Development of Hybrid Artificial Neural Network–Particle Swarm Optimization Model and Comparison of Genetic and Particle Swarm Algorithms for Optimization of Machining Fixture Layout

In this research paper, a methodology is proposed by combining taguchi’s parametric design, hybrid artificial neural network–particle swarm optimization (ANN–PSO) and evolutionary techniques to optimize the fixture layout by minimizing the maximum workpiece deformation on a 2D fixture workpiece syst...

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Veröffentlicht in:	International journal of precision engineering and manufacturing Jg. 23; H. 12; S. 1411 - 1430
Hauptverfasser:	Ramesh, M., Sundararaman, K. A., Sabareeswaran, M., Srinivasan, R.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Seoul Korean Society for Precision Engineering 01.12.2022 Springer Nature B.V
Schlagworte:	Artificial neural networks Deformation End milling Engineering Finite element method Genetic algorithms Industrial and Production Engineering Layouts Materials Science Neural networks Optimization Optimization models Particle swarm optimization Regular Paper Response surface methodology Scientific papers Workpieces Finite element method Evolutionary techniques Artificial neural network Genetic algorithm Particle swarm optimization Fixture layout optimization
ISSN:	2234-7593, 2005-4602
Online-Zugang:	Volltext
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