MechProNet: Machine Learning Prediction of Mechanical Properties in Metal Additive Manufacturing

TL;DR

This paper presents a comprehensive machine learning framework for predicting mechanical properties in metal additive manufacturing, utilizing an extensive dataset, physics-aware features, and explainability techniques to improve prediction accuracy and interpretability.

Contribution

The study introduces a benchmarking framework with a large dataset, physics-informed features, and explainable AI methods for predicting mechanical properties in MAM, advancing the field's predictive capabilities.

Findings

Developed a dataset from 90+ MAM articles with 140 data sheets.

Implemented physics-aware feature engineering for ML models.

Applied SHAP for model interpretability and developed explicit data-driven models.

Abstract

Predicting mechanical properties in metal additive manufacturing (MAM) is essential for ensuring the performance and reliability of printed parts, as well as their suitability for specific applications. However, conducting experiments to estimate mechanical properties in MAM processes can be laborious and expensive, and they are often limited to specific materials and processes. Machine learning (ML) methods offer a more flexible and cost-effective approach to predicting mechanical properties based on processing parameters and material properties. In this study, we introduce a comprehensive framework for benchmarking ML models for predicting mechanical properties. We compiled an extensive experimental dataset from over 90 MAM articles and data sheets from a diverse range of sources, encompassing 140 different MAM data sheets. This dataset includes information on MAM processing conditions, machines, materials, and resulting mechanical properties such as yield strength, ultimate tensile strength, elastic modulus, elongation, hardness, and surface roughness. Our framework incorporates physics-aware featurization specific to MAM, adjustable ML models, and tailored evaluation metrics to construct a comprehensive learning framework for predicting mechanical properties. Additionally, we explore the Explainable AI method, specifically SHAP analysis, to elucidate and interpret the predicted values of ML models for mechanical properties. Furthermore, data-driven explicit models were developed to estimate mechanical properties based on processing parameters and material properties, offering enhanced interpretability compared to conventional ML models.