一种基于原因机制独立性估计的新型跨域学习方法,用于加工变形预测

IF 12.2 1区 工程技术 Q1 ENGINEERING, INDUSTRIAL
Yang Ni , Yingguang Li , Changqing Liu , Xu Liu
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引用次数: 0

摘要

基于数据监测的加工变形预测是精确控制变形和保证产品质量的基础。对于涉及残余应力等不可观测变量、可能导致数据分布偏差的问题,跨域因果学习方法通过转移原因分布和机制,与其他纯数据驱动方法相比具有突出优势。然而,现有的因果学习方法都是基于原因和机制相互独立的假设,忽略了机制在不同领域间的相应变化,会限制学习的准确性。本文提出了一种基于原因-机制独立性估计的新型因果跨域学习方法,该方法通过考虑原因和机制的依赖性打破了这一假设。通过引入机制导数乘以原因分布的结构积分,建立了原因-机制独立性估计器,其估计值可以衡量机制的跨域变化。因此,所提出的基于预测模型的方法可以根据估计值进行有效的分布转移。以航空发动机壳体的加工为例,实验结果表明所提出的方法能在有限的目标域数据下很好地预测变形。此外,提出的方法还可扩展到其他涉及不可观测变量的跨域回归问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A new cause-mechanism independence estimation based cross-domain learning method for machining deformation prediction
Monitoring data-based machining deformation prediction is fundamental for accurate deformation control and product quality guarantee. For problems where involved unobservable variables like residual stress that can lead to data distribution bias, causal cross-domain learning methods have prominent advantages over other pure data-driven methods by shifting cause distributions and mechanisms. However, existing causal methods are based on the hypothesis that cause and mechanism are independent, which ignores the corresponding changes of mechanism across domains and can limit accuracies. This paper proposes a new causal cross-domain learning method based on cause-mechanism independence estimation, where the hypothesis is broken by taking the dependence of cause and mechanism into consideration. A cause-mechanism independence estimator is established by introducing the structural integral of mechanism derivative multiplies cause distribution, and the estimation value can measure the cross-domain changes of mechanism. As a result, the proposed method based predicting model can make efficient distribution shifts according to the estimation. The machining of aero-engine casings is taken as a case study, and experimental results show that the proposed method could predict the deformation well with limited target domain data. Besides, the proposed method can be readily extended to other cross-domain regression problems involved with unobservable variables.
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来源期刊
Journal of Manufacturing Systems
Journal of Manufacturing Systems 工程技术-工程:工业
CiteScore
23.30
自引率
13.20%
发文量
216
审稿时长
25 days
期刊介绍: The Journal of Manufacturing Systems is dedicated to showcasing cutting-edge fundamental and applied research in manufacturing at the systems level. Encompassing products, equipment, people, information, control, and support functions, manufacturing systems play a pivotal role in the economical and competitive development, production, delivery, and total lifecycle of products, meeting market and societal needs. With a commitment to publishing archival scholarly literature, the journal strives to advance the state of the art in manufacturing systems and foster innovation in crafting efficient, robust, and sustainable manufacturing systems. The focus extends from equipment-level considerations to the broader scope of the extended enterprise. The Journal welcomes research addressing challenges across various scales, including nano, micro, and macro-scale manufacturing, and spanning diverse sectors such as aerospace, automotive, energy, and medical device manufacturing.
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