基于深度强化学习的激光粉末床熔化温度控制
Thermal Control of Laser Powder Bed Fusion Using Deep Reinforcement Learning
摘要：
基于粉末的增材制造技术提供了构建难以使用常规方法制造的复杂结构的工具。在激光粉末床融合中，通过选择性地熔化粉末床的特定区域来构建部件，以形成特定部件的二维横截面。然而，缺陷的高发生率影响了该方法在精密应用中的采用。因此，一个控制策略，用于动态地改变工艺参数，以避免导致缺陷发生的现象是必要的。提出了一种深度强化学习（DRL）框架，该框架可以导出一种通用的控制策略，以最大限度地减少这些缺陷的可能性。生成的控制策略在熔化过程中改变激光的速度，以确保熔池的一致性并减少生成的产品中的过热。控制策略的训练和有效的模拟各种激光轨迹下的粉末床层的连续温度分布的验证。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
