Standard Deviation Method Based PSO: An Instigated Approach to Optimize Multi-Objective Manufacturing Process Parameters

Standard Deviation Method Based PSO: An Instigated Approach to Optimize Multi-Objective Manufacturing Process Parameters

Arindam Majumder (Mechanical Engineering Department, National Institute of Technology Agartala, Agartala, India) and Abhishek Majumder (Computer Science and Engineering Department, Tripura University (A Central University), Agartala, India)
Copyright: © 2016 |Pages: 21
DOI: 10.4018/IJSIR.2016040102
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Abstract

Nowadays, optimization of process parameters in manufacturing process deals with a number of objectives. However, the optimization of such process becomes more complex if selected attributes are conflicting in nature. Therefore, to overcome this problem in this study a SDM based PSO algorithm is proposed for optimizing the manufacturing process having multi attribute. In this proposed approach the SDM is used to convert multi attributes into single attribute, named as multi performance index, while the optimal value of this multi performance index is predicted by PSO. Finally, three instances related to optimization of advanced manufacturing process parameters are solved by the proposed approach and are compared with the results of the other established optimization techniques such as Desirability based RSM, SDM-GA and SDM-CACO. From the comparison it has been revealed that the proposed approach performs better as compare to the existing approaches.
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Introduction

In today’s world manufacturing process become more complex due to the drastic advancement in technology since world-war II. Modern manufacturing processes now-a-days include various sophistication to fulfill the growing needs of quality and safety. Most of the heavy industries employed highly automated and computer controlled manufacturing units in order to meet the demands of ever changing comparative market. However the increase in technological sophistication and automation makes the manufacturing operations economically unviable. Thus, the success of any manufacturing process lies in selection of optimal process parameters which can reduce the manufacturing cost and increase the productivity without compromising product quality. An efficient way to solve this problem is to develop and implement an approach in manufacturing process for optimizing all the attributes (product quality, manufacturing cost and productivity) simultaneously.

In the previous days a lot of investigations were carried out to obtain the best operating conditions of manufacturing systems by implementing various optimization techniques. The optimization techniques typically used by these researchers are: Simulated Annealing, Threshold Acceptance Algorithm, Genetic Algorithm, Particle Swarm Optimization, Bat Algorithm, Ant Colony Algorithm, Artificial Bee Colony Algorithm etc. All these methods mainly based on biological, molecular or neurological phenomenon that imitate the metaphor of biological evaluation or social behavior of species. These algorithms are comparatively new and gaining popularity due to certain properties, which the conventional deterministic techniques do not have.

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