预测汽车粘合剂粘接接头的疲劳寿命:采用实验和数值数据集相结合的数据驱动方法

IF 4.2 2区 工程技术 Q2 ENGINEERING, MANUFACTURING Advances in Manufacturing Pub Date : 2024-08-16 DOI:10.1007/s40436-024-00500-5
Chen-Di Wei, Qiu-Ren Chen, Min Chen, Li Huang, Zhong-Jie Yue, Si-Geng Li, Jian Wang, Li Chen, Chao Tong, Qing Liu
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引用次数: 0

摘要

大多数车辆结构故障都源于连接部位。循环载荷是导致接头失效的主要因素之一,因此接头的疲劳性能是车辆结构设计中的一个重要考虑因素。由于缺乏粘合剂寿命数据和接头几何形状的多样性,传统疲劳分析方法的使用受到限制。因此,汽车行业迫切需要一种精确的接头疲劳寿命估算方法。在这项工作中,我们根据实验数据和有限元分析 (FEA) 结果,提出了一种嵌入物理知识指导参数的数据驱动方法。我们采用不同的机器学习(ML)算法来研究三种典型粘接接头(即搭接剪切、教练剥离和 KSII 接头)的疲劳寿命。在对 ML 模型进行特征工程和调整后,建立了使用高斯过程回归算法的优选模型,并输入八个输入参数,即从有限元分析中获得的基材厚度、线力和粘合接头的弯矩。利用测试数据集和具有复杂加载状态的部件级物理测试对所提出的方法进行了验证,以进行无偏评估。结果表明,对于粘合接头的寿命预测,数据驱动的解决方案比传统解决方案更有优势。
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Predicting fatigue life of automotive adhesive bonded joints: a data-driven approach using combined experimental and numerical datasets

The majority of vehicle structural failures originate from joint areas. Cyclic loading is one of the primary factors in joint failures, making the fatigue performance of joints a critical consideration in vehicle structure design. The use of traditional fatigue analysis methods is constrained by the absence of adhesive life data and the wide variety of joint geometries. Therefore, there is a pressing need for an accurate fatigue life estimation method for the joints in the automotive industry. In this work, we proposed a data-driven approach embedding physical knowledge-guided parameters based on experimental data and finite element analysis (FEA) results. Different machine learning (ML) algorithms are adopted to investigate the fatigue life of three typical adhesive joints, namely lap shear, coach peel and KSII joints. After the feature engineering and tuned process of the ML models, the preferable model using the Gaussian process regression algorithm is established, fed with eight input parameters, namely thicknesses of the substrates, line forces and bending moments of the adhesive bonded joints obtained from FEA. The proposed method is validated with the test data set and part-level physical tests with complex loading states for an unbiased evaluation. It demonstrates that for life prediction of adhesive joints, the data-driven solutions can constitute an improvement over conventional solutions.

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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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