用于激光粉末床聚变发射预测的物理引导长短期记忆网络

IF 2.4 3区 工程技术 Q3 ENGINEERING, MANUFACTURING
Rong Lei, Y.B. Guo, W. Guo
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引用次数: 1

摘要

粉末床融合(PBF)是一种增材制造工艺,在该工艺中,激光加热使粉末床顶部吹出的粉末颗粒液化,冷却使熔化的粉末颗粒固化。在此过程中,激光束热量与粉末相互作用,导致热发射并影响熔池。本文旨在利用递归神经网络的优势来预测PBF中的热排放。长短期记忆(LSTM)网络是为了从顺序数据(发射读数)中学习而开发的,而学习是由过程物理指导的,包括激光功率、激光速度、层数和扫描模式。为了减少模型训练的计算工作量,LSTM模型与一种新方法集成,用于对高温计原始数据进行下采样,并从原始数据中提取有用的统计特征。LSTM模型的结构和超参数反映了基于高温计读数数据训练的几次调整迭代。结果揭示了关于如何处理原始高温计数据以使LSTM最佳工作的有用知识,物理特征如何在预测过热时提供信息,以及物理引导的LSTM在排放预测中的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PHYSICS-GUIDED LONG SHORT-TERM MEMORY NETWORKS FOR EMISSION PREDICTION IN LASER POWDER BED FUSION
Powder Bed Fusion (PBF) is an additive manufacturing process in which laser heat liquefies blown powder particles on top of a powder bed, and cooling solidifies the melted powder particles. During this process, the laser beam heat interacts with the powder causing thermal emission and affecting the melt pool. This paper aims to predict heat emission in PBF by harnessing the strengths of recurrent neural networks. Long Short-Term Memory (LSTM) networks are developed to learn from sequential data (emission readings), while the learning is guided by process physics including laser power, laser speed, layer number, and scanning patterns. To reduce the computational efforts on model training, the LSTM models are integrated with a new approach for down-sampling the pyrometry raw data and extracting useful statistical features from raw data. The structure and hyperparameters of the LSTM model reflect several iterations of tuning based on the training on the pyrometer readings data. Results reveal useful knowledge on how raw pyrometer data should be processed to work the best with LSTM, how physics features are informative in predicting overheating, and the effectiveness of physics-guided LSTM in emission prediction.
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来源期刊
CiteScore
6.80
自引率
20.00%
发文量
126
审稿时长
12 months
期刊介绍: Areas of interest including, but not limited to: Additive manufacturing; Advanced materials and processing; Assembly; Biomedical manufacturing; Bulk deformation processes (e.g., extrusion, forging, wire drawing, etc.); CAD/CAM/CAE; Computer-integrated manufacturing; Control and automation; Cyber-physical systems in manufacturing; Data science-enhanced manufacturing; Design for manufacturing; Electrical and electrochemical machining; Grinding and abrasive processes; Injection molding and other polymer fabrication processes; Inspection and quality control; Laser processes; Machine tool dynamics; Machining processes; Materials handling; Metrology; Micro- and nano-machining and processing; Modeling and simulation; Nontraditional manufacturing processes; Plant engineering and maintenance; Powder processing; Precision and ultra-precision machining; Process engineering; Process planning; Production systems optimization; Rapid prototyping and solid freeform fabrication; Robotics and flexible tooling; Sensing, monitoring, and diagnostics; Sheet and tube metal forming; Sustainable manufacturing; Tribology in manufacturing; Welding and joining
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