Machine learning for metal additive manufacturing: Towards a physics-informed data-driven paradigm

Machine learning (ML) has shown to be an effective alternative to physical models for quality prediction and process optimization of metal additive manufacturing (AM). However, the inherent black box nature of ML techniques such as those represented by artificial neural networks has often presented a challenge to interpret ML outcomes in the framework of the complex thermodynamics that govern AM. While the practical benefits of ML provide an adequate justification, its utility as a reliable modeling tool is ultimately reliant on assured consistency with physical principles and model transparency. To facilitate the fundamental needs, physics-informed machine learning (PIML) has emerged as a hybrid machine learning paradigm that imbues ML models with physical domain knowledge such as thermomechanical laws and constraints. The distinguishing feature of PIML is the synergistic integration of data-driven methods that reflect system dynamics in real-time with the governing physics underlying AM. In this paper, the current state-of-the-art in metal AM is reviewed and opportunities for a paradigm shift to PIML are discussed, thereby identifying relevant future research directions.

Automated summary

Machine learning has proven to be an effective alternative to physical models in quality prediction and process optimization of metal additive manufacturing. However, the interpretability of machine learning outcomes within the complex thermodynamics of additive manufacturing has been a challenge. Physics-informed machine learning (PIML) addresses this challenge by integrating data-driven methods with physical domain knowledge.