a good variety of more sophisticated image processing and defect extraction algorithms to effectively separate the effective defects of the image. the anomalies. The conversion control system uses the defect information to control the way in which a network is ultimately converted to products, based on one or more product selection parers. Specifically, the conversion control system applies the image processing and defect extraction algorithms to generate defect information for a number of potential, network-based products, for example, the products into which the defect could be converted. net. The conversion control system then identifies which product best achieves the selected parers, such as maximum utilization of the network. Other examples of product selection parers that can be used to influence the conversion selection process include the unit product produced, the estimated revenue or the benefit of the product produced, the processing time required to convert the network, the current capacity of the machine for each process line, the current demand for different products or other parers. In one embodiment, a method comprises the information of an image of a sequential portion of a network, to provide digital information, and the processing of digital information with at least one initial algorithm to identify regions on the network that contain anomalies. The method further comprising analyzing at least a portion of the information, with at least a plurality of subsequent algorithms to determine which anomalies have effective defects in the network, for a plurality of different products, the determination of a value of at least one parer of product selection for each of the products, the selection of one of the products based on the value determined for each of the products and the conversion of the network into the selected product. In yet another embodiment, a system comprises an image forming device, an analysis computer and a conversion control system. The imaging device imagines a sequential portion of a network to provide digital information. The analysis computer processes the digital information with an initial algorithm to identify the regions on the network that contain anomalies. The conversion control system analyzes at least a portion of the digital information with at least one subsequent algorithm, to determine which anomalies have effective defects in the network, for a plurality of different products. In addition, the conversion control system determines a value for at least one product selection parer for each of the products, and selects one of the products for network conversion., based on the respective determined value for one of the products.

In yet another embodiment, a conversion control system comprises a database that stores data defining a group of rules, and an interconnection to receive anomaly information from an analysis machine where the anomaly information identifies the regions of a network that still have anomalies. The conversion control subject further comprises a conversion control machine which applies the rules to anomaly information to determine a value for at least one product selection parameter for each of the plurality of products. The conversion control machine selects one of the products for network conversion, based on the determined values. In yet another embodiment, a computer-readable medium comprises instructions that cause a processor to store data defining a group of rules, and receives anomaly information from a machine of analysis located within a manufacturing plane, wherein the information of the anomalies identifies the regions of a network, which contains anomalies. The instructions further cause the processor to apply the rules to the anomaly information, to determine a value for at least one product selection parameter for each of a plurality of products; and select one of the products for the conversion of the network based on the determined values. The invention can offer one or more advantages. For example, the capture and storage of anomaly information for subsequent analysis allows application-specific defect detection methods to be applied, which can provide increased defect detection capability. In addition, the techniques allow conversion decisions for a given roll or network to be based on one or more parameters such as network or product performance, revenue, benefits, current process line capacity, current product demand and other parameters. The details of one or more embodiments of the invention are described in the appended figures and in the following description. Other features, objects and advantages of the invention will be apparent from the description and the figures and from the claims. DEFINITIONS For the purposes of the present invention, the following terms used in this application are defined as follows: "network" means a sheet of material having a fixed dimension in one direction, and a length either predetermined or indeterminate in the orthogonal direction; "sequential" means that an image is formed by a succession of simple lines, or areas of the network that optically map to a single row of sensor elements (pixels); "pixel" means an image element representing one or more digital values; "spot" means a connected group of pixels in a binary image; "defect" means an undesirable occurrence in a product; "anomaly" or "anomalies" mean a deviation from a normal product that may or may not be a defect, depending on its characteristics or its severity. "gray scale" means the pixels that have a plurality of possible values, for example the 256 digital values; "binarization" is an operation for the transformation of a pixel into a binary value. "filter" is a mathematical transformation of an input image to a desired output image, filters are typically used to increase the contrast of a desired property within an image; "application specific" means defining the requirements, for example, grade or graduation levels, based on the intended use of the network; "performance" represents a use of a network expressed in percentages of material, unitary member of products or some other way; "fiducial marks" means the reference points or annotations used to define specific, physical locations on the network; "products" are the individual sheets (also referred to as components) produced from a network, for example, a rectangular film sheet for a cell phone screen or a television screen; and "conversion" means the process of physically cutting a network into products. BRIEF DESCRIPTION OF THE FIGURES Figure 1 is a block diagram illustrating a global network environment in which a conversion control system controls the conversion of network material according to the invention. Figure 2 is a block diagram illustrating an exemplary embodiment of a network manufacturing plant. Figure 3 is a flow chart illustrating the operation and operation of the network manufacturing plant. Figure 4 is a block diagram illustrating an exemplary embodiment of a conversion control system. Figure .5 is an exemplary user interconnection, presented by a user interconnect mode with which a user interacts to configure the conversion control system. Figure 6 provides another exemplary user interface, presented by the user interconnect module. Figure 7 is a flow diagram illustrating the exemplary processing of the anomaly information by the conversion control system. Figure 8 is a flow chart illustrating an exemplary method in which a conversion control machine generates a conversion plan for a given network roll, to maximize the utilization of the network. Figure 9 is a flow chart illustrating an exemplary method in which the conversion control machine generates a conversion plan to maximize the number of components produced from the roll of the network. Figure 10 is a flow chart illustrating an exemplary method in which the conversion control machine generates a conversion plan for a given roll of network, to maximize a total volume of unit sales made from the roll of the network. Figure 11 is a flow chart illustrating an exemplary method in which the conversion control machine generates a conversion plan to maximize a total benefit realized from the roll of the network. Figure 12 is a flow diagram illustrating an exemplary method in which the conversion control machine generates a conversion plan to minimize the processing time for a network roll to achieve a defined minimum throughput. Figure 13 is a flow chart illustrating an exemplary method in which the conversion control machine generates a conversion plan to maximize the utilization of the process lines at one or more conversion sites, still achieving performance minimum defined for the network roll. Figure 14 is a flowchart illustrating an exemplary method in which the conversion control machine generates a conversion plan based on a composite defect map, to convert the roll of the network into two or more products, to elaborate maximum use of the network roll. Fig. 15 is a flow chart illustrating an exemplary method in which the conversion control machine generates a conversion plan for a given network roll, based on a weighted average of a plurality of configurable parameters. Figure 16 is a block diagram illustrating a modality of a given conversion site.

Figure 17 is a flow diagram illustrating the exemplary operation of the conversion site in the processing of a network according to a conversion plan, to achieve maximum performance or other configurable parameter. DETAILED DESCRIPTION OF THE INVENTION Figure 1 is a block diagram illustrating a global network environment 2 in which the conversion control system 4 controls the conversion of the network material. More specifically, the 6A-6N network manufacturing plants represent manufacturing sites that produce and ship network materials in the form of a web roll 10. The 6A-6N network manufacturing plants can be geographically distributed. The fabricated web material can include any material in sheet form having a fixed dimension in one direction, and a length either predetermined or undetermined in the orthogonal direction. Examples of network materials include, but are not limited to, metals, papers, woven materials, nonwovens, glass, polymer films, flexible circuits or combinations thereof. The metals may include materials such as steel or aluminum. Woven materials generally include various fabrics. Nonwoven materials include materials such as paper, filter media or insulating material. The films include, for example, clear and opaque polymeric films including laminates and coated films. For many applications, the network materials of the network rolls 10 may have an applied coating, which are generally applied to an exposed surface of the base network material. Examples of coatings include adhesives, optical density coatings, low adhesion backing coatings, and metallized coatings, optically active coatings, electrically conductive or non-conductive coatings, or combinations thereof. The coating may be applied to at least a portion of network material or may completely cover a surface of base network material. In addition, the network materials may or may not have a pattern. The network rolls 10 are shipped to the 8A-8N conversion sites which can be geographically distributed within different countries. The conversion sites 8A-8N ("conversion sites 8") convert each web roll 10 into one or more products. Specifically, each of the conversion sites 8 includes one or more process lines that physically sever the network for a given network roll 10 in numerous individual sheets, individual parts, or numerous network rolls, designated as products 12A-12N. As an example, the conversion site 8A can convert the film network rolls 10 into individual sheets for end-use applications. Similarly, other forms of network materials can be converted to products 12 of different shapes and sizes, depending on the application intended by customers 14A-14N. Each of the conversion sites 8 may be able to receive different sites of network rolls 10, and each conversion site may produce different products 12, depending on the location of the conversion site and the particular needs of the customers 14. As described in detail herein, each of the network manufacturing plants 6 includes one or more detection systems (not shown in Figure 1) that acquires anomaly information for the networks produced. The systems of inspection of the plants 6 of manufacture of networks, realize preliminary examinations of the networks using a first algorithm typically less sophisticated to identify the anomalies of manufacture, accepting the probability of that although some of. the anomalies can prove to be defective, many could be "false positives", for example, anomalies that are not defective. In fact, products 12 have different grade or grade levels, also referred to as quality levels, and have different tolerances for manufacturing anomalies. As a result, some of the anomaly areas may ultimately be classified as defective if the corresponding network roll 10 is converted to a particular product 12, but it is not defective if the network roll is converted to a different product. The network manufacturing plants 6 communicate the information of the images with respect to the regions of the network containing anomalies, to the conversion control system 4 via the network 9, for the subsequent processing. The conversion control system 4 applies one or more defect detection algorithms that may be specific to the application, for example, specific to the products 12. Based on the analysis, the conversion control system 4 determines, from a automatically or semi-automatically, which of the products 12 should allow a particular network roll 10 to achieve maximum performance (for example, the use) of the network. Based on the determination, the compression control system 4 generates a compression plan for each network roll 10, for example, the instructions defined for the processing for the network roll, and communicates to the conversion plan via the network 9 to conversion site 8, appropriate, for use in converting the network into the selected product. The conversion control system 4 may consider other product selection parameters either in addition to, or independently of the performance, when conversion plans are generated for each of the network rolls 10. For example, the conversion control system 4 may consider the number of units that could be produced by each of the network rolls 10 for the different products 12. Other exemplary product selection parameters and the conversion control system 4 can consider when a conversion plan is generated, includes an estimated amount of revenue or benefit that could be produced by the network roll for each potential product 12, a process time that would be required to convert the network for each of the different products, a current capacity of the machine for each process line within the conversion sites 8, the current levels of demand for each of the products 12 and other parameters. In certain embodiments, the conversion control system 4 can make such determinations for individual conversion sites 8. In other words, the conversion control system 4 can identify the network rolls destined for each conversion site 8 and generate the plans of conversion based on the products 12 associated with the individual conversion sites. For example, the conversion control system 4 can identify the network rolls destined for the 8A conversion site, and generate the conversion plans to maximize the performance of the network rolls, based on the 12A products produced by the 8A conversion site. Alternatively, the conversion control system 4 can generate the conversion plans for the network rolls 10, before they are shipped to the conversion sites 8. Consequently, the conversion control system 4 can consider all the potential available products 12. , when the corresponding conversion plans are generated for the network rolls 10. In this way, the conversion control system 4 can consider all the commercially available products 12 in order to maximize the performance of each network roll 10. In this configuration, the conversion control system 4 generates the conversion plans and sends out the instructions identifying the specific conversion sites 8 to which each of the network rolls 10 must be shipped. In some embodiments, the conversion control system 4 considers the parameters when selecting the respective conversion sites 8 for the network rolls 10. Such parameters include, but are not limited to, the current inventory levels of the products 12 in each of the conversion sites 8, the recent orders received from the clients 14, the shipping time and the cost associated with each one of them. the conversion sites 8, the available shipping methods and other parameters. In this way, the conversion control system 4 applies defect detection algorithms, specific to the application, to the anomaly information received from the manufacturing plants 6 and networks, and ultimately directs the conversion of the rolls 10 of network in products 12, based on one or more parameters. As illustrated below, these factors can be selected by the user, and can be applied independently or collectively using a weighting function or other technique. Figure 2 is a block diagram illustrating an exemplary embodiment of the network manufacturing plant 6A of Figure 1. In. the exemplary mode, a segment of a continuously mobile network 20 is placed between two support rollers 22, 24. The image acquisition devices 26A-26N are placed in close proximity to the network 20, continuously mobile. The image acquisition devices 26 scan sequential portions of the continuously mobile network 20 to obtain "image data." The acquisition computers 27 collect the image data from the image acquisition devices 26, and transmit the image data. The images to the analysis computer 28 for the determined analysis The image acquisition devices 26 can be conventional imaging devices that are capable of reading a sequential portion of the mobile network 20 and providing the output in the form of a digital data stream As shown in Fig. 2, the image information devices 26 can be cameras that directly provide a digital data stream or an analog camera with an additional analog to digital converter Other sensors, such as For example, laser scanners can be used as the acquisition device. n of images. A sequential portion of the network indicates that data are acquired by a succession of single lines. Simple lines comprise an area of the continuously mobile network, which maps the map optically to a single row of sensor elements or pixels. Examples of suitable devices for image acquisition include line scan cameras such as Model # LD21 by Perkin Elmer (Sunnyvale, California), Piranha Models by Dalsa (aterloo, Ontario, Canada), or Model # TH78H15 by Thompson CSF (Totawa, NJ). Additional examples include laser scanners from Surface Inspection Systems GmbH (Munich, Germany) in conjunction with an analog-to-digital converter. The image can be optionally acquired through the use of optical assemblies that help in the procuring of the image. Mounts can be any part of a camera, or they can be separated from the camera. The optical assemblies use reflected light, transmitted light, or translucent light during the imaging process. Reflected light, for example, is often suitable for the detection of defects caused by deformations of the surface of the network such as surface scratches. The bar code controller 30 controls the bar code reader 29 for entering roll and position information from the network 20. The bar code controller 30 communicates the roll and position information to the computer 28 of the bar code. analysis. The analysis computer 28 processes the image streams from the acquisition computers 27. The analysis computer 28 processes the digital information with one or more initial algorithms to generate the anomalies information that directs any regions of the network 20 that contain anomalies that can ultimately qualify as defects. For each identified anomaly, the analysis computer 28 extracts from the image data an anomaly image containing pixel data encompassing the anomaly and possibly a portion surrounding the network 20. The analysis computer 28 stores the information of the roll, the position information and the anomaly information within the database 32. The database 32 may be implemented in any one of a number of different ways, including a data storage file or one or more management systems of databases (DBMS) that run one or more of the database servers. The database management systems can be, for example, a relational database management system (RDBMS), hierarchical (HDBMS), multidimensional (MDBMS), object oriented (ODBMS or OODMBS) or related to objects (ORDBMS). As an example, the database 32 is implemented as a relational database provided by SQL Server1111 of Microsoft Corporation. The analysis computer 28 communicates the information of the rolls, as well as the anomalies information, and the respective secondary images to the conversion control system 4 for detailed, off-line, subsequent analysis. For example, the information may be communicated by means of a synchronization of the database between the analysis computer 28 and the conversion control system 4. Figure 3 is a flowchart illustrating the exemplary operation of plant 6A of network fabrication. Initially, the image acquisition devices 26, and the acquisition computers 27 acquire image data from the mobile network 20 (40). The image data can be digitally formed, for example, by means of a digital video camera, or can be converted into digital information (42). In any case, the acquisition computers 27 send out streams of digital image information to the analysis computer 28 (44). The analysis computer 28 applies an initial anomaly detection algorithm to identify regions of the network that contain anomalies (46). In some convenient embodiments, the initial anomaly detection algorithm is very fast to be able to operate in real time by the general purpose computer, even if an online speed of the mobile network 20 is large. As a result, some of the identified regions that contain anomalies may include "false positives." Even when there may be many false positives, the initial algorithm is preferably designed such that, for example, true "defects" are not detected as anomalies, rarely, if indeed they occur. After the application of the initial anomaly detection algorithm, the analysis computer 28 assembles the anomaly data around the identified regions, and stores the anomaly data within the database 32 (48). The data typically includes an initial position of the anomaly within the network and a pixel spanning area of each identified region. Within this process, the analysis computer 28 extracts a portion of the image data for each identified region that contains an anomaly (50). Specifically, only a fraction of the original digital image information needs to be extracted for further sophisticated analysis by the conversion control system 4. The regions identified typically contain information, for example, at least an order of magnitude less than the digital information, as indicated by the size in any measurement, by, such as the file size in bytes. In some applications, the present invention has demonstrated effective reduction of data by an order of magnitude between 3 and 12. Images of extracted anomalies can be stored in a database 32 or a file server (not shown) (52 ) and subsequently communicated to the conversion control system 4 together with the information (54) of the anomalies and the rolls. Alternatively, the information of the rolls, the anomaly information and the anomaly images can be transferred directly for processing by the conversion control system 4. Fig. 4 is a block diagram illustrating an exemplary embodiment of the conversion control system 4 with additional detail. In the exemplary embodiment, the application server 58 provides an operating environment for the software modules 61. The software modules include a plurality of modules 60A-60M, defect processing, a user interconnect module 62, and a machine 64 of conversion control. The software modules 61 interact with the database 70 to access the data 72, which may include anomaly data 72A, roll data 72B, image data 72C, product data 72D, conversion site data 72F, defect maps 72F, composite defect maps 72G, conversion control rules 72H, and conversion plans 721. Database 70 can be implemented in any of a number of different ways including a data storage file or one or more database management systems (DBMS) running on one or more servers in the database of data. As an example, the database 32 is implemented with a relational database provided by SQL Server1111 of Microsoft Corporation. The anomaly data 72A, roll data 72B, and image data 72C, represent the information of the rolls, the reception of the anomalies and the respective anomaly images, received from the network manufacturing plants 6 (Figure 1). The product data 72D represents the data associated with the products 12 (figure 1). More specifically, the product data 72D defines each type of product producible for each conversion site 8. For each product 12, the product data 72D specifies one or more data processing modules 60 that are required to determine whether a roll 10 This network meets the quality requirements for the particular product. In other words, the product data 72D specifies one or more defect processing modules 60 that have to be used to analyze the anomaly data 72A and the image data 72C for each product 12. In addition, the product data 72D stores other information related to the products 12 that can be used by the conversion control system 4 when the conversion sites 8 are selected, and the conversion plans for the network rolls 10 are generated. For example, the 72D product data may also include data specifying an estimated revenue per unit for each of the products 12. The 72D product data may also include data specifying an estimated revenue per unit for each of the products. 12, an estimated conversion time to convert a roll of network to the product, a current level of industry demand for each of the products or other data that may be useful in the selection of conversion plans. The conversion of the site data 72E represents the data associated with the conversion sites 8. For example, the conversion of the 72E site data can store the location of the site, the number of lines in process and a current available capacity of each process line for one of the conversion sites 8. The data 72E of the conversion site may store other data, including but not limited to, the data specifying a current level of inventory for each product 12 in each conversion site 8, the shipping costs associated with the shipment of a roll of network to each conversion site, the shipping options available for each conversion site or the current order information of the clients 14 received by each conversion site, and the data that specify the new or preferred customers for each conversion site, and other data that may be useful in the selection of conversion plans. As described in more detail below, the defect processing modules 60, send out defect maps 72F which specify which anomalies are considered effective defects for the different products 12. In other words, each defect map 72F corresponds to a particular network roll 10 and a specific product 12. Each defect map 72F specifies the location of the particular defects of a network roll 10, particular based on the specific requirements of the corresponding product 12. The conversion control machine 64 analyzes the defect maps 72F according to the 72H rules of conversion control, to select the final conversion used for each of the network rolls 10. For example, the conversion control machine 64 can analyze the defect maps 72F to determine which of the products 12 could allow a particular network roll 10 to achieve maximum performance (eg, utilization) of the network. The conversion control rules 72H specify one or more parameters for consideration by the conversion control machine 64, when defect maps 72F are processed, such as the use of the network material, the number of units that could be produced by each of the network rolls 10 for the different products 12, an estimated amount of revenue or profit that could be produced by the network roll for each potential product 12, a processing time that would be required to convert the network for each one of the different products, a capacity of the current machine for each process line within the conversion sites 10, the current levels of demand for the products 12 and other parameters. By this process, the conversion control machine 64 can determine that a particular network roll 10 can be used better (for example, it can reach a maximum performance) if it is converted to multiple products 12. In other words, the machine 64 of Conversion control can determine that a first person in the network can be better used when it is converted to a first product, and a second portion for a different product. In this case, the conversion control machine 64 generates a defect map 72G "composite" specifies the locations of the defects within each portion of the network, based on the corresponding product to which the portion is to be converted. The conversion control machine 64 can create composite defect maps during the splicing of the portions of two or more defect maps 72F to form a composite, complete defect map for the entire network. After selecting a particular product or group of products for a given roll 10, the conversion control machine 64 generates with conversion plan 721, respectively. Each conversion plan 721 provides the precise instructions to specify the respective network roll. More specifically, each conversion plan 721 defines the configurations for the processing lanes for physically splicing the network into individual product sheets. The conversion control system 4 sends out the shipment inspections, directing the shipment of each network roll 10 to a respective destination conversion site 8. In addition, the conversion control system 4 communicates the conversion plans via network 9 to the appropriate conversion sites 8, for the use of conversion of the rolls with selected products. The user interconnect module 62 provides an interconnection whereby a user can configure the parameters used for the conversion of the control machine 64. For example, as illustrated below, the means of interconnection with the user 62 allows the The user directs the conversion control machine 64 to consider one or more of a maximum network usage, the number of units produced, the estimated revenue, the estimated benefit, the capacity of the machine, the current levels of demand and / or other parameters.

Figure 5 is an exemplary user interconnect 80 presented by the interconnection module 62 with the user with which a user interacts to configure the conversion control machine 64. The exemplary interconnect 80 includes the input mechanism 82 by which the user enters a unique identifier for a network roll. Other mechanisms for selecting a roll can be used, such as a descending drop menu, search function, selectable list of rolls recently manufactured, or the like. In addition, the user interface 80 provides a plurality of input mechanisms 86-94 by which the user can select one or more product selection parameters for consideration by the conversion control machine 64 when a conversion plan is generated. recommended. In this example, the user interconnect 80 includes a first input selection mechanism 86 for directing the conversion control machine 64 to select a conversion plan that seeks to optimize the use of the network for the selected network roll. The input mechanism 88 directs the conversion control machine 64 to maximize a number of components produced from the selected network roll. Similarly, the input mechanisms 90, 92 direct the conversion control machine 64 to maximize the revenue from the benefit generated from the selected network roll, respectively. The input mechanism 94 directs the conversion control machine 64 to select a conversion plan that minimizes the processing time for the selected network roll. After the selection of one or more parameters, the user selects the SEND button 98, which directs the conversion control system 4 to process the selected network flight with the defect processing modules 60, followed by the selection of analysis and conversion plan, by the conversion control machine 64. In this way, the user interface 80 provides a simplistic illustration of how a user can configure the conversion control machine 64 based on one or more parameters. The user interconnect 80 may require the user to select one and only one of the 86-94 input mechanisms. In certain embodiments, the user interconnect 80 includes an input mechanism 96 that allows the user to define a minimum network usage. This can be advantageous in situations where the user selects a primary parameter, such as the benefit, to be maximized, but wants a baseline usage to be satisfied. Figure 6 provides another exemplary user interwork 100 presented by the user interconnect module 62. In this embodiment, the modular interconnect 100 includes the input mechanisms 102-110 by which the user enters the respective weighting functions for each parameter. Specifically, the input mechanism 112 allows the user to enter a weighting function in the range of 0 to 100 for each parameter, where 0 directs the conversion of the conversion control machine 64 to exclude the parameter and 100 represents the highest possible weighting The defect processing modules 60 analyze the anomaly data for the selected network roll, when the user selects the SEND button 112, followed by the selection of the analysis and conversion plan by the conversion control machine 64. When a conversion plan for a given network roll 10 is selected, the conversion control machine 64 can analyze the defect maps 72F for each potential product 12 for each of the parameters having non-zero weighting. In the example in figure 6, the conversion control machine 64 analyzes the defect maps 72F and the product data 72D to compute the utilization of the network, the number of components produced, the benefit generated and the processing time for each potential product. As described in detail below, the conversion control machine 64 can then normalize the computed results of each parameter for each product, and then compute the weighted values from the standardized results. Finally, the conversion control machine 64 selects a conversion plan as a function (for example a sum) of the weighted values. Another technique can be used, in which the conversion control system 4 uses multiple parameters when a conversion plan for a network roll 10 is selected. Figure 7 is a flow diagram illustrating the processing of anomaly information by the conversion control system 4, with additional detail. In particular, Figure 7 illustrates the processing of the anomaly data 72A and the image data 72C by the defect processing modules 60. The conversion control system 4 receives the image data of the anomalies, such as the images 144, 146, which were initially extracted from a network 20 or an analysis computer 28 located in a network coding plant 6, using a first detection algorithm, simple. As illustrated in Figure 7, defect processing modules 60 apply different "M" algorithms (designated A? -Am 158 in FIG. 7) as necessary for up to four different N requirements 150 for products 12. The cross reference table 152 of FIG. 7 is used to illustrate the mapping between the requirements 150 and the modules 60 of defect processing. Specifically, the cross-reference table 152 shows which defect processing modules 60 are used in determining whether each anomaly is a defect or a positive step for a given requirement 150. In some embodiments, a large number of algorithms plus simple, they are conveniently used in parallel. In particular, it is frequently desirable that at least one of the subsequent defect processing modules 60 apply an algorithm that includes the comparison of each anomaly against combination threshold criteria-pixel size. In effective practice, such as, for example, optical film, an anomaly that has only a subtle difference in brightness value from an objective is unacceptable if the area is large, and an anomaly that has a greater difference in brightness from an objective value, it is unacceptable, even if the area is very small. In addition, the algorithms applied by the defect processing modules 60 can incorporate the processing of very complex images and the extraction of defects including, but not limited to, neighbor processing, neighboring range, contrast expansion, manipulations from diverse monadic and dyadic images, digital filtering, such as Laplacian filters, Sobel operators, high pass filtering and low pass filtering, texture analysis, fractal analysis, frequency processing such as Fourier transformations and transformations of the wave train, convolutions, morphological processing, threshold determination, analysis of connected components, spot processing, spot quantification, combinations thereof. Other algorithms can be applied based on the specific types of network and defects to achieve a desired level of accuracy of defect detection. Each of the N product requirements 150 can be carried out using selected combinations of individual defect processing algorithms 158. The algorithms can use very simple threshold and minimum spot processing and more complex algorithms such as spatial filters, morphological operations, frequency filters, wavelet processing, or any other known algorithms of image processing. With this exemplary cross-reference table 152, the product requirement Ri uses a combination of A2, A4 and AM algorithms, each applied to each anomaly image, to determine which anomalies are effective defects for Ri. In more convenient modes, it is using a logic 0 (OR in English), for example, if any of A2, A4 and A report the anomaly as an effective defect, that proportion of network 20 does not satisfy the product requirement Ri. For specialized applications, it is logical through which the reports of the subsequent algorithms 158 are combined in a determination of whether a product requirement 150 is satisfied can be more complex than a simple OR logic. Similarly, the product R2 requirement uses A2 / A3 and A4, etc. In this way, the anomalies that are identified as defects for R2 can be similar to or significantly different from the defects for Ri. After determining which anomalies are considered effective defects by using the cross reference table 152, the control machine 64 Conversion formulates defect maps 72F of the effective defect sites corresponding to the various product requirements for the roll. In some situations, the conversion control machine 64 can generate one or more composite defect maps 72G by splicing one or more portions of the defect maps 72F. In this illustrated example, the conversion control machine 64 generates a composite map 72G having a first portion 160 spliced from a defect map for a first product requirement (MPA-Rl) and a second portion 162 from a defect map for a second product requirement (MAP-R2). In this way, the conversion control machine 64 can determine that a network can be better utilized if some of the portions of the network are converted to different products. Once this has been done, it is often possible to discard secondary image information to minimize the necessary storage media. Further details of the image processing and the subsequent application of the anomaly detection algorithms detected by the defect processing modules 60 are described by the co-pending and commonly assigned United States patent application No. 10 / 669,197 , entitled "APPARATUS AND METHOD FOR THE AUTOMATIC INSPECTION OF NETWORKS", which has the Case of the Lawyer no. 58695US002, filed on April 24, 2003, the entire contents of which is incorporated by reference herein. Figures 8-15 are flowcharts illustrating various exemplary embodiments in which the conversion control machine 64 applies the compression rules 72H to generate the conversion plans 721 based on one or more parameters configurable for the user, such as such as the use of the network material, the number of units produced, the revenues, the benefits, the process point, the capacity of the machine, the demand of the product and other parameters. Fig. 8 is a flowchart illustrating an exemplary method in which the conversion control machine 64 provides a conversion plan 721 for a given network roll 10 to maximize the utilization of the network. Initially, the conversion control machine 64 identifies a group of potential products 12 into which the network roll 10 (200) can be converted. As described above, if the web roll is or is currently being shipped to a particular conversion site 8, the conversion control machine 64 selects one or more of the products associated with the specific conversion site for which the roll network is adequate. Alternatively, if the network roll that is considered has not been shipped, the conversion control system 4 may select all products 12 for which the network roll is suitable. The conversion control machine 64 accesses the product data 72D of the database 70 to identify the product requirements for the identified group of suitable products, and selects one or more of the defect processing modules 60, based on in the identified requirements (202).

Next, the conversion control machine 64 invokes the selected defect processing modules 60, which apply the respective defect detection algorithms to the anomaly data 72A and the image data 72C received from a plant 60 of manufacture of networks, to formulate the information of defects for each one of the requirements of the product. The conversion control machine 64 generates the defect maps 72F based on the defects identified by the defect processing modules (204). In the example of figure 8, the conversion control machine 64 selects a first of the defects maps 206, and analyzes the map to calculate the performance for the network, either in percentage of material used, localized effective area and some another convenient metric (208). The conversion control machine 64 repeats this process for each defect map (210, 212). The conversion control machine 64 then selects the product that could result in the maximum performance for the network roll (214). The conversion control machine 64 identifies the defect map associated with the selected product, and generates a conversion plan 721 according to the selected defect map (216). The conversion control machine 64 can subsequently communicate the conversion plan to the appropriate conversion site 8 and send out (for example, display or print) the attachment instructions to ship the particular network roll 10 to the site. Conversion (218). Figure 9 is a flow diagram illustrating an exemplary method in which the conversion control machine 64 generates a conversion plan 721 for a given network roll 10 to maximize the number of components produced from the network roll. As described above, the conversion control machine 64 identifies a group of potential products 12 into which the network roll 10 can be converted, and selectively invokes one or more of the effects processing modules 60, to apply the algorithms of defect detection, and generates defect maps 72F for the network roll (220-224). In the example method of Figure 9, the conversion control machine 64 selects a first of the defect maps (226), and analyzes the map to calculate a total number of components that could be produced by the respective product (228). ). The conversion control machine 64 repeats this process for each defect map (230, 232). The conversion control machine 64 selects the new product that would give the result in the maximum number of components produced by the network roll (234). For example, based on the specific location of the defects, other components may be realizable for a larger-sized product (eg, a film for a computer screen) versus a smaller-sized product (eg, a film). of mobile phone screen). The conversion control machine 64 generates a conversion plan 721 based on the selected product, communicates the conversion plan to the appropriate conversion site 8, and outputs (for example, viewing or printing) the shipping instructions for the loading of the network roll 10, particular to the conversion site (236-238). Figure 10 is a flow diagram illustrating an exemplary method in which the conversion control machine 64 generates a conversion plan 721 for a given roll of network 10 to bring the maximum total volume of unit sales made from the network roll. As described above, the conversion control machine 64 identifies a group of potential products 12, into which the network roll 10 can be converted, selectively invokes one or more of the defect processing modules 60, to apply algorithms of defect detection, and generates defect maps 72F for the network roll (250-254). Next, the conversion control machine 64 selects a first of the defect maps (256) and analyzes the map to calculate the total number of components that could be produced for the respective product (257). Next, the conversion control machine 64 accesses the product data 72D to retrieve an estimated sale price per unit for the particular product. Based on the estimated sales price, the conversion control machine 64 calculates the estimated total sales (for example, in dollars) that could be generated from the web roll and the roll converted to the product (258). The conversion control machine 64 repeats this process for each defect map (260, 262). The conversion control machine 64 then selects the product that could result in the maximum amount of sales made, for example, revenues, for the network roll (264). For example, certain components can better capture a principal price than other components due to market factors. In this exemplary embodiment, the conversion control machine 64 can select a product that does not achieve maximum utilization of the network roll, but which is nonetheless expected to generate higher sales relative to the other suitable products. The conversion control machine 64 generates a conversion plan 721 based on the selected product, communicates the conversion plan to the appropriate conversion site 8, and outputs (for example, viewing or printing) the shipping instructions for shipping the particular network roll 10 to the conversion site (266-268). Figure 11 is a flow chart illustrating an exemplary method in which the conversion control machine 64 generates a conversion plan 721 for a given network roll 10 to maximize a total realized benefit of the network roll . As described above, the conversion control machine 64 identifies a group of potential products 12 into which the network roll 10 can be converted, and selectively invokes one or more of the defect processing modules 60 to apply the algorithms of defect detection, and generates the defect maps 72F for the network roll 10 (270-274). The conversion control machine 64 selects a first of the defect maps (276) and analyzes the map to calculate a total number of components that could be produced for the respective product (277). Next, the conversion control machine 64 accesses the 72D product data to retrieve an estimated sale price, and the estimated cost per unit for the particular product. Based on the estimated price and cost of sales, the conversion control machine 64 calculates a total estimated benefit realized from the network roll, if the network roll was converted to the product (278). The conversion control machine 64 repeats this process for each defect map (280, 282). The conversion control machine 64 then selects the product that could result in the maximum amount of benefit realized for the network roll (284). The conversion control machine 64 a conversion plan 721 based on the selected product, communicates the conversion plan to the appropriate conversion site 8, and sends out (for example, display or printing) the shipping instructions to ship the network roll 10, particular to the conversion site (286-288). Fig. 12 is a flowchart illustrating an exemplary method in which the conversion control machine 64 generates a conversion plan 721 for a given network roll 10, minimizing process time and achieving minimal performance required . As described above, the conversion control machine 64 identifies a number of potential products 12 into which the network roll 10 can be converted., and selectively invokes one or more of the defect processing modules 60, to set defect detection algorithms and generates defect maps 72F for the network roll (300-304). Next, the conversion control machine 64 selects a first of the defect maps (306), and analyzes the map to calculate a yield that could be produced by the respective product, either as a percentage of material used, the area effective used or some other convenient metric (308). The conversion control machine 64 repeats this process for each defect map (310, 312). The conversion control machine 64 then grades the products according to the estimated yield (314), and selects a subset of the products, including only those products that would achieve a defined minimum yield (316). Next, the conversion control machine 64 qualifies the subgroup of products according to the processing time, as specified in the product data 72D (318). The conversion control machine 64 then selects the product from the subgroup of products that has the lowest estimated process time (320). The conversion control machine 64 generates a compression plan 721, based on the selected products, communicates the conversion plan to the appropriate conversion site 8, and outputs (for example, viewing or printing) the shipping instructions for shipping the network roll 10, particular, to the conversion site (322-324). In this way, the conversion control machine 64 defines a conversion plan 721 for the network roll 10, to achieve an acceptable level of performance, while minimizing the conversion time (e.g. performance) of the network at the conversion sites 8. Figure 13 is a flowchart illustrating an exemplary method in which the conversion control machine 64 generates a conversion plan 721 for a given network roll 10. to maximize the use of the process lines in the conversion sites 8, still achieving the minimum performance required for the network roll. As described above, the conversion control machine 64 identifies a group of potential products 12 into which the network roll 10 can be converted, and selectively invokes one or more of the defect processing modules 60 to apply the algorithms of defect detection and generates defect maps 72F for the network roll (340-344). Next, the conversion control machine 64 selects a first of the defect maps (346), and analyzes the map to calculate a performance that could be produced for the respective product, either as a percentage of the material used, the area effective used or some other convenient metric (348). The conversion control machine 64 repeats this process for each defect map (350, 352). The conversion control machine 64 then grades the products according to the estimated yield (354) and selects a subset of the products, including only those products that would achieve a defined minimum yield (356). Next, the conversion control machine 64 accesses the conversion data 72E to determine a group of process lines of the conversion sites 8, suitable for converting the product subgroup. The conversion control machine 64 qualifies the identified process lines according to the current unused capacity (358). The conversion control machine 64 then selects the product from the product subgroup corresponding to the process line having the highest unused capacity (360). The conversion control machine 64 generates a conversion plan 721 based on the selected product, communicates the conversion plan to the appropriate conversion site 8, and sends out (for example, viewing or printing) the shipping instructions for boarding the network roll 10, particular, to the conversion site (362-364). In this way, the conversion control machine 64 defines a conversion plan 621 for the network roll 10, to achieve an acceptable level of performance, while maximizing the utilization of the process lines of the sites. conversion 8. Figure 14 is a flow chart illustrating an exemplary method in which the conversion control machine 64 generates a conversion plan 721 for a given network roll 10, based on a composite defect map for convert the network roll to two or more products, to maximize the use of the network roll. As described above, the conversion control machine 64 identifies a group of potential products 12 into which the network roll 10 can be converted, and selectively invokes one or more of the defect processing modules 60 to apply the algorithms of defect detection, and generates the defect maps 72F for the network roll 10 (380-384). Next, the conversion control machine 64 analyzes the defect maps to define the regions of the maps, based on the performance (386). For example, as illustrated in Figure 7, based on the analysis, the conversion control machine 64 can define a first region of one of the defect maps that could result in a relatively high yield for a first product, and a second non-overlapping region of a different product map that could result in a high throughput for a second product. The conversion control machine 64 qualifies and selects the non-overlapping regions based on the estimated throughput (390), and generates a composite defect map 72G by applying the non-overlapped regions to form the composite defect map (392). In this way, the conversion control machine 64 can determine that a network can be better utilized if certain portions of the network are converted into different products. The conversion control machine 64 generates a conversion plan 721 based on the defect map of the compound, communicates the conversion plan to the appropriate conversion site 8 and sends out (for example, displays or prints) the shipping instructions to ship the particular network roll 10 to the conversion site (362-364). In this way, the conversion control machine 64 defines a conversion plan 721 for the network roll 10, to convert the network roll into two or more products to maximize the utilization of the network roll. Fig. 15 is a flow diagram illustrating an exemplary method in which the conversion control machine 64 generates a conversion plan 721 for a given network roll 10, based on a weighted average of a plurality of configurable parameters . The conversion control machine 64 identifies a group of potential products 12 into which the network roll 10 can be converted, and selectively invokes one or more of defect processing to apply the defect detection algorithms and generates defect maps 72F for the network roll (400-404). Next, the conversion control machine 64 employs any of the described techniques to calculate the specified parameters, for example, network utilization, component performance, profit, sales, processing capacity, time of process or other parameters for each of the products (406). The conversion control machine 64 then normalizes each of the parameters to a common range, such as from 0 to 100 (408). The conversion control machine 64 then adjusts each of the parameters according to a weight configurable by the user, as shown in Figure 6. (410), and computes a total weighted average for each product (412). The conversion control machine 64 then selects the product corresponding to the maximum weighted average of the parameters (414), generates a conversion plan 721 for the selected product, based on the respective defect map (416). The conversion control machine 64 communicates the conversion plan to the appropriate conversion site 8, and sends out (for example display or printing) the shipping instructions to ship the "roll 10" network, particularly to the conversion site (418). In this way, the conversion control machine 64 can consider multiple parameters when a conversion plan 721 is defined to convert the network roll into products, based on the stored image anomaly information. Figure 16 is a block diagram illustrating one embodiment of an 8A conversion site. In this exemplary embodiment, the conversion site 8A includes a network roll 10A that has been loaded and enlisted for conversion. The conversion server 508 receives the conversion maps from the conversion control system 4, and stores the conversion maps in the database 506. A bar code is read from the roll 10A, which informs the conversion server 508 of the particular network 503, allowing the conversion server to access the database 506 and retrieve the corresponding conversion map. The bar code can be read by the input device 500 when the network 503 is placed in motion or via a manual bar code device before loading. The conversion server 508 displays a conversion plan, thereby allowing the workers to configure the conversion unit 504. Specifically, the conversion unit 504 is configured to physically sever the network 503 on numerous individual sheets (e.g., products 12A) according to the conversion plan. As the network 503 passes through the system during the dial operation, the input device 500 reads the bar codes and the associated fiducial marks are regularly detected. The combination of the bar code and the fiducial mark makes it possible for someone to accurately record the physical position of the 503 network to the defects identified in the conversion plan. Regular reregistration ensures the accuracy of the record to come. A person skilled in the art is capable of establishing the re-registration through the conventional techniques of transformation of physical coordinates. Once the network 503 is registered to the conversion map, the physical position of the specific defects is known. When the defects pass under the marker 502 of the network, marks are applied to the network 503 to visually identify the defects. Specifically, the conversion server 508 sends a series of organs to a network marker 502, which then applies the location marks to the network 503. In many applications of the present invention, the network marker 502 places the marks of the network. location on or adjacent to the defects within the 503 network, according to the respective conversion plan. However, in some specialized applications the location marks are spaced in a predetermined way from the anomalies whose position they identify. The marker 502 of the network may include, for example, a series of inkjet modules, each having a series of jet nozzles. The type of mark, and the exact position of the mark on or near the defect can be selected based on the network material, the classification of the defects, the processing of the network required to address the defect, and the application of use. intended end of the network. In the case of the arranged ink marker, the markers are immediately and preferably depending on their cross-network position, as the defects pass the unit in the downward direction of the network. With this method, marking accuracies of less than 1 mm have been regularly achieved in high-speed networks with production speeds greater than 150 feet / minute. However, higher speed networks greater than 1000 meters / minute are within the capacity of the invention. The conversion server 508 can pause the conversion of the network 503 at any point according to the conversion plan, to allow the reconfiguration of the conversion unit 504. For example, in the uniform network 503 that is going to be converted to different products, the conversion server 508 interrupts the conversion process after the first product is produced, to allow the conversion unit 504 to be reconfigured for the subsequent product. The positioning of the cutting devices and other mechanisms, for example, can be reconfigured as necessary to produce the second product. Figure 17 is a flow diagram illustrating the exemplary operation of a conversion site, such as conversion site 8A of Figure 16, in the processing of a network according to the conversion plans to achieve, for example, maximum performance or other configurable parameter. Initially, the conversion server 508 receives and stores the roll information and the conversion plans from the conversion control system 520. This can happen before or after receiving the network rolls. For example, the conversion server 508 may receive the roll information and a conversion plan for a particular network roll, weeks before the physical network roll arrives at the conversion sites. Alternatively, the conversion server 508 may receive the roll information and a conversion plan for a roll of network, already stored within the inventory at the conversion site. Then, the conversion server 508 receives the barcode information, for a particular network roll to be converted, causing the conversion server 508 to access the database 506 and retrieve the conversion map (522) correspondent. As noted above, the bar code can be read before loading (eg, by a manual barcode device, as illustrated in Figure 17, or via the input device 500 after the 503 network it is loaded and readied for conversion The conversion server 508 shows a conversion plan, whereby it allows workers to configure conversion unit 504 to physically cut network 503 into numerous individual sheets (e.g., products 12A) according to the conversion plan (526) Alternatively, the conversion unit 504 can be configured in an automatic or semi-automatic manner according to the conversion plan Once the conversion unit is configured 504, the network 503 is set in motion and the data entry device 500 reads the bar codes and detects the associated fiducial marks (528), and the network marker 502 can be used to visually mark the network 503, with In order to help in the visual recognition of defective products (530). The conversion unit 504 converts the received network 503 to form the products 12A (532). At any point within the conversion plan, the conversion server 508 may determine that a reconfiguration is required by the plan (534). If so, the conversion server 508 directs the reconfiguration of the conversion unit 504 (536). This process continues until the entire network 503 is converted to one or more products 12A according to the conversion plan (538). Various embodiments of the invention have been described. These and other modalities are within the scope of the following claims.

It is noted that in relation to this date, the best method known to the applicant to carry out the aforementioned invention is that which is clear from the present description of the invention.

Claims (23)

CLAIMS Having described the invention as above, the content of the following claims is claimed as property:

1. A method, characterized in that it comprises: the formation of an image of a sequential portion of a network, to provide digital information; processing of the digital information with at least one initial algorithm, to identify the regions on the network containing anomalies, analyze at least a portion of the digital information with a plurality of subsequent algorithms to determine which anomalies represent effective defects in the network for a plurality of different products; determine a value of at least one product selection parameter for each of the products; select one of the products based on the value determined for each of the products; and convert the network into the selected product. The method according to claim 1, characterized in that the determination of a value comprises computing the respective determined value for each of the products, based on the actual defects determined, for the respective products. 3. The method according to claim 1, characterized in that the determination of a value comprises the computation of a use of the network for each of the products, based on the actual defects determined, for the respective products. 4. The method according to claim 1, characterized in that the determination of a value comprises: computing an estimated number of components that could be produced for each of the products, based on the actual defects determined for the respective products; and compute an estimate of total sales for each of the products, based on the computed number of components. The method according to claim 1, characterized in that the determination of a value comprises the determination of a process time for the conversion of the network for the respective products, and wherein the selection of one of the products comprises the selection of the product to minimize the processing time for the network. The method according to claim 1, characterized in that the determination of a value comprises determining the use of a machine for one or more conversion sites, and wherein the selection of one of the products comprises selecting the product based on the determined use of the machine. The method according to claim 1, further characterized in that it comprises: determining a value for a first product selection parameter, for each of the products, determining a value for a second product selection parameter, for each one of the products; and select the product based on the first and second determined values, for each of the products. The method according to claim 1, characterized in that the determination of a value comprises determining the values for a plurality of product selection parameters, the method further comprising: computing a weighted average of the values for each of the products; and select the product based on the computed weighted averages, respectively. The method according to claim 1, characterized in that it further comprises: selecting non-overlapping regions of the network for at least two of the products; generate a composite defect map, based on the non-overlapped regions, selected; generate a conversion plan based on the composite defect map; and convert the network according to the conversion plan. The method according to claim 1, characterized in that the conversion of the network comprises: generating a conversion plan for the network, based on the determined effective defects and the selected product; and convert the network according to the generated conversion plan. 11. The method according to the claim 1, characterized by at least one subsequent algorithm disteingue at least a portion of the network in quality classifications. 1

2. A system, characterized in that it comprises: an image forming device, which imagines a sequential portion of a network for providing digital information; an analysis computer that processes digital information with an initial algorithm to identify regions on the network that contain anomalies; and a conversion control system that analyzes at least a portion of the digital information with at least one subsequent algorithm, to determine which anomalies represent effective defects in the network, for a plurality of different products, wherein the conversion control system determines a value of at least one product selection parameter for each of the products, and selects one of the products for the conversion of the network based on the value determined for each of the products. The system according to claim 12, characterized in that the conversion control system generates a conversion plan for the network, based on the determined effective defects and the selected product. The system according to claim 13, further characterized in that it comprises: a conversion server located within one of the conversion sites and coupled to the conversion control system, by a work network, wherein the control system The conversion plan electronically communicates the conversion plan to the localized conversion server with the conversion sites. 15. The system according to claim 14, characterized in that the conversion server displays the conversion plan for the conversion of the network to the selected product. The system according to claim 14, characterized in that the conversion server controls the configuration of a process line within the conversion, according to the conversion plan. 17. The system according to claim 12, characterized in that the conversion control system computes the respective value for each of the products, based on the actual defects determined, for the respective products. 18. The system according to claim 12, characterized in that the conversion control system computes one or more of an estimated utilization of the network for each of the products, an estimated number of components that could be produced for each of the products. the products, an estimate of total sales for each of the products, a processing time for the conversion of the network to each of the respective products, or a use of the machine for one or more conversion sites associated with the products . The system according to claim 12, characterized in that the conversion control system determines the values for a plurality of product selection parameters, computes a weighted average of the values for each of the products, and selects the product. based on the weighted, computed, respective averages. The system according to claim 12, characterized in that the conversion control system selects the non-overlapping regions of the network by at least two of the products, and generates a defect map, composite, over the non-overlapping regions selected. . The system according to claim 12, characterized in that the analysis computer processes the digital information with the initial algorithm and extracts a portion of the digital information for each of the identified regions, and the conversion control system analyzes the portions extracted from the digital information, to determine the effective defects for the plurality of different products. 22. The system according to claim 12, characterized in that the conversion control system comprises: an interconnection module with the user that presents an interconnection with the user to visually show the product selection parameter, as one of a plurality of product selection parameters, selected by the user, a database that stores data that define a group of conversion control rules; and a conversion control machine that, for each of the products, applies the conversion control rules to determine the values for the selection parameters of products selected by a user, and selects the product based on the determined values. 2

3. A conversion control system, characterized in that it comprises: a database that stores data defining a group of rules; an interconnection to receive anomalies information from the analysis machine, where the anomalies information identifies the regions of a network, which contain anomalies; and a conversion control machine that applies the rules to the anomaly information, to determine a value for at least one product selection parameter for each of a plurality of products, wherein the conversion control machine selects one of the products for the conversion of the network based on the determined values. 2 . The conversion control system according to claim 23, further characterized in that it comprises a plurality of defect processing modules that apply the image processing algorithms to determine which anomalies represent effective defects in the network, for the different products. 25. The conversion control system according to claim 23, characterized in that the database stores product data that defines each of the products in which the network can be converted. 26. The conversion control system according to claim 25, characterized in that the product database stores data specifying an estimated revenue per unit for each of the products, an estimated gain per unit for each of the products. , an estimated conversion time to convert a roll of network to each product, a current level of demand in the industry for each of the products, and where the conversion machine uses the product data when the rules are applied. 27. A computer-readable medium, characterized in that it comprises the instructions that cause a processor to: store data that define a group of rules; receives anomaly information from a localized analysis machine inside a manufacturing plant, where the anomaly information identifies the regions of a network that contains anomalies; apply the rules to the anomaly information to determine a value for at least one product selection parameter, for each of a plurality of products; and select one of the products for the conversion of the network based on the determined values. 28. The computer readable medium according to claim 27, characterized in that the instruction causes the processor to compute one or more of an estimated utilization of the network for each of the products, an estimated number of the components that could be produced for each of the products, a total estimated sales for each of the products, a processing time for the conversion of the network to each of the respective products, or a use of the machine for one or more conversion sites associated with the products. 29. The computer readable medium according to claim 27, characterized in that the instruction causes the processor: generates a conversion plan for the network, based on the actual defects determined, and the selected product; communicate the conversion plan to a conversion site to control the conversion of the network; and send the shipping instructions to ship the network to the conversion site, for the conversion.