Designing Composites with Target Effective Young's Modulus using Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning framework for designing composite materials with a specific Young's modulus, significantly reducing the required training data and achieving high success rates in complex design spaces.

Contribution

The work presents a novel RL-based method for composite design that eliminates the need for user-selected training data and efficiently explores large design spaces.

Findings

Model trained with only 2.78% of the design space.

Achieved success rates over 90% in finding optimal designs.

Demonstrated potential for RL in advanced material system design.

Abstract

Advancements in additive manufacturing have enabled design and fabrication of materials and structures not previously realizable. In particular, the design space of composite materials and structures has vastly expanded, and the resulting size and complexity has challenged traditional design methodologies, such as brute force exploration and one factor at a time (OFAT) exploration, to find optimum or tailored designs. To address this challenge, supervised machine learning approaches have emerged to model the design space using curated training data; however, the selection of the training data is often determined by the user. In this work, we develop and utilize a Reinforcement learning (RL)-based framework for the design of composite structures which avoids the need for user-selected training data. For a 5 $\times$ 5 composite design space comprised of soft and compliant blocks of constituent material, we find that using this approach, the model can be trained using 2.78% of the total design space consists of $2^{25}$ design possibilities. Additionally, the developed RL-based framework is capable of finding designs at a success rate exceeding 90%. The success of this approach motivates future learning frameworks to utilize RL for the design of composites and other material systems.