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

Advances in Manufacturing 2024年第3期12卷 538-555页

作者： Jian Wang qiu-ren chen Li Huang chen-Di Wei Chao Tong Xian-Hui Wang Qing Liu Nanjing University of Science and Technology Nanjing210094People’s Republic of China Material Academy JitriSuzhou215100JiangsuPeople’s Republic of China Key Laboratory for Light-weight Materials Nanjing Tech UniversityNanjing210009People’s Republic of China Xi’an Jiaotong-Liverpool University Suzhou215123JiangsuPeople’s Republic of China

In lightweight automotive vehicles,the application of self-piercing rivet(SPR)joints is becoming increasingly *** the importance of automotive service performance,the fatigue performance of SPR joints has received considerable ***,this study proposes a data-driven approach to predict the fatigue life and failure modes of SPR *** dataset comprises three specimen types:cross-tensile,cross-peel,and *** ensure data consistency,a finite element analysis was employed to convert the external loads of the different *** selection was implemented using various machine-learning algorithms to determine the model *** Gaussian process regression algorithm was used to predict fatigue life,and its performance was compared with different kernel functions commonly used in the *** results revealed that the Matern kernel exhibited an exceptional predictive capability for fatigue *** 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 *** predict the failure location,various tree and artificial neural network(ANN)models were *** findings indicated that the ANN models slightly outperformed the tree *** ANN model accurately predicts the failure of joints with varying dimensions and ***,minor deviations were observed for the joints with the same ***,this data-driven approach provided a reliable predictive model for estimating the fatigue life and failure location of SPR joints.

关键词： Self-piercing rivet(SPR)joints Fatigue life prediction Failure mode prediction Machine learning

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Predicting fatigue life of automotive adhesive bonded joints:a data-driven approach using combined experimental and numerical datasets

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Advances in Manufacturing 2024年第3期12卷 522-537页

作者： chen-Di Wei qiu-ren chen Min chen Li Huang Zhong-Jie Yue Si-Geng Li Jian Wang Li chen Chao Tong Qing Liu School of Advanced Technology Xi’an Jiaotong-Liverpool UniversitySuzhou215123People’s Republic of China Key Laboratory for Light-weight Materials Nanjing Tech UniversityNanjing210009People’s Republic of China Material Academy JITRISuzhou215100People’s Republic of China Nanjing University of Science and Technology Nanjing210094People’s Republic of China

The majority of vehicle structural failures originate from joint *** loading is one of the primary factors in joint failures,making the fatigue performance of joints a critical consideration in vehicle structure *** use of traditional fatigue analysis methods is constrained by the absence of adhesive life data and the wide variety of joint ***,there is a pressing need for an accurate fatigue life estimation method for the joints in the automotive *** this work,we proposed a data-driven approach embedding physical knowledge-guided parameters based on experimental data and finite element analysis(FEA)*** machine learning(ML)algorithms are adopted to investigate the fatigue life of three typical adhesive joints,namely lap shear,coach peel and KSII *** 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 *** proposed method is validated with the test data set and part-level physical tests with complex loading states for an unbiased *** demonstrates that for life prediction of adhesive joints,the data-driven solutions can constitute an improvement over conventional solutions.

关键词： Fatigue life Adhesive bonded joints Finite element analysis(FEA) Machine learning(ML)

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Data-driven approach to predict the fatigue properties of ferrous metal materials using the cGAN and machine-learning algorithms

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Advances in Manufacturing 2024年第3期12卷 447-464页

作者： Si-Geng Li qiu-ren chen Li Huang Min chen chen-Di Wei Zhong-Jie Yue Ru-Xue Liu Chao Tong Qing Liu School of Advanced Technology Xi’an Jiaotong-Liverpool UniversitySuzhou215123JiangsuPeople’s Republic of China Key Laboratory for Light-weight Materials Nanjing Tech UniversityNanjing210009People’s Republic of China Materials Bigdata and Application Division Material Academy JitriSuzhou215131JiangsuPeople’s Republic of China School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai200240People’s Republic of China

The stress-life curve(S–N)and low-cycle strain-life curve(E–N)are the two primary representations used to characterize the fatigue behavior of a *** material fatigue curves are essential for structural fatigue ***,conducting material fatigue tests is expensive and *** address the challenge of data limitations on ferrous metal materials,we propose a novel method that utilizes the Random Forest Algorithm and transfer learning to predict the S–N and E–N curves of ferrous *** addition,a data-augmentation framework is introduced using a conditional generative adversarial network(cGAN)to overcome data *** incorporating the cGAN-generated data,the accuracy(R2)of the Random Forest Algorithm-trained model is improved by 0.3–*** is proven that the cGAN can significantly enhance the prediction accuracy of the machine-learning model and balance the cost of obtaining fatigue data from the experiment.

关键词： Fatigue life curve Machine learning Transfer learning Conditional generative adversarial network(cGAN)

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A machine learning-based calibration method for strength simulation of self-piercing riveted joints

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Advances in Manufacturing 2024年第3期12卷 465-483页

作者： Yu-Xiang Ji Li Huang qiu-ren chen Charles K.S.Moy Jing-Yi Zhang Xiao-Ya Hu Jian Wang Guo-Bi Tan Qing Liu Key Laboratory for Light-weight Materials Nanjing Tech UniversityNanjing210009People’s Republic of China Design School Xi’an Jiaotong-Liverpool UniversitySuzhou215123JiangsuPeople’s Republic of China Materials Bigdata and Applications Division Materials Academy JitriSuzhou215131JiangsuPeople’s Republic of China School of Mechanical Engineering Nanjing University of Science and TechnologyNanjing210094People’s Republic of China

This paper presents a new machine learning-based calibration framework for strength simulation models of self-piercing riveted(SPR)*** simulations were conducted through the integrated modeling of SPR joints from process to performance,while physical quasi-static tensile tests were performed on combinations of DP600 high-strength steel and 5754 aluminum alloy sheets under lap-shear loading conditions.A sensitivity study of the critical simulation parameters(e.g.,friction coefficient and scaling factor)was conducted using the controlled variables method and Sobol sensitivity analysis for feature ***,machine-learning-based surrogate models were used to train and accurately represent the mapping between the detailed joint profile and its load-displacement *** of the simulation model is defined as a dual-objective optimization task to minimize errors in key load displacement features between simulations and experiments.A multi-objective genetic algorithm(MOGA)was chosen for *** three combinations of SPR joints illustrated the effectiveness of the proposed framework,and good agreement was achieved between the calibrated models and experiments.

关键词： Machine learning Self-piercing riveting(SPR) Sensitivity analysis Multi-objective optimization

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Improving RSW nugget diameter prediction method:unleashing the power of multi-fidelity neural networks and transfer learning

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Advances in Manufacturing 2024年第3期12卷 409-427页

作者： Zhong-Jie Yue qiu-ren chen Zu-Guo Bao Li Huang Guo-Bi Tan Ze-Hong Hou Mu-Shi Li Shi-Yao Huang Hai-Long Zhao Jing-Yu Kong Jia Wang Qing Liu Key Laboratory for Light-weight Materials Nanjing Tech UniversityNanjing210009People’s Republic of China School of Advanced Technology Xi’an Jiaotong-Liverpool UniversitySuzhou215123JiangsuPeople’s Republic of China Material Academy JITRISuzhou215100JiangsuPeople’s Republic of China School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai200240People’s Republic of China

This research presents an innovative approach to accurately predict the nugget diameter in resistance spot welding(RSW)by leveraging machine learning and transfer learning ***,low-fidelity(LF)data were obtained through finite element numerical simulation and design of experiments(DOEs)to train the LF machine learning ***,high-fidelity(HF)data were collected from RSW process experiments and used to fine-tune the LF model by transfer learning *** accuracy and generalization performance of the models were thoroughly *** results demonstrated that combining different fidelity datasets and employing transfer learning could significantly improve the prediction accuracy while minimize the costs associated with experimental trials,and provide an effective and valuable method for predicting critical process parameters in RSW.

关键词： Resistance spot welding(RSW) Nugget diameter prediction Multi-fidelity neural networks Transfer learning

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Aggregation of CeAlO_(3) inclusions in heavy ingot of a steel containing 0.007% aluminum

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Journal of Iron and Steel Research International 2024年第8期31卷 1960-1972页

作者： qiu-yue Zhou Jun-tao Ba Lun Zhang Wei chen Ying ren Li-feng Zhang School of Metallurgical and Ecological Engineering University of Science and Technology BeijingBeijing 100083China School of Mechanical Engineering Yanshan UniversityQinhuangdao 066004HebeiChina School of Mechanical and Materials Engineering North China University of TechnologyBeijing 100144China

The distribution of inclusions at the bottom of a Ce-treated heavy steel ingot was detected and *** three-dimensional morphology and spatial distribution of CeAlOs clusters were characterized using the electrolytic extraction and Micro-CT detection.A model of inclusion collision to predict the aggregation of CeAlO_(3) inclusions in the ingot was established and validated by measured *** were mainly CeAlO_(3) and a small amount of Ce_(2)O_(2)S in the tundish after cerium *** collision and aggregation of inclusions led to the formation of large clusters in the ingot during the solidification *** slag entrainment inclusions,large CeAO_(3) clusters and small CeAlO_(3) particles were observed from the center to the edge of the ingot *** inclusions were mainly concentrated at the *** number density of inclusions larger than 200μm was 0.21 mm^(-3).The maximum diameter of CeA1O_(3) clusters was 1340μ*** the edge to the radial center and from the bottom to the top,the average diameter of inclusions gradually increased due to the longer solidification time of the ingot.

关键词： Inclusion Heavy ingot Cerium treatment Collision

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Tuning the cross-linked structure of basic poly(ionic liquid)to develop an efficient catalyst for the conversion of vinyl carbonate to dimethyl carbonate

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Chinese Journal of Chemical Engineering 2024年第8期72卷 106-116页

作者： Zhaoyang Qi Shiquan Zhong Huiyun Su Changshen Ye Limei ren Ting qiu Jie chen College of Chemical Engineering Fuzhou UniversityFuzhou 350108China Department of Chemical Engineering Shijiazhuang UniversityShijiazhuang 050035China Qingyuan Innovation Laboratory Quanzhou 362801China Fuzhou University International Joint Laboratory of Thermochemical Conversion of Biomass Fuzhou UniversityFuzhou 350108China

Dimethyl carbonate(DMC)is a crucial chemical raw material widely used in organic synthesis,lithiumion battery electrolytes,and various other *** current primary industrial process employs a conventional sodium methoxide basic catalyst to produce DMC through the transesterification reaction between vinyl carbonate and ***,the utilization of this catalyst presents several challenges during the process,including equipment corrosion,the generation of solid waste,susceptibility to deactivation,and complexities in separation and *** address these limitations,a series of alkaline poly(ionic liquid)s,i.e.[DVBPIL][PHO],[DVCPIL][PHO],and[TBVPIL][PHO],with different crosslinking degrees and structures,were synthesized through the construction of cross-linked polymeric monomers and *** poly(ionic liquid)s exhibit cross-linked structures and controllable cationic and anionic *** was conducted to investigate the effect of the cross-linking degree and structure on the catalytic performance of transesterification in synthesizing *** was discovered that the appropriate cross-linking degree and structure of the[DVCPIL][PHO]catalyst resulted in a DMC yield of up to 80.6%.Furthermore,this catalyst material exhibited good stability,maintaining its catalytic activity after repeated use five times without significant *** results of this study demonstrate the potential for using alkaline poly(ionic liquid)s as a highly efficient and sustainable alternative to traditional catalysts for the transesterification synthesis of DMC.

关键词： Poly(ionic liquid) Cross-linking degree Dimethyl carbonate production Transesterification reaction Mechanism

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In-situ interfacial passivation and self-adaptability synergistically stabilizing all-solid-state lithium metal batteries

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Journal of Energy Chemistry 2024年第1期88卷 282-292,I0007页

作者： Huanhui chen Xing Cao Moujie Huang Xiangzhong ren Yubin Zhao Liang Yu Ya Liu Liubiao Zhong Yejun qiu Shenzhen Engineering Lab of Flexible Transparent Conductive Films School of Materials Science and EngineeringHarbin Institute of TechnologyShenzhen 518055GuangdongChina Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems School of Materials Science and EngineeringHarbin Institute of TechnologyShenzhen 518055GuangdongChina College of Chemistry and Environmental Engineering Shenzhen UniversityShenzhen 518060GuangdongChina School of Resource and Environmental Sciences Wuhan UniversityWuhan 430072HubeiChina

The function of solid electrolytes and the composition of solid electrolyte interphase(SEI)are highly significant for inhibiting the growth of Li ***,we report an in-situ interfacial passivation combined with self-adaptability strategy to reinforce Li_(0.33)La_(0.557)TiO_(3)(LLTO)-based solid-state ***,a functional SEI enriched with LiF/Li_(3)PO_(4) is formed by in-situ electrochemical conversion,which is greatly beneficial to improving interface compatibility and enhancing ion *** the polarized dielectric BaTiO_(3)-polyamic acid(BTO-PAA,BP)film greatly improves the Li-ion transport kinetics and homogenizes the Li *** expected,the resulting electrolyte offers considerable ionic conductivity at room temperature(4.3 x 10~(-4)S cm^(-1))and appreciable electrochemical decomposition voltage(5.23 V)after electrochemical *** Li-LiFePO_(4) batteries,it shows a high specific capacity of 153 mA h g^(-1)at 0.2C after 100 cycles and a long-term durability of 115 mA h g^(-1)at 1.0 C after 800 ***,a stable Li plating/stripping can be achieved for more than 900 h at 0.5 mA cm^(-2).The stabilization mechanisms are elucidated by ex-situ XRD,ex-situ XPS,and ex-situ FTIR techniques,and the corresponding results reveal that the interfacial passivation combined with polarization effect is an effective strategy for improving the electrochemical *** present study provides a deeper insight into the dynamic adjustment of electrode-electrolyte interfacial for solid-state lithium batteries.

关键词： Solid-state lithium batteries Composite solid electrolyte In-situ polymerization Interfacial passivation layer Self-adaptability

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The First Five Years of a Phase Theory for Complex Systems and Networks

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1728-1743页

作者： Dan Wang Wei chen Li qiu Division of Decision and Control Systems KTH Royal Institute of TechnologyStockholm 10044Sweden Department of Mechanics and Engineering Science&the State Key Laboratory for Turbulence and Complex Systems Peking UniversityBeijing 100871China Hong Kong University of Science and Technology Hong KongChinaand also with the Southern University of Science and TechnologyShenzhen 518055China

In this paper,we review the development of a phase theory for systems and networks in its first five years,represented by a trilogy:Matrix phases and their properties;The MIMO LTI system phase response,its physical interpretations,the small phase theorem,and the sectored real lemma;The synchronization of a multi-agent network using phase *** the end,we also summarize a list of ongoing research on the phase theory and speculate what will happen in the next five years.

关键词： Consensus and synchronization matrix phases multi-agent systems phase theory sectored real lemma small phase theorem.

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The joint Laplace transforms for killed diffusion occupation times

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Applied Mathematics(A Journal of Chinese Universities) 2024年第3期39卷 398-415页

作者： LI Ying-qiu chen Ye School of Mathematics and Statistics Changsha University of Science and TechnologyChangsha 410114China College of Mathematics and Computational Science Hunan University of Arts and ScienceChangde 415000China

The approach of Li and Zhou(2014)is adopted to find the Laplace transform of occupation time over interval(0,a)and joint occupation times over semi-infinite intervals(-∞,a)and(b,∞)for a time-homogeneous diffusion process up to an independent exponential time e_(q)for 0rential equations associated with the diffusion *** these results,we obtain explicit expressions on the Laplace transform of occupation time and joint occupation time for Brownian motion with drift.

关键词： time-homogeneous diffusion process occupation time joint occupation time Laplace transform Brownian motion with drift

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