NDLI: Performance analysis of non-traditional algorithmic parameters in machining operation

Content Provider	Springer Nature Link
Author	Gayatri, R. Baskar, N.
Copyright Year	2014
Abstract	Non-traditional algorithms are the realistic models that are used for solving many complex engineering optimization problems. Machining is one of the most important and widely used manufacturing processes which rely on optimization. Optimization problems may be either constrained or unconstrained in nature. The complication factor with constrained optimization is that there is a possible existence of one or more complex constraints. These constraints must be incorporated into the problem otherwise solution is unacceptable from a practical standpoint. Non-traditional algorithms are the best way for solving the constrained optimization problems. The advantages of non-traditional techniques are its tolerant of imprecision, uncertainty, and meta-heuristics. In this work, three different non-traditional algorithms such as genetic algorithm (GA), simulated annealing (SA), and particle swarm optimization (PSO) are used in multi-pass turning machining operation to identify the set of optimal parameter values for minimizing unit production cost. The mathematical model is taken from the literature (Chen and Tsai, Int J Prod Res 34:2803–2825, 1996 and it is used for the evaluation of algorithmic parameters. The main aim of this work is to analyze the effect of algorithmic parameters of non-traditional technique in turning operation. The analyses performed are (i) sensitivity analysis in GA, PSO, and SA, (ii) analysis to find the best reproduction method in GA (iii) analysis to analyze the performance of GA, PSO, and SA by varying algorithmic parameters, and (iv) analysis to compare the results of the algorithms with the literature, and the best one is proposed.
Starting Page	443
Ending Page	460
Page Count	18
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	77
Issue Number	1-4
e-ISSN	14333015
Language	English
Publisher	Springer London
Publisher Date	2014-10-18
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Optimization Non-traditional algorithms Genetic algorithm (GA) Simulated annealing (SA) Particle swarm optimization (PSO) Machining 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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