# MeltpoolNet: Melt pool Characteristic Prediction in Metal Additive Manufacturing Using Machine Learning

arXiv: https://arxiv.org/abs/2201.11662 · HTML: https://mlsanity.com/paper/2201.11662/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 104 references that are in the map.
- First posted on arXiv 26 Jan 2022.

## Builds on (most cited first, 5 of 5)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [AI Feynman: a Physics-Inspired Method for Symbolic Regression](https://mlsanity.com/paper/1905.11481/) (2019)
- [When Gaussian Process Meets Big Data: A Review of Scalable GPs](https://mlsanity.com/paper/1807.01065/) (2018)
- [Hyperparameters and Tuning Strategies for Random Forest](https://mlsanity.com/paper/1804.03515/) (2018)
- [Thermal Control of Laser Powder Bed Fusion Using Deep Reinforcement Learning](https://mlsanity.com/paper/2102.03355/) (2021)

## Built on by (most cited first, 19 of 19)

- [Inexpensive High Fidelity Melt Pool Models in Additive Manufacturing Using Generative Deep Diffusion](https://mlsanity.com/paper/2311.16168/) (2023)
- [Surrogate Modeling of Melt Pool Thermal Field using Deep Learning](https://mlsanity.com/paper/2207.12259/) (2022)
- [An Augmented Surprise-guided Sequential Learning Framework for Predicting the Melt Pool Geometry](https://mlsanity.com/paper/2401.05579/) (2024)
- [AdditiveLLM: Large Language Models Predict Defects in Additive Manufacturing](https://mlsanity.com/paper/2501.17784/) (2025)
- [Review of Machine Learning Methods for Additive Manufacturing of Functionally Graded Materials](https://mlsanity.com/paper/2309.16571/) (2023)
- [ThermoPore: Predicting Part Porosity Based on Thermal Images Using Deep Learning](https://mlsanity.com/paper/2404.16882/) (2024)
- [Agentic Additive Manufacturing Alloy Evaluation](https://mlsanity.com/paper/2510.02567/) (2025)
- [A Reinforcement Learning Approach for Process Parameter Optimization in Additive Manufacturing](https://mlsanity.com/paper/2211.09545/) (2022)
- [MechProNet: Machine Learning Prediction of Mechanical Properties in Metal Additive Manufacturing](https://mlsanity.com/paper/2209.12605/) (2022)
- [A hybrid machine learning framework for clad characteristics prediction in metal additive manufacturing](https://mlsanity.com/paper/2307.01872/) (2023)
- [Integrating Multi-Physics Simulations and Machine Learning to Define the Spatter Mechanism and Process Window in Laser Powder Bed Fusion](https://mlsanity.com/paper/2405.07823/) (2024)
- [Computational, Data-Driven, and Physics-Informed Machine Learning Approaches for Microstructure Modeling in Metal Additive Manufacturing](https://mlsanity.com/paper/2505.01424/) (2025)
- [Deep-Learning Quantitative Structural Characterization in Additive Manufacturing](https://mlsanity.com/paper/2302.06389/) (2023)
- [A Systematic Review of Available Datasets in Additive Manufacturing](https://mlsanity.com/paper/2401.15448/) (2024)
- [A Data-Efficient Sequential Learning Framework for Melt Pool Defect Classification in Laser Powder Bed Fusion](https://mlsanity.com/paper/2411.10822/) (2024)
- [Multimodal learning of melt pool dynamics in laser powder bed fusion](https://mlsanity.com/paper/2509.03029/) (2025)
- [Comparison of Deterministic and Probabilistic Machine Learning Algorithms for Precise Dimensional Control and Uncertainty Quantification in Additive Manufacturing](https://mlsanity.com/paper/2509.16233/) (2025)
- [Enhanced geometry prediction in laser directed energy deposition using meta-learning](https://mlsanity.com/paper/2512.22241/) (2025)
- [Material Database Agent: A Multimodal Agentic Framework for Scientific Literature Mining](https://mlsanity.com/paper/2605.04278/) (2026)

## Abstract (arXiv)

Characterizing meltpool shape and geometry is essential in metal Additive Manufacturing (MAM) to control the printing process and avoid defects. Predicting meltpool flaws based on process parameters and powder material is difficult due to the complex nature of MAM process. Machine learning (ML) techniques can be useful in connecting process parameters to the type of flaws in the meltpool. In this work, we introduced a comprehensive framework for benchmarking ML for melt pool characterization. An extensive experimental dataset has been collected from more than 80 MAM articles containing MAM processing conditions, materials, meltpool dimensions, meltpool modes and flaw types. We introduced physics-aware MAM featurization, versatile ML models, and evaluation metrics to create a comprehensive learning framework for meltpool defect and geometry prediction. This benchmark can serve as a basis for melt pool control and process optimization. In addition, data-driven explicit models have been identified to estimate meltpool geometry from process parameters and material properties which outperform Rosenthal estimation for meltpool geometry while maintaining interpretability.