Editorial
Authors
1 M.S. Student, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin
2 M.S. student in mechanical engineering. campus2. Iran University of Science and Technology. Tehran
3 INDUSTRIAL ENGINEERING- SYSTEM ANALYSIS, Bahman Group
Abstract
Abstract
In this paper, the critical parameters of a method of welding with shielding
gas arc welding (GMAW) are discussed; this method is an important process
in creating high quality metal permanent connections in various industries,
including the automobile industry to improve the quality of stem
diameter welding parameters. One of the most useful techniques for modeling
and solving the problems is Response Surface Method. In this paper,
considering five most important factors such as speed welder, torch angle
with the work piece, electrode diameter, wire speed, gas consumption ,and
CO2 levels as input variables, can be controlled independently from the
level of response, the relationship between the input variables and the response
variables were determined using linear regression. Then optimum
value for each factor was calculated using non-linear programming model
to evaluate the results obtained along with the comparison of output of the
Simulation Annealing Algorithm.
In this study, both qualitative and quantitative variables are considered to
evaluate and optimize all response variables regarding that these variables
are not the same, and then fuzzy set theory and LP metric are used to find
answers for multi-objective optimization methods.
Keywords
1. Gupta VK, Parmar RS. Fractional factorial technique to predict dimensions of
the weld bead in automatic submerged arc welding. IE(I) J-MC
1989;70(November):67–75.
2. Murugan N, Parmar RS. Effect of welding conditions on microstructureand
properties of tupe 316L stainless steel submerged arc weldingcladding. Weld J,
AWS 1997;76(5):210-230.
3. Pine T, Lee MM, Jones TB. “Factors affecting torsional properties of box
sections”. J Ironmarking Steelmarking, 25(3):PP.205–209, (1998).
4. Sathavornvichit, N., Bookkamana, P., Plubin, B., “Central Composite Design in
Optimization of the Factors of Automatic Flux Cored Arc Welding for Steel
ST37”, proceedings of the 2nd IMT-GT Regional conference on mathematics,
statistics and applications university sains Malaysia, penang, PP.13-15, (2006)
5. Yang, L. J., Chandel, R. S., and Bibby, M. J., “An analysis of curvilinear
regression equations for modeling the submerged-arc welding process”, J Mater
Process Technol, Vol. 37, pp.601–611, 1993.
6. 2- Kim, I.S., Son, J.S., Kim, I.G., Kim, J.Y., and Kim O.S., “A study on
relationship between process variables and bead penetration for robotic CO2 arc
welding”, J Mater Process Technol, Vol.136, pp.139–145, 2003).
7. Zhou, M., Zhang, H., and Hu, S.J., “Relationship between quality and attributes
of spot welds”, Weld J Suppl, pp.72s–79s, 2003.
8. Rowlands, H., and Antony, F.,” Application of design of experiments to a spot
welding process”, Assembly Autom, Vol.23, pp.273–279, 2003.
9. Markelj, F., Tusek, J.,” Algorithmic optimization of parameters in tungsten inert
gas welding of stainless-steel sheet”, Sci Technol Weld Join, Vol.6, pp.375–
382, 2001.
10. Kim K.-J., Lin D. K. J., "Dual response Surface Optimization: A Fuzzy
Modeling Approach", Journal of Quality Technology, 30, 1-10, (1998).
11. Venter G., Haftka R.T., "Using response surface approximations in fuzzy set
based design optimization", Structural Optimization, 18, 218-227, (1999).
12. Choi, S.H. and Buckley, J.J.”Fuzzy regression using least absolute deviation
estimators", Soft Computing, 12(3), pp. 257-263, (2007).
13. Bashiri, M., M.Hosseininezhad, S.J. “A Fuzzy Programming for Optimizing
Multi Response Surface in Robust Designs”, Journal of Uncertain Systems,
Vol.3, No.3, pp.163-173, (2009).
14. Amiri, M.,” Application of Response Surface Methodology and Fuzzy
Regression Method to Determine Optimum Amount of Effective Factors in
Vehicel Brake Drum Assembling Problem”, International Journal of Industrial
Engineering and Production Management, Sharif University of
Technology.Vol.27, No. 1, PP. 133-143, (2011).
تعیین میزان بهین هى عوامل مؤثر بر کیفیت جوش... 179
15. Tatsuyuki. A, Response Surface Methodology and Its application to Automotive
Suspension design , Toyota central R&D Labs.Inc ,Japan ,(2001)
16. Marklund P-O, Nilsson L. “Optimization of a car body component subjected to
impact.” Structural and Multisciplinary Optimization.Vol.21, No.5, PP.383-392.
(2001).
17. Amiri, M., Mousakhani, M., Alaghebandha, M. and Saeeidi, S.R. “DOE by
RSM Approach. 1st Ed. Chapter 2”, Farhikhtegane Daneshghah Pub. Co.,
Tehran. (2010).
18. Kunter, M. Nachtsheim, C. Neter, J. Li, W., “Applied Linear Statistical
Methods. 5th Ed”. McGraw – Hill, New york , (2005).
19. Santner, T.J. Williams, B.J. Notz, W.I. “The Design and Analysis of Computer
Experiments”. Springer Verlag, New York. (2003).
20. Neter, J.; Kutner, M.H., Wasserman, W., Nachtsheim, C. and Neter, J., Applied
Linear Statistical Methods, 4th ed., McGraw-Hill, New york (1996).
21. Chen-Tung, C., Ching-Torng, L. and Sue-Fn, H. “A fuzzy approach for
supplier evaluation and selection in supply chain management”, International
Journal of Production Economics, 102(2), pp. 289-301 (2006).
22. Asgharpour, M.G., “Multiple Objective Decision Making. 2nd Ed”, Tehran
University Publishes (2002).
23. Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P. “Optimization by Simulated
Annealing”, Science, Vol. 220, PP.671-680. (1983).
24. Cerny, V., “A Thermodynamical Approach to the Traveling Salesman Problem:
An Efficient Simulation Algorithm”, Journal of Optimization Theory and
Applications, Vol. 45, PP.41-51. (1985).
25. Alamtabriz, A., Zandiyeh, M., Mohammad Rahimi, A.R., “Meta Heuristic
Algorithms in Combinatorial Optimization. 2nd Ed”, Saffar Publishes (2011).
)