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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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Correlation between magnetic resonance T2 image signal intensity ratio and cell apoptosis in a rabbit spinal cord cervical myelopathy model

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Chinese Medical Journal 2014年第2期127卷 305-313页

作者： Ma Lei Zhang di chen wei Shen Yong Zhang Yingze ding Wenyuan Zhang wei Wang Linfeng Yang Dalong Department of Spinal Surgery Third Hospital of Hebei Medical University Orthopaedic Biomechanics Laborafory of Hebei Province Shijiazhuang Hebei 050051 China

Background Cervical spondylotic myelopathy （CSM） is a common cause of disability in elderly *** studies have shown that spinal cord cell apoptosis due to spinal cord compression plays an important role in the pathology of *** changes in magnetic resonance imaging （MRI） T2 signal intensity ratio （SIR） are considered to be an indicator of CSM,little information is published supporting the correlation between changes in MRI signal and pathological *** study aims to testify the correlation between MRI T2 SIR changes and cell apoptosis using a CSM animal *** Forty-eight rabbits were randomly assigned to four groups：one control group and three experimental chronic compression groups,with each group containing 12 *** compression of the cervical spinal cord was implemented in the experimental groups by implanting a screw in the C3 *** control group underwent sham *** groups were observed for 3,6,or 9 months after *** T2-weighted SIR Tarlov motor scores and cortical somatosensory-evoked potentials （CSEPs） were periodically *** each time point,rabbits from one group were sacrificed to determine the level of apoptosis by histology （n=6） and Western blotting （n=6）.Results Tarlov motor scores in the compression groups were lower at all time points than the control group scores,with the lowest score at 9 months （P ＜0.001）.Electrophysiological testing showed a significantly prolonged latency in CSEP in the compression groups compared with the control *** rabbits in the compression groups showed higher MRI T2 SIR in the injury epicenter compared with controls,and higher SIR was also found at 9 months compared with 3 or 6 *** analysis showed significant apoptosis in the spinal cord tissue in the compression groups,but not in the control *** were significant differences in apoptosis degree over time （P ＜0.001）,with the 9-month group displaying the most

关键词： cervical myelopathy model apoptosis magnetic resonance imaging T2 signal intensity ratio compression

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公平感对供应链成员定价决策影响的研究

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管理科学学报 2017年第7期20卷 115-126页

作者：刘威志李娟张迪陈炜南京大学工程管理学院南京210093

本文研究公平感对由一个供应商和一个零售商组成的二级供应链中的定价决策的影响,其中供应商决定批发价格,零售商在接受供应商批发价格合同之后决定零售价格,市场需求受到零售价格的线性影响.采用管理实验方法得出,首先,供应商的批发价... 详细信息

本文研究公平感对由一个供应商和一个零售商组成的二级供应链中的定价决策的影响,其中供应商决定批发价格,零售商在接受供应商批发价格合同之后决定零售价格,市场需求受到零售价格的线性影响.采用管理实验方法得出,首先,供应商的批发价格和零售商的零售价格均分别低于完全理性假设下的均衡解;供应商是利他性的,即,乐于看到零售商收益的增加,并且,供应商认为零售商是完全理性的,即零售商的决策目标是最大化自身收益;然而,零售商却是刻毒性的,即乐于看到供应商收益的减少.其次,批发价格的变异度大于零售价格的,即供应商决策的难度大于零售商的.给管理者的启示是:供应商应考虑零售商的刻毒性的特征,降低批发价格,以提高零售商接受供应商所提批发价格的概率;此外,还应该为供应商提供辅助决策手段,以降低批发价格的变异度。

关键词：公平感变异度管理实验

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Brain region-specific roles of brain-derived neurotrophic factor in social stress-induced depressive-like behavior

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Neural Regeneration Research 2025年第1期20卷 159-173页

作者： Man Han Deyang Zeng wei Tan Xingxing chen Shuyuan Bai Qiong Wu Yushan chen Zhen wei Yufei Mei Yan Zeng Brain Science and Advanced Technology Institute Wuhan University of Science and TechnologyWuhanHubei ProvinceChina Geriatric Hospital Affiliated to Wuhan University of Science and Technology WuhanHubei ProvinceChina School of Public Health Wuhan University of Science and TechnologyWuhanHubei ProvinceChina

Brain-derived neurotrophic factor is a key factor in stress adaptation and avoidance of a social stress behavioral *** studies have shown that brain-derived neurotrophic factor expression in stressed mice is brain region–specific,particularly involving the corticolimbic system,including the ventral tegmental area,nucleus accumbens,prefrontal cortex,amygdala,and *** how brain-derived neurotrophic factor participates in stress processing in different brain regions will deepen our understanding of social stress *** this review,we discuss the expression and regulation of brain-derived neurotrophic factor in stress-sensitive brain regions closely related to the pathophysiology of *** focused on associated molecular pathways and neural circuits,with special attention to the brain-derived neurotrophic factor–tropomyosin receptor kinase B signaling pathway and the ventral tegmental area–nucleus accumbens dopamine *** determined that stress-induced alterations in brain-derived neurotrophic factor levels are likely related to the nature,severity,and duration of stress,especially in the above-mentioned brain regions of the corticolimbic ***,BDNF might be a biological indicator regulating stress-related processes in various brain regions.

关键词： amygdala chronic mild stress chronic social defeat stress corticolimbic system depression hippocampus medial prefrontal cortex nucleus accumbens social stress models ventral tegmental area

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Role of iron ore in enhancing gasification of iron coke:Structural evolution,influence mechanism and kinetic analysis

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International Journal of Minerals,Metallurgy and Materials 2025年第1期32卷 58-69页

作者： Jie Wang wei Wang Xuheng chen Junfang Bao Qiuyue Hao Heng Zheng Runsheng Xu School of Chemistry and Materials Science Hubei Engineering UniversityXiaogan 432000China Key Laboratory for Ferrous Metallurgy and Resources Utilization of Ministry of Education Wuhan University of Science and TechnologyWuhan 430081China Institue of Ironmaking Technology Baosteel Central Research InstituteShanghai 201999China State Key Laboratory of Advanced Metallurgy University of Science and Technology BeijingBeijing 100083China

The utilization of iron coke provides a green pathway for low-carbon *** uncover the influence mechanism of iron ore on the behavior and kinetics of iron coke gasification,the effect of iron ore on the microstructure of iron coke was ***,a comparative study of the gasification reactions between iron coke and coke was conducted through non-isothermal thermogravimetric *** findings indicate that compared to coke,iron coke exhibits an augmentation in micropores and specific surface area,and the micropores further extend and *** provides more adsorption sites for CO_(2) molecules during the gasification process,resulting in a reduction in the initial gasification temperature of iron *** the heating rate in non-isothermal gasification can enhance the reactivity of iron *** metallic iron reduced from iron ore is embedded in the carbon matrix,reducing the orderliness of the carbon structure,which is primarily responsible for the heightened reactivity of the carbon *** kinetic study indicates that the random pore model can effectively represent the gasification process of iron coke due to its rich pore ***,as the proportion of iron ore increases,the activation energy for the carbon gasification gradually decreases,from 246.2 kJ/mol for coke to 192.5 kJ/mol for iron coke 15wt%.

关键词： low-carbon ironmaking iron coke gasification structural evolution kinetic model

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论服饰作为社会交互工具的功能和意义

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郑州轻工业学院学报（社会科学版） 2010年第4期11卷 52-54页

作者：陈巍娣广东工业大学艺术设计学院广东广州510090

服饰在社会发展过程中被赋予越来越多的社会意义,成为人们社会交往的重要工具。运用马斯洛的需求层次理论,可以将人类着装行为目的分为基本生理需求、安全需求、社交和归属感的需求、尊重的需求和自我实现的需求五个层次。服饰企业、设... 详细信息

服饰在社会发展过程中被赋予越来越多的社会意义,成为人们社会交往的重要工具。运用马斯洛的需求层次理论,可以将人类着装行为目的分为基本生理需求、安全需求、社交和归属感的需求、尊重的需求和自我实现的需求五个层次。服饰企业、设计师及销售商应对服饰消费者与他人的交互性给予高度重视,力求满足他们以服饰提升自身层次的需求。

关键词：服饰社会交互工具层次需求论

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浅谈科技服装

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广东工业大学学报（社会科学版） 2010年第3期10卷 78-80,88页

作者：陈巍娣广东工业大学艺术设计学院广东广州510090

将诸如先进纺织材料以及照相机、播放器、计算机芯片、传感器等各种与数字化相关科技产品,通过附加、编织等结合方式与服装融为一体的科技服装,代表了服装设计与服装行业未来科技发展的趋势。由此形成的服装内建环境和所使用的普适计算... 详细信息

将诸如先进纺织材料以及照相机、播放器、计算机芯片、传感器等各种与数字化相关科技产品,通过附加、编织等结合方式与服装融为一体的科技服装,代表了服装设计与服装行业未来科技发展的趋势。由此形成的服装内建环境和所使用的普适计算模式,将为着装者带来更多的舒适、便利和全新的功能。源于西方先进国家的这一科技服装潮流,有待我国服装设计师、社会学者、科研人员和企业家关注、借鉴,并开展探索与努力。

关键词：科技服装科技服装材料内建环境普适计算发展现状发展趋势

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Dative B–N bonds weaven toward two-dimensional purely organic materials for single crystals

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Science China Chemistry 2025年第01期 12-14页

作者： wei-Lei Zhou Siwei Wang Yong chen College of Chemistry and Materials Science Inner Mongolia Key Laboratory of Chemistry for Nature Products and Synthesis for Functional Molecules Innovation Team of Optical Functional Molecular Devices Inner Mongolia Minzu University College of Chemistry Nankai University

Molecular topology is one of the important topics to study and understand the relationship between molecular structure and properties [1]. Particularly, two-dimensional (2D) materials with extremely high surface area and ultra-thin morphology have attracted considerable attention due to their unexpected properties in materials and chemistry [2,3].

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