# Computational, Data-Driven, and Physics-Informed Machine Learning Approaches for Microstructure Modeling in Metal Additive Manufacturing

arXiv: https://arxiv.org/abs/2505.01424 · HTML: https://mlsanity.com/paper/2505.01424/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 146 references that are in the map.
- First posted on arXiv 2 May 2025.

## Builds on (most cited first, 12 of 12)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data](https://mlsanity.com/paper/2003.06097/) (2020)
- [From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning](https://mlsanity.com/paper/2410.13228/) (2024)
- [Failure-informed adaptive sampling for PINNs](https://mlsanity.com/paper/2210.00279/) (2022)
- [MeltpoolNet: Melt pool Characteristic Prediction in Metal Additive Manufacturing Using Machine Learning](https://mlsanity.com/paper/2201.11662/) (2022)
- [Hybrid thermal modeling of additive manufacturing processes using physics-informed neural networks for temperature prediction and parameter identification](https://mlsanity.com/paper/2206.07756/) (2022)
- [GrainGNN: A dynamic graph neural network for predicting 3D grain microstructure](https://mlsanity.com/paper/2401.03661/) (2024)
- [Review of Machine Learning Methods for Additive Manufacturing of Functionally Graded Materials](https://mlsanity.com/paper/2309.16571/) (2023)
- [Physics-Informed Machine Learning for Smart Additive Manufacturing](https://mlsanity.com/paper/2407.10761/) (2024)
- [Physics-Informed Machine Learning of Argon Gas-Driven Melt Pool Dynamics](https://mlsanity.com/paper/2307.12304/) (2023)
- [Thermal-Mechanical Physics Informed Deep Learning For Fast Prediction of Thermal Stress Evolution in Laser Metal Deposition](https://mlsanity.com/paper/2412.18786/) (2024)
- [Tusas: A fully implicit parallel approach for coupled phase-field equations](https://mlsanity.com/paper/2006.16764/) (2020)

## Built on by (most cited first, 1 of 1)

- [Compressing the Validation Bottleneck: An Agentic Self-Driving Lab for Scientific Discovery](https://mlsanity.com/paper/2607.04508/) (2026)

## Abstract (arXiv)

Metal additive manufacturing enables unprecedented design freedom and the production of customized, complex components. However, the rapid melting and solidification dynamics inherent to metal AM processes generate heterogeneous, non-equilibrium microstructures that significantly impact mechanical properties and subsequent functionality. Predicting microstructure and its evolution across spatial and temporal scales remains a central challenge for process optimization and defect mitigation. While conventional experimental techniques and physics-based simulations provide a physical foundation and valuable insights, they face critical limitations. In contrast, data-driven machine learning offers an alternative prediction approach and powerful pattern recognition but often operate as black-box, lacking generalizability and physical consistency. To overcome these limitations, physics-informed machine learning, including physics-informed neural networks, has emerged as a promising paradigm by embedding governing physical laws into neural network architectures, thereby enhancing accuracy, transparency, data efficiency, and extrapolation capabilities. This work presents a comprehensive evaluation of modeling strategies for microstructure prediction in metal AM. The strengths and limitations of experimental, computational, and data-driven methods are analyzed in depth, and highlight recent advances in hybrid PIML frameworks that integrate physical knowledge with ML. Key challenges, such as data scarcity, multi-scale coupling, and uncertainty quantification, are discussed alongside future directions. Ultimately, this assessment underscores the importance of PIML-based hybrid approaches in enabling predictive, scalable, and physically consistent microstructure modeling for site-specific, microstructure-aware process control and the reliable production of high-performance AM components.