Machine Learning-Driven Optimization of TPMS Architected Materials Using Simulated Annealing

TL;DR

This paper introduces a machine learning and simulated annealing based method to optimize TPMS structures for tensile stress, achieving high predictive accuracy and improved material performance.

Contribution

It presents a novel integration of simulated annealing with machine learning models to optimize TPMS structures, with the SA-XGBoost model showing superior performance.

Findings

SA-XGBoost achieved R-squared of 0.96

SA optimization improved tensile stress predictions

Random Forest and Decision Tree less effective

Abstract

The research paper presents a novel approach to optimizing the tensile stress of Triply Periodic Minimal Surface (TPMS) structures through machine learning and Simulated Annealing (SA). The study evaluates the performance of Random Forest, Decision Tree, and XGBoost models in predicting tensile stress, using a dataset generated from finite element analysis of TPMS models. The objective function minimized the negative R-squared value on the validation set to enhance model accuracy. The SA-XGBoost model outperformed the others, achieving an R-squared value of 0.96. In contrast, the SA-Random Forest model achieved an R squared value of 0.89 while the SA-Decision Tree model exhibited greater fluctuations in validation scores. This demonstrates that the SA-XGBoost model is most effective in capturing the complex relationships within the data. The integration of SA helps in optimizing the hyperparameters of these machine learning models, thereby enhancing their predictive capabilities.