基于熔池监测的熔头几何形状预测数据驱动方法

IF 2.4 3区 工程技术 Q3 ENGINEERING, MANUFACTURING Journal of Manufacturing Science and Engineering-transactions of The Asme Pub Date : 2023-06-21 DOI:10.1115/1.4062800
Zoe Alexander, T. Feldhausen, K. Saleeby, Thomas Kurfess, Katherine Fu, Christopher Saldaña
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引用次数: 0

摘要

在增材制造中,选择工艺参数以防止过度沉积和沉积不足是一个耗时和资源密集的试错过程。由于每个零件几何形状的独特性,需要进一步发展实时过程监测和控制,以获得可靠的零件尺寸精度。该研究表明,由于支持向量回归(SVR)和卷积神经网络(CNN)模型能够以高精度识别复杂的非线性模式,因此为实时过程控制提供了一个很有前途的解决方案。设计了一个新的实验,比较了SVR模型和CNN模型的性能,从单层单熔头构建的熔池同轴图像中间接检测熔头高度。研究表明,基于同轴光学相机采集的熔池数据训练的SVR和CNN模型均能准确预测熔池高度,平均绝对百分比误差分别为3.67%和3.68%。
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Data-Driven Approaches for Bead Geometry Prediction via Melt Pool Monitoring
In additive manufacturing, choosing process parameters to prevent over and under deposition is a time and resource intensive trial-and-error process. Due to the uniqueness of each part geometry, further development of real-time process monitoring and control is needed for reliable part dimensional accuracy. This research shows that support vector regression (SVR) and convolutional neural network (CNN) models offer a promising solution for real-time process control due to the models' abilities to recognize complex, nonlinear patterns with high accuracy. A novel experiment was designed to compare the performance of SVR and CNN models to indirectly detect bead height from a coaxial image of a melt pool from a single layer, single bead build. The study showed that both SVR and CNN models trained on melt pool data collected from a coaxial optical camera can accurately predict the bead height with a mean absolute percentage error of 3.67% and 3.68%, respectively.
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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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