Data-Driven Multi-Objective Optimization of Conformal Cooling Channels for Energy-Efficient Injection Molding
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https://datarepositorium.uminho.pt/citation?persistentId=doi:10.34622/datarepositorium/HUBDNN
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资源简介：
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
提供机构：
Repositório de Dados da Universidade do Minho
创建时间：
2026-02-09
