scholarly journals Multi-Objective Optimization Methods Based on Artificial Neural Networks

10.5772/15113 ◽  
2011 ◽  
Author(s):  
Sara Carcangiu ◽  
Alessandra Fanni ◽  
Augusto Montisci
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Optimization Methods ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Artificial Neural

Multi-Objective Optimization of Parameters for Milling Using Evolutionary Algorithms and Artificial Neural Networks

2019 ◽  
Author(s):  
Amit Banerjee ◽  
Issam Abu-Mahfouz ◽  
AHM Esfakur Rahman
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Evolutionary Algorithms ◽  
Cost Function ◽  
Machine Learning Algorithms ◽  
Machining Parameters ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Artificial Neural

Abstract Model-based design of manufacturing processes have been gaining popularity since the advent of machine learning algorithms such as evolutionary algorithms and artificial neural networks (ANN). The problem of selecting the best machining parameters can be cast an optimization problem given a cost function and by utilizing an input-output connectionist framework using as ANNs. In this paper, we present a comparison of various evolutionary algorithms for parameter optimization of an end-milling operation based on a well-known cost function from literature. We propose a modification to the cost function for milling and include an additional objective of minimizing surface roughness and by using NSGA-II, a multi-objective optimization algorithm. We also present comparison of several population-based evolutionary search algorithms such as variants of particle swarm optimization, differential evolution and NSGA-II.

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