Article(id=1322594946742333450, tenantId=1146029695717560320, journalId=1189873562199433220, issueId=1322594807147512718, articleNumber=null, orderNo=null, doi=10.19710/J.cnki.1003-8817.20260248, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793260807928, onlineDateStr=2026-10-29, pubDate=1792425600000, pubDateStr=2026-10-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793260807928, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793260807928, creator=13701087609, updateTime=1793260807928, updator=13701087609, issue=Issue{id=1322594807147512718, tenantId=1146029695717560320, journalId=1189873562199433220, year='2026', volume='', issue='10', pageStart='1', pageEnd='72', issueExtLink='null', onlineDate='null', pubDate='1792425600000', pubDateStr='2026-10-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1793260774647, creator='13701087609', updateTime=1793261345543, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322597202086703686, tenantId=1146029695717560320, journalId=1189873562199433220, issueId=1322594807147512718, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322597202086703687, tenantId=1146029695717560320, journalId=1189873562199433220, issueId=1322594807147512718, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=54, endPage=64, ext={EN=ArticleExt(id=1322594946905911307, articleId=1322594946742333450, tenantId=1146029695717560320, journalId=1189873562199433220, language=EN, title=Research on High Efficiency Numerical Simulation and Application of Steel-Aluminum Hybrid SPR Joint, columnId=null, journalTitle=Automobile Technology & Material, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To achieve accurate and efficient prediction of mechanical response and crash failure of steel‑aluminum hybrid self‑piercing riveted joints, and support the lightweight and crashworthiness design of steel‑aluminum hybrid vehicle bodies, adaptive mesh technology is adopted to establish an SPR forming simulation model for high‑strength steel and cast aluminum, and the influence law of process parameters on joint forming quality is analyzed. A refined solid model incorporating process history is constructed through residual stress‑strain field mapping, and the joint failure mechanism is revealed combined with mechanical tests. An efficient numerical simulation model for self‑piercing riveting (SPR) is developed and calibrated, and applied to the analysis of vehicle small‑overlap crash conditions. The results show that the error of characteristic cross‑sectional dimensions from forming simulation is no more than 10%; the peak load prediction error of the model considering process history is less than 6%, and that of the efficient SPR model is below 2%. Vehicle‑level simulation can accurately reproduce joint failure and structural deformation behaviors.

, authors=Ting Li, Ruidie Zhu, Zhigao He, Jiajie Shao, Mengqi Zhang, authorsList=Ting Li, Ruidie Zhu, Zhigao He, Jiajie Shao, Mengqi Zhang, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322594948755599372, articleId=1322594946742333450, tenantId=1146029695717560320, journalId=1189873562199433220, language=CN, title=钢铝混合自冲铆接头高效数值仿真及应用研究, columnId=1322594810385515408, journalTitle=汽车工艺与材料, columnName=2026中国汽车工程学会年会优秀论文（选登）, runingTitle=null, highlight=null, articleAbstract=

为实现钢铝混合自冲铆接头力学响应与碰撞失效的精准高效预测，支撑钢铝混合车身轻量化与耐撞性设计，采用自适应网格技术建立高强钢与铸铝自冲铆成形仿真模型，分析工艺参数对接头成形质量的影响规律；通过残余应力应变场映射构建含工艺历史的精细化实体模型，结合力学试验揭示接头失效机理；开发并标定自冲铆接(SPR)高效数值仿真模型，应用于整车小偏置碰撞工况分析。结果表明，成形仿真截面特征尺寸误差不超过10%，考虑工艺历史的模型峰值载荷预测误差低于6%，SPR高效模型峰值载荷误差低于2%，整车仿真可准确复现接头失效与结构变形行为。

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李婷（1996—），女，工程师，博士学位，研究方向为汽车新材料及连接工艺失效表征与工程应用。

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李婷（1996—），女，工程师，博士学位，研究方向为汽车新材料及连接工艺失效表征与工程应用。

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