System and method for end-to-end item management

Abstract

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

Description

Background Art

[0001] Traditional fabric manufacturing processes can take a long time from the concept of a garment to the production of a 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 produced. By the time the manufacturing process for making a garment has changed to accommodate the new trend and take into account available resources, another new clothing 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. Pat. No. 9,623,578 generally describes a system for on-demand apparel manufacturing, the system comprising a textile printer, a textile cutting machine, and a computing device, wherein the computing device is configured to perform a process comprising aggregating product orders, organizing the orders according to yield factors, and arranging panels of products in the orders into an aggregated textile panel template. However, improvements are needed.

[0003] As another example, U.S. Patent No. 9,782,906 generally describes a system for on-demand garment manufacturing, the system comprising a textile cutting machine and a computing device. The computing device is configured to perform a process comprising capturing an image of a textile piece on a textile cutting machine, wherein the textile piece comprises a panel for a product. The method further comprises using the image of the textile piece to identify a cutting control factor to illustrate a textile line, a weaving, a pile or a weaving pattern of the textile piece, alignment of a textile print pattern on the textile piece, or deformation of a panel of the textile piece on the textile cutting machine. However, changes in material may occur outside the textile cutting machine or the table. Therefore, there is a need to improve textile cutting and alignment. Summary of the invention

[0004] In one or more aspects, the present disclosure relates to an end-to-end process for item management. Such items may include clothing, garments, accessories, components including fabrics, etc. In one or more aspects, the present disclosure relates to producing items within the design tolerance range of such items. Typically, in conventional processes, the steps of item management are discrete and separate, and the transitions between process steps introduce errors or differences from the intended design. The end-to-end process of the present disclosure can minimize such differences and can facilitate the production of items such as clothing within the strict tolerances of the intended design. In particular, the color of the finished item can be within a predetermined tolerance of the design color. Alternatively or in addition, the methods and systems of the present disclosure can facilitate dynamic pricing, dynamic lead times, dynamic batching, dynamic delivery, and can provide customers with personalized or customized processes.

[0005] Conventional approaches are locked into a long-term forecast driven supply chain. The present disclosure provides a demand driven garment 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 example methods for manufacturing an article. The example methods may include receiving consumer data including at least biometric information associated with one or more consumers. The example methods may include receiving design input indicating a design for an article, wherein the design of the article is based on the consumer data. The example methods may include causing interactive content to be output to a user interface associated with one or more consumers, wherein the interactive content includes at least a representation of the design of the article. The example methods may include outputting manufacturing data indicating instructions associated with the manufacture of the article, wherein the instructions are 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 generally illustrate various examples discussed in the present disclosure by way of example and not by way of limitation. In the drawings:

[0008] Figures 1A-1E An example diagram showing the manufacturing process.

[0009] Figure 2 Example diagram showing the design process.

[0010] Figure 3 is a flow chart of an example method.

[0011] Figure 4 is a flow chart of an example method.

[0012] Figure 5 An example diagram showing nesting.

[0013] Figure 6 A sample diagram showing the item management process.

[0014] Fig. 7A is example data based on the processing of the present disclosure.

[0015] Figure 7B Formulations for example treatments are shown.

[0016] Figure 7C Formulations for example treatments are shown.

[0017] Fig.7D is a flow chart of an example method.

[0018] Figure 8 is a flow chart of an example method.

[0019] Fig.9A is a flow chart of an example method.

[0020] Figure 9B-9EAn example traceability mechanism is shown.

[0021] Fig.10 It is a process diagram.

[0022] Fig.11 An example system is shown.

[0023] Fig.12 An example system is shown.

[0024] Fig.13 An example system is shown.

[0025] Fig.14 An example process flow is shown.

[0026] Fig.15 is a flow chart of an example method.

[0027] Figure 16-17 An example system is shown. DETAILED DESCRIPTION

[0028] Systems and methods for managing items such as clothing / garments are described, which include, by way of example only, shirts, pants, shorts, footwear, and bags. Systems and / or methods may include, for example, end-to-end item management for manufacturing. Systems and / or methods may include every manufacturing aspect from the design of a garment to the delivery of the garment to a customer. Systems and / or methods may capture information from one or more steps of a management or manufacturing process to influence other steps of the management or manufacturing process. Reference may be made herein to fabrics or textiles as an illustration. However, it is contemplated that it may be applied to a wider range of materials and therefore should not be limited to such illustrative terms.

[0029] The systems and / or methods described herein may include one or more tools, units or factories for managing items from fabrics to customers, such as clothing or clothes. The systems and / or methods described herein may include one or more clothing factories. The systems and / or methods described herein may include one or more computing devices associated with one or more clothing factories, and the one or more clothing factories are associated with one or more corresponding clothing factories. The systems and / or methods described herein may include one or more cloud computing environments associated with one or more clothing factories. The systems and / or methods described herein may include one or more client devices, such as laptop computers, desktop computers, smart phones, wearable devices, tablet computers, etc. One or more client devices may communicate with one or more computing devices and / or one or more cloud computing environments via a network. One or more client devices may include one or more applications executed on one or more client devices.

[0030] The systems and / or methods described herein may include business processes. A business process may include a process for creating a job file (e.g., Figure 1A 110 in the process. The job file may include instructions for authoring tools, instructions for digital asset management, and / or instructions for patterns and / or marks. The authoring tools may include 2D design tools, 3D polygonal design tools, and / or 3D parameter design tools. Digital asset management may include information about digital materials, graphics, images, 3D assets, color characteristic curves, matching blocks, design library management, material development, line plans, bills of materials, material testing, supplier collaboration, and / or financial plans. As described herein, digital asset management may include or may be based on data about material physical properties, material spectral reflectance and refractive properties, material performance properties, material provenance and related resource consumption, batch serialization, etc. Patterns and / or marks may include information about patterns, reference points, cutting data, grading, graphic images, colors, cutting plans, job status management, and / or material utilization. The job file may include options to be selected by the user. The job file may include options selected by the user. The job file may include options selected without user intervention.

[0031] A job file may be created by one or more computing devices (a "job file creator"). The job file creator may communicate with one or more computing devices configured to collect real-time and / or near real-time manufacturing and / or consumer data via a network (a "data collection"). The job file and / or usable parameters associated with the job file may be affected by the real-time data received from the data collection. The job file creator may communicate with one or more computing devices configured to cause one or more manufacturing steps to be performed via a network (a "controller"). The job file creator may provide the job file to the controller. The controller may cause one or more manufacturing steps to be performed according to the job file.

[0032] The systems and / or methods described herein may include a feedback loop for designers. Biometric data and / or consumer data may be captured, and trends may be identified. The options available to designers on their design tools may be influenced by the captured biometric and / or consumer data. Designers may design garments based on the options available on their design tools. Biometric data and / or consumer data associated with the designed garments may be captured, and trends may be identified, thereby restarting the feedback loop.

[0033] The systems and / or methods described herein may use nesting to efficiently use the material that needs to be cut. Nesting involves arranging the patterns cut from the material to reduce waste caused by cutting. Nesting may involve arranging components with similar or identical colors and / or patterns at boundaries so that two such boundaries of two components are adjacent to each other. Nesting may involve using color overlap between two or more components. Nesting may involve dynamic batching of orders.

[0034] 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 content. The foam pretreatment process may reduce energy consumption. The foam pretreatment process may reduce the use of chemicals. The foam pretreatment process may achieve darker and / or richer colors. The foam pretreatment process may dry more easily than a traditional dipping process.

[0035] The system and / or method described herein may include a plasma pre-cleaning / activation process. The atmospheric plasma pre-cleaning / activation process may include a 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. The atmospheric plasma pre-cleaning / activation process may be used to vaporize and remove (decompose) pollutants (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 pad 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 may be anhydrous and occurs at ambient temperature. The plasma pre-cleaning / activation process may be applied by 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 morphology, chemical properties, and surface energy to the fabric and / or material. Some grafting reactions (functionalization reactions) may occur between the fabric and / or material and the plasma carrier gas. After the plasma treatment, the chemical composition of the fabric and / or material on the surface may be changed. The systems and / or methods described herein may include a coloring analysis process. The coloring analysis process may compare the expected color and the actual color. The coloring analysis process may determine the expected color from digital data (e.g., data from a job file). The coloring analysis process may use computer vision to determine the actual color.

[0036] 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 the material before, during, and / or after the 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 in the manufacturing process, the one or more sensors may be checked to ensure quality. The one or more sensors may be manually checked by a person. The one or more sensors may be checked by one or more computing devices. Checking the one or more sensors by the one or more computing devices may include comparing an observed data set with an expected data set. Checking the one or more sensors by the one or more computing devices may include triggering an alarm when the difference between the observed data set and the expected data set is greater than a predetermined threshold. In one aspect, for example, an identification may be added to a material such as a barcode, a QR code, an invisible mark, etc., so that the code can be read or sensed using a reader device such as a spectrometer.

[0037] 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 a pattern on the fabric after one or more manufacturing steps. The observation process may include determining an increment between an observed pattern on the fabric before the one or more manufacturing steps and an observed pattern on the fabric after the one or more manufacturing steps. The observation process may be performed by one or more computing devices ("observation devices"). The observation device may provide the determined increments to one or more computing devices in communication with a machine for cutting fabric.

[0038] Digital product creation

[0039] Conventional processes for item creation include isolated and manual steps / operations. The present disclosure enables consumers to customize products via an ordering system, and may include the ability for users to input data such as their measurements. The software can generate automatic patterns, and based on the customization rules being established, the solution will pick the appropriate pattern. Such software may include customized selections of colors or graphics that may be used in the automatic generation of a pattern or in the selection of an existing pattern. This pattern may then be associated with a design bill of materials including work instructions to the manufacturing site. The systems and methods may be integrated with back-end systems that enable on-demand manufacturing.

[0040] Tailor-made

[0041] Today, consumer-facing front-end systems offer the ability to customize products from a list of options. These options are mapped to, and therefore limited by, the back-end manufacturing system. The ability to customize to fit unique sizes is limited. In addition, the options for personalizing products are also limited. The option to do this on demand does not exist. The present disclosure provides the ability to customize products or add user input information to products. Systems and methods can dynamically configure products to meet individual requirements and create manufacturing-ready packaging. The systems and methods can be further expanded to automate much of the product creation.

[0042] Image Predistortion

[0043] In conventional textile processing, textile materials are processed in "web" form, whereby mechanical forces and / or mechanical forces in combination with heat can cause distortions throughout the "web". This means that an image printed on a digital printing press can be controlled to within nanometer range accuracy, but subsequent processing may result in distortions that were not the author's intent. The present disclosure receives information associated with the distortion of the raw material web through downstream processing, and then maps the distortion so that the image applied to the web at the digital printing stage can be "pre-distorted" so that the final product matches the author's intent. As an example, the present disclosure may use feedback loops / validation to address one or more shortcomings of conventional processes, such as Fig.12 shown.

[0044] The handling of materials during the manufacturing process (e.g., handling of printed upper material around the last) can cause bending and deviation of the image or pattern from the intended print. Fig.9A By including pre-distortion in the print job file to illustrate the downstream process, a final product that is more faithful to the original form can be produced, as shown in the example.

[0045] Material Classification

[0046] Current industry practices are piecemeal and do not consider substrates and their corresponding effects on multi-spectral color refractive index, translucency, opacity, etc. Additionally, substrate construction has a significant impact on how a fabric drapes and / or flows in real life. Additional input data such as whiteness index, pH, degree of mercerization, refractive and reflectivity indices, thickness, compressibility, curvature, roughness, friction, thermal properties, smoothness, softness, warmth, wrinkle, distortion, combined measurements thereof, or one or more of naturally observed performance histories and variances. 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 characteristics can be digitally recreated in a digital format, i.e., creating a digital twin that we can produce an honest recreation of in real life.

[0047] Order creation and job management

[0048] Figures 1A-1E An example diagram of a management (e.g., manufacturing) process is shown. Although an example sequence is shown, it should be understood that the various steps can be implemented in any order and can be selectively implemented or not implemented. Feedback loops from one or more downstream processes can be received and can be used to update one or more upstream processes. As an example, data collected at any one of the manufacturing steps can be shared upstream or downstream in an end-to-end process and can be used to update other processes. As another example, all manufacturing steps can be implemented at a single facility to provide complete end-to-end control. However, the data shared between the steps can allow one or more processes to be performed in different facilities without losing control or standardization. At 100, project requests and / or orders can be received. Project requests and / or orders can be received at one or more computing devices associated with a clothing manufacturer. Project requests and / or orders can be received in a cloud computing environment associated with a clothing manufacturer. As used herein, manufacturing or manufacturers can refer to operations or entities associated with any part of the management of article production and delivery. Project requests and / or orders can be received from a client device associated with a customer.

[0049] In response to receiving a project request and / or order, a job (e.g., order, project, etc.) file 110 may be created. The job file 110 may include a graphic design file 112, grading information 114, quantity / yardage request 116, kitting data 118, order data 120, pattern file 122, substrate 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 may be referenced in / by the job file 110.

[0050] In response to receiving the project request and / or order, it may be determined at 102 whether the fabric associated with the project request and / or order has been created. One or more computing devices and / or cloud computing environments associated with the apparel manufacturer may determine whether the fabric associated with the project request and / or order has been created. If the fabric has not been created, the process may move to 106. If the fabric has been created, the process may move to 104.

[0051] At 104, it may be determined whether the 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 apparel manufacturer may determine whether the 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.

[0052] 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 apparel manufacturer may order fabric from a fabric supplier. After the 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.

[0053] At 108, a process of processing a purchase order and / or a payment cycle associated with the fabric may be caused, the process of the purchase order and / or the payment cycle being associated with the item request and / or the order. One or more computing devices and / or cloud computing environments associated with the garment manufacturer may process the process of the purchase order and / or the payment cycle. One or more computing devices and / or cloud computing environments associated with the garment manufacturer may cause another one or more computing devices to process the process of the purchase order and / or the payment cycle.

[0054] Following the process leading to processing of a purchase order and / or payment cycle associated with the fabric associated with the project request and / or order, inventory management may be performed at 132. One or more computing devices and / or cloud computing environments associated with the apparel manufacturer may perform inventory management and / or cause inventory management to be performed. Performing inventory management may include the process of updating inventory to reflect the purchase order and / or payment cycle associated with the fabric associated with the project request and / or order. Performing inventory management may include using inventory information as part of a fabric intake step at 138. Performing inventory management may include updating inventory information based on the fabric intake step at 138. Performing inventory management may include updating inventory information based on the fabric pre-processing step at 140.

[0055] The material testing database 134 may communicate with one or more computing devices associated with a fabric supplier factory 136. The material testing database 134 may cause the fabric to be placed with the fabric supplier factory 136. The fabric supplier factory 136 may cause the fabric to be delivered to the garment manufacturer as part of the fabric intake step at 138.

[0056] At 138, a material operator or manager, such as a garment manufacturer, may have or may receive material (e.g., fabric from a fabric supplier factory 136 as part of a fabric intake step at 138). Other materials may be used. After the fabric intake step, the process may move to a fabric pre-treatment step at 140. It should be 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 capabilities for a particular product, may be considered in order to determine a lead time for a quote (e.g., in real time), and surge pricing / preferred pricing may be implemented.

[0057] After the fabric ingestion step at 138, a laboratory and / or visual inspection may be performed at 142. The inspection may include a human inspection. The inspection may include an inspection using computer vision. The inspection may include an inspection of one or more sensors in communication with the fabric. If the fabric fails the inspection, the material testing database 134 may be updated (which in turn may cause the material testing database 134 to order more fabric from the fabric supplier factory 136). The results of the laboratory and / or visual inspection may be communicated to the laboratory and / or visual inspection at 144.

[0058] The fabric pretreatment step at 140 may include a foam pretreatment process. The foam pretreatment process may replace a traditional dipping process. The foam pretreatment process may reduce water content. The foam pretreatment process may reduce energy consumption. The foam pretreatment process may reduce the use of chemicals. The foam pretreatment process may achieve deeper and / or richer colors. The foam pretreatment process may dry more easily than a traditional dipping process. The fabric that has undergone the foam pretreatment process may be used in the fabric conditioning step at 146.

[0059] After the fabric pre-treatment step at 140, a laboratory and / or visual inspection may be performed at 144. The inspection may include a human inspection. The inspection may include computer vision, machine vision, and machine learning. The inspection may include an inspection of one or more sensors in communication with the fabric. If the fabric fails the inspection, the material testing database 134 may be updated (which in turn may cause the material testing database 134 to order more fabric from the fabric supplier factory 136). If the fabric fails the inspection, depending on the type of failure / defect, it may also result in a new order being generated to backfill the failed material. The failure may result in a change in the lead time of the quote. The results of the laboratory and / or visual inspection may be communicated to one or more computing devices involved in the fabric conditioning step at 146.

[0060] The fabric conditioning step at 146 may include an atmospheric plasma pre-cleaning / activation process. The atmospheric plasma pre-cleaning / activation process may include a 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. The atmospheric plasma pre-cleaning / activation process may be used to vaporize and remove (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 the foam pretreatment and / or pad process. 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 may be anhydrous and occur at ambient temperature. The plasma pre-cleaning / activation process may be applied by 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 morphology, chemical properties and surface energy for the fabric and / or material. Some grafting reactions (functionalization reactions) may occur between the fabric and / or material and the plasma carrier gas. After the plasma treatment, the chemical composition of the fabric and / or material on the surface may be changed. The fabric that has undergone the plasma pre-cleaning / activation process can be used in the printing step at 158.

[0061] After the fabric conditioning step at 146, a laboratory and / or visual inspection may be performed at 148. The inspection may include a human inspection. The inspection may include an inspection using computer vision. The inspection may include an inspection of one or more sensors in communication with the fabric. If the fabric fails the inspection, the material testing database 134 may be updated (which in turn may cause the material testing database 134 to order more fabric from the fabric supplier factory 136). The results of the laboratory and / or visual inspection may be communicated to one or more computing devices involved in the printing step at 158.

[0062] At 150, the job file 110 may be used as part of a nested pattern step. One or more computing devices may use the job file 110 as part of the nested pattern step. The pattern file 122 of the job file 110 may be used as part of the nested pattern step. Other parts of the job file 110 may also be used, such as the graphic design file 112, the hierarchical information 114, etc. The nested pattern step will refer to Figure 3152. As described herein, the nesting may be generated or updated based on upstream or downstream information. Alternatively or in addition, the nesting may be updated based on information received related to downstream process or device performance. For example, if a cutting process or machine, or if a material handling process or machine / system performs in a particular manner, the nesting may be updated based on such performance information.

[0063] At 152, a cutting file may be generated. The cutting file may be generated in response to the nested pattern step. One or more computing devices may generate the cutting file. The cutting file may include information for cutting components from the fabric. The cutting file may be used in the cutting step at 182. After the cutting file is generated, the process may move to 154. The description is made with reference to various files. It should be understood that several files or a single file may be used.

[0064] At 154, a color separation step may be performed. One or more computing devices may perform the color separation step. Job file 110 may be used to perform the color separation step. After the color separation step, the process may move to 156.

[0065] 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 .

[0066] At 158, a printing step may be performed. Although the term printing is used, it is understood that drop-on-demand refers to a general selective process, for example, including selective deposition of materials and digital printing. The printing step may include causing colors and / or graphics to be printed on the fabric. One or more computing devices may cause colors and / or graphics to be printed on the fabric. The results of the raster image processing step at 156 may be used to affect the printing of colors and / or graphics on the fabric. The job file 110 may be used to affect the printing of colors and / or graphics on the fabric. The fabric that has undergone the printing process may be used in a post-printing dyeing step at 166.

[0067] After the printing step at 158, a laboratory and / or visual inspection may be performed at 160. The inspection may include a human inspection. The inspection may include an inspection using computer vision. The inspection may include an inspection of one or more sensors in communication with the fabric. The inspection may determine whether and to what extent there is a difference between the expected color and the color actually printed on the fabric during the printing step at 158. The results of the laboratory and / or visual inspection may be communicated to one or more computing devices associated with the 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 assist in the inspection at 160. The results of the laboratory and / or visual inspection may be communicated to one or more computing devices involved in the post-printing dyeing step at 166.

[0068] At 162, one or more computing devices associated with the color control / printer calibration step may determine and / or receive information indicating a difference between the expected color and the color actually printed on the fabric at 158. The one or more computing devices associated with the color control / printer calibration step may determine a new paint color to associate with the expected color. The one or more computing devices associated with the color control / printer calibration step may determine that the new paint color requires more or less of a particular color, such as red, blue, and / or green, to more closely approximate the expected color. The one or more computing devices associated with the color control / printer calibration step may communicate with one or more computing devices associated with an update library of addressable color steps at 164.

[0069] At 164, one or more computing devices associated with the updated library of addressable colors step may update the library of addressable colors based on information from one or more computing devices associated with the color control / press calibration step. One or more computing devices associated with the updated library of addressable colors step may assign the determined new paint color to the desired color. One or more computing devices associated with the updated library of addressable colors step may generate a new project request and / or order at 110 using the updated library of addressable colors to be created.

[0070] At 166, a post-printing drying step may be performed. The post-printing dyeing step may include drying the fabric. One or more computing devices may cause the fabric to be dried. The fabric that has undergone the post-printing dyeing process may be used in the fixing / steaming step at 170.

[0071] After the post-print drying step at 166, a laboratory and / or visual inspection may be performed at 168. The inspection may include a human inspection. The inspection may include an inspection using computer vision. The inspection may include an inspection of one or more sensors in communication with the fabric. If the fabric fails the inspection, the material testing database 134 may be updated (which in turn may cause the material testing database 134 to order more fabric from the fabric supplier factory 136). The results of the laboratory and / or visual inspection may be communicated to one or more computing devices involved in the fixing / steaming step at 170.

[0072] At 170, a fixing / steaming step may be performed. The fixing / steaming step may include steaming the fabric that has been printed and / or dyed. One or more computing devices may cause the fabric to be steamed. The fabric that has undergone the fixing / steaming step may be used in a post-printing washing step at 174.

[0073] After the fixing / steaming step at 170, a laboratory and / or visual inspection may be performed at 172. The inspection may include a human inspection. The inspection may include an inspection using computer vision. The inspection may include an inspection of one or more sensors in communication with the fabric. The results of the laboratory and / or visual inspection may be communicated to one or more computing devices involved in the post-printing cleaning step at 174.

[0074] At 174, a post-printing washing step may be performed. The post-printing washing step may include washing the fabric that has been steamed and / or fixed. 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.

[0075] After the post-printing step at 174, a laboratory and / or visual inspection may be performed at 176. The inspection may include a human inspection. The inspection may include an inspection using computer vision. The inspection may include an inspection of one or more sensors in communication with the fabric. The results of the laboratory and / or visual inspection may be communicated to one or more computing devices involved in the post-printing drying step at 178.

[0076] 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 cause the fabric to be dried. The fabric that has undergone the post-printing drying step may be used in the cutting step at 182.

[0077] After the post-print drying step at 178, a laboratory and / or visual inspection may be performed at 180. The inspection may include a human inspection. The inspection may include an inspection using computer vision. The inspection may include an inspection of one or more sensors in communication with the fabric. If the fabric fails the inspection, the material testing database 134 may be updated (which in turn may cause the material testing database 134 to order more fabric from the fabric supplier factory 136). The results of the laboratory and / or visual inspection may be communicated to one or more computing devices involved in the cutting step at 182.

[0078] At 182, a cutting step may be performed. The dried fabric may be cut. The dried fabric may be cut according to the cutting file generated at 152. One or more computing devices may cause the fabric to be cut. The fabric that has undergone the post-printing cutting step may be used in a batch processing step at 184.

[0079] At 184, a batch processing step may be performed. The cut fabric may be batch processed. One or more computing devices may cause the fabric to be batch processed. The fabric that has undergone the batch processing step may be used in the kitting step at 186.

[0080] At 186, a kitting step may be performed. The batch of fabrics may be kitted. One or more computing devices may cause the fabrics to be woven. The fabrics that have undergone the kitting step may be used in the assembly step at 188.

[0081] At 188, an assembly step may be performed. The woven fabric may be assembled. One or more computing devices may cause the fabric to be assembled. The fabric that has undergone the assembly step may be shipped to a customer.

[0082] 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.

[0083] Design / Product Development

[0084] Current design and product development tools are not digitally connected to any real-world production methods. According to the present disclosure, digital product creation can include manufacturing (printing) instructions created from the design platform. Coloring feasibility will have a feedback loop to inform the design platform and define the designer's choices for the product.

[0085] Figure 2An example diagram of a design process is shown. 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.

[0086] At 202, the designer user interface may be influenced by the consumer data. The colors and / or designs in the design tool may be influenced by the consumer data. The colors and / or designs as options in the design tool may be influenced by business reasons, such as preferred materials highlighted in the designer user interface or undesirable colors and / or patterns not allowed by the designer user interface. The designer user interface may be associated with a 2-dimensional and / or 3-dimensional design and / or development tool.

[0087] At 204, the visualization tool may be influenced by the 2-dimensional and / or 3-dimensional design and / or development tool. The visualization tool may be influenced by the consumer data. The colors and / or designs that appear as options and / or in the visualization created by the visualization tool may be influenced by the consumer data.

[0088] At 206, an 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 2-dimensional and / or 3-dimensional design and / or development tools. The interactive consumer experience presented to the consumer may be influenced by consumer data. The colors and / or designs that appear as options and / or in the interactive consumer experience may be influenced by consumer data. Feedback from the interactive consumer experience may be new consumer data at 200.

[0089] At 208, drip-on-demand (e.g., digital) and / or traditional manufacturing may be influenced by visualization tools. Digital and / or traditional manufacturing may be influenced by 2-dimensional and / or 3-dimensional design and / or development tools. Digital and / or traditional manufacturing may be influenced by consumer data. Colors and / or designs that appear as options and / or in digital and / or traditional manufacturing may be influenced by consumer data. Feedback from digital and / or traditional manufacturing may be new consumer data at 200.

[0090] At 210, high speed manufacturing may be influenced by visualization tools. High speed manufacturing may be influenced by 2-dimensional and / or 3-dimensional design and / or development tools. High speed manufacturing may be influenced by consumer data. Colors and / or designs that appear as options and / or in high speed manufacturing may be influenced by consumer data. Feedback from high speed manufacturing may be new consumer data at 200.

[0091] Currently, design and development for fashion trends is fragmented and not directly driven by consumer demand-designers. The creator will make the best estimate of the best trends and desired situations. This conventional approach is not optimized. According to the present disclosure, an improved on-demand feedback loop can allow the desired color combinations and designs to be predicted in a data-driven manner.

[0092] refer to Figure 3 , a method for manufacturing an article is shown. The method may allow customization. The method may allow dynamic pricing. The method may allow dynamic lead time determination. The method may allow dynamic delivery.

[0093] 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.

[0094] At step 320, design input indicative of a design for an item may be received. One or more computing devices may receive design input indicative of a design for an item. The design of the item may be based on consumer data. The design input indicative of a design for an item may be consumer-oriented, such as design input for made-to-measure items or personalized and / or customized items. The design input indicative of a design for an item may be used for product design of mass-produced items. The design input indicative of a design for an item may include automatic pattern creation. The design input indicative of a design for an item may come directly from a manufacturer. The design input indicative of a design for an item may fit a design model. A "fit model" is a model of size parameters that a brand uses to design a product line, i.e., a standard set of sizes scaled for each available size.

[0095] At step 330, the interactive content may be caused to be output to a user interface associated with one or more consumers. One or more computing devices may cause the interactive content to be output to a user interface associated with one or more consumers. The interactive content may include at least a representation of a design of an item.

[0096] At step 340, manufacturing data indicating instructions associated with manufacturing of the article may be output. One or more computing devices may output manufacturing data indicating instructions associated with manufacturing of 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 be provided directly from a customer to a manufacturer.

[0097] Coloration data indicating coloration feasibility may be received. One or more computing devices may receive coloration data indicating coloration feasibility. The design of the article may depend on the coloration data.

[0098] Sensors in clothing can detect when clothing is worn. The sensors can communicate with an application executing on a client device. The application can relay information from the sensors to a centralized server. The centralized server can include an application for determining trend information, such as which colors, patterns, and / or fabrics are most commonly worn. The centralized server can provide the determined trend information to a server associated with an e-commerce website or browser executing on a user device accessing the e-commerce website. The e-commerce website can make recommendations based on the determined trend information.

[0099] refer to Figure 4 , a method for article development is shown. 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 consumer data including at least biometric information associated with one or more consumers. The consumer data may include consumer preference information.

[0100] At step 420, trend data indicative of trends in one or more of item design or item coloration may be received. One or more computing devices may receive trend data indicative of trends in one or more of item design or item coloration.

[0101] At step 430, output of one or more design options may be caused based at least on the consumer data and the trend data and via a user interface.One or more computing devices may cause output of one or more design options based at least on the consumer data and the trend data and via a user interface.

[0102] At step 440, design input indicative of a design for an article may be received. One or more computing devices may receive design input indicative of a design for an article.

[0103] In response to receiving the design input, types of fabrics may be presented to the designer. The designer may select one or more of the presented types of fabrics. In response to the selected one or more types of fabrics, an integrated technology package may be created for the designer. The integrated technology package may fit the design input and the selected one or more types of fabrics. An engineering bill of materials may be generated for the integrated technology package. The engineering bill of materials may be generated on demand.

[0104] Sensors in clothing can detect when clothing is worn. The sensors can communicate with an application executing on a client device. The application can relay information from the sensors to a centralized server. The centralized server can include an application for determining trend information, such as which colors, patterns, and / or fabrics are most commonly worn. The centralized server can provide the determined trend information to a server associated with a remotely accessible designer tool or browser executing on a user device accessing the remotely accessible designer tool. The designer tool can make suggestions based on the determined trend information. The user can create a design based on the suggestions. The user can create an order based on the design. A bill of materials can be automatically generated based on the order.

[0105] As an example, a method for item 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 input indicating a design of an item. The design of the item may be based on the consumer data and / or other inputs. The method may include causing interactive content to be output to a user interface associated with one or more consumers. The interactive content may include at least a representation of the design of the item. The method may include outputting item data including at least manufacturing data indicating instructions associated with the manufacture of the item. 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 item. The item data may be configured to be received by one or more computing devices associated with one or more manufacturing processes, wherein the one or more manufacturing processes are updated based at least on the item data. The method further includes receiving coloring data indicating coloring feasibility, wherein the design of the item depends on the coloring data. The method may include generating a technical package based on the design of the item and the fabric selection. The method may include outputting a bill of materials based on the design of the fabric. Other steps may be used. As another example, one or more methods may include: wherein the item data includes nesting information indicating a spatial arrangement of one or more portions of the item, and wherein 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 portions of the item; one or more treatments applied to the material used to form at least a portion of the one or more portions of the item; a desired web speed or an operation performed by a pick and place system configured to move the one or more portions of the item once the one or more portions of the item are separated from the material. The nesting information is 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 the data may be shared upstream and / or downstream to enable updates in one or more processes.

[0106] Order aggregation and batch processing

[0107] Conventional order processing for digital printing presses does not address the entire manufacturing process for a single order fulfillment system. This is largely driven by a fragmented value chain where each process takes into account the efficiency of its respective process rather than the entire manufacturing process and its associated overall costs. The present disclosure provides dynamic nested optimization. As an example, dynamic nested optimization can include individual customer orders - particularly a theoretical minimum order quantity (MOQ) of one customer - and batch order components to maximize production and delivery speed back to the end customer within business-oriented product performance, unit cost / profit, and sustainability parameters.

[0108] In a single unit order execution, the amount of ink applied to individual components may vary greatly. During the subsequent cleaning process, components with high ink content may cross-contaminate adjacent components with low ink content (e.g., a bright red component adjacent to a white component), resulting in reduced quality. According to the present disclosure, by analyzing the amount of ink required to print each discrete component, a nested pattern can be created that starts with the lowest level of ink and accumulates to the highest level of ink. Therefore, dark and saturated components (e.g., bright red components) will be adjacent to components that are also dark, thereby avoiding cross-contamination of dark and light colors. When the material is run through the cleaning process, the lightest colors will be cleaned first (when the water on the washing machine is cleanest) and the dark colors will be cleaned last. This can allow less water and chemicals to be used for lighter colors and make the entire process more efficient and sustainable.

[0109] Conventional production planning processes do not consider optimizing smaller batches (down to a single unit) into large batch production utilizing both digital manufacturing processes (e.g., digital printing) as well as conventional '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 routes, whereby small batches can be aggregated for a common process and then split into smaller batches for separate routes in a manner that can be efficiently scheduled in production.

[0110] Nesting

[0111] The current process of creating RIP and print job files does not take into account the individual throughput speeds of actual printing or downstream processing. The present disclosure can integrate printing, finishing, assembly and other manufacturing process considerations into batch processing throughput to improve efficiency and overall speed.

[0112] Nesting optimization in currently used digital printing processes is about 60%-70% (30%-40% material waste), which compares extremely poorly to material optimization for conventional garment manufacturing of about 80%-95% (5%-20% material waste). Nesting optimization needs to be improved in the digital printing space to make the process sustainable and viable at commercial scale. The present disclosure can use optimized nesting of components produced on demand to achieve the efficiency of conventional manufacturing of about 80%-95% material utilization.

[0113] Figure 5A set of example garments illustrating nesting is shown. A first garment 500 may include two sets of colors. A first color may include an upper portion of the first garment 500. A second color may include a lower portion of the first garment 500. A second garment 502 may include two sets of colors. The upper right half of the second garment 502 may include a second color. The lower left half of the second garment 502 may include a third color. A third garment 504 may include a color - a second color. A fourth garment 506 may include a color - a first color. A fifth garment 508 may include a color - a third color.

[0114] Nesting may include arranging garments 500, 502, 504, 506, 508 so that the colors of adjacent borders of garments 500, 502, 504, 506, 508 may be similar. The third garment 506 may be arranged adjacent to the upper half of the first garment 500. The lower left half of the second garment 502 may be arranged adjacent to the fifth garment. Two or more of the lower half of the first garment 500, the upper right half of the second garment 502, and the third garment 504 may be arranged adjacent.

[0115] In an illustrative example, a robotic arm (or robot) may be used to transfer and / or stack (aggregate) garment parts. Multiple such robotic arms with corresponding end effectors may comprise a pick and place production line. The pick and place process (involving transfer and stacking) is typically much slower than other processes in the envisioned system and may therefore be considered a "bottleneck". However, the process may be improved using a nesting protocol that takes into account the specific arrangement and transfer characteristics of the robotic arms to maximize throughput. The nesting arrangement may vary based on, for example: fabric properties (e.g., porosity, stiffness, etc.); the type of treatment applied to the fabric; the desired web speed; other operations performed by the robotic arms, etc. The initial nesting arrangement may be performed by a person or by nesting software. As the pick and place process occurs, the robotic arms may send feedback to a computer that may cause changes to the nesting arrangement, thereby maximizing the overall throughput and / or pick and place speed.

[0116] As an example, the components of a garment may be grouped into sizes, such as small and large. The threshold for grouping and the number of groups may be determined for a particular operation or desired output. In the case of textile materials, a different pick and place approach may be used for small components compared to the approach used for large components. Special nesting may be created that takes into account the various time delays associated with any particular robotic arm (e.g., adjusting the size and arrangement of the grippers so that a large component is picked up immediately after a small component, etc.). The pick and place systems mentioned above may be configured with nesting optimization to allow the system to process multiple smaller components at once or in a chronological order. The pick and place systems may be configured with nesting optimization to handle individual large components or a mixture of small and large components. The pick and place systems may be configured with nesting optimization to maximize throughput speed and fabric utilization. Other optimizations may be used.

[0117] Component Manufacturing

[0118] Figure 6 An example diagram of a component manufacturing process is shown. At 600, a printed fabric may be received. The fabric may have been printed at the printing step at 158 ​​in FIG. 1. The fabric may have been dried at the post-printing drying step at 166 in FIG. 1. The fabric may include a customized upper portion of a shoe. The fabric may include rows and / or columns, wherein each row and column combination may include the same print. The fabric may include cotton canvas.

[0119] At 602, the fabric may be finished. Finishing the fabric may include steaming the fabric. The fabric may be steamed in the fixing / steaming step at 170 in FIG. 1. Finishing the fabric may include washing the fabric. The fabric may be washed at the post-printing washing step at 174 in FIG. 1. Finishing the fabric may include drying the fabric. The fabric may be dried at the post-printing drying step at 178 in FIG. 1.

[0120] At 604, a lining may be applied to the fabric. The lining may be glued to the back of the fabric. The fabric may be a printed and finished cotton canvas. Alternative or additional methods may be used.

[0121] At 606, components can be cut in the fabric. A laser, router, or knife can be used to cut the components in the fabric. Some debris may remain in the cut components. Each row and column combination can be completely cut.

[0122] At 608, the combination of fully cut fabrics (rows and columns) can be stacked. The fully cut fabrics can be stacked so that partially cut components of one fabric layer are aligned with corresponding partially cut components of the fabric layers stacked above and / or below. The stacked fabrics can be sent to an assembler for assembly.

[0123] Color Control

[0124] Conventional coloring methods rely heavily on manual processes and require multiple time-consuming iterations in trial and error over extended periods of time. The present disclosure can combine precise substrate characterization data, color-by-color ink chemistry profiles, and precise wet finishing data to preempt the extended trial and error process.

[0125] Conventional design tools are fragmented, incompatible, and in many ways completely isolated from the manufacturing process, thus requiring a prolonged trial-and-error process to produce as designed, creating the need to make changes to the original design for manufacturing. The present disclosure includes an integrated manufacturing job file creation function that provides customers, designers, and other end users with only achievable design and material properties, excluding colors and features that cannot be maintained within allowed performance attributes and standards, thereby seamlessly creating manufacturing job files directly from input designs.

[0126] Typically performed as a separate ad hoc process as a post-process QA / QC function - 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., desired end color, substrate construction, and subsequent wet processing and lamination processes. The present disclosure can integrate this into our inline coloring and fixing processes to more proactively inform color matching and repeatability.

[0127] Preprocessing

[0128] Foam Application

[0129] In direct-to-fabric digital printing in the textile industry, pre-treatment chemicals are applied to textiles in the form of open widths through a process called filling, whereby the entire textile is immersed in chemicals and the excess is squeezed out before the drying / fixing of the chemicals. Under conventional processes, the amount of moisture absorbed by the textile can be in the range of 70% to >100% of the textile weight (referred to as "moisture absorption" in the industry), and when the textile is dried before subsequent treatment, all of this moisture must be evaporated in an energy-intensive process. Another problem with conventional filling of pre-treatment chemicals is that in most cases, chemicals are only applied to the entire textile and run through the entire textile when chemicals are needed on the surface to be printed. Therefore, conventional processes require the use of more energy, water and chemicals than what is needed to increase value in subsequent processes. The textile industry is the second largest freshwater consumer in the world and one of the largest surface water polluters after the agricultural industry. The industry is looking for novel ways to reduce water, energy and chemical consumption.

[0130] Foam application of chemicals has been used commercially for decades. In the emerging industry of direct to fabric digital printing, production speeds are increasing to levels that are rapidly growing the industry and attracting the attention of the investment community. In the present disclosure, a process includes a pre-treatment chemical applied via a foam applicator that has several benefits important to the textile industry, such as: reduced energy consumption, reduced water consumption, reduced chemical consumption, more accurate application of needed chemicals, and reduced chemical load on wastewater treatment systems. It has also been demonstrated that deeper, richer colors can be achieved through the foam application process compared to conventional processes.

[0131] Fig. 7A Positive results for foam application pretreatment in four preliminary chemical formulations are shown. These preliminary formulations show higher average results in multiple categories compared to the average results in the control case or the conventional process. R is the reflectance at the wavelength of maximum absorption in a decimal manner (20% R = 0.20R)

[0132] All 4 samples (3C, 5B, 2B and 2D) were subjected to foam application and the results were compared to the corresponding conventional filled or pad coated samples. For example, samples 3C and 5B were compared to a filled sample (pad 1 below), and 2B and 2D were compared to another filled sample (pad 2 below).

[0133] SWL values ​​> 100% associated with the foam samples mean that higher color yields were achievable with the foam formulation and conditions, at least compared to the conventional samples.

[0134] Figure 7B Positive results are shown for four different chemical formulations, with 2B and 2D showing similar performance results to the baseline control case, and 3C and 5B showing improved performance compared to the control and results for the other formulation variables.

[0135] The present disclosure includes formulations for foam treatments such as the following (although other chemistries may be used):

[0136] -Pad 1

[0137] -Urea 10% 100g / kg

[0138] -Alkali (sodium carbonate) 1% 10g / kg

[0139] -Migration inhibitor (Thermacol MP) 10% 100g / kg

[0140] -Pad 2

[0141] o Urea 10% 100g / kg

[0142] o Alkali (sodium carbonate) 2% 20g / kg

[0143] o Migration inhibitor (Prepajet Uni) 8% 80g / kg

[0144] o Reduction inhibitor (Lyoprint RG) 2% 20g / kg

[0145] A foam treatment of a durable water repellent (DWR) may be used. As an example, Figure 7C A DWR formulation with specific parameters for applying the foam to a polyester substrate was demonstrated, showing that the improvement in performance could result in a 50% reduction in chemical savings with a potential 80-85% reduction in chemical consumption.

[0146] refer to Fig.7D , a method for pre-treating a textile is shown. At step 710, a textile may be received. A material manufacturer may receive the textile. Step 138 in FIG. 1 may include step 710.

[0147] At step 720, a selected area of ​​the textile to be printed may be determined. The material manufacturer may determine the selected area of ​​the textile to be printed. Step 140 in FIG. 1 may include step 720.

[0148] At step 730, the applicator may be caused to apply the foam chemical to the selected area of ​​the textile. The material manufacturer may cause the applicator to apply the foam chemical to the selected area of ​​the textile. Application of the foam chemical to areas of the textile outside the selected area may be minimized. Step 140 in FIG. 1 may include step 730.

[0149] At step 740, the selected area of ​​the textile may be dried so that the surface of the selected area can be printed. The material manufacturer may dry the selected area of ​​the textile so that the surface of the selected area can be printed. Step 140 in FIG. 1 may include step 740.

[0150] The material manufacturer may receive a textile and a corresponding job file. The job file may indicate specific areas of the textile that should be printed. The material manufacturer may cause the specific areas of the textile to be subjected to a foam pre-treatment. The material manufacturer may cause the specific areas of the textile to be dried. The material manufacturer may cause the specific areas of the textile to be printed according to the instructions of the job file.

[0151] Plasma pre-cleaning / activation

[0152] Textile materials must be thoroughly cleaned to optimize wettability and adhesion of chemicals (e.g., durable water-repellent finishes, colorants, polymer coatings, laminations, etc.). With increasingly stringent environmental restrictions on the use of solvents and surfactants, it is becoming increasingly difficult to achieve the same level of cleanliness that was obtained with aggressive chemicals (e.g., solvents) in the past. Today, most textile cleaning is based on water using large amounts of thermal energy and the mildest detergent chemistry possible. Unfortunately, modern cleaning systems, while environmentally friendly, do not protect textiles from contaminants that can interfere with the coloring and finishing of textiles. Applying chemicals to contaminated fabrics often results in poor performance, poor durability of functional finishes, or the need to use more chemicals to achieve acceptable grades than would be required if the fabric were completely clean. Atmospheric plasma treatment can change the surface chemistry and surface morphology of fabrics and / or materials to improve adhesion to different materials. Each plasma carrier gas can provide different surface chemistry and surface morphology.

[0153] According to the present disclosure, the corona plasma process can use ionized gases to vaporize and remove (decompose) contaminants (oils, waxes, etc.) on the surface of textiles. It is a water-free process and makes the cleaner surface easier to "wet" by changing the surface chemistry and surface energy of fabrics and / or materials with water-based chemistry. A corona plasma unit can be placed before the chemical application step to help the chemical penetrate (wettability) and activate the textile surface. Plasma can be used to improve the performance of some chemical applications (such as DWR) and obtain deeper, more saturated colors using less dyes and chemicals.

[0154] refer to Figure 8 , a method for pre-treating a textile is shown. At step 810, a textile may be received. A material manufacturer may receive the textile. Step 138 in FIG. 1 may include step 810.

[0155] At step 820, at least a portion of the textile may be conditioned to remove one or more contaminants from at least a portion of the textile using plasma. The material manufacturer may condition at least a portion of the textile using 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 corona plasma. Step 140 in FIG. 1 may include step 820. Step 146 in FIG. 1 may include step 820.

[0156] At step 830, one or more chemicals may be applied to at least a portion of the textile. The material manufacturer may apply the one or more chemicals to at least a portion of the textile. Step 140 in FIG. 1 may include step 830. Step 146 in FIG. 1 may include step 830. Activation of the 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.

[0157] A material manufacturer may receive a textile that has 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.

[0158] Coloring by numbers

[0159] Drop-on-demand (e.g., digital printing)

[0160] In the apparel industry, conventional products are currently manufactured under a forecasting model where wholesalers and retailers place orders for clothing and footwear based on forecasts before consumers actually purchase the final product. In this scenario, products are manufactured using large batch inputs (e.g., materials such as textiles) that are successively broken down into smaller and smaller batches until the final process whereby the final product is completed as a '1 batch' unit, which is Fig. 9B An illustrative example is shown in . Under this system, the final product's unique identifier is not assigned until the last step in the process. In one scenario where a product is to be manufactured under a "mass customization" model, the consumer may purchase the final product before it is manufactured. Under this model, it is critical to identify each component of the final product throughout the manufacturing process in order to track the order from inception to delivery. The identification of each component can be generated at the digital printing step using a unique identifier (e.g., a barcode or QR code). However, most consumers do not want to see a unique identifier on the final product and in Fig. 9B Illustrative examples are shown in . Furthermore, material utilization is a key driver for the efficient and sustainable use of raw materials. In order to address the identification issue without using excessive materials, a unique identifier needs to be placed on each component in a manner that is clear but invisible to the consumer.

[0161] In the present disclosure, quality control may be implemented via invisible alignment dots, such as using invisible ink that can be viewed via computer vision or some other process. The systems and methods of the present invention may embed data via a unique dither pattern. For example, the present disclosure includes applying a unique identifier (e.g., Fig. 9BThe present disclosure includes the use of a unique identifier applied to each component using an invisible ink that is readable by machine vision, but uses ink outside the visible spectrum of human perception (e.g., ultraviolet, infrared, etc.).

[0162] Attribution and traceability

[0163] In the apparel industry, "to-scale" industry production does not support digital custom manufacturing driven by consumer generated or aggregated content. In order to manage a unique, single-unit workflow that can be used to make claims and prove the provenance of products and components, a digitally generated tagging system for each textile component is needed, which can track each textile component from the point of generation to the point of sale, so that the tag can be connected to the entire value chain through manufacturing integration and intelligent systems. In the present disclosure, systems and methods can embed customer order data with visible and invisible attribution via digital printing using visible and / or invisible codes.

[0164] Methods 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 set on at least a portion of the material. One or more unique identifiers may be invisible to the human eye and may be visible by means of a predetermined visual method. One or more unique identifiers may represent item data including at least a portion of the order data. One or more methods may include processing materials via one or more manufacturing processes to form at least a portion of the item. 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 corresponding manufacturing process. Each (or one or more) of the manufacturing processes may include reading the item data and adjusting one or more actions associated with the corresponding manufacturing process based on the item data. The item data may indicate the provenance of the item. As used herein, the item data may be or include other data, such as nesting data, order data, color data, etc.

[0165] Methods 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 set on at least a portion of the material. One or more unique identifiers are visible to the human eye and are configured to be hidden by one or more manufacturing processes. One or more unique identifiers may represent item data including at least a portion of the order data. Other data may be represented. One or more methods may include processing the material via one or more manufacturing processes to form at least a portion of the item 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 corresponding manufacturing process. Each (or one or more) of the manufacturing processes may include reading the item data and adjusting one or more actions associated with the corresponding manufacturing process based on the item data. The item data may indicate the provenance of the item.

[0166] Digital marking of apparel / footwear components requires unique markings and / or serialized unique markings (e.g. Fig. 9B ), the mark being large enough to be clearly visible via machine vision to enable automated manufacturing (reliable legibility). The size of the digital mark varies depending on the substrate - for example, very flat, even substrates are marked with smaller marks due to the physical properties of the flat surface, while substrates with a greater degree of Z-direction texture (e.g., seersucker weave, waffle weave) require relatively larger marks due to the physical properties of light reflected from the substrate surface. In the present disclosure, a manufacturing integration and intelligence system (MII) can generate a unique digital identifier (e.g., QR code, barcode) that is generated to be reliably legible based on data collected on a given substrate. The present disclosure can automatically select a mark of appropriate size based on the substrate data and the size of the printed component.

[0167] Current product storytelling requires months or years of planning with the upstream supply chain, whereby the provenance of inputs (e.g., organic content, recycled content) is managed through underwriting or legal documentation to manage risk contrary to a defined digital chain of custody. As the market moves toward smaller batch sizes and higher degrees of customization, it is increasingly difficult to track inputs and processes to prepare marketing claims. The systems and methods of the present disclosure can generate a unique identifier that can be accessed digitally (e.g., QR code, barcode) to connect the consumer to the history and provenance of the final product, thereby compiling inputs and 'ingredients' as the product moves through the supply chain and will be accessible to the end consumer by interacting with the unique identifier (e.g., through a mobile device, scanner, digital camera, etc.).

[0168] In the apparel and footwear industries, textile substrates, while being 'engineered' materials, can have a large degree of variation in dimensional changes through the steps in the manufacturing process, particularly during the finishing stages of processing. Dimensional changes can be the result of a variety of factors: variations in moisture / wetting of the material; mechanical forces in wet and dry processing, such as stretching or compaction of the material; permanent / semi-permanent changes in thermoplastic substrates due to the application of heat fixation (heat setting), chemicals (e.g., coatings, etc.). Dimensional variations manifest not only at a macro level (batch to batch), but also at a micro level within a yard or meter of substrate, making local predictions of dimensional changes unpredictable. Due to the aforementioned dimensional variations, the relationship of the alignment marks of the cutting process of individual components (applied in a previous process, such as on a digital printing press) can vary significantly through processing, such that there may be significant variations throughout the process, such that cutting based on the initial design dimensions will result in components that are out of specification. To address this issue, a more robust process is needed to identify dimensional changes prior to cutting in order to provide accurate cutting, and also provide a data feedback loop to improve the prediction of dimensional changes and / or identify quality issues using machine vision. Adding sizing reference points across the width and length of a textile web is critical. Placing a high density of reference marks on the end product is commercially unacceptable to the consumer, so markings that are invisible to the consumer (outside the visual spectrum) but visible to machine vision are critical for high-speed single-ply cutting of textiles. If a mark large enough to be reliably detected were visible on every component of a garment / footwear product, the mark would likely be objectionable to the average consumer. In the present disclosure, invisible ink can be used to create registration points that track changes to the original pattern as a quality control measure. This can be corrected during the cutting process, or the order can be referenced back to a queue in a pre-formatted job file for reproduction.

[0169] Conventional components are often cut from a monolithic print - this results in wasted ink, trimming of the finish, and increased difficulty in recycling unused materials (e.g., fabric or other components). In the present disclosure, precise application of trimming allows recycling of unused fabric.

[0170] Traditionally, adhesives are applied in a monolithic and analog manner. This creates a lot of waste, both in excess chemicals and in the ability to recycle unused materials (e.g., fabrics or other components). The present disclosure can utilize digital printing / printing of adhesives using proprietary formulations to apply chemicals only when needed. The platform can identify visual alignment points, reference layers in a digital technology pack database, and use precise application of chemicals only for necessary areas of corresponding component-level (or engineering) prints.

[0171] Fig.9AAn illustrative example of component-level printing combined with precision laser cutting to facilitate simplified assembly and automation was presented. Demonstration of component-level engineered printing, nested in pairs and batched according to downstream processing, enabled flexibility in downstream sewing and assembly - benefits that can be realized in order-by-order printing, which is largely commoditized in practice. Fig. 9B An example of QR codes for upstream and downstream traceability is shown. QR codes are 'invisible' identifiers in jitter patterns and formulations to contain essential data for supply chain sustainability and CSR to facilitate manufacturing throughout the shipping process and create opportunities for marketing and attribution. Fig. 9C The incremental improvements shown in the figure, such as precise cutting to leave residual uncut debris, translate into material savings in direct labor costs while improving the quality and consistency of the base product compared to existing manufacturing methods. Fig.9D -E shows an example of a traditional manufacturing process being eliminated through large-scale engineering printing and digital manufacturing. In this case, simply replacing the label with digitally printed information (with applicable care and size information and necessary country of origin certification data) eliminates the direct labor costs attributed to the manufacture of the goods, while also providing the opportunity for countermeasures to prevent the circulation of fraudulent and counterfeit goods.

[0172] Alternatively or in addition to being used as an information carrier, the UID may be used to "grade" or evaluate one or more process steps. For example, if a particular UID has a reflective property, measuring the reflectivity before the PU coating is applied (the "process") and again after the process is complete will yield "local" information or information specific to the garment component about the thickness and / or quality of the PU coating applied. In other cases, the UID itself will change (e.g., a color change or visibility change corresponding to the highest and / or lowest temperature used in the process or indicating a specific temperature range). Evaluations of other production process characteristics are conceivable. These UIDs can be applied within the seam allowance of each garment component and / or within the binding area of ​​the fabric roll.

[0173] Wet dressing

[0174] Conventional finishing processes are typically fragmented processes, physically and digitally separated from the printing process. The current state presents an extremely long feedback loop. It is currently performed manually with a high degree of variation (especially from one site to another). In the present disclosure, an online spectrophotometer can be implemented to measure the variation and create algorithms to optimize settings for local 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 corresponding conditions (e.g., water quality, chemicals, environmental conditions, etc.), read data from previous processes and settings, and then write conditions from this part of the process.

[0175] Fig.10 An example diagram of a trimming process is shown. The wet trimming process may include a process data set 1000 and a product data set 1050. The process data set 1000 may receive and / or extract data from a digital input and / or command 1040. The process data set 1000 may communicate with the product data set 1050.

[0176] The process data set 1000 may include a process recipe database 1010. The process recipe database 1010 may include substrate data 1012, coloring data 1014, feel data 1016, trimming data 1018, etc. The process data set 1000 may include data about various process steps, such as a substrate pretreatment step 1020, a substrate coloring step 1022, a substrate steaming step 1024, a substrate cleaning step 1026, a substrate curing step 1028, a substrate drying step 1030, a substrate functional trimming step 1032, a substrate flipping step 1034, etc. Data about various process steps may be obtained from sensors. Data about various process steps may be obtained from a spectrometer. Data about various process steps may be obtained from an online spectrophotometer. An online spectrophotometer may measure differences in data about various process steps. Differences measured in data about various process steps may be used to create algorithms to obtain better settings. The process data set 1000 may include settings and / or conditions. Settings and / or conditions may be attributed to direct performance outputs. The settings and / or conditions may be associated with an inline dryer, steamer, washer, tenter frame, etc.

[0177] The product data set 1050 may include a product feedback set 1060. The product feedback set 1060 may include data on 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 reflectivity feedback 1078, substrate heat absorption feedback 1080, substrate heat retention feedback 1082, etc. Data on various aspects of the product can be obtained from sensors. Data on various methods can be obtained from spectrometers. Data on various aspects can be obtained from online spectrophotometers. Online spectrophotometers can measure data differences on various aspects. The differences measured in the data on various aspects can be used to create algorithms to obtain better settings. The product data set 1050 may include settings and / or conditions. Settings and / or conditions can be attributed to direct performance outputs. Settings and / or conditions can be associated with online dryers, steamers, washing machines, tenter frames, etc.

[0178] Digital retouching

[0179] Component-level application of DWR (e.g., fluorine-free or conventional fluorine)

[0180] Conventional DWR processes are done in batches in an overall chemical application where the chemical is applied to the entire web of textile material with the same level of application across the entire surface. The problem with this approach is that the chemical is used on a material that ends up as waste and there is no control over the level of repellency in an 'engineered' approach to create new performance applications. In the present disclosure, the digital application of DWR at the component level and in a roll-to-roll process will allow for engineered patterns that can digitally enable moisture management. This will create a more sustainable process where less chemicals are used to create performance. And, the fabric that ends up as waste after the cutting process can be more easily recycled since the waste does not contain chemical contaminants. Furthermore, in the digital application of chemicals, new performance functions can be achieved through engineered placement of chemicals that can be scaled across components of different sizes to enable customization of individual units.

[0181] Engineering Applications of Chemical Substances

[0182] Conventional application of chemicals covers all fabrics, creates a lot of waste in chemicals, and hinders the recycling of unused fabrics. In the present disclosure, precise digital application of chemicals such as adhesives can reduce chemical usage, save costs and allow recycling of unused fabrics.

[0183] Cutting

[0184] The typical conventional method is to cut a pre-programmed pattern from the whole print, which requires repeated component patterning and causes ink waste, and hinders scaled customization. In the present disclosure, dynamic identification of cutting patterns allows for increased overall throughput, reduced waste and mass customization.

[0185] Current automated single-ply cutting in the textile materials industry does not have sufficient throughput to scale in the apparel / footwear industry. Conventional technology utilizes gantry-driven X / Y axis equipment, typically using a mechanical knife, and sometimes also using laser energy. The disclosed system and method can utilize a high-speed galvanometer-driven laser, which has a throughput that is 2 orders of magnitude higher than a gantry-driven system. Fig.11 Shown is the design of an early high-speed single-layer galvanometer-driven laser cutting prototype that far exceeds existing conventional textile cutting methods.

[0186] Conventional wet finishing processes (e.g., post-print steaming, washing, and stenting) create distortions in fabrics that are nonlinear and difficult to predict consistently (especially on knitted fabrics). These distortions hinder the ability to print both patterns and components with consistent accuracy. The disclosed systems and methods can match a database of pre-graded component shapes and patterns and make cutting adjustments to correct observed shape distortions while adjusting recipe changes for the next printing iteration. Fig.12 yes Fig.11 The infeed section of the machine where visual recognition and real-time job file correction data is collected and transmitted.

[0187] Printing at the component level can lead to inefficient sorting and handling of downstream processes, where engineered printed components are difficult to discern from waste. The disclosed system and method can nest prints and cut components so that unused substrate remains attached as a web. This web of unused substrate is rolled off a conveyor belt, leaving the associated cut components to be sorted, woven and assembled, allowing for efficient batch processing and recycling of waste. Fig.13 Rollers are shown for waste removal and subsequent down-circulation or recycling.

[0188] Conventional cutting methods involve manually cutting components using machine tools with analog capabilities, or automatically cutting materials using gantry-driven knives, routers, or lasers. Specifically, this labor-intensive process creates significant inefficiencies, especially for customization. The systems and methods of the present disclosure provide nesting through unique custom orders, batch processing, and precise laser cutting to keep the scraps together, thereby enabling manual separation from the block.

[0189] A method for cutting alignment may include using computer vision to analyze a first pattern configuration set on a printed material. One or more methods may include implementing a trimming process on the printed material to produce a second pattern configuration different from the first pattern configuration. One or more methods may include using computer vision to analyze a second pattern configuration set on the printed material. One or more methods may include determining cutting control information based on the first pattern configuration and the second pattern configuration. One or more methods may include sending the cutting control information to a cutting system to facilitate cutting the printed material. The cutting system may include a high-speed single-layer galvanometer-driven laser cutting system. The trimming process includes steaming the material, cleaning the material, and / or drying the material. The trimming process may include a digital trimming process. The cutting control information may depend on the type of material. One or more methods may include batching one or more customer orders into batches and nesting multiple item components on the printed material based on the batches.

[0190] The cutting method may include batching one or more customer orders into batches, nesting a plurality of item components based on the batches, and cutting the nested components from a substrate such that one or more tabs connect the cut components to a portion of the substrate. Prior to the cutting step, a method may include: analyzing a first pattern configuration disposed on a substrate using computer vision; performing a trimming process on the substrate to produce 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 trimming process may include one or more of steaming the material, washing the material, or drying the material. The trimming process may include one or more of conveyor belt compaction, mechanical compaction, dewaxing, sponging, shrink proofing, relaxation drying, continuous tumbling, or batch tumbling. The finishing process may include one or more of the following: suede raising, shearing, napping, open width compaction, tubular compaction, calendaring, vaporizing, sponging, atmospheric plasma finishing, continuous dewaxing, semi-continuous dewaxing, crushing, coating, laminating, embossing, tension-free drying, relaxation drying, stretching, tentering, napping, brushing, singeing, beating, heat setting, heat fixing, filling, digital printing, roller printing, fleecing, sputtering or applying. The cutting control information may depend on material properties, which include one or more of the following: type of material, thickness of material, mass per unit area of ​​material, porosity of material or yarn properties.

[0191] Fig.14 An example diagram of a laser cutting process is shown. At 1400, a fabric can be observed. The fabric can be a digitally printed fabric. The digitally printed fabric can include a pattern. Robot vision can be used to capture pattern dimensions. The captured pattern dimensions can include original pattern dimensions. The captured pattern dimensions can include pre-trimmed pattern dimensions.

[0192] 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 in the fixing / steaming step at 170 in FIG. 1. The fabric finishing process may include washing the fabric. The fabric may be washed at the post-printing washing step at 174 in FIG. 1. The fabric finishing process may include drying the fabric. The fabric may be dried at the post-printing drying step at 178 in FIG. 1. The fabric may undergo a traditional and / or digital finishing process. The fabric finishing process may produce a fabric including a changed pattern size.

[0193] At 1404, fabric changes can be observed. Robot vision can be used to capture the pattern size of the fabric after the fabric finishing process. The pattern size of the fabric captured after the fabric finishing process may include the changed pattern size. The pattern size of the fabric captured after the fabric finishing process may include the trimmed pattern size. The changed pattern size can be compared to the original pattern size to obtain an increment (e.g., a change, a change, etc.). The increment can be provided to the laser control system. The laser control system can use the increment to more accurately and precisely cut a pattern from the fabric.

[0194] refer to Fig.15 , a method for cutting alignment is shown. At step 1510, a first pattern configuration disposed on a printed fabric may be analyzed using computer vision. A material manufacturer may analyze a first pattern configuration disposed on a printed fabric using computer vision.

[0195] At step 1520, a finishing process may be performed on the printed fabric to produce a second pattern configuration different from the first pattern configuration. The material manufacturer may perform a finishing process on the printed fabric to produce a second pattern configuration different from the first pattern configuration.

[0196] The second pattern configuration provided on the printed fabric may be analyzed using computer vision at step 1530. The material manufacturer may analyze the second pattern configuration provided on the printed fabric using computer vision.

[0197] At 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.

[0198] At step 1550, cutting control information may be sent to a cutting system to facilitate cutting of the printed fabric. The material manufacturer may send cutting control information to a cutting system to facilitate cutting of the printed fabric.

[0199] A material manufacturer may receive 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 altered printed design on the fabric. The material manufacturer may use the original printed design and the altered printed design to determine increments. The material manufacturer may provide the increments to a cutting system. The cutting system may use the increments to cut the fabric with the altered printed design.

[0200] Material Handling

[0201] Traditional automation approaches for handling and kitting functions within the textile and apparel industry are highly specialized for specific product applications. Because traditional apparel and footwear assembly lines are capital intensive and product specific, high volumes and throughput with low labor costs are required to justify the investment. This investment threshold is prohibitively high for most businesses, often hindering manufacturing operations in high-cost developed markets.

[0202] In this disclosure, Figure 16-17 As shown, the design for automated kitting and assembly creates a flexible platform that can be reconfigured for different product types and categories and can be adjusted based on differences in corresponding throughput speeds and outputs. For example, by using an overhead track system, pick-and-place robot types (e.g., SCARA vs. delta) have variable concentrations and overlapping radii. The throughput speed / output can be adjusted by using pallets and a sorting system that allows parts to be dynamically sorted into a pile of similar components or as batch orders, thereby simplifying the problem set of navigation via visual recognition and minimizing the mechanical distance traveled. Other examples of platform agility can be illustrated by interchangeable end effectors (electrostatic, water, vacuum, etc.) to illustrate substrate types, extended conveyors to manage larger cut components, and replacing packaging with pallets for off-site assembly of custom orders elsewhere.

[0203] Conventional item management processes remain completely isolated from printing, nesting, and batching considerations. This can lead to excessive cost inefficiencies, especially when printing at the component level. The present disclosure can view the end-to-end manufacturing value chain as a closed system and feedback loop.

[0204] Fiber web defect tracking method:

[0205] In this system, there may be multiple "subsystems" (printers, steamers, cutters, etc.). Each of the subsystems can produce defects under certain circumstances. For example, one of the printers may print an error, or the deposited ink may be smudged by physical contact with an object. As another example, a steamer set to execute the wrong recipe may produce unusable fragments of a fabric roll. In another example, an operator may choose to cut fragments of a fabric roll and then reconnect them (via sewing). Such changes can be recorded and transmitted to the designated subsystem so that, for example, a cutter can accurately and precisely cut garment parts from a fiber web and / or so that a robotic arm knows the desired parts and their locations.

[0206] Thus, the nesting program (e.g., after executing the nesting protocol) can deposit UIDs along the "gutter" area of ​​the fabric roll. The UIDs can be scannable bar codes, data matrices, or equivalent, and are equally spaced along the long edge of the fabric. Other UIDs can be used. For example, one can create unique identifiers every 5 inches (with the same copy of the UID contained within another parallel gutter); alternatively, these UIDs can be spaced in any other regular manner along the gutter. The space between each UID (virtual fabric slice) can be called a "segment" in which the printed garment parts or graphic designs will be contained. The nesting program can record the specific garment parts or graphic designs contained within each virtual segment (i.e., between two consecutive UIDs). It should be noted that some parts in a particular segment will not be "complete" because parts of the parts will be contained in segments after or before the current segment.

[0207] As an illustrative example, when a defect is detected, one of the following operations may be performed: 1) The operator cuts out the defective segment of the fabric, scans two UIDs (one before the defective area and one after the defective area), and reconnects the fabric. 2) No cutting of the fabric occurs. The operator (or camera) scans the two UIDs containing the defective area (which may span multiple segments). Other operations may be performed. Information from the scanned UIDs may be transmitted to a computer system, which makes appropriate adjustments to the master nesting file to exclude the cut segment from subsequent operations (e.g., cutting). Additionally or alternatively, information about the components (and / or graphic designs) contained within the defective area may be stored. The stored information may be further supplemented with other defect data from other rolls of the same fabric material. At the end of the production process (or at any time), the stored information may be sent back to the nesting software, which may summarize and re-nest the "missing" components. Once the components are nested, the production process (i.e., printing, steaming, cutting, etc.) is carried out until all necessary components are produced. Alternatively or additionally, the same process as above may be implemented, but with the UIDs placed between nested components rather than in the gutter.

[0208] The present disclosure includes at least the following aspects:

[0209] Aspect 1: A method for item management, the method comprising: receiving consumer data including at least biometric information associated with one or more consumers; receiving design input indicating a design of an item, wherein the design of the item is based on the consumer data; causing interactive content to be output to a user interface associated with the one or more consumers, wherein the interactive content includes at least a representation of the design of the item; and outputting manufacturing data indicating instructions associated with the manufacture of the item, wherein the instructions are based on the design of the item.

[0210] Aspect 2: The method according to aspect 1, wherein the consumer data further includes consumer preference information.

[0211] Aspect 3: The method according to aspect 1, further comprising: receiving coloring data indicating coloring feasibility, wherein the design of the item depends on the coloring data.

[0212] Aspect 4: The method according to aspect 1, wherein the outputting the manufacturing data comprises outputting at least a portion of the manufacturing data to a digital printing system.

[0213] Aspect 5: A method for direct-to-manufacturer item management, the method comprising: receiving consumer data including at least biometric information associated with one or more consumers; receiving design input indicating a design of an item, wherein the design of the item is based on the consumer data; automatically generating a pattern including one or more components of the item and one or more components of a second item; and outputting manufacturing data indicating instructions associated with the manufacture of the item, wherein the instructions are based on the pattern.

[0214] Aspect 6: The method according to aspect 1, wherein the automatically generating a pattern comprises performing nesting optimization.

[0215] Aspect 7: A method for item development, the method comprising: receiving consumer data including at least biometric information associated with one or more consumers; receiving trend data indicating trends in one or more of item design or item coloring; causing output of one or more design options based at least on the consumer data and the trend data and via a user interface; and receiving design input indicating a design of the item.

[0216] Aspect 8: The method according to aspect 7, wherein the consumer data further includes consumer preference information.

[0217] Aspect 9: The method of aspect 7, wherein the one or more design options include a type of fabric.

[0218] Aspect 10: The method of aspect 7, wherein the one or more design options are limited based on available fabrics.

[0219] Aspect 11: The method of aspect 7, further comprising generating a tech pack based on the design and fabric selection of the article.

[0220] Aspect 12: The method of aspect 7, further comprising outputting a bill of materials based on the design of the fabric.

[0221] Aspect 13: A method for color control, the method comprising: receiving data indicating one or more characteristics of a substrate used to form an article; selecting based on the data indicating one or more characteristics of the substrate, a chemical profile or a finishing process, or both; and forming at least a portion of the article using the selected chemical profile or finishing process, or both.

[0222] Aspect 14: The method according to aspect 13, wherein the article exhibits a color within a tolerance range of a designed color.

[0223] Aspect 15: A method of color control, the method comprising: executing a first process among multiple item management processes to output a first-stage item; capturing color data associated with the first-stage product using an online spectrophotometer; comparing the color data with expected data; and performing repairs based at least on the comparison of the color data with the expected data.

[0224] Aspect 16: A method for pretreating a textile, the method comprising: 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 the selected area is minimized; and drying the selected area of ​​the textile so that a surface of the selected area can be printed.

[0225] Aspect 17: A method for pretreating a textile, the method comprising: receiving a textile; conditioning at least a portion of the textile using plasma to remove one or more contaminants from at least the portion of the textile and to change one or more of the surface chemistry and surface morphology of at least the portion of the textile; and applying one or more chemicals to at least the portion of the textile.

[0226] Aspect 18: A method according to Aspect 17, wherein the conditioning of at least the portion of the textile further activates the surface of the textile, and wherein the activation of the surface of the textile improves the performance of the one or more chemicals compared to an unactivated surface of the textile to which the same one or more chemicals are applied.

[0227] Aspect 19: A method for attribution and / or traceability, the method comprising setting one or more unique identifiers on at least a portion of an article, wherein the one or more unique identifiers are invisible to the human eye and are visible by a predetermined visual method, and wherein the one or more unique identifiers are referenced during a manufacturing process including digital printing to provide quality control data for one or more steps in the manufacturing process.

[0228] Aspect 20: The method of aspect 19, wherein the one or more unique identifiers indicate an alignment mechanism for one or more components of the article.

[0229] Aspect 21: A method for attribution and / or traceability, the method comprising setting one or more unique identifiers on an item, wherein the one or more unique identifiers include an invisible component that is invisible to the human eye and visible with the aid of a predetermined visual method and a visible component that is visible to the human eye, and wherein the one or more unique identifiers at least indicate attribution data.

[0230] Aspect 22: The method according to aspect 21, wherein the attribution data includes information indicating the provenance of the item.

[0231] Aspect 23: A method for component-level application, the method comprising: receiving information indicating a location for applying a material on a surface of an article; and using digital printing or digital extrusion and selectively setting the material only at the location based on the location, whereby the material is not set on at least a portion of the article.

[0232] Aspect 24: The method according to aspect 23, wherein the material includes an adhesive.

[0233] Aspect 25: The method according to aspect 23, wherein the selectively setting is based on an alignment point associated with the item.

[0234] Aspect 26: A method for cutting alignment, the method comprising: using computer vision to analyze a first pattern configuration set on a printed fabric; implementing a trimming process on the printed fabric to produce a second pattern configuration different from the first pattern configuration; using computer vision to analyze the second pattern configuration set on the printed fabric; determining cutting control information based on the first pattern configuration and the second pattern configuration; and sending the cutting control information to a cutting system to facilitate cutting of the printed fabric.

[0235] Aspect 27: A cutting method comprising: batching one or more customer orders into batches; nesting a plurality of item components based on the batches; and cutting the nested components from a substrate such that one or more tabs connect the cut components to a portion of the substrate.

[0236] Aspect 28: A method of material handling, comprising: arranging a pallet and a sorting system, wherein the pallet and the sorting system have an overhead track system configured for material handling; receiving a plurality of material components; and using the arranged pallet and the sorting system and the overhead track system to sort the plurality of material components based on one or more types of components or batch orders or one or more of both.

[0237] Aspect 29: A system for implementing any one of the methods of Aspects 1 to 28.

Claims

1. A method for attribution and / or traceability, the method comprising: receiving order data associated with one or more first customer orders; disposing one or more unique identifiers on at least a portion of a material, wherein the one or more unique identifiers represent item data including at least a portion of the order data, wherein the order data further includes substrate data regarding a material type of the material, and wherein the disposing further includes: automatically selecting a unique identifier of an appropriate size based on the substrate data and the size of the unique identifier; and processing the material via one or more manufacturing processes to form at least a portion of an article, wherein the article data is represented by the one or more unique identifiers, and wherein the article data is dynamic and updated throughout the one or more manufacturing processes based on applying at least one of the one or more manufacturing processes to the portion of the material to include information associated with the respective manufacturing process, wherein each of the manufacturing processes comprises: read updated item data, and adjusting one or more actions associated with the corresponding manufacturing process based on the updated item data; and The item data indicates a history and provenance of the item and one or more item inputs and is accessible using the unique identifier.

2. A method for attribution and / or traceability, the method comprising: receiving order data associated with one or more first customer orders; Disposing one or more unique identifiers on at least a portion of a material, wherein the one or more unique identifiers are visible to a human eye and are configured to be concealed via one or more manufacturing processes, and wherein the one or more unique identifiers represent item data including at least a portion of the order data, wherein the order data further includes substrate data regarding a material type of the material, and wherein the disposing further includes: automatically selecting a unique identifier of an appropriate size based on the substrate data and the size of the unique identifier; and processing the material via the one or more manufacturing processes to form at least a portion of an item and concealing at least a portion of the one or more unique identifiers, wherein the item data is represented by the one or more unique identifiers, and wherein the item data is dynamic and updated throughout the one or more manufacturing processes based on applying at least one of the one or more manufacturing processes to the portion of the material to include information associated with the respective manufacturing process, wherein each of the manufacturing processes comprises: Read updated item data; and adjusting one or more actions associated with the corresponding manufacturing process based on the updated item data; and The item data indicates a history and provenance of the item and one or more item inputs and is accessible using the unique identifier.

3. The method of claim 2, further comprising selecting the material based at least on the order data and batch data associated with one or more second customer orders, wherein the one or more unique identifiers are disposed on at least a portion of the selected material.

4. The method of claim 2, wherein the one or more unique identifiers indicate an alignment mechanism for one or more components of the article.

5. The method of claim 2, wherein the order data comprises one or more of: biometric information associated with the one or more consumers, color data associated with a color of the item, or material information indicating the material to be processed.

6. A method according to claim 2, wherein the one or more manufacturing processes include a chemical pretreatment process, and wherein the chemical pretreatment process is selected and implemented based at least on the item data before the chemical pretreatment process, and wherein the item data is updated to indicate the implemented chemical pretreatment process.

7. A method according to claim 2, wherein the one or more manufacturing processes include a drop-on-demand process, wherein the color of at least a portion of the material is evaluated after the drop-on-demand process based at least on the item data, and remedial action is taken if the evaluated color is not within a tolerance range.

8. A method according to claim 2, wherein the one or more manufacturing processes include a finishing process, wherein at least the one or more unique identifiers indicate a first spatial characteristic of the material before the finishing process, and wherein the item data is updated to indicate a second spatial characteristic of the material after the finishing process.

9. 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.

10. The method of claim 2, wherein the item data includes grade information indicating the quality of one or more previous processes in the one or more manufacturing processes.

11. The method of claim 10, wherein the rating information is derived at least in part from physical characteristics of the one or more unique identifiers.

12. The method of claim 2, wherein the one or more unique identifiers are disposed on at least a portion of the material to indicate a defect condition or a defect location, or both.

13. The method of claim 12, wherein the one or more manufacturing processes include repairing a defect based on one or more of the defect condition or the defect location.

14. An article formed using the method of claim 2.

15. A system configured to implement the method according to claim 2.

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