Tailoring the mechanical properties of 3D microstructures: a deep learning and genetic algorithm inverse optimization framework

TL;DR

This paper introduces a deep learning and genetic algorithm framework for inverse microstructure design, enabling tailored mechanical properties in materials like Ti-6Al-4V efficiently and transferably.

Contribution

The study presents an integrated deep learning and genetic algorithm approach for inverse microstructure optimization, improving accuracy and efficiency over traditional methods.

Findings

Successfully tailored Ti-6Al-4V microstructures for desired mechanical properties.

Achieved large property variations with minimized stress concentration.

Framework is versatile and transferable to other materials and properties.

Abstract

Materials-by-design has been historically challenging due to complex process-microstructure-property relations. Conventional analytical or simulation-based approaches suffer from low accuracy or long computational time and poor transferability, further limiting their applications in solving the inverse material design problem. Here, we establish a deep learning and genetic algorithm framework that integrates forward prediction and inverse exploration. This framework provides an end-to-end solution to achieve application-specific mechanical properties by microstructure optimization. In this study, we select the widely used Ti-6Al-4V to demonstrate the effectiveness of this framework by tailoring its microstructure and achieving various yield strength and elastic modulus across a large design space, while minimizing the stress concentration factor. Compared with conventional methods, our framework is efficient, versatile, and readily transferrable to other materials and properties. Paired with additive manufacturing's potential in controlling local microstructural features, our method has far-reaching potential for accelerating the development of application-specific, high-performing materials.