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    <identifier identifierType="DOI">10.34622/datarepositorium/HUBDNN</identifier>
    <creators><creator><creatorName>Pereira, Carlos</creatorName><affiliation>((IPC - Instituto de Polímeros e Compósitos/DEP))</affiliation></creator><creator><creatorName>Gaspar-Cunha, António</creatorName><nameIdentifier schemeURI="https://orcid.org/" nameIdentifierScheme="ORCID">0000-0001-7777-7625</nameIdentifier><affiliation>((IPC - Instituto de Polímeros e Compósitos/DEP))</affiliation></creator></creators>
    <titles>
        <title>Data-Driven Multi-Objective Optimization of Conformal Cooling Channels for Energy-Efficient Injection Molding</title>
    </titles>
    <publisher>Repositório de Dados da Universidade do Minho</publisher>
    <publicationYear>2026</publicationYear>
    <resourceType resourceTypeGeneral="Dataset"/>
    
    <descriptions>
        <description descriptionType="Abstract">The article presents an integrated methodology combining NL-PCA, neural-network surrogate models, and evolutionary algorithms to optimize conformal cooling channel geometries and processing conditions in injection molding, reducing cycle time, energy consumption, and part defects</description>
    </descriptions>
    <contributors><contributor contributorType="ContactPerson"><contributorName>Gaspar-Cunha, António</contributorName><affiliation>((IPC - Instituto de Polímeros e Compósitos/DEP))</affiliation></contributor></contributors>
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