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

Shenghan Guo, Mohit Agarwal, Clayton Cooper, Qi Tian, Robert X. Gao, Weihong Grace Guo, Y. B. Guo

Research output: Contribution to journalReview articlepeer-review

70 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)145-163
Number of pages19
JournalJournal of Manufacturing Systems
StatePublished - Jan 2022


  • Additive manufacturing
  • Deep learning
  • Machine learning
  • Physics of manufacturing processes

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Hardware and Architecture
  • Industrial and Manufacturing Engineering


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