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

  • 제16권6호
  • /
  • Pages.125-132
  • /
  • 2017
  • /
  • 1598-6721(pISSN)
  • /
  • 2288-0771(eISSN)
한국기계가공학회 (The Korean Society of Manufacturing Process Engineers)
권호 표지
DOI QR코드

DOI QR Code

ANN을 이용한 절삭성능의 예측과 ACO를 이용한 훈련

Prediction of Machining Performance using ANN and Training using ACO

  • 오수철 (부경대학교 시스템경영공학부)
  • Oh, Soo-Cheol (Department of Systems Management & Engineering, Pukyong National University)
  • 투고 : 2017.08.18
  • 심사 : 2017.10.10
  • 발행 : 2017.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Generally, in machining operations, the required machining performance can be obtained by properly combining several machining parameters properly. In this research, we construct a simulation model, which that predicts the relationship between the input variables and output variables in the turning operation. Input variables necessary for the turning operation include cutting speed, feed, and depth of cut. Surface roughness and electrical current consumption are used as the output variables. To construct the simulation model, an Artificial Neural Network (ANN) is employed. With theIn ANN, training is necessary to find appropriate weights, and the Ant Colony Optimization (ACO) technique is used as a training tool. EspeciallyIn particular, for the continuous domain, ACOR is adopted and athe related algorithm is developed. Finally, the effects of the algorithm on the results are identified and analyzsed.

키워드

참고문헌

  1. Ahilan, et al., "Modeling and prediction of machining quality in CNC turning process using intelligent hybrid decision making tools", Applied Soft Computing, Vol. 13, No. 3, 1543-1551, 2013. https://doi.org/10.1016/j.asoc.2012.03.071
  2. Charles, S. C., Punuhsingon, and Oh, S-C., "Prediction of Surface Roughness and Electric Current Consumption in Turning Operation using Neural Network with Back Propagation and Particle Swarm Optimization", Journal of the Korean Society of Manufacturing Process Engineers, Vol. 14, No. 3, 65-73, 2015. https://doi.org/10.14775/ksmpe.2015.14.3.065
  3. Dorigo, M., Maniezzo, V. and Colorni, A., "Ant System: Optimization by a colony of cooperating agents," IEEE Transactions on Systems, Man, and Cybernetics, Part B(Cybernetics), Vol. 26, No. 1, 29-41, 1996. https://doi.org/10.1109/3477.484436
  4. Ezilarasan et al., "An experimental analysis and measurement of process performances in machining of nimonic C-263 super alloy", Measurement, Vol. 46, No. 1, 185-199, 2013. https://doi.org/10.1016/j.measurement.2012.06.006
  5. Gen, M. and Cheng, R., Genetic algorithms & Engineering Design, John Wiley & Sons, 1997.
  6. Pandian, A., "Training neural networks with ant colony optimization," Master thesis, California State University, spring 2013.
  7. Socha, K. and Dorigo, M., "Ant colony optimization for continuous domains", European journal of operational research, Vol. 185, No. 3, 1155-1173, 2008. https://doi.org/10.1016/j.ejor.2006.06.046
  8. Socha, K. and Blum, C., "An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training", Neural Computing and Applications, Vol. 16, No. 3, 235-247, 2007. https://doi.org/10.1007/s00521-007-0084-z
  9. Wasserman, P. D., Neural Computing: Theory and Practice, Van Nostrand Reinhold, 1989.

피인용 문헌

  1. Application of Open Source, Big Data Platform to Optimal Energy Harvester Design vol.17, pp.2, 2018, https://doi.org/10.14775/ksmpe.2018.17.2.001