Parametric Fit Modeling for Custom Product Manufacturing
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Solution Overview
Problem
Current systems lack a standardized method for integrating user data to customize and refine the fit of manufactured products, particularly in the clothing industry, where customization of ornamentation, coloring, and fit is complex and not efficiently implemented in computerized systems.
Innovation Solution
A computer-based approach using parametric grading and modeling techniques to generate customizable product models, allowing users to interact with interfaces for adjusting fit and collaborating with agents to produce tailored products, with functionalities for generating manufacturing instructions and transmitting them to manufacturers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If parametric modeling and grading techniques are implemented to enable product customization, then adaptability and ease of operation are improved, but device complexity increases
Solution Approach 1:
The system divides the customization process into distinct modules: data input module, parametric modeling module, grading module, and manufacturing instruction generation module. Each module handles specific tasks independently, allowing complex customization functionality to be built from manageable components that can be developed and maintained separately.
Solution Approach 2:
The parametric modeling system is designed to handle multiple product types and customization parameters through a unified framework. The same core engine processes different product geometries, material properties, and design constraints, eliminating the need for separate systems for each product category and reducing overall system complexity.
2Productivity
If automated parametric modeling and grading systems are deployed, then productivity is improved, but manufacturing precision may be compromised due to lack of expert intervention
Solution Approach 1:
The system incorporates feedback mechanisms where manufacturing results and fit measurements are fed back into the parametric model to refine future predictions. This continuous improvement loop allows the automated system to learn from actual manufacturing outcomes and adjust parameters to maintain high precision without requiring constant expert intervention.
Solution Approach 2:
Expert knowledge and manufacturing constraints are pre-encoded into the parametric model and grading rules before production begins. This preliminary configuration of expert criteria into the system allows automated processing to achieve expert-level precision while maintaining high productivity during actual manufacturing operations.
Data Source
AI summary
In some implementations, a method for custom fitting and manufacturing parametric products comprises: generating a plurality of parametric models of a custom product based on past learned data pertaining to the custom product; generating a first user interface comprising at least a first graphical representation of the custom product, and displaying the first user interface on a computer display device; receiving a plurality of adjustment parameters for improving a fit of the custom product in relation to the user-specific data; based on the plurality of adjustment parameters and the plurality of parametric models, determining two particular parametric models; generating, for the custom product, a parametric fit model having a plurality of fit model parameters, by interpolating a corresponding parameter of a first particular parametric model and a corresponding parameter of a second particular parametric model of the two particular parametric models; transmitting the parametric fit model to a manufacturer to cause the manufacturer to produce a parametric physical product based on the plurality of fit model parameters of the parametric fit model.