<?xml version='1.0' encoding='UTF-8'?><metadata xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns="http://dublincore.org/documents/dcmi-terms/"><dcterms:title>Data-Driven Multi-Objective Optimization of Conformal Cooling Channels for Energy-Efficient Injection Molding</dcterms:title><dcterms:identifier>https://doi.org/10.34622/datarepositorium/HUBDNN</dcterms:identifier><dcterms:creator>Pereira, Carlos</dcterms:creator><dcterms:creator>Gaspar-Cunha, António</dcterms:creator><dcterms:publisher>Repositório de Dados da Universidade do Minho</dcterms:publisher><dcterms:issued>2026-02-09</dcterms:issued><dcterms:modified>2026-02-09T09:42:46Z</dcterms:modified><dcterms:description>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</dcterms:description><dcterms:subject>Engineering</dcterms:subject><dcterms:contributor>Pereira, Carlos</dcterms:contributor><dcterms:dateSubmitted>2025-11-20</dcterms:dateSubmitted><dcterms:license>CC0</dcterms:license><dcterms:rights>CC0 Waiver</dcterms:rights></metadata>