Article(id=1309475151444533799, tenantId=1146029695717560320, journalId=1189873562199433220, issueId=1309475137360060880, articleNumber=null, orderNo=null, doi=10.19710/J.cnki.1003-8817.20250293, 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=1790132804975, onlineDateStr=2026-09-23, pubDate=1781884800000, pubDateStr=2026-06-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790132804975, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790132804975, creator=13701087609, updateTime=1790132804975, updator=13701087609, issue=Issue{id=1309475137360060880, tenantId=1146029695717560320, journalId=1189873562199433220, year='2026', volume='', issue='6', pageStart='1', pageEnd='72', issueExtLink='null', onlineDate='null', pubDate='1781884800000', pubDateStr='2026-06-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1790132801618, creator='13701087609', updateTime=1790132983936, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309475902124286252, tenantId=1146029695717560320, journalId=1189873562199433220, issueId=1309475137360060880, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309475902124286253, tenantId=1146029695717560320, journalId=1189873562199433220, issueId=1309475137360060880, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=31, endPage=39, ext={EN=ArticleExt(id=1309475152266617384, articleId=1309475151444533799, tenantId=1146029695717560320, journalId=1189873562199433220, language=EN, title=A Response Surface Approach to Optimizing Welding Parameters for Power Battery Anodes, columnId=null, journalTitle=Automobile Technology & Material, columnName=null, runingTitle=null, highlight=null, articleAbstract=

The welding quality of the positive electrode in lithium-ion batteries directly affects internal resistance, safety, and cycle life. To address issues such as insufficient strength, overwelding, and poor consistency caused by overly broad welding parameter windows (amplitude, energy, pressure) in current production, a Design of Experiments (DOE) method is introduced for process optimization. Initially, through a full factorial experiment with three factors and two levels, it is found that the initial parameter range (amplitude 30~60 μm, energy 40~100 J, pressure 30~60 MPa) is generally in an "over-welded" state, with amplitude identified amplitude identified as the most critical influencing factor. Based on this, the study further adopts the Box-Behnken response surface design to construct quantitative models for tensile force, peel force, and welding effectiveness within a narrowed parameter space. The results indicate that within lower parameter intervals, the contribution of energy and amplitude to welding quality shifts from negative to positive. Through multi-response variable optimization, the optimal process parameter combination is determined as amplitude 36 μm, energy 50 J, and pressure 40 MPa. Verification experiments confirms that under this parameter combination, the welded joints exhibit excellent mechanical properties, ideal foil adhesion rates, and significantly improved process stability. This provides a reliable basis for achieving refined control and mass production consistency in positive electrode welding.

, authors=Xiaoxiong Hu¹, Jun Wang², Lirong Liu², authorsList=Xiaoxiong Hu, Jun Wang, Lirong Liu, 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=1309475153009009193, articleId=1309475151444533799, tenantId=1146029695717560320, journalId=1189873562199433220, language=CN, title=动力电池负极集流体的焊接参数响应面优化方法, columnId=1190284220841955836, journalTitle=汽车工艺与材料, columnName=生产现场, runingTitle=null, highlight=null, articleAbstract=

锂离子电池的正极焊接质量直接影响电池的内阻、安全性与循环寿命，为解决当前生产中因焊接参数（振幅、能量、压力）窗口宽泛导致的强度不足、过焊及一致性差等问题，引入试验设计（DOE）方法进行工艺优化。首先通过三因子两水平全因子试验，发现初始参数范围（振幅30~60 μm，能量40~100 J，压力30~60 MPa）已普遍处于过焊状态，振幅为最关键的影响因素。基于此，采用Box-Behnken响应面设计，在缩小的参数空间内构建了拉力、撕力与焊接效果的量化模型。结果表明，在较低参数区间内，能量与振幅对焊接质量的贡献由负转正。通过多响应变量优化，确定了最佳工艺参数组合为振幅36 μm、能量50 J、压力40 MPa。验证试验表明，该参数组合下的焊接接头力学性能优异、粘箔率理想且工艺稳定性显著提升，为实现负极集流体焊接的精细化控制与量产一致性提供了可靠依据。

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胡晓雄（1988—），男，高级工程师，硕士学位，研究方向为新能源动力电池系统设计与研发。

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胡晓雄（1988—），男，高级工程师，硕士学位，研究方向为新能源动力电池系统设计与研发。

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