Machine learning-based identification of interpretable process-structure linkages in metal additive manufacturing

Abstract

The use of data-driven methods for metal additive manufacturing (AM) is currently gaining importance as indicated by the increasing number of scientific literature in this field. Incorporation of data-driven methods has the potential to eliminate current bottlenecks in microstructure design given the diverse and complex nature of microstructures in additively manufactured metals. So far, coupling of existing simulation methods, e.g. physics-based process and microstructure models, to simulate AM microstructures with desired morphological characteristics requires extensive computational resources, high computation times and therefore, allows no scalable output. The extension of experimental- and simulation-based approaches by machine learning (ML) algorithms enables fast and computationally efficient predictions. However, the underlying architecture of ML algorithms often does not allow domain experts to interpret how predictions of the model were made and which features are responsible to what extent. This is why ML models are often referred to as black- box models. In this study, we present a data-driven framework based on physics-based simulation data to reveal explainable process-(micro)structure (P-S) linkages for metal AM. We provide an open-source dataset of 960 unique 3D microstructures created by simulation of powder bed fusion in metal AM. We employed the stochastic parallel particle kinetic simulator (SPPARKS) that is based on the kinetic Monte Carlo (kMC) method as an exemplary AM microstructure generator. Selected ML regression algorithms aim to predict 3D chord length distributions (CLDs), as a morphology descriptor, depending on the associated process parameter combinations. Various dimension reduction algorithms are applied for computationally efficient use of the data space. The proposed methodology allows (i) microstructure predictions under given processing conditions and (ii) to navigate experts in the process parameter space to achieve target microstructures. In this context, SHAP (SHapley Additive exPlanations) values are used to decipher the contribution of individual process parameters to the microstructure evolution. In particular, SHAP values calculated in this study unfold the width of the melt pool and the heat-affected zone as dominant features on the model output. We provide open-access to the used dataset and methods for the scientific community to gain experience with the proposed approach.