Optimization of a Wear Property of Die Cast AZ91D Components via a Neural Network

Autor:	Yung-Kuang Yang, Jeong-Lian Wen, Jie-Ren Shie
Rok vydání:	2009
Předmět:	Materials science Artificial neural network Scanning electron microscope Mechanical Engineering Delamination Mechanical engineering medicine.disease_cause Die casting Industrial and Manufacturing Engineering Die (integrated circuit) Mechanics of Materials Mold Slurry medicine General Materials Science Sequential quadratic programming
Zdroj:	Materials and Manufacturing Processes. 24:400-408
ISSN:	1532-2475 1042-6914
DOI:	10.1080/10426910802714274
Popis:	This study integrated a trained general regression neural network (GRNN) and a sequential quadratic programming (SQP) method to determine an optimal parameter setting for a die casting process of AZ91D. Nine experiments were prepared under different die casting processes by selecting slurry pressure, the fusion slurry velocity and the mold temperature as three controlled parameters and the wear mass loss as a quality target. A field-emission scanning electron microscope (FE-SEM) was applied to realize wear mechanisms and AZ91D components with a low wear mass loss showed a low friction coefficient as well as small scratching marks and delamination on the worn surfaces.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b3b0c55105261d207a3c6b7a0a1f7727 https://doi.org/10.1080/10426910802714274 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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