Stiffness minimisation of graded microstructural configurations using asymptotic analysis and machine learning

TL;DR

This paper combines asymptotic analysis and machine learning within an advanced topology optimization framework to enable fast, accurate stiffness design of microstructured materials, significantly reducing computational time.

Contribution

It introduces a novel integration of asymptotic analysis with machine learning for efficient microstructure stiffness optimization, ensuring positive definiteness and high accuracy.

Findings

The proposed method achieves rapid optimization in about 300 seconds.

It maintains high accuracy and efficiency in complex microstructure design.

The approach is validated through numerical demonstrations.

Abstract

The article is aimed to address a mutually boosting use of asymptotic analysis and machine learning, for fast stiffness design of configurations infilled with smoothly-varying graded microstructures. The discussion is conducted in the context of an improved asymptotic-homogenisation topology optimisation (AHTO plus) framework. It is demonstrated that on one hand, machine learning can be employed to represent the key but implicit inter-relationships revealed from asymptotic analysis, and the evaluations of the homogenised quantities, as well as the sensitivities of the design variables, become quite efficient. On the other hand, the use of asymptotic analysis identifies a computational routine for data acquisition, thus the training data here are inexhaustible in theory. Key issues regarding integration of the two methods, such as ensuring the positive definiteness of the homogenised elasticity tensor represented with neural networks, are also discussed. The accuracies and the efficiencies of the present scheme are numerically demonstrated. For two-dimensional optimisation, it takes the present algorithm roughly 300 seconds on a standard desktop computer, and this qualifies the present scheme as one of the most efficient algorithms used for the compliance optimisation of configurations infilled with complex microstructures.