Customized Garment Fabrication Using Body Scan and Neural Network Knitting
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Solution Overview
Problem
Conventional garment manufacturing systems face inefficiencies in sizing, neglecting individual body shapes and fit preferences, leading to poorly fitting apparel, high return rates, environmental strain, and inefficiencies in global supply chains, along with time and cost inefficiencies in custom clothing options.
Innovation Solution
A method and system utilizing body scans to extract measurements and shape analysis, combined with artificial neural networks, to create customized knitting instructions for personalized garments, incorporating fit and style preferences, and integrating 3D printing and thermoforming for precise fit and functionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional garment manufacturing systems use standardized sizing, then production efficiency is maintained, but fit precision deteriorates
Solution Approach 1:
The system changes the sizing parameters from standardized categories to personalized measurements derived from 3D body scans. The neural network processes body scan data to generate customized pattern dimensions, transforming the manufacturing approach from fixed size charts to dynamic, individual-specific parameters while maintaining automated production workflows.
Solution Approach 2:
The patent replaces manual measurement and fitting processes with automated 3D body scanning and neural network-based pattern generation. The mechanical system of traditional tailoring and manual pattern making is substituted with digital scanning, AI processing, and computer-controlled knitting machines, enabling personalized garments through automation rather than manual craftsmanship.
2Manufacturing precision
If customized garments are produced for each individual, then fit precision improves, but device complexity increases
Solution Approach 1:
The system employs a universal neural network model that can process various body scan inputs and generate appropriate knitting patterns for different garment types and body shapes. This multi-functional AI system handles diverse customization requirements through a single integrated platform, reducing the need for multiple specialized systems while maintaining high fit precision across different applications.
3Measurement precision
If body scan data is processed through neural networks, then measurement accuracy improves, but computational resources increase
Solution Approach 1:
The system performs preliminary processing of body scan data by the neural network to extract key measurements and generate a digital body model before final pattern production. This pre-processing step prepares and optimizes the data structure, allowing subsequent pattern generation to be more efficient. By performing the computationally intensive measurement extraction in advance, the system reduces overall computational resource requirements while maintaining high measurement accuracy.
Data Source
AI summary
Methods of producing a customized garment for a subject are disclosed. The methods include acquiring a plurality of physical parameters of the subject, providing the plurality of physical parameters to an artificial neural network trained to extract measurements and define a shape analysis of the subject to produce a digital body, responsive to the extracted measurements and shape analysis, producing a custom pattern, displaying the custom pattern on the digital body, generating production instructions from the custom pattern, and directing the production instructions to a knitting machine programmed to produce at least a portion of the customized garment in accordance with the production instructions. Systems for producing a customized garment are also disclosed. The systems include a body scanner, a computing unit, and a production subsystem including a knitting machine.