A Multi-Scale AI-Driven Optimization Framework for Industrial Product Design

Abstract

Metal Additive Manufacturing (AM) has emerged as a crucial technology for fabricating high-performance, lightweight components for aerospace, biomedical, and energy applications. However, consistent part quality remains one of the most significant challenges because complex nonlinear interactions between material properties, process parameters, thermal histories, and defect formation mechanisms control this process. Traditional modeling approaches usually treat the scales individually, thus poorly predicting key outcomes such as porosity, melt pool morphology, and mechanical performance. In this work, a physics-aware, multi-scale Artificial Intelligence (AI)-driven optimization framework is proposed that unifies material level attributes, process settings, thermal/in-situ responses, and final mechanical properties into a single predictive pipeline. To model realistic AM behavior, a synthetic, physics-informed dataset is generated to represent the Laser Powder Bed Fusion (LPBF), Electron Beam Melting (EBM), and Directed Energy Deposition (DED) processes. Multi-level surrogate models are proposed for predictions of melt-pool width, peak temperature, porosity, and tensile properties. Defect classification models are used to predict builds with porosity exceeding a 2.5% threshold. Sensitivity analysis, feature attribution, and energy-density sweeping are conducted to identify critical cross-scale interactions and the optimal process window. The framework demonstrates the overall importance of energy density and in-situ thermal properties to defect formation and mechanical results, and it indicates that it highly predicts all tasks. The work will become part of smart digital twins in the future, providing a single, data-driven basis for stable process optimization in AM, enabling better parameter selection and improved quality assurance.