In an embodiment, a method for optimizing computer machine learning includes receiving an optimization goal. The optimization goal is used to search a database of base option candidates (BOC) to identify matching BOCs that at least in part matches the goal. A selection of a selected base option among the matching BOCs is received. Machine learning prediction model(s) are selected based at least in part on the goal to determine prediction values associated with alternative features for the selected base option, where the model(s) were trained using training data to at least identify weight values associated with the alternative features for models. Based on the prediction values, at least a portion of the alternative features is sorted to generate an ordered list. The ordered list is provided for use in manufacturing an alternative version of the selected base option with the alternative feature(s) in the ordered list.

A tool allows a user to create new designs for apparel and preview these designs in three dimensions before manufacture. Software and lasers are used in finishing apparel to produce a desired wear pattern or other design. Based on a laser input file with a pattern, a laser will burn the pattern onto apparel. With the tool, the user will be able to create, make changes, and view images of a design, in real time, before burning by a laser. The tool can be accessed or executes via a Web browser.

Computer implemented method, system and computer program product for simulating the behavior of a woven fabric at yarn level. The method comprises. retrieving the layout of warp yarns (1), weft yarns (2) and yarn crossing nodes (3): describing each yarn crossing node (3) by a 3D position coordinate (x) and two sliding coordinates, warp sliding coordinate (u) and weft sliding coordinate (v) representing the sliding of warp (1) and weft (2) yarns; measuring forces on each yarn crossing node (3) based on a force model, the forces being measured on both the 3D position coordinate (x) and the sliding coordinates (u, v); calculating the movement of each yarn crossing node (3) using equations of motion derived using the Lagrange-Euler equations, and numerically integrated over time, wherein the equations of motion account for the mass density distributed uniformly along yarns, as well as the measured forces and boundary conditions.

A system for predicting the performance of a rotary unit operation on a web. The system includes a first rotary roll, a second rotary roll, a nip gap between the first rotary unit and the second rotary roll, a web enabled to travel through the nip, a predetermined pattern, and a computing device comprising a processor and a memory component. The first rotary roll and the second rotary roll are enabled to imprint the predetermined pattern on the web. The memory component stores logic that, when executed by the processor, causes the system to perform at least the following: receive input characteristics of the first rotary roll and the second rotary roll, the web, and the initial nip-gap; receive the predetermined pattern; simulate a transformation performance on the web by the first rotary roll and the second rotary roll; output an analysis demonstrating a load impact factor; and analyze one or more load impact factors to determine if the system will output the predetermined pattern and if the transformation is robust.

Provided are methods and systems for automatic creation of a customized avatar animation of a user. An example method commences with receiving production parameters and creating, based on the production parameters, a multidimensional array of a plurality of blank avatar animations. Each blank avatar animation has a predetermined number of frames and a plurality of features associated with each frame. The method further includes receiving user parameters including body dimensions, hair, and images of a face of a user. The method continues with selecting, from the plurality of blank avatar animations, two blank avatar animations closest to the user based on the body dimensions. The method further includes interpolating corresponding frames of the two blank avatar animations to produce an interpolated avatar animation. The method continues with compositing the face and the hair with the interpolated avatar animation using a machine learning technique to render the customized avatar animation.

A computer-aided design system enables physical articles to be customized via printing or embroidering and enables digital content to be customized and electronically shared. A user interface may be generated that includes an image of a model of an article of manufacture and user customizable design areas. Customization permissions associated with a selected design area are accessed. User provided content to be used in customizing a design area may be analyzed in real time or in batch mode using a trained engine to determine if it complies with one or more rules. If the user provided content satisfies a corresponding rule, manufacturing instructions and a design file may be transmitted to a printing system.

The invention relates to computer implemented methods for generating a garment finish preset comprising assembly instructions for a garment finish for a garment to be fabricated, for automatically generating a garment finish preset comprising assembly instructions for a garment finish for a garment to be fabricated, and for automatically determining at least one candidate from a plurality of garment finish presets, each of said garment finish presets comprising assembly instructions for a garment finish for a garment to be fabricated from garment panels.

A method of augmenting reality of an image, comprising providing a lenticular lens for viewing an image, wherein the image is a lenticular image comprising at least two interlaced images. At least two fiducial markers are provided in the image. A first sensor located at a first distance from the image detects a first fiducial marker, and a second sensor located at a second distance from the image detects a second fiducial marker, wherein the detection of the each of the first and the second fiducial markers triggers a computing process delivering augmented reality to a user via a screen. The second distance is closer to the image than the first distance, and the second fiducial marker is capable of being detected by the second sensor from the second distance, but not from the first distance.

The present document describes techniques associated with a textile-material model for vibroacoustic structural simulation. The techniques described herein provide a nontrivial methodology to test a textile and simplify its representation, which can enable prediction of acoustic performance (e.g., rub and buzz) of an electronic-speaker device having a textile mounted thereon. The textile is modeled as a textile-material model based on an elongation stiffness obtained from a time-temperature superposition curve of the textile, which is based on a dynamic mechanical analysis test of the textile in each of course and wale directions. The textile-material model is then applied to an assembly model of the electronic-speaker device to simulate a vibroacoustic response of the textile relative to the assembly model to predict a likelihood of rub and buzz.

Systems and methods generating textiles with repeating design elements based at least in part on Voronoi diagrams are provided. In one example implementation, the method can include generating a plurality of seed points in a graphic area. The seed points are utilized to create a Voronoi diagram. A Voronoi diagram is thereafter propagated within the graphic area based upon the seed points. The method also includes receiving a first user input defining a design area. The design area includes a plurality of boundaries within the graphic area. The design area is then correlated to a textile segment and a textile design is generated by replicating the cells in the design area. The cells that intersect the boundaries of the design area are replicated with identical instances placed at the adjacent sides of the design area and the corners of the design area.