Thermal Control of Laser Powder Bed Fusion Using Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning-based control policy for Laser Powder Bed Fusion that dynamically adjusts laser velocity to minimize defects and improve process consistency, validated through simulations.

Contribution

It presents a novel DRL framework for real-time control of laser parameters in additive manufacturing, enhancing defect prevention in Laser Powder Bed Fusion.

Findings

Control policy effectively maintains melt pool stability.

Reduces overheating and defect occurrence.

Validated through comprehensive simulations.

Abstract

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.