Abstract
Artificial intelligence (AI) methods exhibit surprising ability to capture nonlinear relationship and interaction effect with great success. Due to complicacy during the welding and lots of interferential factors, especially short-time property of the spot welding process, of late, AI methods are used more frequently by the researchers to estimate output responses of the process. The present study is aimed at investigating failure load of spot-welded B.S. 1050 aluminum sheets using two most commonly used AI methods such as adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM). Data generated from an experimental study is fed into the paradigm of ANFIS and SVR for the formulation of mathematical model between input and output process parameters. Based on the input data, AI models estimate the failure load of welded joint and results are compared in terms of percentage of relative error. The details of experimentation, model development, and comparisons of modeling methods are summarized in this paper which will guide welders about the proper setting of the process parameters for a strong weld joint.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jou, M.: Real time monitoring weld quality of resistance spot welding for the fabrication of sheet metal assemblies. J. Mater. Process. Technol. 132, 102–113 (2003)
Aslanlar, S., Ogur, A., Ozsarac, U., Ilhan, E.: Welding time effect on mechanical properties of automotive sheets in electrical resistance spot welding. J. Mater Des. 29, 1427–1431 (2008)
Tsai, C.L., Jammal, O.A., Papritan, J.L., Dickinson, D.W.: Modelling of resistance spot weld nugget growth. Welding J. Res. Suppl. 71, 47–54 (1992)
Muhammad, N., Manurang, H.P.Y.: Design parameters selection and optimization of weld zone development in resistance spot welding. World Acad. Sci. Eng. Technol. 71, 1220 (2012)
Muhammad, N., Manurung, Y., Hafidzi, M., Abas, S.K., Tham, G., Rahim, M.R.: A quality improvement approach for resistance spot welding using multi-objective Taguchi method and response surface methodology. Int. J. Adv. Sci. Eng. Inf. Technol. 2, 2088–5334 (2012)
Muhammad, N., Manurung, Y., Hafidzi, M., Abas, S.K., Tham, G., Haruman, E.: Optimization and modeling of spot welding parameters with simultaneous multiple response consideration using multi-objective Taguchi method and RSM. J. Mech. Sci. Technol. 26(8), 2365–2370 (2012)
Muhammad, N., Manurung, Y., Hafidzi, M., Abas, S.K., Tham, G., Haruman, E.: Model development for quality features of resistance spot welding using multi-objective Taguchi method and response surface methodology. J. Intell. Manuf. (2012). doi:10.1007/s1084506483
Zhao, D., Wang, Y., Sheng, S., Lin, Z.: Multi-objective optimal design of small scale resistance spot welding process with principal component analysis and response surface methodology. J. Intell. Manuf. doi:10.1007/s10845(2013)0733-2
Zhao, D., Wang, Y., Wang, X., Wang, X., Chen, F., Liang, D.: Process analysis and optimization for failure energy of spot welded titanium alloy. J. Mater. Des. 60, 479–489 (2014)
Kim, I.S., Park, C.E., Jeong, Y.J., Son, J.S.: Development of an intelligent system for selection of the process variables in gas metal arc welding processes. Int. J. Adv. Manuf. Technol. 18, 98–102 (2001)
Tzeng, Y.-F., Chen, F.-C.: Effects of operating parameters on the static properties of pulsed laser welded zinc-coated steel. Int. J. Adv. Manuf. Technol. 18, 641–647 (2001)
Darwish, S.M., Al-Dekhial, S.D.: Statistical models for spot welding of commercial aluminium sheets. Int. J. Mach. Tools Manuf. 39, 1589–1610 (1999)
Gunaraj, V., Murugan, N.: Application of response surface methodology for predicting weld bead quality in submerged arc welding of pipes. J. Mater. Process. Technol. 88, 266–275 (1999)
Elangovan, K., Balasubramaniam, V., Babu, S.: Predicting tensile strength of friction stir welded AA6061 aluminium alloys joints by a mathematical model. Mater. Des. 30, 188–193 (2009)
Gill, S., Singh, J.: Artificial intelligent modeling to predict tensile strength of inertia friction-welded pipe joints. Int. J. Adv. Manuf. Technol. 69, 2001–2009 (2013)
Singh, J., Singh, S.G.: Modeling for tensile strength of friction welded aluminium pipes by ANFIS. J. Intell. Eng. Inf 1(1), 3–20 (2010)
Tran, H.T., Kim, K.Y., Yang, H.J.: Weldability prediction of AHSS stack ups using support vector machines. Int. J. Comput. Electr. Eng. 6(3), 207–210 (2014)
Shuangsheng, G., Xingwei, T., Shude, J., Zhitao, Y.: Prediction of mechanical properties of welded joints based on support vector regression. J. Proc. Eng. 29, 1471–1475 (2012)
Na, M.G., Kim, J.W., Lim, D.H., Kang, Y.: Residual stress prediction of dissimilar metals welding at NPPs using support vector regression. Nucl. Eng. Des. 238(7), 1503–1510 (2008)
Nagesh, D.S., Datta, G.L.: Genetic algorithm for optimization of welding variables for height to width ratio and application of ANN for prediction of bead geometry for TIG welding process. Appl. Soft Comput. 10, 897–907 (2010)
Ates, H.: Prediction of gas metal arc welding parameters based on artificial neural networks. J. Mater. Des. 28, 2015–2023 (2007)
Cevika, A., Kutuk, A.M., Erklig, A., Guzelbey, I.H.: Neural network modeling of arc spot welding. J. Mater. Process Technol. 202, 137–449 (2008)
Tseng, H.Y.: Welding parameters optimization for economic design using neural approximation and genetic algorithm. Int. J. Adv. Manuf. Technol. 27, 897–901 (2006)
Bouyousfi, B., Sahraoui, T., Guessasma, S., Chaouch, K.T.: Effect of process parameter on physical characteristic of spot weld joints. J. Mater. Des. 28, 414–419 (2003)
Nizamettin, K.: The influence of welding parameters on the joints of strength of resistance spot welded. J. Mater. Des. 28, 421–427 (2007)
Waller, D.N., Knowlson, P.M.: Electrode separation applied to quality control in resistance welding. Weld. J. 44(12), 168-s–174-s (1965)
Fratini, L., Buffa, G., Palmeri, D.: Using a neural network for predicting the average grain size in friction stir welding processes. J. Comput. Struct. 87, 1166–1174 (2009)
Martin, O., Tiedra, P.D., Lopez, M.: Artificial neural networks for pitting potential prediction of resistance spot welding joints of AISI 304 austenitic stainless steel. J. Corros. Sci. 52, 2397–2402 (2010)
Martin, O., Tiedra, P.D., Lopez, M., Manuel, S.J., Cristina, G., Fernando, M.: Quality prediction of resistance spot welding joints of 304 austenitic stainless steel. J. Mater. Des. 30, 68–77 (2009)
Kennard, R.W., Stone, L.A.: Computer aided design of experiments. Technometrics 11, 137–148 (1969)
Kecman, V.: Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models. MIT Press, Cambridge (2001)
Andrés, E., Salcedo-Sanz, S., Monge, F., Pérez-Bellido, A.M.: Efficient aero-dynamic design through evolutionary programming and support vector regression algorithms. Expert Syst. Appl. 39, 10700–10708 (2012)
Pelckmans, K., Suykens, J.A.K., Vangestel, T., De Brabanter, J., Lukas, L., Hamers, B., et al.: LS SVMlab: a MATLAB/c toolbox for least squares support vector machines. Tutorial. KU Leuven-ESAT, Leuven (2002)
Jang, J.S.R.: Adaptive-network-based fuzzy inference system (ANFIS). IEEE Trans. Syst. Man Cybern. 23, 665–685 (1993)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer India
About this paper
Cite this paper
Panda, B.N., Babhubalendruni, M.V.A.R., Biswal, B.B., Rajput, D.S. (2015). Application of Artificial Intelligence Methods to Spot Welding of Commercial Aluminum Sheets (B.S. 1050). In: Das, K., Deep, K., Pant, M., Bansal, J., Nagar, A. (eds) Proceedings of Fourth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 335. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2217-0_3
Download citation
DOI: https://doi.org/10.1007/978-81-322-2217-0_3
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2216-3
Online ISBN: 978-81-322-2217-0
eBook Packages: EngineeringEngineering (R0)