AI Embroidery Planning for Fabric-Adaptive Stitch Digitization
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Solution Overview
Problem
Embroidery digitization requires significant manual effort and expertise, and existing systems lack the ability to adapt designs to varying fabric types or sizes, often leading to inconsistent and suboptimal results.
Innovation Solution
An AI-driven embroidery system that includes a design generator, Global Decisions Module, Local Decisions Module, visualizer, reranker, and trainer, which processes multiple input types, generates context-aware embroidery plans, and learns from human feedback to improve over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual embroidery digitization is used, then design quality and expertise can be maintained, but significant manual effort and time are required
Solution Approach 1:
The patent replaces the manual mechanical process of embroidery digitization with an AI-based automated system. The AI model processes input images and automatically generates embroidery stitch instructions, eliminating the need for manual digitization while maintaining design quality through learned patterns from training data.
Solution Approach 2:
The patent introduces an AI model as an intermediary between the input design image and the embroidery machine instructions. This intermediary processes the design through multiple modules (segmentation, stitch generation, optimization) to produce high-quality embroidery patterns without direct human intervention in the digitization process.
2Extent of automation
If existing automated systems are used, then manual effort is reduced, but the ability to adapt to different fabric types and sizes is limited
Solution Approach 1:
The patent implements a dynamic system that adapts to different fabric types and design requirements. The AI model adjusts its processing based on input characteristics, and the optimization module modifies stitch parameters according to the specific fabric properties and design complexity, enabling versatile adaptation across different embroidery applications.
Solution Approach 2:
The patent applies different processing strategies to different regions of the design based on local characteristics. The segmentation module identifies distinct regions with different properties, and the stitch generation module creates region-specific embroidery patterns optimized for each area's fabric type and design requirements.
3Ease of operation
If manual digitization is used, then design customization is possible, but productivity and output efficiency are reduced
Solution Approach 1:
The patent replaces manual digitization operations with automated AI processing that maintains customization capabilities. The system accepts various input formats and design parameters, processes them through intelligent algorithms, and generates optimized embroidery instructions, achieving both high productivity and design flexibility simultaneously.
4Extent of automation
If existing automated systems are used, then manual intervention is reduced, but error correction and optimization require extensive human involvement
Solution Approach 1:
The patent implements a feedback mechanism where the AI model learns from training data and optimizes its output based on evaluated results. The system includes modules for generating multiple stitch pattern variations, evaluating them against quality criteria, and selecting the optimal solution, reducing the need for manual error correction while maintaining high automation levels.
Data Source
AI summary
In some embodiments, a computer-implemented AI-driven method for generating and refining embroidery designs comprises: receiving, by a design generator of a system, a reference design and a text prompt; generating an embroidery-friendly design image; determining, by a global decisions module of the system, high-level embroidery plans for the embroidery-friendly design image; refining, by a local decisions module of the system, the high-level embroidery plans to generate detailed stitch instructions; rendering, by a visualizer of the system, a visualization of based on the embroidery-friendly design image and the detailed stitch instructions; scoring and reordering, by a reranker of the system, variations of the high-level embroidery plans; receiving feedback and corrections from various sources; updating, by a trainer of the system, parameters of the design generator, the global decisions module, and the local decisions module based on the feedback and the corrections to improve future embroidery designs.