Garment Pattern Grading Using Constrained Mesh Optimization
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Solution Overview
Problem
Existing methods for grading garment patterns from a standard size to a custom size often result in inconsistencies and limitations, such as the need for grading rules that are not applicable to all types of garments and require extensive expertise, and the inefficiency of transforming 3D designs into 2D panels, which can lead to impractical sewing and high computational demands.
Innovation Solution
A computer-implemented method that represents each panel as a 2D closed domain, imposes constraints on its segments to achieve the desired size, generates a mesh, combines these with constraints into a system of equations, and solves for a new set of meshes that correspond to the desired size, avoiding the need for grading rules and allowing for efficient grading of any type of garment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If grading rules with multiplication coefficients are used to transform panels from standard size to custom size, then the grading process can be automated, but inconsistencies arise over different panels making sewing impracticable
Solution Approach 1:
The system implements feedback by continuously monitoring and adjusting the transformation of characteristic points across panels. The optimization algorithm uses feedback from seam point correspondence requirements to refine the transformation parameters, ensuring that graded panels maintain consistent dimensions and can be seamlessly sewn together.
Solution Approach 2:
The invention creates a universal grading system that works across different garment types and size ranges. The optimization-based approach is garment-agnostic, applying the same mathematical framework to transform patterns for various clothing items while maintaining panel consistency through constrained optimization that enforces seam compatibility.
2Manufacturing precision
If grading rules are created for specific garment types, then grading can be performed for those garments, but the rules are not applicable to new or different types of garments
Solution Approach 1:
The system achieves universality by using a general optimization framework that can handle any garment pattern. Instead of garment-specific rules, the patent applies a unified mathematical approach that transforms characteristic points based on optimization criteria, making it adaptable to any garment type including new designs without requiring pre-defined rules.
Solution Approach 2:
The invention changes the approach from fixed grading rules to dynamic parameter optimization. The system adjusts transformation parameters for each grading operation based on the specific pattern and size requirements, allowing accurate grading for any garment type by optimizing the transformation that best preserves pattern geometry and seam compatibility.
3Ease of manufacture
If 3D garment designs are transformed into 2D panels using flattening techniques, then the garment can be fabricated, but intensive computer power is needed creating a bottleneck
Solution Approach 1:
The system extracts the essential grading function from complex 3D flattening processes. Instead of performing full 3D to 2D transformations, the patent directly optimizes 2D pattern transformations using characteristic points, eliminating the computationally intensive 3D modeling and flattening steps while achieving the same fabrication-ready output.
Solution Approach 2:
The invention replaces the mechanical 3D flattening process with a mathematical optimization system. The complex geometric transformations of 3D flattening are substituted with optimization algorithms that directly compute the transformed pattern coordinates, significantly reducing computational requirements while maintaining accuracy.
4Manufacturing precision
If characteristic points are shifted and twisted according to grading rules, then the pattern can be transformed to custom size, but the knowledge and know-how lies in tailor experience making automation difficult
Solution Approach 1:
The system makes the grading process self-service by using optimization algorithms that automatically determine the best transformation without requiring expert input. The optimization framework self-adjusts the shifting and twisting of characteristic points based on mathematical criteria, eliminating the need for tailor experience while achieving accurate results.
Solution Approach 2:
The patent replaces the manual expert-based grading process with an automated optimization system. The complex judgment and adjustments that previously required tailor knowledge are substituted with mathematical optimization that automatically finds the optimal transformation, simplifying the system while improving consistency.
Data Source
AI summary
According to an embodiment, the invention relates to a computer implemented method for grading a pattern from a first size to a second size, the pattern comprising one or more panels, the method comprising the steps of a representation step comprising representing each panel of the one or more panels by a contour, wherein a contour comprises one or more segments, a constraint step comprising imposing constraints on segments for grading to the second size; generating a mesh of each panel of the one or more panels thereby obtaining a first set of meshes; combining the first set of meshes with the constraints into a system of equations; solving the system of equations into a second set of meshes, wherein the contours of the second meshes correspond to the pattern in the second size and representing the contours of the second set of meshes.