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

  • 제14권3호
  • /
  • Pages.65-73
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  • 2015
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  • 1598-6721(pISSN)
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  • 2288-0771(eISSN)
한국기계가공학회 (The Korean Society of Manufacturing Process Engineers)
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BP와 PSO형 신경회로망을 이용한 선삭작업에서의 표면조도와 전류소모의 예측

Prediction of Surface Roughness and Electric Current Consumption in Turning Operation using Neural Network with Back Propagation and Particle Swarm Optimization

  • ;
  • 오수철 (부경대학교 시스템경영공학부)
  • Punuhsingon, Charles S.C (Department of Systems Management & Engineering, Graduate School, Pukyong National University) ;
  • Oh, Soo-Cheol (Department of Systems Management & Engineering, Pukyong National University)
  • 투고 : 2015.03.13
  • 심사 : 2015.04.07
  • 발행 : 2015.06.30
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초록

This paper presents a method of predicting the machining parameters on the turning process of low carbon steel using a neural network with back propagation (BP) and particle swarm optimization (PSO). Cutting speed, feed rate, and depth of cut are used as input variables, while surface roughness and electric current consumption are used as output variables. The data from experiments are used to train the neural network that uses BP and PSO to update the weights in the neural network. After training, the neural network model is run using test data, and the results using BP and PSO are compared with each other.

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참고문헌

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