Intelligent modeling using genetic algorithm for optimizing a-tig welding process parameters of mod. 9Cr-1Mo steel
Muthukumaran Vasudevan1
, Krishnamoorthy Nadimuthu Gowtham2, Tammana Jayakumar1
1Metallurgy and Materials Group Indira Gandhi Centre for Atomic Research, Kalpakkam.
2Department of Metallurgical Engineering PSG College of Technology, Coimbatore.
DOI:
https://doi.org/10.7494/cmms.2011.1.0306
Abstract:
Modified 9Cr-1Mo ferritic steel is used as structural material for stream generator components of power plants. Generally, Tungsten Inert Gas (TIG) welding is preferred for welding these steels in which the depth of penetration achievable during autogenous welding is very limited and hence productivity is less. Therefore, Activated flux Tungsten Inert Gas (A-TIG) welding, a novel welding technique has been developed in house to increase the depth of penetration. In modified 9Cr-1Mo steel joints produced by A-TIG welding process, weld bead width, depth of penetration and Heat Affected Zone (HAZ) width play an important role in determining the mechanical properties and also the performance of the weld joints during service. To obtain the desired weld bead geometry, HAZ width and make a good weld, it becomes important to set up the welding process parameters. Since the experimental optimization of these parameters is time consuming, Genetic Algorithm based computational model is developed for optimization of the welding process parameters. First Adaptive Neuro Fuzzy Inference System (ANFIS), one of the soft-computing tools is used to develop independent models correlating the welding process parameters like current, voltage and speed with weld bead shape parameters like depth of penetration, bead width and HAZ width. Then Genetic Algorithm is employed to determine the optimum A-TIG welding process parameters in order to obtain the desired weld bead shape parameters and HAZ width. Validation of the GA model is completed by carrying out experiments to compare the target values with that of the actual values of the weld bead shape parameters obtained. There is good agreement between the target values and the actual values.
Cite as:
Vasudevan, M., Gowtham, K., & Jayakumar, T. (2011). Intelligent modeling using genetic algorithm for optimizing a-tig welding process parameters of mod. 9Cr-1Mo steel. Computer Methods in Materials Science, 11(1), 16 – 22. https://doi.org/10.7494/cmms.2011.1.0306
Article (PDF):
Keywords:
ANFIS, Genetic algorithm, A-TIG welding, Welding process optimization, Mod. 9Cr-1Mo steel
References:
Bag, S., De, A., 2009, Development of efficient numerical heat transfer model coupled with genetic algorithm based optimization for prediction of process variables in GTA spot welding. Science and Technology of welding and Joining, 14, 633-645.
Chakraborti, N., 2004, Genetic algorithm in materials design and Processing. International Material Reviews, 49 (3-4), 246-260.
Chen, M.Y., Linkens, D.A., 2006, Impact Toughness Prediction for TMCP Steels Using Knowledge-based Neural-fuzzy Modelling. ISIJ International, 46, 586-590.
Datta, S., Pettersson, F., Ganguly, S., Saxen, H., Chakraborti, N., 2007, Designing high strength multi-phase steel for improved strength-ductility balance using neutral networks and Multi-objective genetic algorithm. ISIJ International, 47, 1193-2001.
Deb, K., 1999, An Introduction to Genetic Algorithms. Sadhana, 24, 239-315.
Dey, S., Datta, S., Chattopadhyay, P.P., Sil, J., 2008, Modeling the properties of TRIP steel using AFIS: A distributed approach. Computational Materials Science, 43, 501-511.
Dhas, J.E.R., Kumanan, S., 2007, ANFIS for predicted of weld bead width in a submerged arc welding process. Journal of Scientific and Industrial Research, 66,335-338.
Ghanty, P., Paul, S., Roy, A., Mukherjee, D.P., Pal, N.R., Vasudevan, M., Kumar, H., Bhaduri, A.K., 2008, Fuzzy rule based approach for predicting weld bead geometry in gas tungsten arc welding. Science and technology of Welding and Joining, 13, 167-175.
Hancheng, Q., Bocai, X., Shangzheng, L., Fagen, W., 2002, Fuzzy neural network modelling of material properties. Journal of Materials Processing Technology, 122, 196-200.
Kovacevic, R., Zhang, Y.M., 1997, Neurofuzzy Model-Based weld Fusion State Estimation. IEEE control Systems, 30-42.
Lucas, W., 2000, Activating flux-improving the performance of the TIG process. Welding and Metal Fabrication, 68(2),7-10.
Odetunji A. Odejobi and Laisisi E. Umoru, 2009, Applications of soft computing techniques in materials engineering: A review, African Journal of Mathematics and Computer Science Research Vol. 2(7), 104-131.
Paskell, T., Lundin, C, Castner, H., 1997, GTAW flux increases weld joint penetration. Welding Journal, 76(4), 57-62.
Vasudevan, M., 2007, Computational and experimental studies on arc welded austenitic stainless steels. PhD thesis, Indian Institute of Technology, Chennai, India.
Vasudevan, M., Bhaduri, A.K., Raj, B., 2008, Developmental Evaluation of activated Flux for TIG welding of Type 304LN and 316LN stainless steels. Proceedings of the International Institute of Welding, International congress, January, 8-10, Chennai.
Vasudevan, M., Raj, B., 2009, Soft computing in Materials Science and Engineering. IIM Metal News, 12, 15-21.
Vasudevan, M., 2009, Soft computing techniques in Stainless steel Welding. Materials and Manufacturing Processes, 24, 209-218.
Vasudevan, M., Kupuswamy, M.V., Bhaduri, A.K., 2010, Optimizing process parameters for Gas Tunsten arc welding of an austenitic stainless steel using genetic algorithm. Transactions of The Indian Institute of Metals, 63 (3), 1-10.
Vasudevan, M., ArunKumar, V., Chandrasekhar, N., Maduraimuthu, V., 2010, Genetic Algorithm for optimisation of A-TIG welding process for modified 9Cr-1Mo steel. Science and Technology of welding and Joining, 15,117-123.
Vasudevan, M., Bhaduri, A.K., Raj, B., Rao, K.P., 2007, Genetic Algorithm-Based Computational Models for Optimizing the Process Parameters in A-TIG Welding to Achieve Target Bead geometry in Type 304 L(N) and 316 L(N) Stainless Steels. Materials Manufacturing Process, 22, 641-649.