3D-Printed Wearables via Mobile Scanning and ML
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The high cost of ownership and skill requirements for 3D printing, combined with the complexity of generating customized 3D models that fit individual wearers, make personalized 3D printing inaccessible to most people, as existing methods struggle to efficiently convert disparate body data into usable 3D models for printing.
Innovation Solution
A system utilizing commonly available mobile devices to capture images of users, which are then processed by a server using computer vision and machine learning to generate tessellation models for customized 3D printed wearables, allowing for the creation of personalized items like shoe insoles, bracelets, and other body-contoured products without the need for specialized equipment or expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If 3D printing is used to create customized items, then personalization and fit quality are improved, but cost of ownership and skill requirements increase
Solution Approach 1:
The patent introduces an automated image processing system as an intermediary between the user and the 3D printing process. The system automatically captures body measurements through images, processes them through algorithms to generate 3D models, and prepares print-ready files, eliminating the need for users to manually create complex 3D models while preserving customization capability
Solution Approach 2:
The system enables users to perform their own body scanning and model generation using standard mobile devices without requiring specialized equipment or expertise. The automated processing pipeline allows users to independently create customized 3D printed items through simple image capture, making the technology self-service accessible to non-experts
2Manufacturing precision
If manual 3D model generation is used for customization, then fit precision is improved, but processing time and complexity increase
Solution Approach 1:
The patent replaces manual mechanical measurement and model creation processes with automated computer vision and image processing algorithms. The system captures body measurements through standard images and automatically generates accurate 3D models through computational processing, eliminating time-consuming manual operations while maintaining fit precision
Solution Approach 2:
The system performs preliminary automated processing of body images to extract measurements and generate 3D models before the actual 3D printing process. By pre-processing the data automatically, the system eliminates the need for time-consuming manual measurements and model creation during the manufacturing process
3Measurement precision
If specialized body scanning equipment is used, then measurement accuracy is improved, but device cost and accessibility worsen
Solution Approach 1:
The patent uses standard, inexpensive mobile devices with built-in cameras instead of expensive specialized scanning equipment. The system processes multiple standard images to achieve accurate measurements, making the technology accessible to ordinary consumers without requiring costly specialized hardware
Solution Approach 2:
The system uses universal mobile devices that most people already own for body scanning purposes, rather than requiring dedicated scanning equipment. The same device can be used for various functions including body measurement, image capture, and model generation, making the technology broadly accessible while maintaining measurement accuracy through software-based processing
Data Source
AI summary
Disclosed is a platform for generating and delivering 3-D printed wearables. The platform includes scanning, image processing, machine learning, computer vision, and user input to generate a printed wearable. Scanning occurs in a number of ways across a number of devices. The variability of scanning generates a number of scanning output types. Outputs from the scanning process are normalized into a single type during image processing. The computer vision and machine learning portions of the platform use the normalized body scan to develop models that may be used by a 3D printer to generate a wearable customized to the user. The platform further provides opportunities for the user to check the work of the scanning, image processing, computer vision, and machine learning. The user input enables the platform to improve and inform the machine learning aspects.