Machine learning tools for flow-related defects detection in friction stir welding

IF 2.4 3区 工程技术 Q3 ENGINEERING, MANUFACTURING
D. Ambrosio, V. Wagner, G. Dessein, J. Vivas, O. Cahuc
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引用次数: 0

Abstract

Flow-related defects in friction stir welding are critical for the joints affecting their mechanical properties and functionality. One way to identify them, avoiding long and sometimes expensive destructive and non-destructive testing, is using machine learning tools with monitored physical quantities as input data. In this work, artificial neural network and decision tree models are trained, validated, and tested on a large dataset consisting of forces, torque, and temperature in the stirred zone measured when friction stir welding three aluminum alloys such as 5083-H111, 6082-T6, and 7075-T6. The built models successfully classified welds between sound and defective with accuracies over 95%, proving their usefulness in identifying defects on new datasets. Independently from the models, the temperature in the stirred zone is found to be the most influential parameter for the assessment of friction stir weld quality.
用于搅拌摩擦焊中流动相关缺陷检测的机器学习工具
搅拌摩擦焊中与流动相关的缺陷对影响其机械性能和功能的接头至关重要。识别它们的一种方法是使用机器学习工具,将监测到的物理量作为输入数据,从而避免漫长且有时昂贵的破坏性和非破坏性测试。在这项工作中,人工神经网络和决策树模型在由搅拌摩擦焊接三种铝合金(如5083-H111、6082-T6和7075-T6)时测量的搅拌区的力、扭矩和温度组成的大型数据集上进行了训练、验证和测试。所建立的模型成功地将焊缝分为完好焊缝和缺陷焊缝,准确率超过95%,证明了它们在新数据集上识别缺陷的有用性。与模型无关,搅拌区的温度是评估搅拌摩擦焊接质量的最有影响的参数。
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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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