# Physics-Informed Machine Learning for Smart Additive Manufacturing

arXiv: https://arxiv.org/abs/2407.10761 · HTML: https://mlsanity.com/paper/2407.10761/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 15 references that are in the map.
- First posted on arXiv 15 Jul 2024.

## Builds on (most cited first, 1 of 1)

- [Machine learning for metal additive manufacturing: Predicting temperature and melt pool fluid dynamics using physics-informed neural networks](https://mlsanity.com/paper/2008.13547/) (2020)

## Built on by (most cited first, 3 of 3)

- [Computational, Data-Driven, and Physics-Informed Machine Learning Approaches for Microstructure Modeling in Metal Additive Manufacturing](https://mlsanity.com/paper/2505.01424/) (2025)
- [Physics-Informed Machine Learning Regulated by Finite Element Analysis for Simulation Acceleration of Melt Pool Dynamics in Laser Powder Bed Fusion](https://mlsanity.com/paper/2506.20537/) (2025)
- [Real-Time AI-Driven Milling Digital Twin Towards Extreme Low-Latency](https://mlsanity.com/paper/2512.13482/) (2025)

## Abstract (arXiv)

Compared to physics-based computational manufacturing, data-driven models such as machine learning (ML) are alternative approaches to achieve smart manufacturing. However, the data-driven ML's "black box" nature has presented a challenge to interpreting its outcomes. On the other hand, governing physical laws are not effectively utilized to develop data-efficient ML algorithms. To leverage the advantages of ML and physical laws of advanced manufacturing, this paper focuses on the development of a physics-informed machine learning (PIML) model by integrating neural networks and physical laws to improve model accuracy, transparency, and generalization with case studies in laser metal deposition (LMD).