NDLI: Hard turning behavior improvement using NSGA-II and PSO-NN hybrid model

Content Provider	Springer Nature Link
Author	Bouacha, Khaider Terrab, Asma
Copyright Year	2016
Abstract	The present work concerns an investigation and optimization of hard turning behavior of AISI 52100 bearing steel with CBN tool. The combined effects of the process parameters (cutting speed, feed rate, depth of cut, cutting time, and workpiece hardness) on performance characteristics (tool wear, surface roughness, and cutting forces) are investigated through the analysis of variance (ANOVA). Also, the relationship between process parameters and performance characteristics through the artificial neural networks and response surface methodologies are modeled. Moreover, an attempt has been made to find the best process parameters combination that optimizes simultaneously the performance characteristics. For this purpose, the particle swarm optimization-based neural network (PSO-NN) hybrid model and the non-dominated sorting genetic algorithm (NSGA-II)-based RSM models are proposed and compared for their performances. The results show that the workpiece hardness strongly influences the cutting forces, and it plays a dominant role in the tool wear and the surface roughness variations as compared to other process parameters. The depth of cut affects strongly the cutting forces. The cutting time has a considerable effect on all performance characteristics. They show, also, that the artificial neural network (ANN) model estimates the performance characteristics with high accuracy as compared to the RSM models. Moreover, it was found that NSGA II and PSO-NN approaches performed efficiently and they predicted near similar results. Furthermore, the NSGA-II exhibits better performance when compared to the PSO-NN methodology. However, the PSO-NN has been shown to outperform the NSGA-II methodology in computational time.
Starting Page	3527
Ending Page	3546
Page Count	20
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	86
Issue Number	9-12
e-ISSN	14333015
Language	English
Publisher	Springer London
Publisher Date	2016-02-15
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	ANN PSO NSGA-II Hard turning Tool wear Surface roughness Industrial and Production Engineering Production Mechanical Engineering Computer-Aided Engineering (CAD, CAE) and Design
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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