Determining thresholds of the primary control loops (System 1) of an additive manufacturing (AM) process is challenging when realizing System 1 with its fast and intuitive capability for adapting to different metal powers, machine configurations, and process parameters. Based on the convolution neural network and long short-term memory models, this letter presents a secondary tuning loop (System 2) to classify the types of melt-pool images (MPIs) from a coaxial camera online, suggest polishing parameters, and determine the control thresholds of System 1 offline. Case studies indicate that the thresholds and parameters of System 1 including smoke discharging, powder coating, and laser polishing of control loops of a laser powder bed fusion (LPBF) machine can be more deliberatively and logically decided by the proposed MPI-based System 2.

中文翻译：

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用于确定 LPBF 过程控制阈值和参数的基于 MPI 的系统 2


在实现系统 1 时，确定增材制造 (AM) 工艺的主控制回路（系统 1）的阈值具有挑战性，因为系统 1 具有快速、直观的适应不同金属功率、机器配置和工艺参数的能力。基于卷积神经网络和长短期记忆模型，这封信提出了一个二次调谐循环（系统 2），用于对来自同轴相机在线的熔池图像（MPIs）类型进行分类，建议抛光参数，并确定系统1离线控制阈值。案例研究表明，系统 1 的阈值和参数，包括激光粉末床熔合 (LPBF) 机器控制回路的排烟、粉末涂层和激光抛光，可以由所提出的基于 MPI 的系统 2 更加深思熟虑和逻辑地决定。