한국생산제조학회 학술지 영문 홈페이지
[ Best Paper of This Month ]
Journal of the Korean Society of Manufacturing Technology Engineers - Vol. 28, No. 3, pp.139-147
ISSN: 2508-5107 (Online)
Print publication date 15 Jun 2019
Received 20 Dec 2018 Revised 19 Apr 2019 Accepted 20 May 2019
DOI: https://doi.org/10.7735/ksmte.2019.28.3.139

구배율 기반 알고리즘을 이용한 문자마킹 용접공정 최적화에 대한 연구

이보람a ; 윤태종a ; 오원빈a ; 짱강a ; 심지연b ; 박민호c ; 김일수a, *
A Study on Welding Parameter for Character Marking Using the Gradient-Based Optimization
Bo-Ram Leea ; Tae-Jong Yuna ; Won-Bin Oha ; Gang Zhanga ; Ji-Yeon Shimb ; Min-Ho Parkc ; Ill-Soo Kima, *
aDepartment of Mechanical Engineering, Mokpo National University, 1666 Youngsan-ro, Chungkye-myun, Muan-gun, Jeonnam-do, 58554, Korea
bCarbon&Light Materials Application R&D Group, KITECH, 222, Palbok-ro, Deokjin-gu, Jeonju, Jeonbuk-do, 54853, Korea
cResearch Institute of Medium & Small Shipbuilding, 55, Daebuljugeo 3-ro, Samho-eup, Yeongam-gun, Jeonnam-do, 58457, Korea

Correspondence to: *Tel.: +82-61-454-3455 Fax: +82-61-452-6376 E-mail address: ilsookim@mokpo.ac.kr (Ill-Soo Kim).

Abstract

Welding is one of the most fundamental and essential assembly processes in manufacturing. It is an indispensable production technology for modern industries such as shipbuilding, aircraft, and automobile manufacturing. However, owing to the strong arc light, heat, smoke, and noise, one of the main challenges is to ensure the quality of welding because of the poor working environment. In this paper, a mathematical model to determine the correlation between welding parameters and bead width in the GMA welding process is developed and verified. Based on the prediction model using the GBO algorithm, the model is employed as an objective function and a deterministic constraint function to predict the optimal welding parameters on a given top-bead width for the letter-marked system.

Keywords:

GMAW, Process variable, Regression method, Optimization method, GBO (Gradient-Based Optimization)

References

  • Park, C. S., Park, J. W., Ryu, Y. S., 2009, Development of Marking Robot by using Arc Welding for Shipbuilding, The Korean Welding and Joining Society, 19-2 Mathematical Theory of Heat Distribution during Welding and Cutting, 200-207.
  • Paris, A. M., John, B. T., 2002, A Recurrent Fuzzy-neural Model for Dynamic System Identification, IEEE Tran. Syst., Man, and Cybern. Part B, 32:2 176-190. [https://doi.org/10.1109/3477.990874]
  • Kuhu, Pal, R. K. M., 2002, A New Scheme for Fuzzy Rule-based System Identification and its Application to Self-tuning Fuzzy Controllers, IEEE Tran. Syst., Man, and Cybern. Part B, 32:4 470-481. [https://doi.org/10.1109/TSMCB.2002.1018766]
  • Huebner, K. H., Thornton, E. A., 1982, The Finite Element Method for Engineers, The Finite Element Method for Engineers, John Wiley Sons, New York, 411-412.
  • Snyder, M. D., Bathe, K. J., 1981, A Solution Procedure for Thermo Elastic-plastic and Creep Problems, Nuclear Engineering and Design, 64 49-80. [https://doi.org/10.1016/0029-5493(81)90032-7]
  • Poliak, E. I., 1998, Application of Linear Regression Analysis in Accuracy Assessment of Rolling Force Calculations, Metals and Materials, 4:5 1047-1056. [https://doi.org/10.1007/BF03025975]
  • Beck, A., Teboulle, M., 2009, A Fast Iterative Shrinkage-thresholding Algorithm for Linear Inverse Problems, SIAM Journal on Imaging Sciences, 2:1 183-202. [https://doi.org/10.1137/080716542]
  • Starling, M. D., 1995, Statistical Modeling of Narrow-Gap GTA Welding with Magnetic Arc Oscillation, Materials Processing Technology, 51 37-49. [https://doi.org/10.1016/0924-0136(94)01356-6]
  • Kim, J. E., 1997, Panel Blasting and Marking Equipment, Republic of Korea.
  • Seong, J.S., Lee, H.Y., Yun, K.S., 2018, Marking System and Method for Recognizing Marking Information, Republic of Korea.
  • Oreper, G. M., Eagar, T. W., Szekely, J., 1983, Convection in Arc Weld Pools, Welding Journal, 62:11 307-312.
  • Phadke, M. S., 1989, Quality Engineering Using Robust Design, New Jersey:Prentice-Hill, 67-113. [https://doi.org/10.1007/978-1-4684-1472-1_3]
  • Gupta, V. K., Sorroshian, S.,1985, The Automatic Calibrating of Conceptual Catchment Models Using Derivative-Based Optimization Algorithms, Water Resources Research, 21 473-485. [https://doi.org/10.1029/WR021i004p00473]
  • Sorooshian, S., 1990, Parameter Indentifiability in Conceptual Rainfall-Runoff Models, Computerized Decision Support Systems for Water Managers, 173-183.
  • Ebert, R. S., 1946, Generalization Abilities in Mathematics, The Journal of Educational Research, 39:9 671-681. [https://doi.org/10.1080/00220671.1946.10881478]
  • El Mouhayar, R. R., Jurdak, M. E., 2013, Teachers’ Ability to Identify and Explain Students’ Actions in Near and Far Figural Pattern Generalization Tasks, Educational Studies in Mathematics, 82:3 379-396. [https://doi.org/10.1007/s10649-012-9434-6]
  • Jurow, A. S., 2004, Generalizing in Interaction: Middle School Mathematics Students Making Mathematical Ceneralizations in a Population-Modeling Project Mind, Culture and Activity, 11:4 279-300. [https://doi.org/10.1207/s15327884mca1104_4]
  • Rosenthal, D., 1941, Mathematical Theory of Heat Distribution during Welding and Cutting, Welding Journal, 20:5 2205-2345.
  • Brick, R. M., Pense, A. W., Gordon, R. B., 1977, Structure and Properties of Engineering Materials, McGraw-Hill Series in Materials Science and Engineering, 4 25-35.