Systems and methods for end-to-end inventory management - Patents.com

Abstract

Systems and methods for managing goods are described. The systems and methods described herein may include exemplary methods for manufacturing goods. The systems and methods provide an end-to-end manufacturing value chain as a closed system and feedback loop.

Description

[Background technology]

[0001] Traditional fabric manufacturing processes can take a long time from conception of a garment to production of the garment. However, changes in consumer clothing trends can occur quickly. Additionally, changes in manufacturing parameters, such as available resources, can affect whether a particular garment can be created. By the time the manufacturing process by which a garment is produced is changed to account for available resources to accommodate a new trend, another new trend may have emerged. What is needed is a fabric manufacturing process that can more efficiently adapt to changing trends and real-time manufacturing parameters.

[0002] As an example, U.S. Patent No. 9,623,578 describes a system for on-demand apparel manufacturing generally including a textile printer, a textile cutter, and a computing device configured to perform processes including aggregating product orders, organizing the orders according to productivity factors, and placing panels for the custom products on the aggregated textile panel templates. However, improvements are needed.

[0003] As a further example, U.S. Patent No. 9,782,906 generally describes a system for on-demand apparel manufacturing including a textile cutter and a computing device. The computing device is configured to perform a process including capturing an image of a textile sheet on the textile cutter, the textile sheet including a panel for a product. The process further includes identifying cutting control factors using the image of the textile sheet to account for textile threads, weave, nap, or knit patterns of the textile sheet, alignment of textile print patterns on the textile sheet, or panel deformation of the textile sheet on the textile cutter. However, changes to the material may occur from the textile cutter or table. Thus, improvements in textile cutting and registration are needed. Summary of the Invention

[0004] The present disclosure, in one or more aspects, relates to an end-to-end process for article management. Such articles may include garments, apparel items, accessories, fabric-containing components, and the like. The present disclosure, in one or more aspects, relates to producing articles within the tolerances of the design of such articles. Often, in conventional processes, the steps of article management are discrete and separate, and transitions between steps of the process result in errors or discrepancies from the intended design. The end-to-end process of the present disclosure may minimize such discrepancies and facilitate the production of articles, such as apparel, within tight tolerances of the intended design. In particular, the color of the finished product may be within a predetermined tolerance of the designed color. Alternatively or additionally, the methods and systems of the present disclosure may facilitate dynamic pricing, dynamic lead times, dynamic batching, dynamic delivery, and may provide customers with an individualized or customized process.

[0005] Traditional methods are locked into long, forecast-driven supply chains. The present disclosure provides a demand-driven apparel manufacturing process by bringing process steps such as coloring closer to the consumer.

[0006] Systems and methods for managing materials such as fabric manufacturing are described. The systems and methods described herein may include an exemplary method for manufacturing an article. The exemplary method may include receiving consumer data including at least biometric information associated with one or more consumers. The exemplary method may include receiving design input indicative of a design of the article, the design of the article being based on the consumer data. The exemplary method may include causing output of interactive content to a user interface associated with the one or more consumers, the interactive content including at least a representation of the design of the article. The exemplary method may include outputting manufacturing data indicative of instructions associated with manufacturing the article, the instructions being based on the design of the article. These and other fabric manufacturing management methods and systems are described herein. [Brief description of the drawings]

[0007] The following drawings illustrate generally, by way of example, but not by way of limitation, various examples discussed in the present disclosure.

[0008] [Figure 1A] An example diagram of the manufacturing process is shown. [Figure 1B] An example diagram of the manufacturing process is shown. [Figure 1C] An example diagram of the manufacturing process is shown. [Figure 1D] An example diagram of the manufacturing process is shown. [Figure 1E] An example diagram of the manufacturing process is shown. [Diagram 2] An example diagram of the design process is shown below. [Diagram 3] FIG. 2 is a flow diagram of an exemplary method. [Figure 4] FIG. 2 is a flow diagram of an exemplary method. [Diagram 5] An example diagram of nesting is shown below. [Figure 6] 1 shows an example of an item management process. [Figure 7A] 1 is exemplary data based on the processes of the present disclosure. [Figure 7B] Illustrates an exemplary treatment formulation. [Figure 7C] Illustrates an exemplary treatment formulation. [Figure 7D] FIG. 2 is a flow diagram of an exemplary method. [Figure 8] FIG. 1 is a flow diagram of an exemplary method. [Figure 9A] FIG. 2 is a flow diagram of an exemplary method. [Figure 9B] 1 illustrates an exemplary traceability mechanism. [Figure 9C] 1 illustrates an exemplary traceability mechanism. [Figure 9D] 1 illustrates an exemplary traceability mechanism. [Figure 9E] 1 illustrates an exemplary traceability mechanism. [Figure 10] FIG. [Figure 11] 1 illustrates an exemplary system. [Figure 12] 1 illustrates an exemplary system. [Figure 13] 1 illustrates an exemplary system. [Figure 14] 1 illustrates an exemplary process flow. [Figure 15] FIG. 2 is a flow diagram of an exemplary method. [Figure 16] 1 illustrates an exemplary system. [Figure 17] 1 illustrates an exemplary system. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] By way of example only, and not by way of limitation, systems and methods are described for managing articles such as clothing / apparel, including shirts, trousers, shorts, footwear, bags, and the like. The systems and / or methods may include end-to-end article management, such as manufacturing. The systems and / or methods may include all aspects of manufacturing, from designing the article of clothing to delivering the article of clothing to the customer. The systems and / or methods may incorporate information from one or more steps of the management or manufacturing process to affect other steps of the management or manufacturing process. Reference may be made herein to fabrics or textiles by way of example. However, application to a broader range of materials is contemplated and thus should not be limited to such exemplary terminology.

[0010] The systems and / or methods described herein may include one or more tools, units, or plants for managing articles such as clothing or garments from fabric to customer. The systems and / or methods described herein may include one or more garment manufacturing plants. The systems and / or methods described herein may include one or more computing devices associated with one or more garment manufacturing plants associated with one or more respective garment manufacturers. The systems and / or methods described herein may include one or more cloud computing environments associated with one or more garment manufacturing plants. The systems and / or methods described herein may include one or more client devices, such as laptops, desktops, smartphones, wearable devices, tablets, etc. The one or more client devices may communicate with the one or more computing devices and / or the one or more cloud computing environments over a network. The one or more client devices may include one or more applications running on the one or more client devices.

[0011] The systems and / or methods described herein may include a business process. The business process may include a process for the creation of a job file (e.g., 110 in FIG. 1A). The job file may include instructions for an authoring tool, instructions for digital asset management, and / or instructions for patterns and / or markers. The authoring tool may include two-dimensional design tools, three-dimensional polygonal design tools, and / or three-dimensional parametric design tools. Digital asset management may include information about digital materials, graphics, images, three-dimensional assets, color profiles, fit blocks, design library management, material development, line planning, bill of materials, material testing, vendor collaboration, and / or financial planning. As described herein, digital asset management may include or be based on data about material physical properties, material spectral reflectance and refractive properties, material performance properties, material provenance and associated resource consumption, batch serialization, and the like. The patterns and / or markers may include information about patterns, fiducials, cut data, grading, graphic images, colors, cut plans, job status management, and / or material utilization. The job file may contain options selected by a user.The job file may contain options selected by a user.The job file may contain options selected without user intervention.

[0012] A job file may be created by one or more computing devices ("job file creators"). The job file creators may communicate over a network with one or more computing devices configured to collect real-time and / or near real-time manufacturing and / or consumer data ("data collection"). The job file and / or available parameters associated with the job file may be affected by the real-time data received from the data collection. The job file creators may communicate over a network with one or more computing devices configured to cause the execution of one or more manufacturing processes ("controllers"). The job file creators may provide a job file to the controller. The controllers may cause the execution of one or more manufacturing processes in accordance with the job file.

[0013] The systems and / or methods described herein may include a feedback loop for the designer. Bio-data and / or consumer data may be captured and trends may be identified. Options available to the designer on his / her design tool may be influenced by the captured bio-data and / or consumer data. The designer may design a garment based on the options available on his / her design tool. Bio-data and / or consumer data associated with the designed garment may be captured and trends may be identified, thus restarting the feedback loop.

[0014] The systems and / or methods described herein may use nesting to efficiently use material that needs to be cut. Nesting involves arranging patterns to be cut from material such that there is less waste from cutting. Nesting may involve arranging components with similar or identical colors and / or patterns at their boundaries such that two such boundaries of two components are adjacent to one another. Nesting may involve using color overlap between two or more components. Nesting may involve dynamically batching orders.

[0015] The systems and / or methods described herein may include a foam pretreatment process. The foam pretreatment process may replace a traditional dipping process. The foam pretreatment process may reduce water. The foam pretreatment process may reduce energy. The foam pretreatment process may reduce chemical usage. The foam pretreatment process may achieve deeper and / or richer color. The foam pretreatment process may dry easier than a traditional dipping process.

[0016] The systems and / or methods described herein may include a plasma pre-cleaning / activation process. The atmospheric plasma pre-cleaning / activation process may include corona plasma. The atmospheric plasma pre-cleaning / activation process may be used to clean fabrics and / or materials and increase the surface roughness of fabrics / fibers and / or materials to improve adhesion properties. The atmospheric plasma pre-cleaning / activation process may be used to vaporize (decompose) contaminants (e.g., oils, waxes, etc.) from fabrics and / or materials. The atmospheric plasma pre-cleaning / activation process may clean fabrics and / or materials after and / or before foam pretreatment and / or padding processes. The atmospheric plasma pre-cleaning / activation process may activate fabrics and / or materials. The plasma pre-cleaning / activation process may achieve deeper and / or more saturated colors on fabrics and / or materials while using less dyes and / or chemicals. The plasma pre-cleaning / activation process does not require water and may occur at ambient temperature. The plasma pre-cleaning / activation process may be applied with different carrier gases, such as air, oxygen, nitrogen, helium, argon, hydrocarbon-based gases, fluorocarbon-based gases, and / or mixtures of different gases. Each gas provides a different surface topography, chemistry, and surface energy to the fabric and / or material. Several grafting reactions (functionalization reactions) may occur between the fabric and / or material and the plasma carrier gas. The chemical composition of the fabric and / or material on the surface may change after plasma treatment. The systems and / or methods described herein may include a color analysis process. The color analysis process may compare the intended color to the actual color. The color analysis process may determine the intended color from digital data, such as data from a job file. The color analysis process may determine the actual color using computer vision.

[0017] The systems and / or methods described herein may include inserting and / or adding one or more sensors to a fabric operation, such as for in-line inspection of materials before, during, and / or after operation. The one or more sensors may include a spectrometer. The one or more sensors may include an optical spectrometer. The one or more sensors may include a spectrophotometer. At one or more steps of the manufacturing process, one or more sensors may be inspected to ensure quality. The one or more sensors may be inspected manually by a human. The one or more sensors may be inspected by one or more computing devices. Inspection of the one or more sensors by the one or more computing devices may include comparing an observed data set to an expected data set. Inspection of the one or more sensors by the one or more computing devices may include triggering an alert when a difference between the observed data set and the expected data set exceeds a predetermined threshold. In one aspect, identification may be added to the material, such as a barcode, QR code, invisible marker, etc., to allow the code to be read or sensed with a reader device, such as a spectrometer, for example.

[0018] The systems and / or methods described herein may include an observation process. The observation process may include observing a pattern on a fabric. The fabric may undergo one or more manufacturing steps. The observation process may include observing the pattern on the fabric after the one or more manufacturing steps. The observation process may include determining a delta between a pattern observed on the fabric before the one or more manufacturing steps and a pattern observed on the fabric after the one or more manufacturing steps. The observation process may be performed by one or more computing devices ("observers"). The observers may provide the determined delta to one or more computing devices in communication with a machine for cutting the fabric.

[0019] Digital Product Creation Traditional processes for creating articles include siloed manual steps / operations. The present disclosure allows consumers to customize products via an ordering system and may include functionality for user input data, such as their measurements. The software may generate automatic patterns, and based on established tailoring rules, the solution will select the appropriate pattern. Such software may include custom selection of color or graphic(s) that may be used for automatic generation of patterns or selection of existing patterns. The pattern may then be associated with a design bill of materials that includes work instructions to the manufacturing site. The system and method may be integrated with back-end systems that enable on-demand manufacturing.

[0020] Custom Made Today, front-end consumer systems offer the ability to customize a product from a list of options. These options are mapped to and therefore limited by the back-end manufacturing system. The ability to customize to a unique size is limited. Furthermore, the option to personalize the product is also limited. The option to do this on demand does not exist. The present disclosure provides the ability to customize a product or add user input information to the product. The system and method can dynamically configure the product to fit an individual's requirements and create a package that is ready to be manufactured. The system and method can be further extended to automate much of the product creation.

[0021] Image Predistortion In conventional textile processing, textile materials are processed in a "web" form, whereby mechanical forces and / or mechanical forces combined with heat induce distortions throughout the "web." It is suggested that while images printed on digital printers can be controlled to within nanometers of accuracy, subsequent processing can result in distortions that differ from those intended by the creator. The present disclosure receives information associated with distortions of the raw material web through downstream processing, and then maps the distortions so that the images applied to the web at the digital printing stage can be "pre-distorted" to thereby match the final product to the creator's intent. As an example, the present disclosure can address one or more shortcomings of conventional processes using a feedback loop / verification, for example, as shown in FIG. 12.

[0022] Manipulation of materials in the manufacturing process (e.g., manipulation of top materials printed near the end) can result in warping and skewing of the image or pattern from the intended print. According to the present disclosure, at least the example illustrated in Figure 9A can produce a final product that is truer to the original form by including pre-distortion in the print job file to account for downstream processes.

[0023] Material classification Current industry practices are fragmented and do not consider substrates and their respective effects on multi-spectral color refraction, translucency, opacity, etc. Additionally, substrate construction has a substantial effect on how a fabric drapes and / or flows in real life. Additional input data such as whiteness index, pH, mercury level, refraction and reflection index, thickness, compression, bending, roughness, friction, thermal properties, smoothness, softness, warmth, puckering, distortion, composite measurements thereof, or one or more of naturally observed performance history and variations. According to aspects of the present disclosure, by collecting substrate properties and performance data (e.g., whiteness index, pH, etc.) and integrating these properties, key design and performance properties can be digitally reproduced in a digital format, i.e., a digital twin can be created that can produce an authentic reproduction of real life.

[0024] Order Creation and Job Management 1A-1E show an example diagram of a management (e.g., manufacturing) process. While an example sequence is shown, it is understood that the various steps may be performed in any order, selectively performed, or not performed. Feedback loops from one or more downstream processes may be received and used to update one or more upstream processes. As an example, data collected at any one of the manufacturing steps may be shared upstream or downstream in the end-to-end process and used to update other processes. As a further example, all manufacturing steps may be performed at a single facility to provide complete end-to-end control. However, data shared between steps may allow one or more processes to be located at different facilities without losing control or standardization. At 100, a project request and / or order may be received. The project request and / or order may be received at one or more computing devices associated with the garment manufacturer. The project request and / or order may be received at a cloud computing environment associated with the garment manufacturer. As used herein, manufacturing or manufacturer may refer to an operation or entity associated with any part of the management of the production and delivery of an article. A project request and / or order may be received from a client device associated with a customer.

[0025] In response to project requests and / or orders being received, a job (e.g., order, project, etc.) file 110 may be created. The job file 110 may include graphic design files 112, grading information 114, quantity / yardage requests 116, kitting data 118, order data 120, pattern files 122, substrates 124, finishing data 126, assembly data 128, and / or tracking and / or routing data 130. The job file 110 may include a bill of materials and / or serialization data. Other information may be included in the job file 110 or referenced within / by the job file 110.

[0026] In response to receiving a project request and / or order, a determination may be made at 102 whether fabric associated with the project request and / or order has already been created. One or more computing devices and / or cloud computing environments associated with the garment manufacturer may determine whether fabric associated with the project request and / or order has already been created. If fabric has not yet been created, the process may move to 106. If fabric has already been created, the process may move to 104.

[0027] At 104, a determination may be made whether fabric associated with the project request and / or order is in stock. One or more computing devices and / or cloud computing environments associated with the garment manufacturer may determine whether fabric associated with the project request and / or order is in stock. If the fabric is in stock, the process may move to 108. If the fabric is not in stock, the process may move to 106.

[0028] At 106, fabric associated with the project request and / or order may be ordered. One or more computing devices and / or cloud computing environments associated with the garment manufacturer may order the fabric from a fabric supplier. After fabric associated with the project request and / or order is ordered, the material testing database 134 may be updated and the process may move to 108.

[0029] At 108, a cycle of purchase orders and / or payment procedures associated with the fabric associated with the project request and / or order may be processed. One or more computing devices and / or cloud computing environments associated with the garment manufacturer may process the cycle of purchase orders and / or payment procedures. One or more computing devices and / or cloud computing environments associated with the garment manufacturer may have another one or more computing devices process the cycle of purchase orders and / or payment procedures.

[0030] After the cycle of purchase orders and / or payment procedures associated with the fabrics associated with the project request and / or order has been processed, inventory control may be performed at 132. One or more computing devices and / or cloud computing environments associated with the garment manufacturer may perform and / or cause inventory control to be performed. Performing inventory control may update inventory to reflect the cycle of purchase orders and / or payment procedures associated with the fabrics associated with the project request and / or order. Performing inventory control may include using inventory information as part of a fabric intake process at 138. Performing inventory control may include updating inventory information based on the fabric intake process at 138. Performing inventory control may include updating inventory information based on a fabric pretreatment process at 140.

[0031] The material testing database 134 may be in communication with one or more computing devices associated with the fabric supplier mill 136. The material testing database 134 may place fabric orders with the fabric supplier mill 136. The fabric supplier mill 136 may have the fabric delivered to a garment manufacturer as part of the fabric intake process at 138.

[0032] At 138, a material operator or manager, such as a garment manufacturer, may have or receive material (e.g., fabric from fabric supplier mill 136 as part of the fabric intake step at 138). Other materials may be used. After the fabric intake step, the process may move to a fabric pretreatment step at 140. It is understood that such a process is not limited to garment manufacturing, but is a non-limiting example. Other entities and operators may perform the same or similar operations. In one aspect, operational capabilities, such as manufacturing capacity of a particular product, may be considered to determine the estimated lead time (e.g., in real time), allowing for surge pricing / priority pricing.

[0033] After the fabric intake step at 138, lab and / or visual inspection may be performed at 142. Inspection may include human inspection. Inspection may include inspection using computer vision. Inspection may include inspection of one or more sensors in communication with the fabric. If the fabric fails inspection, the material testing database 134 may be updated (which may cause the material testing database 134 to order more fabric from the fabric supplier mill 136). The results of the lab and / or visual inspection may be passed to lab and / or visual inspection at 144.

[0034] The fabric pretreatment step at 140 may include a foam pretreatment process. The foam pretreatment process may replace a traditional soaking process. The foam pretreatment process may reduce water. The foam pretreatment process may reduce energy. The foam pretreatment process may reduce chemical usage. The foam pretreatment process may achieve deeper and / or richer color. The foam pretreatment process may dry easier than a traditional soaking process. Fabric that has undergone the foam pretreatment process may be used for a fabric conditioning step at 146.

[0035] After the fabric pre-treatment step at 140, lab and / or visual inspection at 144 may be performed. Inspection may include human inspection. Inspection may include computer vision, machine vision, and machine learning. Inspection may include inspection of one or more sensors in communication with the fabric. If the fabric fails inspection, the material testing database 134 may be updated (which may cause the material testing database 134 to order more fabric from the fabric supplier mill 136). If the fabric fails inspection, depending on the type of failure / defect, it may also result in the generation of a new order to make up for the failed material. The failure may result in a change to the estimated lead time. The results of the lab and / or visual inspection may be passed to one or more computing devices associated with the fabric conditioning step at 146.

[0036] The fabric conditioning step at 146 may include an atmospheric plasma pre-cleaning / activation process. The atmospheric plasma pre-cleaning / activation process may include corona plasma. The atmospheric plasma pre-cleaning / activation process may be used to clean the fabric and / or material and increase the surface roughness of the fabric / fiber and / or material to improve adhesion properties. The atmospheric plasma pre-cleaning / activation process may be used to vaporize (decompose) contaminants (e.g., oils, waxes, etc.) from the fabric and / or material. The atmospheric plasma pre-cleaning / activation process may clean the fabric and / or material after and / or before the foam pretreatment and / or pad process. The atmospheric plasma pre-cleaning / activation process may activate the fabric and / or material. The plasma pre-cleaning / activation process may achieve deeper and / or more saturated colors on the fabric and / or material while using less dyes and / or chemicals. The plasma pre-cleaning / activation process does not require water and may occur at ambient temperature. The plasma pre-cleaning / activation process may be applied with different carrier gases, such as air, oxygen, nitrogen, helium, argon, hydrocarbon-based gases, fluorocarbon-based gases, and / or mixtures of different gases. Each gas provides a different surface topography, chemistry, and surface energy to the fabric and / or material. Several grafting reactions (functionalization reactions) may occur between the fabric and / or material and the plasma carrier gas. The chemical composition of the fabric and / or material on the surface may change after plasma treatment. The fabric that has undergone the plasma pre-cleaning / activation process may be used in the printing step at 158.

[0037] After the fabric conditioning process at 146, a lab and / or visual inspection at 148 may be performed. The inspection may include human inspection. The inspection may include inspection using computer vision. The inspection may include inspection of one or more sensors in communication with the fabric. If the fabric fails inspection, the material testing database 134 may be updated (which may cause the material testing database 134 to order more fabric from the fabric supplier mill 136). The results of the lab and / or visual inspection may be passed to one or more computing devices involved in the printing process at 158.

[0038] At 150, the job file 110 may be used as part of a nested pattern process. One or more computing devices may use the job file 110 as part of the nested pattern process. The pattern file 122 of the job file 110 may be used as part of the nested pattern process. Other parts of the job file 110, such as the graphic design file 112, the grading information 114, etc. may also be used. The nested pattern process will be described in more detail with reference to FIG. 3. After the nested pattern process, the process may move to 152. As described herein, the nesting may be generated or updated based on upstream or downstream information. Alternatively or additionally, the nesting may be updated based on information received related to downstream process or device performance. For example, in the case of a cutting process or machine, or if a material handling process or machine / system is performing in a particular manner, the nesting may be updated based on such performance information.

[0039] At 152, a cut file may be generated. The cut file may be generated in response to a nested pattern process. One or more computing devices may generate the cut file. The cut file may contain information for cutting components from the fabric. The cut file may be used in a cutting process at 182. After the cut file is generated, the process may move to 154. For purposes of illustration, various files are referenced. It should be understood that several files or a single file may be used.

[0040] At 154, a color separation step may be performed. One or more computing devices may perform the color separation step. The job file 110 may be used to perform the color separation step. After the color separation step, the process may move to 156.

[0041] At 156, a raster image processing step may be performed. One or more computing devices may perform the raster image processing step. The job file 110 may be used to perform the raster image processing step. After the raster image processing step, the process may move to 158.

[0042] At 158, a printing step may be performed. Although the term printing is used, it should be understood that drop-on-demand refers to a general selective process including, for example, selective deposition of materials and digital printing. The printing step may include printing color and / or graphics onto the fabric. One or more computing devices may print color and / or graphics onto the fabric. The results of the raster image processing step at 156 may be used to affect the printing of color and / or graphics onto the fabric. The job file 110 may be used to affect the printing of color and / or graphics onto the fabric. Fabric that has undergone the printing process may be used in a post-printing dyeing step at 166.

[0043] After the printing step at 158, a lab and / or visual inspection at 160 may be performed. The inspection may include human inspection. The inspection may include inspection using computer vision. The inspection may include inspection of one or more sensors in communication with the fabric. The inspection may determine whether and to what extent a difference exists between the expected color and the color actually printed on the fabric during the printing step at 158. Results of the lab and / or visual inspection may be passed to one or more computing devices associated with a color control / printer calibration step at 162. One or more computing devices associated with the color control / printer calibration step at 162 may provide information to aid in the inspection at 160. Results of the lab and / or visual inspection may be passed to one or more computing devices involved in the post-printing dyeing step at 166.

[0044] At 162, one or more computing devices associated with the color control / printer calibration process may determine and / or receive information indicating a discrepancy between an expected color and a color actually printed on the fabric at 158. One or more computing devices associated with the color control / printer calibration process may determine a new paint color to correlate with the expected color. One or more computing devices associated with the color control / printer calibration process may determine that the new paint color requires more or less of a particular color, such as red, blue, and / or green, to approximate the expected color. One or more computing devices associated with the color control / printer calibration process may communicate with one or more computing devices associated with the addressable color process update library at 164.

[0045] At 164, one or more computing devices associated with the addressable color process update library may update the addressable color library based on information from one or more computing devices associated with the color control / printer calibration process. One or more computing devices associated with the addressable color process update library may assign the determined new paint colors to expected colors. One or more computing devices associated with the addressable color process update library may trigger new project requests and / or orders at 110 using the updated library of addressable colors to be created.

[0046] At 166, a post-print drying step may be performed. A post-print dyeing step may include drying the fabric. The one or more computing devices may dry the fabric. The fabric that has undergone the post-print dyeing process may be used in a fixing / steaming step at 170.

[0047] After the post-print drying step at 166, a lab and / or visual inspection at 168 may be performed. The inspection may include human inspection. The inspection may include inspection using computer vision. The inspection may include inspection of one or more sensors in communication with the fabric. If the fabric fails inspection, the material testing database 134 may be updated (which may cause the material testing database 134 to order more fabric from the fabric supplier mill 136). The results of the lab and / or visual inspection may be passed to one or more computing devices involved in the fixing / steaming step at 170.

[0048] At 170, a fix / steaming step may be performed. The fix / steaming step may include steaming the printed and / or dyed fabric. One or more computing devices may cause the fabric to be steamed. The fabric that has undergone the fix / steaming step may be used in a post-printing wash step at 174.

[0049] After the fixation / steaming step at 170, a lab and / or visual inspection at 172 may be performed. The inspection may include human inspection. The inspection may include inspection using computer vision. The inspection may include inspection of one or more sensors in communication with the fabric. The results of the lab and / or visual inspection may be passed to one or more computing devices involved in the post-printing wash step at 174.

[0050] At 174, a post-printing cleaning step may be performed. The post-printing cleaning step may include steaming and / or cleaning the fixed fabric. The one or more computing devices may cause the fabric to be washed. The fabric that has undergone the post-printing washing step may be used in a post-printing drying step at 178.

[0051] After the post-printing process at 174, a lab and / or visual inspection at 176 may be performed. The inspection may include human inspection. The inspection may include inspection using computer vision. The inspection may include inspection of one or more sensors in communication with the fabric. The results of the lab and / or visual inspection may be passed to one or more computing devices involved in the post-printing drying process at 178.

[0052] At 178, a post-printing drying step may be performed. The post-printing drying step may include drying the washed fabric. One or more computing devices may dry the fabric. The fabric that has undergone the post-printing drying step may be used in a cutting step at 182.

[0053] After a post-print drying process at 178, a lab and / or visual inspection at 180 may be performed. The inspection may include human inspection. The inspection may include inspection using computer vision. The inspection may include inspection of one or more sensors in communication with the fabric. If the fabric fails inspection, the material testing database 134 may be updated (which may cause the material testing database 134 to order more fabric from the fabric supplier mill 136). The results of the lab and / or visual inspection may be passed to one or more computing devices associated with the cutting process at 182.

[0054] At 182, a cutting step may be performed. The dried fabric may be cut. The dried fabric may be cut according to the cut file generated at 152. One or more computing devices may cause the fabric to be cut. The fabric that has undergone the cutting step may be used in a batch processing step at 184.

[0055] At 184, a batching process may be performed. The cut fabric may be batched. One or more computing devices may cause the fabric to be batched. Fabric that has undergone the batching process may be used in a kitting process at 186.

[0056] At 186, a kitting step may be performed. The batched fabric may be kitted. One or more computing devices may cause the fabric to be kitted. The fabric that has undergone the kitting step may be used in an assembly step at 188.

[0057] At 188, an assembly process can be performed. The kitted fabric can be assembled. One or more computing devices can cause the fabric to be assembled. The fabric that has undergone the assembly process can be shipped to a customer.

[0058] Other steps and processes may be performed. Steps may be selectively performed or not performed. Data may be shared between processes and processes may be updated based on shared data related to the performance of upstream and / or downstream processes and equipment.

[0059] Design / Product Development Current design and product development tools are not digitally linked to any actual production method. According to this disclosure, the creation of a digital product may include manufacturing (printing) instructions created from a design platform. The feasibility of coloring will have a feedback loop to inform the design platform and define the designer's choices for the product.

[0060] 2 shows an example diagram of a design process. At 200, consumer data may be received (e.g., collected, etc.). The consumer data may include biometric data. The consumer data may be collected from one or more consumers. The consumer data may be collected from one or more wearable devices. The consumer data may be collected from one or more e-commerce websites. The consumer data may be collected from a feedback loop. The consumer data may be collected from a repository.

[0061] At 202, the designer user interface may be influenced by consumer data. The design tool's choice of color and / or design may be influenced by consumer data. The design tool's choice of color and / or design may be influenced by business reasons, such as preferred materials to be prominently featured in the designer user interface, or non-preferred colors and / or patterns not permitted by the designer user interface. The designer user interface may be associated with two-dimensional and / or three-dimensional design and / or development tools.

[0062] At 204, the visualization tool may be influenced by two-dimensional and / or three-dimensional design and / or development tools. The visualization tool may be influenced by consumer data. Colors and / or designs that are options for and / or appear in visualizations created by the visualization tool may be influenced by consumer data.

[0063] At 206, the interactive consumer experience may be presented to the consumer via the e-commerce website. The interactive consumer experience presented to the consumer may be influenced by visualization tools. The interactive consumer experience presented to the consumer may be influenced by two-dimensional and / or three-dimensional design and / or development tools. The interactive consumer experience presented to the consumer may be influenced by consumer data. Colors and / or designs that are options and / or appear in the interactive consumer experience may be influenced by consumer data. Feedback from the interactive consumer experience may be new consumer data at 200.

[0064] At 208, drop-on-demand (e.g., digital) and / or traditional manufacturing can be influenced by visualization tools. Digital and / or traditional manufacturing can be influenced by two-dimensional and / or three-dimensional design and / or development tools. Digital and / or traditional manufacturing can be influenced by consumer data. Colors and / or designs that are and / or appear in digital and / or traditional manufacturing options can be influenced by consumer data. Feedback from digital and / or traditional manufacturing can be new consumer data at 200.

[0065] At 210, rapid manufacturing can be influenced by visualization tools. Rapid manufacturing can be influenced by two-dimensional and / or three-dimensional design and / or development tools. Rapid manufacturing can be influenced by consumer data. Colors and / or designs that are and / or appear in rapid manufacturing options can be influenced by consumer data. Feedback from rapid manufacturing can be new consumer data at 200.

[0066] Design and development for fashion trends is currently fragmented and not directly driven by consumer demand, designers make their best guesses as to what the trends will be and hope for the best. This traditional approach is not optimized. According to the present disclosure, an improved on-demand feedback loop can enable data-driven prediction of the color usage and design required.

[0067] Referring to Figure 3, a method for manufacturing an article is illustrated. The method may enable customization. The method may enable dynamic pricing. The method may enable dynamic lead time determination. The method may enable dynamic shipping.

[0068] At step 310, consumer data including at least biometric information associated with one or more consumers may be received. One or more computing devices may receive consumer data including at least biometric information associated with one or more consumers. The consumer data may include consumer preference information.

[0069] In step 320, design inputs indicative of a design for an article may be received. One or more computing devices may receive the design inputs indicative of a design for an article. The design for the article may be based on consumer data. The design inputs indicative of a design for an article may be consumer-oriented, such as design inputs made to measure an article, or a personalized and / or custom article. The design inputs indicative of a design for an article may be used in product design for mass-produced articles. The design inputs indicative of a design for an article may include automated pattern creation. The design inputs indicative of a design for an article may come directly from a manufacturer. The design inputs indicative of a design for an article may be fitted to a design model. A "fit model" is a model utilized by a brand to design sizing parameters for a product line, i.e., a standard collection of dimensions scaled to each available size.

[0070] At step 330, output of interactive content may be triggered to a user interface associated with one or more consumers. One or more computing devices may trigger output of interactive content to a user interface associated with one or more consumers. The interactive content may include at least a representation of a design of the article.

[0071] In step 340, manufacturing data indicative of instructions associated with manufacturing the article may be output. One or more computing devices may output manufacturing data indicative of instructions associated with manufacturing the article. The instructions may be based on a design of the article. Outputting the manufacturing data may include outputting at least a portion of the manufacturing data to a digital printing system. The manufacturing data may be provided directly from a designer to a manufacturer. The manufacturing data may also be provided directly from a customer to a manufacturer.

[0072] Coloring data indicative of the feasibility of the coloring may be received. The one or more computing devices may receive the coloring data indicative of the feasibility of the coloring. The design of the article may depend on the coloring data.

[0073] Sensors on the garment may detect when the garment is being worn. The sensors may communicate with applications running on the client devices. The applications may relay information from the sensors to a central server. The central server may include applications that determine trend information, such as which colors, which patterns, and / or which fabrics are being worn most often. The central server may provide the determined trend information to a server associated with the e-commerce website or to a browser running on a user device accessing the e-commerce website. The e-commerce website may make suggestions based on the determined trend information.

[0074] 4, a method for article development is illustrated. At step 410, consumer data including at least biometric information associated with one or more consumers may be received. One or more computing devices may receive the consumer data including at least biometric information associated with the one or more consumers. The consumer data may include consumer preference information.

[0075] At step 420, trend data indicative of one or more trends in article design or article coloring may be received. One or more computing devices may receive the trend data indicative of one or more trends in article design or article coloring.

[0076] At step 430, output of one or more design alternatives may be triggered based at least on the consumer data and the trend data and via a user interface. One or more computing devices may trigger output of one or more design alternatives based at least on the consumer data and the trend data and via a user interface.

[0077] At step 440, design inputs indicating a design of the article may be received. One or more computing devices may receive the design inputs indicating a design of the article.

[0078] In response to receiving the design input, fabric types may be presented to the designer. The designer may select one or more of the presented fabric types. In response to the selected one or more types of fabric, an integrated technology package may be created for the designer. The integrated technology package may match the design input and the selected one or more fabric types. An engineering design bill of materials for the integrated technology package may be generated. The engineering design bill of materials may be generated on demand.

[0079] Sensors on the garment may detect when the garment is being worn. The sensors may communicate with applications running on the client devices. The applications may relay information from the sensors to a central server. The central server may include an application that determines trend information, such as which colors, which patterns, and / or which fabrics are most often worn. The central server may provide the determined trend information to a server associated with the remotely accessible designer tool or to a browser running on a user device accessing the remotely accessible designer tool. The designer tool may make suggestions based on the determined trend information. A user may create a design based on the suggestions. A user may create an order based on the design. A bill of materials may be automatically generated based on the order.

[0080] As an example, a method for article management may include receiving consumer data including at least biometric information associated with one or more consumers. The consumer data may further include consumer preference information. The method may include receiving design inputs indicating a design of the article. The design of the article may be based on the consumer data and / or other inputs. The method may include triggering output of interactive content to a user interface associated with the one or more consumers. The interactive content may include at least a representation of the design of the article. The method may include outputting article data including at least manufacturing data indicating instructions associated with manufacturing of the article. Outputting the manufacturing data may include outputting at least a portion of the manufacturing data to a drop-on-demand system (e.g., a digital printing system). The instructions may be based on the design of the article. The article data may be configured to be received by one or more computing devices associated with one or more manufacturing processes, the one or more manufacturing processes being updated based on at least the article data. The method may include receiving coloring data indicating feasibility of coloring, the design of the article depending on the coloring data. The method may include generating a technical package based on the design of the article and a selection of fabric. The method may include outputting a bill of materials based on the design of the fabric. Other steps may be used. As a further example, the one or more methods may include where the article data includes nesting information indicative of a spatial arrangement of one or more parts of the article, and the nesting information is derived at least in part from one or more of a property of a material used to form at least a portion of the one or more parts of the article, one or more treatments applied to a material used to form at least a portion of the one or more parts of the article, a desired web speed, or an operation performed by a pick-and-place system configured to move the one or more parts of the article once separated from the material. The nesting information may be derived at least in part from a feedback loop associated with the operation of the pick-and-place system.Data may be collected from any number of systems, subsystems, or devices and shared upstream and / or downstream to enable updates in one or more processes.

[0081] Order Aggregation and Batching Conventional order processing for digital printers does not consider the entire manufacturing process of a single order fulfillment system. It is primarily driven by a fragmented value chain where each process considers the efficiency of its respective process but not the entire manufacturing process and its associated overall costs. The present disclosure provides dynamic nesting optimization. As an example, dynamic nesting optimization may include individual consumer orders, specifically one theoretical minimum order quantity (MOQ), and batch processing order components that maximize production and delivery speed to the end customer within business-oriented product performance, unit cost / margin, and sustainability parameters.

[0082] In a single unit order run, individual components may have vastly different levels of ink applied. In the subsequent washing process, components with high levels of ink may cross-contaminate adjacent components with low levels of ink (e.g., a light red component adjacent to a white component), leading to a loss of quality. According to the present disclosure, by analyzing the levels at which ink is required to print each separate component, a nested pattern may be created that starts with the lowest level of ink and builds up to the highest level of ink. Hence, a dark, saturated component (e.g., a light red component) will be adjacent to a component that is also dark in color, thereby hiding cross-contamination from dark to light colors. As the material progresses through the washing process, the lightest colors go in first (when the washer water is at its cleanest) and the dark colors go in last. This may allow for less water and chemicals to be used for the light colors, making the entire process more efficient and sustainable.

[0083] Traditional production planning processes do not consider optimizing small batches (on the order of a single unit) for large scale operations that utilize both digital manufacturing processes (e.g., digital printing) and traditional "continuous" production processes (e.g., drying, washing). According to the present disclosure, rules can be used to aggregate and organize small batches into larger batches while considering different downstream routings, whereby small batches can be aggregated for a common process and then split into smaller batches for separate routings in a manner that can be efficiently scheduled during production.

[0084] nesting The current process of creating the RIP and print job file does not consider the separate throughput speed of the actual printing or downstream processing. The present disclosure can integrate printing, finishing, assembly, and other manufacturing process considerations into the batch throughput for greater efficiency and overall speed.

[0085] Nesting optimization in currently used digital printing processes is very poor (30%-40% material waste) at 60%-70% per unit, compared to 80%-95% (5%-20% material waste) per unit material optimization in traditional clothing manufacturing. Nesting optimization needs to be improved within the digital printing space to make the process sustainable and feasible at commercial scale. The present disclosure may use optimized nesting of components produced on demand to approach the efficiency of traditional manufacturing with 80%-95% per unit material utilization.

[0086] FIG. 5 shows an exemplary set of clothing items illustrating nesting. A first clothing item 500 may include two sets of colors. The first color may include the top half of the first clothing item 500. The second color may include the bottom half of the first clothing item 500. A second clothing item 502 may include two sets of colors. The top right half of the second clothing item 502 may include the second color. The bottom left half of the second clothing item 502 may include the third color. A third clothing item 504 may include one color, i.e., the second color. A fourth clothing item 506 may include one color, i.e., the first color. A fifth clothing item 308 may include one color, i.e., the third color.

[0087] Nesting may include arranging the clothing items 500, 502, 504, 506, 508 such that the colors of adjacent borders of the clothing items 500, 502, 504, 506, 508 may be similar. The third clothing item 506 may be positioned adjacent to the top half of the first clothing item 500. The bottom left half of the second clothing item 502 may be positioned adjacent to the fifth clothing item. Two or more of the bottom half of the first clothing item 500, the top right half of the second clothing item 502, and the third clothing item 504 may be positioned adjacent.

[0088] In an illustrative example, garment parts may be transferred and / or stacked (aggregated) using a mechanical arm (or robot). A number of such mechanical arms with corresponding end effectors may comprise a pick-and-place production line. The pick-and-place process (with transfer and stacking) is typically much slower than other processes of the envisioned system and may therefore be considered a "bottleneck". However, the process may be improved with nesting protocols that take into account the specific placement and transfer characteristics of the mechanical arms to maximize throughput. The nesting placement may vary depending, for example, on fabric properties (porosity, stiffness, etc.), the type of treatment applied to the fabric, the desired web speed, additional operations performed by the mechanical arm, etc. Initiating the nesting placement may be performed by a human or by nesting software. As the pick-and-place process occurs, the mechanical arm sends feedback to the computer, which may result in a modified nesting placement that maximizes the overall throughput and / or pick-and-place speed.

[0089] As an example, garment parts may be grouped by size, e.g., small and large. The grouping threshold and number of groups may be determined for a particular operation or desired output. As far as textile materials are concerned, different pick and place techniques may be used for small parts compared to the technique used for large parts. Special nesting may be created to account for the various time delays associated with any particular machine arm (e.g., adjusting the gripper size and placement so that large parts can be picked up immediately after small parts, etc.). The pick and place system described above may be configured with nesting optimization to allow the system to handle multiple small parts at once or as a series in time. The pick and place system may be configured with nesting optimization to handle individual large parts or a mix of small and large parts. The pick and place system may be configured with nesting optimization to maximize throughput speed and fabric utilization. Other optimizations may be used.

[0090] Component Manufacturing FIG. 6 shows an example diagram of a manufacturing process for a component. At 600, a printed fabric may be received. The fabric may have been printed at a printing step at 158 ​​of FIG. 1. The fabric may have been dried at a post-printing drying step at 166 of FIG. 1. The fabric may include a custom upper portion of a shoe. The fabric may include rows and / or columns, and each row and column combination may include the same print. The fabric may include cotton canvas.

[0091] At 602, the fabric may be finished. Finishing the fabric may include steaming the fabric. The fabric may be steamed at a fixing / steaming step at 170 of FIG. 1. Finishing the fabric may include washing the fabric. The fabric may be washed at a post-printing washing step at 174 of FIG. 1. Finishing the fabric may include drying the fabric. The fabric may be dried at a post-printing drying step at 178 of FIG. 1.

[0092] At 604, a liner may be applied to the fabric. The liner may be adhered to the back side of the fabric. The fabric may be a printed and finished cotton canvas. Alternative or additional methods may be used.

[0093] At 606, components may be cut in the fabric. A laser, router, or knife may be used to cut the components in the fabric. Partial shavings may remain in the cut components. Each row and column combination may be cut completely.

[0094] At 608, the combination of fully cut fabrics (rows and columns) may be stacked. The fully cut fabrics may be stacked such that the partially cut elements of one fabric layer align with the corresponding partially cut elements of the fabric layers stacked above and / or below. The stacked fabrics may be sent to an assembler for assembly.

[0095] Color Control Traditional coloring methods rely heavily on manual processes that involve multiple time-consuming iterations of trial and error. The present disclosure can combine precise substrate property data, chemical profiles from inks by color, and precise wet finish data to preempt lengthy trial and error.

[0096] Traditional design tools are fragmented, incompatible, and in many ways completely separate from the manufacturing process, requiring lengthy trial and error processes to produce as designed, creating the need to make changes to the original design in order to manufacture. The present disclosure includes an integrated manufacturing job file creation feature that presents customers, designers, and other end users with only achievable design and material attributes, filtering out colors and characteristics that are not acceptable within permitted performance attributes and standards, thereby seamlessly creating a manufacturing job file directly from the input design.

[0097] Typically, this is done as a separate ad-hoc process, as a post-processing QA / QC function, but it takes too long and occurs too far away from the coloring process. Other manufacturers fail to integrate data upstream and downstream of the value chain, i.e., the desired final color, the substrate construction, and the subsequent wet processing and lamination processes. This disclosure integrates this into our in-line coloring and fixing process to more proactively inform color matching and repeatability.

[0098] Pretreatment Foam Coating In direct-to-fabric digital printing in the textile industry, pretreatment chemicals are applied to the textile in a spread form through a process called padding, whereby the entire textile is immersed in the chemicals and the excess is squeezed out before the chemicals are dried / fixed. In conventional processes, the amount of water absorbed by the textile can range from 70% to >100% of the textile's weight (known in the industry as "wet pick up"), and all of this water must be evaporated in an energy-intensive process when the textile is dried before subsequent processing. Another problem with conventional padding of pretreatment chemicals is that in most cases the chemicals are applied on and through the entire textile, when they are only needed on the surface that will be printed. Hence, conventional processes require the use of more energy, water, and chemicals than are needed to add value in subsequent processes. The textile industry is the second largest consumer of fresh water in the world and one of the largest polluters of surface water after the agricultural industry. Industry is looking for new ways to reduce water, energy and chemical consumption.

[0099] Foam application of chemicals has been used commercially for several decades. In the early days of direct-to-fabric digital printing industry, production rates have increased to a level where the industry is growing rapidly and attracting attention from the investment community. In this disclosure, a process includes pre-treatment chemicals that are applied via a foam applicator that has several advantages that are important to the textile industry, such as reduced energy consumption, reduced water consumption, reduced chemical consumption, more precise application of chemicals when needed, and reduced chemical loading to wastewater treatment systems. It has also been demonstrated that deeper, richer colors can be achieved through the foam application process compared to conventional processes.

[0100] Figure 7A shows positive results from foam applied pretreatment across four preliminary chemical formulations. These preliminary formulations demonstrate higher average results in many categories compared to the average results of the control case or conventional process. R is the decimal reflectance of the wavelength of maximum absorption (20%R = 0.20R).

[0101] All four samples (3C, 5B, 2B, and 2D) were foam coated and the results were compared to the corresponding conventionally padded or pad coated samples, e.g., samples 3C and 5B were compared to a padded sample (Pad 1 below), and 2B and 2D were compared to another padded sample (Pad 2 below).

[0102] A SWL value >100% associated with a foam sample means that a higher color yield was achieved with that foam formulation and conditions, at least compared to the conventional sample.

[0103] FIG. 7B illustrates positive results with four different chemical combinations, with 2B and 2D demonstrating performance results similar to that of the baseline control, and 3C and 5B demonstrating improved performance over the control and other combination variables results.

[0104] The present disclosure includes formulations for foam treatment such as (although other chemicals may be used): [Table 1]

[0105] A foam treatment for durable water repellent (DWR) may be used. As an example, Figure 7C shows a DWR formulation with specific parameters for foam application on a polyester substrate, demonstrating that performance improvements result in a 50% reduction in chemical savings combined with a potential 80-85% reduction in chemical consumption.

[0106] 7D, a method for pretreating a textile is illustrated. In step 710, the textile may be received. A material manufacturer may receive the textile. Step 138 of FIG. 1 may include step 710.

[0107] In step 720, a selected area of ​​the textile to be printed may be determined. A material manufacturer may determine a selected area of ​​the textile to be printed. Step 140 of FIG. 1 may include step 720.

[0108] In step 730, an applicator can be caused to apply foam chemicals to selected areas of the textile. A material manufacturer can have an applicator apply foam chemicals to selected areas of the textile. Application of foam chemicals to areas of the textile outside of the selected areas can be minimized. Step 140 of FIG. 1 can include step 730.

[0109] In step 740, the selected areas of the textile may be dried so that the surface of the selected areas can be printed. A material manufacturer may dry the selected areas of the textile so that the surface of the selected areas can be printed. Step 140 of FIG. 1 may include step 740.

[0110] A material manufacturer may receive a textile and a corresponding job file. The job file may indicate that certain areas of the textile should be printed. The material manufacturer may have certain areas of the textile receive a foam pretreatment. The material manufacturer may have certain areas of the textile dry. The material manufacturer may have certain areas of the textile printed as directed by the job file.

[0111] Plasma pre-cleaning / activation Textile materials must be thoroughly cleaned to optimize the wetting and adhesion of chemicals (e.g., durable water repellent finishes, colorants, polymer coatings, laminations, etc.). As environmental regulations tighten over the use of solvents and surfactants, it is becoming increasingly difficult to achieve the same level of cleanliness obtained with aggressive chemicals (solvents) of the past. Most cleaning of textiles today is water-based, using large amounts of thermal energy and the most benign cleaning chemicals possible. Unfortunately, modern cleaning systems, while environmentally friendly, do not keep the textile free of contaminants that can interfere with the coloring and finishing of the textile. Applying chemicals to contaminated fabrics often leads to poor performance, poor durability of functional finishes, or the need to use more chemicals to achieve pass rates than would be required if the fabric were perfectly clean. Atmospheric plasma treatments can change the surface chemistry and topography of the fabric and / or material to improve adhesion properties to different materials. Each plasma carrier gas can provide a different surface chemistry and surface topography.

[0112] According to the present disclosure, the corona plasma process may use ionized gas to vaporize (break down) contaminants (oils, waxes, etc.) on the surface of a textile. It is a water-free process, resulting in a cleaner surface that "wets" more easily by changing the surface chemistry and surface energy of the fabric and / or material with water-based chemicals. A corona plasma unit may be placed prior to a chemical application step to assist with chemical penetration (wetting) and activation of the textile surface. Plasma may be used to improve the performance of some chemical applications (e.g., DWR) as well as to achieve deeper, more saturated colors using less dyes and chemicals.

[0113] Referring to Figure 8, a method for pretreating a textile is illustrated. In step 810, the textile may be received. A material manufacturer may receive the textile. Step 138 of Figure 1 may include step 810.

[0114] In step 820, at least a portion of the textile may be conditioned using a plasma to remove one or more contaminants from at least a portion of the textile. A material manufacturer may condition at least a portion of the textile using a plasma to remove one or more contaminants from at least a portion of the textile. Conditioning at least a portion of the textile may activate a surface of the textile. The plasma may include a corona plasma. Step 140 of FIG. 1 may include step 820. Step 146 of FIG. 1 may include step 820.

[0115] In step 830, one or more chemicals may be applied to at least a portion of the fibers. A material manufacturer may apply one or more chemicals to at least a portion of the textile. Step 140 of FIG. 1 may include step 830. Step 146 of FIG. 1 may include step 830. Activation of a surface of the textile may improve the performance of the one or more chemicals as compared to an unactivated surface of the textile having the same one or more chemicals applied thereto.

[0116] A material manufacturer may receive a textile that includes contaminants on specific areas of the textile. The material manufacturer may remove the contaminants by using plasma on the specific areas. The material manufacturer may apply one or more chemicals to the specific areas of the textile to activate the specific areas of the textile.

[0117] Digital coloring Drop-on-demand (e.g. digital printing) In the apparel industry, traditional products are currently manufactured under a predictive model, where wholesalers and retailers order clothing and footwear against predictions before consumers actually purchase the final product. In this scenario, products are manufactured using large batch inputs that are successively broken down into smaller and smaller batches until the final product, whereby the final product is completed as a "one batch" unit, an illustrative example is shown in FIG. 9B. In this system, the unique identifier of the final product is not assigned until the very end of the process. In one scenario where products are to be manufactured in a "mass customization" model, consumers may purchase the final product before its manufacture. In this model, it is important that each component of the final product is identified throughout the manufacturing process to keep track of the order from initiation to delivery. Identifying each component may be generated in a digital printing process using a unique identifier, such as a barcode or QR code. However, most consumers do not want to see a unique identifier on the final product, an illustrative example is shown in FIG. 9B. In addition, material utilization is a key driver of efficient and sustainable use of raw materials. To solve the identification problem without using excessive materials, a unique identifier needs to be placed on each component in a manner that is easy to read, yet invisible to the consumer.

[0118] In the present disclosure, quality control may be performed through invisible registration points, such as using invisible ink that can be seen through computer vision or some other process. The present system and method may embed data through a unique dithering pattern. For example, the present disclosure includes the application of unique identifiers (e.g., barcodes, QR codes, an illustrative example is shown in FIG. 9B) that are "invisible" to the consumer but use methods that are readable from the manufacturing process from digital printing by the point of sale. The present disclosure includes the use of unique identifiers that are applied to each component using invisible ink that is readable by machine vision, but that use ink that is outside the visible spectrum of human perception (e.g., ultraviolet, infrared, etc.).

[0119] Attribution and Traceability In the apparel industry, "big-scale" industrial production does not support digital custom manufacturing driven by consumer generated or aggregated content. To manage unique single unit workflows that can be used to bill products and components and to prove provenance, we need a digitally generated marking system for every textile component that can be tracked from the point of generation to the point of sale, whereby the markings can be linked to the entire value chain through manufacturing integration and intelligence systems. In this disclosure, the system and method can embed customer order data with visible and invisible attribution via digital printing using visible and / or invisible codes.

[0120] The method for attribution and / or traceability may include receiving order data associated with one or more first consumer orders. One or more unique identifiers (UIDs) may be disposed on at least a portion of the material. The one or more unique identifiers may be invisible to the human eye and visible with the aid of a predetermined vision method. The one or more unique identifiers may represent item data including at least a portion of the order data. The one or more methods may include processing the material through one or more manufacturing processes to form at least a portion of an article. The item data represented by the one or more unique identifiers may be updated based on each manufacturing process to include information associated with the respective manufacturing process. Each of the manufacturing processes (or one or more of them) may include retrieving the item data and adjusting one or more measures associated with the respective manufacturing process based on the item data. The item data may indicate the provenance of the item. As used herein, item data may be or include other data, such as nesting data, order data, color data, etc.

[0121] The method for attribution and / or traceability may include receiving order data associated with one or more first consumer orders. One or more unique identifiers may be disposed on at least a portion of the material. The one or more unique identifiers may be configured to be visible to the human eye and hidden through one or more manufacturing processes. The one or more unique identifiers may represent item data including at least a portion of the order data. Other data may be represented. The one or more methods may include processing the material to form at least a portion of an item through one or more manufacturing processes and hiding at least a portion of the one or more unique identifiers. The item data represented by the one or more unique identifiers may be updated based on each manufacturing process to include information associated with the respective manufacturing process. Each of the manufacturing processes (or one or more of them) may include retrieving the item data and adjusting one or more actions associated with the respective manufacturing process based on the item data. The item data may indicate the provenance of the item.

[0122] Digital marking of apparel / footwear components requires unique markers and / or serialized unique markers (e.g., FIG. 9B) large enough to be legible via machine vision to enable automated manufacturing (reliable legibility). The size of the digital markers varies with the substrate, e.g., flatter surfaces may be marked with smaller markers due to the physical nature of flatter surfaces, whereas substrates with a greater degree of Z-directional texture (e.g., seersucker weave, waffle knit) require relatively larger markers due to the physical nature of light reflecting off the substrate surface. In the present disclosure, the MII (Manufacturing Integration and Intelligence System) may generate unique digital identifiers (e.g., QR codes, bar codes) that are generated to ensure legibility depending on the data collected on a given substrate. The present disclosure may automatically select an appropriately sized marker based on the substrate data and the size of the printed component.

[0123] Current product storytelling requires months or years of planning in the upstream supply chain whereby input provenance (e.g., organic content, recycled content) is managed through underwriting or legal documentation rather than a defined digital evidence hold to manage risk. As the market moves to smaller batch sizes and higher degrees of customization, it becomes increasingly difficult to track inputs and processes to make marketing claims. The systems and methods of the present disclosure generate unique identifiers (e.g., QR codes, bar codes) that can be accessed digitally to connect consumers with the history and provenance of the final product, whereby inputs and "ingredients" are compiled as the product moves through the supply chain and are accessible to the end consumer through interaction with the unique identifiers (e.g., through mobile devices, scanners, digital cameras, etc.).

[0124] In the apparel and footwear industry, textile substrates, while being "engineered" materials, can have a large degree of variation in dimensional changes of the substrate through the steps of the manufacturing process, especially the finishing stages of processing. Dimensional changes can be the result of permanent / semi-permanent changes in the thermoplastic substrate as a result of various factors, changes in moisture / wetting of the material, mechanical forces in wet and dry processing such as stretching or compressing the material, heat fixing (thermosetting), application of chemicals (e.g., coatings, etc.). Dimensional changes are manifested not only at a macro level (batch to batch) but also at a micro level within a yard or meter of the substrate, making local prediction of dimensional changes unpredictable. Due to the above-mentioned dimensional changes, the relationship of registration marks (applied by traditional processes, e.g., digital printers) for the cutting process of individual components can shift significantly through processing, thereby resulting in cutting based on initial design dimensions resulting in out-of-spec components. To solve this problem, a much more robust process is needed to identify dimensional changes prior to cutting in order to provide precision cutting and to provide a data feedback loop to improve prediction of dimensional changes and / or to identify quality issues using machine vision. It is important to add dimensional fiducial points across the width and length of the textile web. Placing a high density of fiducial marks on the final product is commercially unacceptable to consumers, so marks that are invisible to consumers (outside the visible spectrum) but visible to machine vision are important for high speed single layer cutting of textiles. Markers large enough to be reliably detected may be objectionable to the average consumer if the markings were visible on all components of the clothing / footwear product. In the present disclosure, invisible inks may be used to create registration points that track changes to the original pattern as a quality control measure. This may be corrected within the cutting process or the order will be returned to the queue of pre-formatted job files for re-production.

[0125] Conventional components are typically cut from a monolithic print, which results in wasted ink and adds difficulty to recycling of the finished, virgin material (e.g., fabric or other components). In the present disclosure, precise application of finishes to enable recycling of virgin fabrics is described.

[0126] Adhesives are traditionally applied monolithically and in an analog fashion. This results in high levels of waste, both in excess chemical consumption and inhibiting the ability to recycle unused materials (e.g., fabrics or other components). The present disclosure may utilize digital printing / extrusion of adhesives using proprietary formulations to apply chemicals only where needed. The platform may identify visible registration points, reference layers in a digital technology package database, and use precision application of chemicals to only the required areas of each component level (or engineered) print.

[0127] FIG. 9A presents an illustrative example of component-level printing combined with precision laser cutting to facilitate assembly and automation. Demonstration of component-level engineered printing nested as pairs and batched following downstream processing allows flexibility of downstream sewing and assembly, with benefits realized with custom printing, whereas downstream sewing and assembly are largely commoditized in practice. FIG. 9B demonstrates an example with QR codes for upstream and downstream traceability. The QR codes are “invisible” in formulation, representing dithering patterns to contain underlying data for supply chain sustainability and CSR purposes, facilitate manufacturing through the shipping process, and provide marketing and attribution opportunities. FIG. 9C shows the incremental improvements demonstrated in the diagram, for example, precision cutting to leave uncut drilling waste leads to material savings in direct labor costs, while improving underlying product quality and consistency compared to existing manufacturing methods. FIG. 9D-FIG. 9E demonstrate examples of traditional manufacturing processes eliminated by large-scale engineered printing and digital manufacturing operational operations. In this case, the simple replacement of the tag (containing appropriate care and size information as well as necessary country of origin data) with digitally printed information eliminates the direct labor costs attributable to the manufacture of the goods, while also preventing the opportunity to thwart measures against fraud and the distribution of counterfeit goods.

[0128] Alternatively, or in addition to being used as a carrier of information, UIDs may also be used to "grade" or evaluate process steps or processes. For example, if a particular UID has reflective properties, measuring the reflectivity before PU coating application (the "process") and again after the process is completed will yield "localized" or garment part specific information regarding the thickness and / or quality of the applied PU coating. In other cases, the UID will itself undergo modification (e.g., a color or visibility change corresponding to the maximum and / or minimum temperatures used in the process or indicating a particular temperature range). Evaluation of other production process characteristics may be envisioned. These UIDs may be applied within the seam allowance of each garment part and / or within the binding margin area of ​​the fabric roll.

[0129] Wet Finishing Traditional finishing processes are generally fragmented processes, physically and digitally separated from the printing process. The current state presents a very long feedback loop. Currently, it is performed manually with a high degree of variability (especially from one site to another). In this disclosure, an in-line spectrophotometer can be implemented to measure the variability and create algorithms that optimize settings for on-premise and networked manufacturing. For example, the system and method can use the aggregated data to create a baseline recipe that can be adjusted for other manufacturing sites and their respective conditions, i.e. water quality, chemistry, ambient conditions, etc., reading data from previous processes and settings and then writing the conditions from this part of the process.

[0130] 10 shows an example diagram of a wet finishing process. The wet finishing process may include process data collection 1000 and product data collection 1050. The process data collection 1000 may receive and / or extract data from digital inputs and / or orders 1040. The process data collection 1000 may be in communication with the product data collection 1050.

[0131] The process data collection 1000 may include a process recipe database 1010. The process recipe database 1010 may include substrate data 1012, coloring data 1014, hand feel data 1016, finishing data 1018, etc. The process data collection 1000 may include data regarding various process steps, such as a substrate pretreatment step 1020, a substrate coloring step 1022, a substrate steam treatment step 1024, a substrate cleaning step 1026, a substrate curing step 1028, a substrate drying step 1030, a substrate functional finishing step 1032, a substrate tumbling step 1034, etc. The data regarding the various process steps may be obtained from a sensor. The data regarding the various process steps may be obtained from a spectrometer. The data regarding the various process steps may be obtained from an in-line spectrophotometer. The in-line spectrophotometer may measure the variation in the data regarding the various process steps. The measured variation in the data regarding the various process steps may be used to create an algorithm to obtain more optimal settings. The process data collection 1000 may include settings and / or conditions. The settings and / or conditions may be attributable to direct performance outputs. The settings and / or conditions may be associated with an in-line dryer, steamer, washer, tenter frame, etc.

[0132] Product data collection 1050 may include product feedback collection 1060. Product feedback collection 1060 may include data regarding various aspects of the product, such as non-metameric color matching feedback 1070, substrate hand feedback 1072, substrate air permeability feedback 1074, substrate water permeability feedback 1076, substrate light reflectance feedback 1078, substrate heat absorptance feedback 1080, substrate heat retention feedback 1082, etc. Data regarding various aspects of the product may be obtained from sensors. Data regarding various aspects of the product may be obtained from spectrometers. Data regarding various aspects of the product may be obtained from in-line spectrophotometers. In-line spectrophotometers may measure variations in data regarding various aspects of the product. The measured variations in data regarding various aspects of the product may be used to create algorithms to obtain more optimal settings. Product data collection 1050 may include settings and / or conditions. Settings and / or conditions may be attributed to direct performance outputs. Settings and / or conditions may be associated with in-line dryers, steamers, washers, tenter frames, etc.

[0133] Digital Finishing Component level application of DWR (e.g., non-fluoro or conventional fluoro) Conventional DWR processes are done in batches with monolithic application of chemicals, whereby chemicals are applied across the entire web of textile material with the same level of application across the entire surface. The problem with this approach is that chemicals are used across the entire material that ends up as waste, and there is no way to control the level of repellency in an "engineered" way to create new performance applications. In this disclosure, the digital application of DWR at the component level and roll-to-roll process will enable engineered patterns of moisture management that can be digitally enabled. This will lead to a more sustainable process, whereby fewer chemicals are used to deliver performance. Also, fabric that becomes waste after the cutting process can be recycled more easily due to the waste not containing chemical contaminants. Also, with the digital application of chemicals, new performance functions can be enabled by engineered placement of chemicals that can be scaled across different sized components to enable single unit customization.

[0134] Chemical Engineering Applications Traditional application of chemicals covers all of the fabric, resulting in significant waste of chemicals and inhibiting recycling of virgin fabrics. In this disclosure, precise digital application of chemicals such as adhesives can reduce chemical usage, save costs, and enable recycling of virgin fabrics.

[0135] cutting Typical conventional methods cut pre-programmed patterns from monolithic prints, requiring repetition of set component patterns, resulting in wasted ink and preventing scaled customization. In this disclosure, dynamic recognition of cut patterns enables increased overall throughput, reduced waste, and mass customization.

[0136] Current automated single layer cutting in the textile materials industry does not have sufficient throughput to scale in the clothing / footwear industry. Conventional techniques utilize gantry-driven X / Y axis instruments, typically involving machine knives and, in some cases, laser energy. The systems and methods of the present disclosure may utilize high speed galvanometer-driven lasers with two orders of magnitude higher throughput than gantry-driven systems. FIG. 11 illustrates an early high speed single layer galvanometer-driven laser cutting prototype design that far exceeds existing conventional textile cutting methods.

[0137] Traditional wet finishing processes such as post-print steaming, washing, stenting, etc., introduce fabric distortions (especially in knits) that are non-linear and difficult to consistently predict. These distortions inhibit the ability to print both patterns and components consistently and precisely. The disclosed system and method queries a database of pre-graded component shapes and patterns and makes cut adjustments to correct observed shape distortions while adjusting recipe changes for the next print iteration. Figure 12 is the infeed portion of Figure 11 where vision recognition and real-time job file correction data is collected and transmitted.

[0138] Printing at the component level creates inefficiencies in sorting and handling down processes where engineered printed components are unreadable from waste. The disclosed systems and methods can nest prints and cut components such that the unused substrate remains attached as a web. This web of unused substrate is unwound from the belt and the associated cut components are left to be sorted, kitted and assembled, effectively batching and recycling the waste. Figure 13 illustrates nip rollers for waste removal and subsequent downcycling or recycling.

[0139] Conventional cutting methods may include manually cutting components with analog function machine tools, automated cutting of material using gantry driven knives, routers, or lasers. This labor-intensive process results in significant inefficiencies, especially with regard to customization. The disclosed system and method provides unique customized sequencing, batching, and nesting with precision laser cutting to leave connecting drilling waste to hold together and allow for hand separation from the block.

[0140] The method for cutting registration may include using computer vision to analyze a first pattern configuration disposed on the printed material. The one or more methods may include performing a finishing process on the printed material, resulting in a second pattern configuration different from the first pattern configuration. The one or more methods may include using computer vision to analyze a second pattern configuration disposed on the printed material. The one or more methods may include determining cutting control information based on the first pattern configuration and the second pattern configuration. The one or more methods may include sending the cutting control information to a cutting system to facilitate cutting of the printed material. The cutting system may include a high speed single layer galvanometer driven laser cutting system. The finishing process includes steaming the material, washing the material, and / or drying the material. The finishing process may include a digital finishing process. The cutting control information may depend on the type of material. The one or more methods may include batching one or more customer orders into batches and nesting multiple article components on the printed material based on the batches.

[0141] The method of cutting may include batching one or more customer orders into a batch, nesting a plurality of article components based on the batch, and cutting the nested components from the substrate such that one or more tabs connect the cut components to a portion of the substrate. Prior to the cutting step, the method may include analyzing a first pattern configuration disposed on the substrate using computer vision, performing a finishing process on the substrate, resulting in a second pattern configuration different from the first pattern configuration, analyzing the second pattern configuration disposed on the substrate using computer vision, and determining cutting control information based on the first pattern configuration and the second pattern configuration. The cutting step may be performed based on at least the cutting control information. The cutting step may be performed using a high speed galvanometer-driven laser cutting system to cut a single layer. The finishing process may include one or more of steaming the material, washing the material, or drying the material. The finishing process may include one or more of belt pressing, mechanical pressing, decatalysis, sponging, sanforizing, gentle drying, continuous tumbling, or batch tumbling. The finishing process may include one or more of sueding, shearing, razing, open width pressing, tubular pressing, calendaring, vaporizing, sponging, atmospheric plasma finishing, continuous decatizing, semi-continuous decatizing, clubbing, coating, laminating, embossing, tensionless drying, gentle drying, tentering, tentering, napping, brushing, singeing, beetling, heat setting, heat fixing, filling, digital printing, roller printing, scratching, sputtering, or Schreinering. The cutting control information may depend on material properties including one or more of material type, material thickness, material mass per unit area, material porosity, or yarn properties.

[0142] FIG. 14 shows an example diagram of a laser cutting process. At 1400, a fabric may be observed. The fabric may be a digitally printed fabric. The digitally printed fabric may include a pattern. Robot vision may be used to capture the pattern dimensions. The captured pattern dimensions may include original pattern dimensions. The captured pattern dimensions may include pre-finished pattern dimensions.

[0143] At 1402, a fabric finishing process may be performed on the fabric. The fabric finishing process may include steaming the fabric. The fabric may be steamed at a fixing / steaming step at 170 of FIG. 1. The fabric finishing process may include washing the fabric. The fabric may be washed at a post-printing washing step at 174 of FIG. 1. The fabric finishing process may include drying the fabric. The fabric may be dried at a post-printing drying step at 178 of FIG. 1. The fabric may undergo conventional and / or digital finishing processes. The fabric finishing process may result in a fabric including altered pattern dimensions.

[0144] At 1404, changes in the fabric may be observed. Robot vision may be used to capture fabric pattern dimensions after the fabric finishing process. The captured fabric pattern dimensions after the fabric finishing process may include altered pattern dimensions. The captured fabric pattern dimensions after the fabric finishing process may include finished pattern dimensions. The altered pattern dimensions may be compared to the dimensions of the original pattern to obtain deltas (e.g., changes, alterations, etc.). The deltas may be provided to a laser control system. The laser control system may use the deltas to more accurately and precisely cut the pattern from the fabric.

[0145] Referring to Figure 15, a method for cutting registration is illustrated. In step 1510, a first pattern configuration disposed on a printed fabric may be analyzed using computer vision. A material manufacturer may use computer vision to analyze a first pattern configuration disposed on a printed fabric.

[0146] In step 1520, a finishing process may be performed on the printed fabric, resulting in a second pattern configuration that is different from the first pattern configuration. The materials manufacturer may perform a finishing process on the printed fabric, resulting in a second pattern configuration that is different from the first pattern configuration.

[0147] In step 1530, the second pattern configuration disposed on the printed fabric may be analyzed using computer vision. The material manufacturer may use computer vision to analyze the second pattern configuration disposed on the printed fabric.

[0148] In step 1540, cutting control information may be determined based on the first pattern configuration and the second pattern configuration. The material manufacturer may determine the cutting control information based on the first pattern configuration and the second pattern configuration.

[0149] In step 1550, cutting control information may be sent to a cutting system to facilitate cutting of the printed fabric. The materials manufacturer may send cutting control information to a cutting system to facilitate cutting of the printed fabric.

[0150] A material manufacturer may receive a fabric with a printed design. The material manufacturer may use computer vision to capture the original printed design on the fabric. The fabric may undergo a finishing process. The material manufacturer may use computer vision to capture the modified printed design on the fabric. The material manufacturer may use the original printed design and the modified printed design to determine a delta. The material manufacturer may provide the delta to a cutting system. The cutting system may use the delta to cut the fabric with the modified printed design.

[0151] Material Handling Traditional automation methods for handling and kitting functions within the textile and garment industry are highly specialized to specific product applications. Traditional garment and footwear assembly lines are capital intensive and product specific, requiring high volume and throughput with low labor costs to justify the investment. This investment threshold is prohibitively high for most companies, typically inhibiting manufacturing operations in high-cost developed markets.

[0152] In this disclosure, as illustrated in Figures 16-17, the automated kitting and assembly design results in a flexible platform that can be reconfigured for different product types and categories, as well as the ability to adjust to variations in their respective throughput speeds and capacities. For example, pick-and-place robot types (e.g., SCARA vs. Delta) with varying degrees of convergence and overlap radii through the use of an overhead rail system. Throughput speed / capacity can be adjusted through the use of trays and sorting systems that allow parts to be dynamically sorted, either as stacks of similar components or batched orders, navigated via vision recognition and in a manner that simplifies the problem set such that mechanical travel distances are minimized. Other examples of the platform's agility can be found in end effectors that can be swapped (electrostatic, water, vacuum, etc.) to account for substrate types, conveyors that are expanded to manage larger cut components, trays that are swapped with packaging materials for off-site assembly of custom orders at other locations.

[0153] Traditional material management processes remain siloed and compartmentalized from printing, nesting, and batching considerations. This leads to prohibitive cost inefficiencies, especially when printing at the component level. The present disclosure can treat the end-to-end manufacturing value chain as a closed system and feedback loop.

[0154] Web Defect Tracking Methods: In this system, there may be multiple "subsystems" (printers, steamers, cutters, etc.). Each one of the subsystems may produce defects in certain instances. For example, one of the printers may misprint or deposited ink may smudge by physical contact with an object. As another example, a steamer set to run the wrong recipe may produce a segment of a fabric roll that is unusable. In another example, an operator may choose to detach a segment of a roll of fabric and then reconnect it (via stitching). Such changes may be recorded and communicated to a designated subsystem so, for example, a cutter can accurately and precisely detach a garment part from a web and / or so a mechanical arm knows which part to expect and where it is.

[0155] Thus, the nesting program (e.g., after executing the nesting protocol) may deposit UIDs along the "gutter" area of ​​the fabric roll. The UIDs may be scannable bar codes, data matrices, or equivalent, and are evenly spaced along the long edge of the fabric. Other UIDs may be used. For example, a unique identifier may be created every 5 inches (with identical copies of the UIDs contained within the other parallel margins), or alternatively, these UIDs may be spaced in any other regular manner along the margin. The space between each UID (virtual fabric slice) may be referred to as a "segment" and will contain the printed garment part or graphic design(s). The nesting program may record the particular garment part or graphic design(s) contained within each virtual segment (i.e., between two consecutive UIDs). Note that a portion of a part within a particular segment will not be "whole" since a portion of the part will be contained within the segment immediately following or immediately preceding the current segment.

[0156] As an illustrative example, when a defect is detected, one of the following operations may be performed: 1) the operator cuts off the defective segment of the fabric, scans two UIDs (one before the defective area, and one immediately after the defective area), and reconnects the fabric; 2) no cutting of the fabric occurs. The operator (or a camera) scans the two UIDs that contain the defective area (which may span multiple segments). Other operations may be performed. Information from the scanned UIDs may be communicated to a computer system that makes appropriate adjustments to the main nesting file to exclude the cut segment in a later operation (e.g., cutting). Additionally or alternatively, information regarding the part (and / or graphic design) that was contained within the defective area may be stored. The stored information may be further appended with other defect data from other rolls of the same fabric material. At the end of the manufacturing process (or at any time), the stored information may be sent back to the nesting software, which aggregates and re-nest the "missing" parts. Once the parts are nested, the manufacturing process continues (i.e., printing, steaming, cutting, etc.) until all necessary parts are manufactured. Alternatively or additionally, the same process as above may be performed, but the UID may be placed between the nested parts instead of within the binding margin.

[0157] The present disclosure includes at least the following aspects:

[0158] Aspect 1: A method for item management, the method including: receiving consumer data including at least biometric information associated with one or more consumers; receiving design input indicative of a design of an item, the design of the item being based on the consumer data; causing output of interactive content to a user interface associated with the one or more consumers, the interactive content including at least a representation of the design of the item; and outputting manufacturing data indicative of instructions associated with manufacturing of the item, the instructions being based on the design of the item.

[0159] Aspect 2: The method of aspect 1, wherein the consumer data further includes consumer preference information.

[0160] Aspect 3: The method of aspect 1, further comprising receiving coloring data indicating the feasibility of coloring, wherein the design of the article depends on the coloring data.

[0161] Aspect 4: The method of aspect 1, wherein outputting the manufacturing data includes outputting at least a portion of the manufacturing data to a digital printing system.

[0162] Aspect 5: A method for article management for manufacturers, the method including: receiving consumer data including at least biometric information associated with one or more consumers; receiving design input indicative of a design of an article, the design of the article being based on the consumer data; automatically generating a pattern including one or more components of the article and one or more components of a second article; and outputting manufacturing data indicative of instructions associated with manufacturing of the article, the instructions being based on the pattern.

[0163] Aspect 6: The method of aspect 1, wherein automatically generating the pattern includes performing nesting optimization.

[0164] Aspect 7: A method for article development, the method including: receiving consumer data including at least biometric information associated with one or more consumers of a mineral; receiving trend data indicating trends in one or more of article design or article coloring; triggering output of one or more design options via a user interface based on at least the consumer data and the trend data; and receiving design input indicating a design of the article.

[0165] Aspect 8: The method of aspect 7, wherein the consumer data further includes consumer preference information.

[0166] Aspect 9: The method of aspect 7, wherein the one or more design options include a type of fabric.

[0167] Embodiment 10: The method of embodiment 7, wherein one or more design options are limited based on available fabric.

[0168] Example 11: The method of example 7, further comprising generating a technical package based on the article design and fabric selection.

[0169] Aspect 12: The method of aspect 7, further comprising outputting a table if the material is based on a cloth design.

[0170] Embodiment 13: A method for color control, the method including: receiving data indicative of one or more characteristics of a substrate for use in forming an article; selecting a chemical profile or a finishing process, or both, based on the data indicative of the one or more characteristics of the substrate; and forming at least a portion of the article using the selected chemical profile or finishing process, or both.

[0171]

[0041] Example 14: The method of example 13, wherein the article exhibits a color that is within the design color tolerance range.

[0172] Aspect 15: A method of color control, the method including: performing a first process of a plurality of item management processes to output a first stage item; capturing color data associated with the first stage product using an in-line spectrophotometer; comparing the color data to expected data; and performing remediation based at least on comparing the color data to the expected data.

[0173] Aspect 16: A method of pretreating a textile, the method including: receiving a textile; determining a selected area of ​​the textile to be printed; causing an applicator to apply a foam chemical to the selected area of ​​the textile, wherein application of the foam chemical to areas of the textile outside of the selected area is minimized; and drying the selected area of ​​the textile such that a surface of the selected area can be printed.

[0174] Aspect 17: A method of pretreating a textile, the method including: receiving a textile; conditioning at least a portion of the textile using a plasma to remove one or more contaminants from at least a portion of the textile and to alter one or more of a surface chemistry and topography of at least a portion of the textile; and applying one or more chemicals to at least a portion of the textile.

[0175] Aspect 18: The method of aspect 17, wherein conditioning at least a portion of the textile further activates a surface of the textile, and the activation of the surface of the textile improves the performance of one or more chemicals compared to an unactivated surface of the textile to which the same one or more chemicals have been applied.

[0176] Aspect 19: A method for attribution and / or traceability, the method comprising disposing one or more unique identifiers on at least a portion of an article, the one or more unique identifiers being invisible to the human eye and visible with the aid of a predetermined vision method, and the one or more unique identifiers being referenced during a manufacturing process including digital printing to provide quality control data for one or more steps of the manufacturing process.

[0177] Example 20: The method of example 19, wherein the one or more unique identifiers indicate a registration mechanism for one or more components of the article.

[0178] Aspect 21: A method for attribution and / or traceability, the method comprising disposing one or more unique identifiers on an article, the one or more unique identifiers comprising an invisible component that is invisible to the human eye and visible with the aid of a predetermined vision method, and a visible component that is visible to the human eye, the one or more unique identifiers indicating at least attribution data.

[0179] Aspect 22: The method of aspect 21, wherein the attribution data includes information indicating the provenance of the item.

[0180] Aspect 23: A method for component level application, the method including receiving information indicating locations for application of a material on a surface of an article, and selectively disposing the material only at the locations based on the locations using digital printing or digital extrusion, whereby the material is not disposed on at least a portion of the article.

[0181] Example 24: The method of example 23, wherein the material comprises an adhesive.

[0182] Example 25: The method of example 23, wherein the selectively disposing is based on a registration point associated with the article.

[0183] Aspect 26: A method for cutting registration, the method including: analyzing a first pattern configuration disposed on a printed fabric using computer vision; performing a finishing process on the printed fabric, the finishing process resulting in a second pattern configuration different from the first pattern configuration; analyzing the second pattern configuration disposed on the printed fabric using computer vision; determining cutting control information based on the first pattern configuration and the second pattern configuration; and transmitting the cutting control information to a cutting system to facilitate cutting of the printed fabric.

[0184] Aspect 27: A method of cutting, comprising: batching one or more customer orders into a batch; nesting a plurality of article components based on the batch; and cutting the nested components from the substrate such that one or more tabs connect the cut components to a portion of the substrate.

[0185] Aspect 28: A method of material handling, comprising: positioning a tray and sorting system having an overhead rail system configured for material handling, receiving a plurality of material components, and sorting the plurality of material components based on one or more of type of component or batched order, or both, using the positioned tray and sorting system and the overhead rail system.

[0186] Example 29: A system for carrying out any one of the methods described in Examples 1 to 28.

Claims

1. 1. A method for attribution and / or traceability, said method comprising: receiving order data associated with one or more first consumer orders; disposing one or more unique identifiers on at least a portion of the material, the one or more unique identifiers being invisible to the human eye and visible with the aid of a predetermined vision method, the one or more unique identifiers representing item data including at least a portion of the order data; processing the material to form at least a portion of an article via one or more manufacturing processes, wherein the article data is represented by the one or more unique identifiers, and the article data is dynamic and updated throughout the one or more manufacturing processes based on application of the one or more manufacturing processes to the at least a portion of the material, and includes information associated with the respective manufacturing processes; each of the manufacturing processes includes retrieving the updated item data and adjusting one or more actions associated with the respective manufacturing process based on the updated item data; The method of claim 1, wherein the item data indicates a provenance of the item.

2. 1. A method for attribution and / or traceability, said method comprising: receiving order data associated with one or more first consumer orders; disposing one or more unique identifiers on at least a portion of a material, the one or more unique identifiers configured to be visible to a human eye and hidden via one or more manufacturing processes, the one or more unique identifiers representing item data including at least a portion of the order data; processing the material through the one or more manufacturing processes to form at least a portion of an article and to hide at least a portion of the one or more unique identifiers, wherein the article data is represented by the one or more unique identifiers, and the article data is dynamic and updated throughout the one or more manufacturing processes based on application of the one or more manufacturing processes to the at least a portion of the material and includes information associated with the respective manufacturing process; each of the manufacturing processes includes retrieving the updated item data and adjusting one or more actions associated with the respective manufacturing process based on the updated item data; The method of claim 1, wherein the item data indicates a provenance of the item.

3. The method of claim 2, wherein the article includes one or more components, and the one or more unique identifiers indicate a registration mechanism for the one or more components of the article.

4. The method of claim 2 , wherein the order data includes one or more of biometric information associated with the one or more consumers, or color data associated with a color of the item.

5. 3. The method of claim 2, wherein the one or more manufacturing processes includes a foam pretreatment process, the foam pretreatment process being selected and performed prior to the foam pretreatment process based at least on the article data, and the article data being updated to indicate the performed foam pretreatment process.

6. The method of claim 2 , wherein the one or more manufacturing processes comprises a drop-on-demand process.

7. The method of claim 2 , wherein the one or more manufacturing processes include one or more of a laser cutting process or a material handling process.

8. The method of claim 2 , wherein the one or more unique identifiers are disposed on at least a portion of the material that includes a defective segment or a defective area, or both.

9. The method of claim 8 , wherein the one or more manufacturing processes include adjusting a main nesting file based on one or more of the defective segments or the defective areas.

10. A system for attribution and / or traceability, comprising: a receiver for receiving order data associated with one or more first consumer orders; a disposing unit that disposes one or more unique identifiers on at least a portion of a material, the one or more unique identifiers configured to be visible to a human eye and hidden via one or more manufacturing processes, the one or more unique identifiers representing item data including at least a portion of the order data; a processing unit that processes the material through the one or more manufacturing processes to form at least a portion of an article and to hide at least a portion of the one or more unique identifiers, the article data being represented by the one or more unique identifiers, the article data being dynamic and being updated throughout the one or more manufacturing processes based on applying the one or more manufacturing processes to the at least a portion of the material and including information associated with the respective manufacturing process; each of the manufacturing processes includes an adjustment unit that reads the updated item data and adjusts one or more actions associated with the respective manufacturing process based on the updated item data; The item data indicates a history of the item.

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