# Thermal Control of Laser Powder Bed Fusion Using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2102.03355 · HTML: https://mlsanity.com/paper/2102.03355/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 41 references that are in the map.
- First posted on arXiv 29 Jan 2021.

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

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Efficient collective swimming by harnessing vortices through deep reinforcement learning](https://mlsanity.com/paper/1802.02674/) (2018)
- [Controlling Rayleigh-B'enard convection via Reinforcement Learning](https://mlsanity.com/paper/2003.14358/) (2020)
- [A Physics-Informed Machine Learning Model for Porosity Analysis in Laser Powder Bed Fusion Additive Manufacturing](https://mlsanity.com/paper/2101.05605/) (2021)
- [A greedy algorithm for optimal heating in powder-bed-based additive manufacturing](https://mlsanity.com/paper/1901.10884/) (2019)

## Built on by (most cited first, 12 of 12)

- [MeltpoolNet: Melt pool Characteristic Prediction in Metal Additive Manufacturing Using Machine Learning](https://mlsanity.com/paper/2201.11662/) (2022)
- [Inexpensive High Fidelity Melt Pool Models in Additive Manufacturing Using Generative Deep Diffusion](https://mlsanity.com/paper/2311.16168/) (2023)
- [An Augmented Surprise-guided Sequential Learning Framework for Predicting the Melt Pool Geometry](https://mlsanity.com/paper/2401.05579/) (2024)
- [A Reinforcement Learning Approach for Process Parameter Optimization in Additive Manufacturing](https://mlsanity.com/paper/2211.09545/) (2022)
- [Deep Reinforcement Learning Based Toolpath Generation for Thermal Uniformity in Laser Powder Bed Fusion Process](https://mlsanity.com/paper/2404.07209/) (2024)
- [Deep Learning for Melt Pool Depth Contour Prediction From Surface Thermal Images via Vision Transformers](https://mlsanity.com/paper/2404.17699/) (2024)
- [Physics-Informed Machine Learning of Argon Gas-Driven Melt Pool Dynamics](https://mlsanity.com/paper/2307.12304/) (2023)
- [Advancing Additive Manufacturing through Deep Learning: A Comprehensive Review of Current Progress and Future Challenges](https://mlsanity.com/paper/2403.00669/) (2024)
- [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)
- [An Efficient and Uncertainty-aware Reinforcement Learning Framework for Quality Assurance in Extrusion Additive Manufacturing](https://mlsanity.com/paper/2503.00971/) (2025)
- [DRL-Based Injection Molding Process Parameter Optimization for Adaptive and Profitable Production](https://mlsanity.com/paper/2505.10988/) (2025)
- [Laser Scan Path Design for Controlled Microstructure in Additive Manufacturing with Integrated Reduced-Order Phase-Field Modeling and Deep Reinforcement Learning](https://mlsanity.com/paper/2506.21815/) (2025)

## Abstract (arXiv)

Powder-based additive manufacturing techniques provide tools to construct intricate structures that are difficult to manufacture using conventional methods. In Laser Powder Bed Fusion, components are built by selectively melting specific areas of the powder bed, to form the two-dimensional cross-section of the specific part. However, the high occurrence of defects impacts the adoption of this method for precision applications. Therefore, a control policy for dynamically altering process parameters to avoid phenomena that lead to defect occurrences is necessary. A Deep Reinforcement Learning (DRL) framework that derives a versatile control strategy for minimizing the likelihood of these defects is presented. The generated control policy alters the velocity of the laser during the melting process to ensure the consistency of the melt pool and reduce overheating in the generated product. The control policy is trained and validated on efficient simulations of the continuum temperature distribution of the powder bed layer under various laser trajectories.