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Tritiya A.R. Arungpadang
Department of Mechanical Engineering, Faculty of Engineering, Universitas Sam Ratulangi, Manado 95115, INDONESIA
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Mechanical Engineering

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Dual Response Approach in Process Capability based on Hybrid Neural Network-Genetic Algorithms Tritiya A.R. Arungpadang; Stenly Tangkuman; Lily S. Patras
Journal of Sustainable Engineering: Proceedings Series Vol 1 No 1 (2019)
Publisher : Fakultas Teknik Universitas Sam Ratulangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35793/joseps.v1i1.16

Abstract

Process capability has long been recognized as an important performance measure to prove how well the process meets the requirements. Process capability can be improved by applying dual response approach, to determine optimal input factors. Using of artificial intelligence can optimize the prediction of the best input combination with a limited number of experiments. This study proposes an alternatives procedure using a dual response approach and artificial intelligence. One of the most common robust design models has been formulated to minimize variability while maintaining the mean on the desired target. A study case was selected to implement the proposed approach and compare it with conventional optimization models to show the improvement in procedures.
Co-Authors Lily S. Patras Stenly Tangkuman