Applying Generative Design to Fashion

Shua Cho

Abstract

This project explores the application of generative design to transform thrift store heels, aiming to enhance their stability, sustainability, and aesthetic appeal. Led by Shua Cho, the initiative leverages computational methods to optimize the performance and longevity of used shoes, reducing their environmental impact.

The process involves multi-iteration, computer-aided shape optimization using Autodesk Fusion 360 to represent shoe and obstacle geometry. By conducting 20-30 rounds of algorithmic generation and shape optimization, the project aims to minimize mass while maintaining safety factors. Techniques such as form synthesis and level-set topology synthesis, inspired by natural growth patterns, are employed to generate innovative shoe designs.

The final designs are 3D-printed in PLA and fused with the original thrift store shoes, demonstrating the feasibility and effectiveness of generative design in creating sustainable and high-performance footwear. This project targets the fashion industry, environmentalists, and consumers seeking innovative and eco-friendly solutions, showcasing the potential of computational design to revolutionize footwear manufacturing.

Key Objectives:

Innovation in Footwear Design: Transform thrift store heels using generative design for enhanced stability, sustainability, and aesthetic appeal.

Leverage computational methods to optimize the performance of used shoes and extend their longevity, thereby reducing environmental impact.

Usefulness: The project aims to revolutionize footwear design by applying computational techniques to improve the stability and durability of thrift store heels.

Target Audience: Fashion industry professionals interested in organic, generative, and tech-driven design.

Environmentalists focused on sustainable practices.

Consumers seeking innovative and eco-friendly footwear solutions.

Application:

Design Representation:

Using Autodesk Fusion 360 to model the shoe and obstacle geometry.

Setup:

General load cases including forces, constraints, obstacle geometry, material properties, and manufacturing specifications for additive FDM manufacturing.

Algorithmic Generation and Shape Optimization:

Conducting 20-30 rounds of generative algorithmic methods for each design outcome.

Utilizing form synthesis, inspired by nature, to grow new forms resembling bone and sinew based on linear-static FEA methods.

Employing a level-set approach to topology synthesis, focusing on surface area as the initial acting stage.

Generating multiple design outcomes by varying load cases.

 

Fabrication:

The project successfully demonstrates how generative design can enhance the stability and aesthetic appeal of thrift store heels while promoting sustainability.

Final designs are presented with additional photos showcasing the enhanced footwear.

 

Conclusion:

This project provides a novel approach to footwear design, leveraging computational methods to create sustainable, innovative, and aesthetically appealing shoes. The integration of generative design principles into the fashion industry has the potential to revolutionize how we think about and produce footwear, with significant benefits for both consumers and the environment.