NDLI: An Improved genetic algorithm for the prediction of surface finish in dry turning of SS 420 materials

Content Provider	Springer Nature Link
Author	Ansalam Raj, T. G. Narayanan Namboothiri, V. N.
Copyright Year	2009
Abstract	Determination of optimal cutting parameters is one of the most important elements in any process planning of metal parts. This paper presents a development of an improved genetic algorithm (IGA) and its application to optimize the cutting parameters for predicting the surface roughness is proposed. Optimization of cutting parameters and prediction of surface roughness is concerned with a highly constrained nonlinear dynamic optimization problem that can only be fully solved by complete enumeration. The IGA incorporating a stochastic crossover technique and an artificial initial population scheme is developed to provide a faster search mechanism. The main advantage of the IGA approach is that the “curse of dimensionality” and a local optimal trap inherent in mathematical programming methods can be simultaneously overcome. The IGA equipped with an improved evolutionary direction operator and a migration operation can efficiently search and actively explore solutions. The IGA approach is applied to predict the influence of tool geometry (nose radius) and cutting parameters (feed, speed, and depth of cut) on surface roughness in dry turning of SS 420 materials conditions based on Taguchi's orthogonal array method. Additionally, the proposed algorithm was compared with the conventional genetic algorithm (CGA), and we found that the proposed IGA is more effective than previous approaches and applies the realistic machining problem more efficiently than does the conventional genetic algorithm (CGA).
Starting Page	313
Ending Page	324
Page Count	12
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	47
Issue Number	1-4
e-ISSN	14333015
Language	English
Publisher	Springer-Verlag
Publisher Date	2009-07-21
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Genetic algorithm Global optimization Improved genetic algorithm Taguchi methods Turning Surface roughness Computer-Aided Engineering (CAD, CAE) and Design Mechanical Engineering Production/Logistics Industrial and Production Engineering
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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