A data-driven approach for predicting the fatigue life and failure mode of self-piercing rivet joints

IF 4.2 2区 工程技术 Q2 ENGINEERING, MANUFACTURING
Jian Wang, Qiu-Ren Chen, Li Huang, Chen-Di Wei, Chao Tong, Xian-Hui Wang, Qing Liu
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Abstract

In lightweight automotive vehicles, the application of self-piercing rivet (SPR) joints is becoming increasingly widespread. Considering the importance of automotive service performance, the fatigue performance of SPR joints has received considerable attention. Therefore, this study proposes a data-driven approach to predict the fatigue life and failure modes of SPR joints. The dataset comprises three specimen types: cross-tensile, cross-peel, and tensile-shear. To ensure data consistency, a finite element analysis was employed to convert the external loads of the different specimens. Feature selection was implemented using various machine-learning algorithms to determine the model input. The Gaussian process regression algorithm was used to predict fatigue life, and its performance was compared with different kernel functions commonly used in the field. The results revealed that the Matern kernel exhibited an exceptional predictive capability for fatigue life. Among the data points, 95.9% fell within the 3-fold error band, and the remaining 4.1% exceeded the 3-fold error band owing to inherent dispersion in the fatigue data. To predict the failure location, various tree and artificial neural network (ANN) models were compared. The findings indicated that the ANN models slightly outperformed the tree models. The ANN model accurately predicts the failure of joints with varying dimensions and materials. However, minor deviations were observed for the joints with the same sheet. Overall, this data-driven approach provided a reliable predictive model for estimating the fatigue life and failure location of SPR joints.

Abstract Image

预测自冲铆接疲劳寿命和失效模式的数据驱动方法
在轻型汽车中,自冲铆钉(SPR)接头的应用越来越广泛。考虑到汽车使用性能的重要性,SPR 接头的疲劳性能受到了广泛关注。因此,本研究提出了一种数据驱动的方法来预测 SPR 接头的疲劳寿命和失效模式。数据集包括三种试样类型:交叉拉伸、交叉剥离和拉伸剪切。为确保数据的一致性,采用了有限元分析来转换不同试样的外部载荷。使用各种机器学习算法进行特征选择,以确定模型输入。高斯过程回归算法用于预测疲劳寿命,并将其性能与该领域常用的不同核函数进行了比较。结果表明,Matern 核对疲劳寿命具有卓越的预测能力。在数据点中,95.9% 的数据在 3 倍误差范围内,其余 4.1% 的数据超出了 3 倍误差范围,原因是疲劳数据存在固有的分散性。为了预测失效位置,对各种树模型和人工神经网络(ANN)模型进行了比较。结果表明,人工神经网络模型的性能略优于树状模型。人工神经网络模型能准确预测不同尺寸和材料接头的失效。不过,在相同板材的接合处也观察到了轻微的偏差。总之,这种数据驱动方法为估计 SPR 接头的疲劳寿命和失效位置提供了可靠的预测模型。
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来源期刊
Advances in Manufacturing
Advances in Manufacturing Materials Science-Polymers and Plastics
CiteScore
9.10
自引率
3.80%
发文量
274
期刊介绍: As an innovative, fundamental and scientific journal, Advances in Manufacturing aims to describe the latest regional and global research results and forefront developments in advanced manufacturing field. As such, it serves as an international platform for academic exchange between experts, scholars and researchers in this field. All articles in Advances in Manufacturing are peer reviewed. Respected scholars from the fields of advanced manufacturing fields will be invited to write some comments. We also encourage and give priority to research papers that have made major breakthroughs or innovations in the fundamental theory. The targeted fields include: manufacturing automation, mechatronics and robotics, precision manufacturing and control, micro-nano-manufacturing, green manufacturing, design in manufacturing, metallic and nonmetallic materials in manufacturing, metallurgical process, etc. The forms of articles include (but not limited to): academic articles, research reports, and general reviews.
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