Journal of the Korean Society of Manufacturing Process Engineers (한국기계가공학회지)

The Korean Society of Manufacturing Process Engineers (한국기계가공학회)
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Application of Ant Colony Optimization and Particle Swarm Optimization for Neural Network Model of Machining Process

절삭가공의 Neural Network 모델을 위한 ACO 및 PSO의 응용

  • Oh, Soo-Cheol (Department of Systems Management & Engineering, Pukyong National University)
  • 오수철 (부경대학교 시스템경영공학부)
  • Received : 2019.06.26
  • Accepted : 2019.07.12
  • Published : 2019.09.30
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Abstract

Turning, a main machining process, is a widespread process in metal cutting industries. Many researchers have investigated the effects of process parameters on the machining process. In the turning process, input variables including cutting speed, feed, and depth of cut are generally used. Surface roughness and electric current consumption are used as output variables in this study. We construct a simulation model for the turning process using a neural network, which predicts the output values based on input values. In the neural network, obtaining the appropriate set of weights, which is called training, is crucial. In general, back propagation (BP) is widely used for training. In this study, techniques such as ant colony optimization (ACO) and particle swarm optimization (PSO) as well as BP were used to obtain the weights in the neural network. Particularly, two combined techniques of ACO_BP and PSO_BP were utilized for training the neural network. Finally, the performances of the two techniques are compared with each other.

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References

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