Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/114077
DC FieldValueLanguage
dc.contributorDepartment of Industrial and System Engineering-
dc.contributorDepartment of Mechanical Engineering-
dc.creatorMa, J-
dc.creatorTang, X-
dc.creatorHou, Y-
dc.creatorLi, H-
dc.creatorLin, J-
dc.creatorFu, MW-
dc.date.accessioned2025-07-11T09:11:26Z-
dc.date.available2025-07-11T09:11:26Z-
dc.identifier.issn0890-6955-
dc.identifier.urihttp://hdl.handle.net/10397/114077-
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectDefect classificationen_US
dc.subjectDefect control and avoidanceen_US
dc.subjectDefect formation mechanismsen_US
dc.subjectDefect predictionen_US
dc.subjectForming defectsen_US
dc.subjectMetal-formingen_US
dc.titleDefects in metal-forming : formation mechanism, prediction and avoidanceen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume207-
dc.identifier.doi10.1016/j.ijmachtools.2025.104268-
dcterms.abstractDefects in metal-forming create numerous bottleneck issues related to product quality, properties and performance, productivity, production cost, and sustainability. Effectively addressing defect issues in the up-front design process via prediction and avoidance of defect formation is the most critical and challenging issue in metal-forming based product development. In this paper, vital insights into defect classification, formation mechanisms, modelling/prediction, and avoidance principles and strategies in metal-forming are orchestrated and articulated. First, almost all the potential defects in metal-forming are exemplified and classified into three categories, viz., stress-induced, flow-induced, and microstructure-related defects. For each defect category, its influencing factors, formation mechanisms, and analysis approaches are delineated. Additionally, the countermeasures are articulated from the aspects of defect identification, control, avoidance or elimination by employing different state-of-the-art techniques, including in-process sensing/monitoring/detection, data-based modelling and online adaptive control. Finally, perspective insights into defect analysis, modelling/prediction, and avoidance are orchestrated and presented, focusing on innovative process developments, real-time in-process monitoring, physics-informed and data-driven through-process modelling, and strategies for intelligent and sustainable manufacturing.-
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationInternational journal of machine tools and manufacture, Apr. 2025, v. 207, 104268-
dcterms.isPartOfInternational journal of machine tools and manufacture-
dcterms.issued2025-04-
dc.identifier.scopus2-s2.0-105001158487-
dc.identifier.eissn1879-2170-
dc.identifier.artn104268-
dc.description.validate202507 bcch-
dc.identifier.FolderNumbera3852ben_US
dc.identifier.SubFormID51419en_US
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextHong Kong Polytechnic Universityen_US
dc.description.fundingTextFundamental Research Funds for the Central Universities, Chinaen_US
dc.description.fundingTextNational Natural Science Foundation of Chinaen_US
dc.description.fundingTextAlexander von Humboldt Foundation, Germanyen_US
dc.description.pubStatusPublisheden_US
dc.date.embargo2027-04-30en_US
dc.description.oaCategoryGreen (AAM)en_US
Appears in Collections:Journal/Magazine Article
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Embargo End Date 2027-04-30
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