Comparison of evolutionary techniques for the optimization of machining fixture layout under dynamic conditions

Comparison of evolutionary techniques for the optimization of machining fixture layout under... Modern manufacturing industries are striving to improve the machining accuracy and productivity to reduce the rejection rate and unit cost of the machined parts. The properly designed fixture layout enables the designer to minimize the vibration so that the requisite machining accuracy can be achieved. During machining, especially in end milling, the intermittent engagement of multitooth cutter induces vibration on the workpiece. When the excitation frequency of multitooth cutter coincides with any one of the natural frequencies of the fixtured workpiece, it leads to the condition of resonance. The vibration increases under these circumstances, which degrades the machining accuracy and surface finish of the machined workpiece. Hence, the issues related to the design of fixture layout are to be addressed by recognizing the dynamic behavior of the fixture–workpiece system. In this research paper, finite element method is utilized to simulate the end milling operation and to determine the natural frequency of the workpiece. The main focus is to maximize the difference between natural frequency of the fixtured workpiece and excitation frequency of the cutter to minimize the vibration on the workpiece. Two different evolutionary techniques genetic algorithm and particle swarm optimization are employed to maximize the difference between these frequencies by optimizing the machining fixture layout. The performance of genetic algorithm and particle swarm optimization on the fixture layout optimization is compared. The comparison of results concludes that particle swarm optimization is the most appropriate approach than the genetic algorithm in achieving the better results. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science SAGE

Comparison of evolutionary techniques for the optimization of machining fixture layout under dynamic conditions

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Publisher
SAGE Publications
Copyright
© IMechE 2017
ISSN
0954-4062
eISSN
2041-2983
D.O.I.
10.1177/0954406217718219
Publisher site
See Article on Publisher Site

Abstract

Modern manufacturing industries are striving to improve the machining accuracy and productivity to reduce the rejection rate and unit cost of the machined parts. The properly designed fixture layout enables the designer to minimize the vibration so that the requisite machining accuracy can be achieved. During machining, especially in end milling, the intermittent engagement of multitooth cutter induces vibration on the workpiece. When the excitation frequency of multitooth cutter coincides with any one of the natural frequencies of the fixtured workpiece, it leads to the condition of resonance. The vibration increases under these circumstances, which degrades the machining accuracy and surface finish of the machined workpiece. Hence, the issues related to the design of fixture layout are to be addressed by recognizing the dynamic behavior of the fixture–workpiece system. In this research paper, finite element method is utilized to simulate the end milling operation and to determine the natural frequency of the workpiece. The main focus is to maximize the difference between natural frequency of the fixtured workpiece and excitation frequency of the cutter to minimize the vibration on the workpiece. Two different evolutionary techniques genetic algorithm and particle swarm optimization are employed to maximize the difference between these frequencies by optimizing the machining fixture layout. The performance of genetic algorithm and particle swarm optimization on the fixture layout optimization is compared. The comparison of results concludes that particle swarm optimization is the most appropriate approach than the genetic algorithm in achieving the better results.

Journal

Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering ScienceSAGE

Published: Jun 1, 2018

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