Machining process optimization not only remains an ongoing activity but is also becoming increasingly important in industry in the drive for reduced cycle time and agile manufacturing. To achieve these goals, one of the considerations is by optimizing the machining process parameters such as the cutting speed, feed rate, tool nose radius, tool rake angle, depth of cut, and cutting environment. Recently, alternative to conventional techniques employed for machining optimization the other techniques include geometric programming, geometric plus linear programming, Non-Linear Programming, goal programming, sequential unconstrained minimization technique and dynamic programming etc. Eleven techniques are considered, namely genetic algorithm (GA), response surface methodology (RSM), simulated annealing (SA), scatter search technique (SS), multiple regression analysis (MRA), particle swarm optimization (PSO), fuzzy logic, Taugchi’s technique, utility concept, artificial neural network (ANN) and ant colony optimization (ACO). This paper provides an overview of these important soft computing techniques and highlights the progress made in this area of modeling of material turning processes.
Surinder Kumar, Meenu, P. S. Satsangi
Machining optimization, genetic algorithm, simulated annealing, scatter search technique, multiple regression analysis, fuzzy logic, Taguchi technique, artificial neural network, utility concept, response surface methodology, ant colony optimization and particle swarm optimization.
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|Published in :
||Volume 2 | Issue 6 | November-December - 2016
|Date of Publication
Cite This Article
Surinder Kumar, Meenu, P. S. Satsangi, "Optimization of Material Turning Operation - A State-of-Art Research Review", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 6, pp.381-390, November-December-2016.
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