한국산학기술학회논문지 (Journal of the Korea Academia-Industrial cooperation Society)

  • 제21권1호
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  • Pages.560-567
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  • 2020
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  • 1975-4701(pISSN)
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  • 2288-4688(eISSN)
한국산학기술학회 (The Korea Academia-Industrial cooperation Society)
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유전 알고리즘-BP신경망을 이용한 Al3004 판재 점진성형 공정변수에 대한 최적화 연구

Optimization of Process Parameters of Incremental Sheet Forming of Al3004 Sheet Using Genetic Algorithm-BP Neural Network

  • 양센 (경북대학교 기계공학과) ;
  • 김영석 (경북대학교 기계공학과)
  • Yang, Sen (Dept. of Mechanical Engineering, Kyungpook Nat'l Univ.) ;
  • Kim, Young-Suk (Dept. of Mechanical Engineering, Kyungpook Nat'l Univ.)
  • 투고 : 2019.09.06
  • 심사 : 2020.01.03
  • 발행 : 2020.01.31
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초록

점진판재성형은 금형을 제작하지 않고 판재를 가공하는 방법으로서 빠른 시제품 제작과 소량 생산에 적합한 성형법이다. 이러한 점진판재성형의 공정 변수로 공구 직경, 매 스탭당 Z-방향 깊이, 공구 이송 속도, 공구 회전 속도 등은 성형품의 품질에 크게 영향을 미친다. 본 연구에서는 두께가 1.0mm인 Al3004판재를 사용하여 원뿔절두체(VWACF: Varying Wall Angle Conical Frustum) 모델의 점진성형을 실시하였으며, 각각의 변수들의 조합에서 성형성을 판단하였다. BP신경망 (BPNN: Back Propagation Neural Network)를 기반으로 Minitab 소프트웨어를 사용하여 성형 각도를 예측하는 2 차 수학적 모델을 구축하였다. 또한 이 모델을 유전 알고리즘의 목적함수로 사용하였으며 최대 성형 각도로 얻기 위한 최적의 변수 조합을 찾아내었다. 공구 직경은 6mm, 회전 속도는 180rpm, Z-방향 피치는 0.401mm, 이송 속도는 772.4mm/min일 경우 가장 큰 성형 각도인 87.071°를 갖는 컵을 성형할 수 있었다.

Incremental Sheet Forming (ISF) is a unique sheet-forming technique. The process is a die-less sheet metal manufacturing process for rapid prototyping and small batch production. In the forming process, the critical parameters affecting the formability of sheet materials are the tool diameter, step depth, feed rate, spindle speed, etc. This study examined the effects of these parameters on the formability in the forming of the varying wall angle conical frustum model for a pure Al3004 sheet with 1mm in thickness. Using Minitab software based on Back Propagation Neural Network (BPNN) and Genetic Algorithm (GA), a second order mathematical prediction model was established to predict and optimize the wall angle. The results showed that the maximum forming angle was 87.071° and the best combination of these parameters to give the best performance of the experiment is as follows: tool diameter of 6mm, spindle speed of 180rpm, step depth of 0.4mm, and feed rate of 772mm/min.

키워드

참고문헌

  1. E. Leszak, "Apparatus and Process for Incremental Die Less Forming," Patent US3342051 Al, 1967.
  2. E. Appleton, B. Mason, "Sheet metal forming for small batches using sacrificial tooling," Production Engineer, Vol. 63, No. 9, pp. 58-61, 1984, DOI: https://doi.org/10.1049/tpe:19840213
  3. J. Jeswiet, F. Micari, G. Hirt, A. Bramley, J. Duflou, J. Allwood, “Asymmetric Single Point Incremental Forming of Sheet Metal,” CIRP Annals, Vol. 54, No. 2, pp. 88-114, 2005. DOI: https://doi.org/10.1016/S0007-8506(07)60021-3
  4. J. Leon, D. Salcedo, C. Ciaurriz, C. J. Luis, J. P. Fuertes, I. Puertas, R. Luri, "Analysis of the Influence of Geometrical Parameters on the Mechanical Properties of Incremental Sheet Forming Parts," Procedia Engineering, Vol.63, pp. 445-453, 2013. DOI: https://doi.org/10.1016/j.proeng.2013.08.206
  5. Y. H. Kim, J. J. Park, "Effect of Process Parameters on Formability in Incremental Forming of Sheet Metal," Journal of Materials Processing Technology, Vol. 130-131, pp. 42-46, 2002. DOI: https://doi.org/10.1016/S0924-0136(02)00788-4
  6. C. I. Kim, V. C. Do, X. Xiao, Y. S. Kim, " Optimization of Single Point Incremental Forming of Al5052-O Sheet," Transactions of the Korean Society of Mechanical Engineers, Vol. 41, No. 3, pp. 181-186, 2017. DOI: https://doi.org/10.3795/KSME-A.2017.41.3.181
  7. R. Varthini, R. Gandhinathan, C. Pandivelan, A. K. Jeevanantham, "Modelling and Optimization of Parameters of the Single Point Incremental Forming of Aluminium5052 Alloy Sheet Using Genetic Algorithm-Back Propagation Neural Network," International Journal of Mechanical And Production Engineering, Vol. 2, No. 6, pp. 55-62, 2014.
  8. E. T. Park, Y. H. Lee, J. Kim, W. J. Song, "Numerical Prediction of Temperature-Dependent Flow Stress on Fiber Metal Laminate Using Artificial Neural Network," Transactions of Materials Processing, Vol.27, No.4, pp. 331-338, 2018. DOI: https://doi.org/10.5228/KSTP.2018.27.4.227
  9. F. Han, J. H. Mo, H. W. Qi, R. F. Long, X. H. Cui, Z. W. Li, "Springback prediction for incremental sheet forming based on FEM-PSONN technology," Transactions of Nonferrous Metals Society of China, Vol. 23, No. 4, pp. 1061-107, 2013.. DOI: https://doi.org/10.1016/S1003-6326(13)62567-4
  10. M. Y. Zhang, X. Y. Wang, J. C. Xia, J. Gang, " Multiple-Target Optimization Design of Pre-forging for Gear Blank Using Back Propagation Neural Network and Genetic Algorithm," Forging Stamping Technology, Vol.35, No.6, 2010.
  11. M. A. Woo, S. M. Lee, K. H. Lee, W. J. Song, J. Kim, "Application of an Artificial Neural Network Model to Obtain Constitutive Equation Parameters of Materials in High Speed Forming Process," Transactions of Materials Processing, Vol.27, No.6, pp. 331-338, 2018. DOI: https://doi.org/10.5228/KSTP.2018.27.6.331
  12. K. F. Man, K. S. Tang, and S. Kwong, "Genetic Algorithms: Concepts and Applications," IEEE Transactions on industrial electronics, Vol. 43, No. 5, 1996. https://doi.org/10.1109/TIE.1996.538730
  13. I. M. Deiab, "Concurrent Optimization of Design and Machining Tolerances Using the Genetic Algorithms Method," Int. J. Mach. Tools Manufact. Vol. 37, No. 12, pp. 1721-1731, 1997. DOI: https://doi.org/10.1016/S0890-6955(97)00033-3
  14. T. Tugrul, "A New Genetic Algorithm Methodology for Design Optimization of Truss Structures: Bipopulation-Based Genetic Algorithm with Enhanced Interval Search," Modelling and Simulation in Engineering, Vol. 2009, Article ID 615162, 28 pages, 2009. DOI: http://dx.doi.org/10.1155/2009/615162
  15. J. Jeswiet, D. Adams, M. Doolan, T. McAnulty, P. Gupta, "Single point and asymmetric incremental forming," Advances in Manufacturing, Vol. 3, No.4, pp. 253-262, 2015. DOI: https://doi.org/10.1007/s40436-015-0126-1
  16. J. I. Choi, J. M. Lee, S. H. Baek, B. M. Kim, D. H. Kim, "The Shoe Mold Design for Korea Standard Using Artificial Neural Network," Transactions of Materials Processing, Vol.24, No.3, pp. 167-175, 2015. DOI: https://doi.org/10.5228/KSTP.24.3.167
  17. J. M. Jorge, "The Levenberg-Marquardt algorithm: Implementation and theory," Numerical Analysis, Vol. 630, pp. 105-116, 2006.