了解工艺条件对热缺陷关系的影响:一种传递机器学习方法

IF 2.4 3区 工程技术 Q3 ENGINEERING, MANUFACTURING Journal of Manufacturing Science and Engineering-transactions of The Asme Pub Date : 2023-03-07 DOI:10.1115/1.4057052
Ayantha Senanayaka, Wenmeng Tian, T. Falls, L. Bian
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引用次数: 1

摘要

本研究旨在基于现有工艺条件的知识转移,开发一种针对不同工艺条件的智能、快速孔隙率预测方法。传统的机器学习算法被广泛用于孔隙度预测。然而,这些方法假设训练(源)和测试(目标)数据遵循相同的概率分布,并且标记的数据在源域和目标域中都可用。在现实世界的制造环境中,源和目标并不遵循相同的分布。工业化过程的多样性导致在不同的生产条件下收集不同的数据,并且标记成本高昂。迁移学习是一种稳健的技术,它能够在源和目标之间转移所学知识,以在目标拥有较少数据的情况下建立关系。因此,本文提出了基于相似性的多源迁移学习(SiMuS-TL)方法来建立源和未知目标之间的关系。源和目标之间的相似性是通过形成一个称为混合域的新域来学习的,该域将数据组织成身份组。然后,指定基于组的学习过程来转移知识以进行目标预测。SiMuS TL的有效性是通过在实际情况下在添加制造的零件中应用孔隙率预测来探索的,即,从单一来源和多来源转移到未知目标孔隙率预测。使用SiMuS TL方法,两种情况下的孔隙率预测准确率均约为90%,但当工艺条件不同时,传统的SVM和CNN分类器在预测孔隙率方面几乎不能很好地发挥作用。
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Understanding the Effects of Process Conditions on Thermal-Defect Relationship: A Transfer Machine Learning Approach
This study aims to develop an intelligent, rapid porosity prediction methodology for varying process conditions based on knowledge transfer from the existing process conditions. Conventional machine learning algorithms are extensively used in porosity prediction. However, these approaches assume that the training (source) and testing (target) data follow the same probability distribution, and the labeled data are available in both source and target domains. The source and target do not follow the same distribution in real-world manufacturing environments. The diversity of industrialization processes leads to heterogeneous data collection in different production conditions, and labeling is costly. Transfer learning is one of the robust techniques that enables transferring learned knowledge between source and target to establish a relationship while the target has less data. Therefore, this paper presents similarity-based multi-source transfer learning(SiMuS-TL) method to develop a relationship between a source and an unknown target. The similarities between sources and targets are learned by forming a new domain called the mixed domain, which organizes data into identity groups. Then, a group-based learning process is designated to transfer knowledge to make target predictions. The effectiveness of the SiMuS-TL is explored with the application of porosity prediction in additively manufactured parts in realistic situations, i.e., single-source and multi-sources transfer to unknown target porosity prediction. The porosity prediction accuracies are approximately 90% for both scenarios with the SiMuS-TL method, but conventional SVM and CNN classifiers barely perform well in predicting porosity while process condition varies.
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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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