Artificial Neural Network Modeling for Prediction of Tensile Strength in Pressure Vessel Welding with Steel ASTM A537 Class 1

Main Article Content

Prachya Peasura


          This research described to the determine a mathematic model using artificial neural network (ANN) for predicting the of tensile strength in the pressure vessel steel ASTM A537 Class1 grade with submerge arc welding process. The following welding parameters were studied: the welding current, voltage and travel speed. The resulting welding samples were examined using tensile strength tests which were observed microstructure with scanning electron microscopy (SEM) and determine a suitable mathematic model. The research results reveal that using a neural network model with the proposed mathematical model, which tensile strength represents 3 neurons for the input 14 neurons and 1 output neurons (3-14-1). The Levenberg-Marquart training algorithm was also train for weight and bias network. The neuron of log-sigmoid for input layer, tan-sigmoid for hidden layer and purelin for output layer activation function was assigned. The mean square error (MSE) and coefficient of determination (R2) for tensile strength predict was showed that of 0.284. The welding conditions which have the highest tensile strength was showed that microstructure phenomenon could be that fine pearlite and spheroidized pearlite with distribute intensity in heat affected zone. The mathematical model that can be effectively applied to predict and quality control of welds to obtain tensile strength according to the standard requirements, which was reduce production costs and increase work efficiency.


Download data is not yet available.

Article Details

Research Article


[1] W. H. Kearns, Welding handbook metals and their weldability, 7th ed. USA: American Welding Society, 1982.

[2] R. L. Obrien, Welding handbook welding processes, 8th ed. USA: American Welding Society, 1992.

[3] The lincon electric company, The Procedure Hand book of Arc Welding, USA: The Lincoln Electric Company, 1995.

[4] V. D. Kalyankar and G. Chudasama, “Effect of post weld heat treatment on mechanical properties of pressure vessel steels,” Materials Today: Proceedings, Vol. 5, No. 11, Part 3, pp. 24675–24684, Jan. 2018.

[5] B. Sadeghi, H. Sharifi, M. Rafiei, and M. Tayebi, “Effects of post weld heat treatment on residual stress and mechanical properties of GTAW: The case of joining A537CL1 pressure vessel steel and A321 austenitic stainless steel,” Engineering Failure Analysis, Vol. 94, pp. 396–406, Dec. 2018.

[6] T. Chinnadurai, S. Saravanan, N. Prabaharan, M. Karthigai Pandian, and S. Deebika, “Analyzing the weld strength of ultrasonic polymer welding using Artificial Neural Networks,” Materials Today: Proceedings, Vol. 5, No. 14, Part 2, pp. 28320–28327, Jan. 2018.

[7] R. Vaira Vignesh and R. Padmanaban, “Artificial neural network model for predicting the tensile strength of friction stir welded aluminium alloy AA1100,” Materials Today: Proceedings, Vol. 5, No. 8, Part 3, pp. 16716–16723, Jan. 2018.

[8] K. Kamal Babu et al., “Parameter optimization of friction stir welding of cryorolled AA2219 alloy using artificial neural network modeling with genetic algorithm,” The International Journal of Advanced Manufacturing Technology, Vol. 94, No. 9, pp. 3117–3129, Feb. 2018.

[9] A. Sarkar, P. Dey, R. N. Rai, and S. C. Saha, “A comparative study of multiple regression analysis and back propagation neural network approaches on plain carbon steel in submerged-arc welding,” Sādhanā, Vol. 41, No. 5, pp. 549–559, May 2016.

[10] L. Yu, K. Saida, S. Hirano, N. Chigusa, M. Mochizuki, and K. Nishimoto, “Application of neural network-based hardness prediction method to HAZ of A533B steel produced by laser temper bead welding,” Welding in the World, Vol. 61, No. 3, pp. 483–498, May 2017.

[11] L. Srinivasan, M. C. Khan, T. D. B. Kannan, P. Sathiya, and S. Biju, “Application of Genetic Algorithm Optimization Technique in TIG Welding of 15CDV6 Aerospace Steel,” Silicon, Vol. 11, No. 1, pp. 459–469, Feb. 2019.

[12] M. W. Dewan, D. J. Huggett, T. Warren Liao, M. A. Wahab, and A. M. Okeil, “Prediction of tensile strength of friction stir weld joints with adaptive neuro-fuzzy inference system (ANFIS) and neural network,” Materials & Design, Vol. 92, pp. 288–299, Feb. 2016.

[13] D. Zhao, D. Ren, K. Zhao, S. Pan, and X. Guo, “Effect of welding parameters on tensile strength of ultrasonic spot welded joints of aluminum to steel – By experimentation and artificial neural network,” Journal of Manufacturing Processes, Vol. 30, pp. 63–74, Dec. 2017.

[14] V. V. Narayanareddy, N. Chandrasekhar, M. Vasudevan, S. Muthukumaran, and P. Vasantharaja, “Numerical Simulation and Artificial Neural Network Modeling for Predicting Welding-Induced Distortion in Butt-Welded 304L Stainless Steel Plates,” Metallurgical and Materials Transactions B, Vol. 47, No. 1, pp. 702–713, Feb. 2016.

[15] N. Pavan Kumar, P. K. Devarajan, S. Arungalai Vendan, and N. Shanmugam, “Prediction of bead geometry in cold metal transfer welding using back propagation neural network,” The International Journal of Advanced Manufacturing Technology, Vol. 93, No. 1, pp. 385–392, Oct. 2017.

[16] The American Society of Mechanical Engineers, Boiler and Pressure Vessel Section IX Welding, Brazing, and Fusing Qualifications, USA: The American Society of Mechanical Engineers, 2015.

[17] B. Venugopal Rao, N. Aravindan, and K. Saraswathamma, “Experimentation of effect of process parameters on mechanical properties in SAW Process,” Materials Today: Proceedings, Vol. 5, No. 13, Part 3, pp. 26961–26967, Jan. 2018.

[18] American Welding Society, AWS A5.17 Specification for Carbon Steel Electrodes and Fluxes for Submerged Arc Welding, USA: American Welding Society, 2019.

[19] American Society for Metal, ASM Handbook Metallography and Microstructures, Vol. 9, American Society for Metal, USA:, 1985.

[20] O. I. Abiodun, A. Jantan, A. E. Omolara, K. V. Dada, N. A. Mohamed, and H. Arshad, “State-of-the-art in artificial neural network applications: A survey,” Heliyon, Vol. 4, No. 11, p. e00938, Nov. 2018.

[21] W.-B. Oh, T.-J. Yun, B.-R. Lee, C.-G. Kim, Z.-L. Liang, and I.-S. Kim, “A Study on Intelligent Algorithm to Control Welding Parameters for Lap-joint,” Procedia Manufacturing, Vol. 30, pp. 48–55, Jan. 2019.

[22] J.-Y. Shim, J.-W. Zhang, H.-Y. Yoon, B.-Y. Kang, and I.-S. Kim, “Prediction model for bead reinforcement area in automatic gas metal arc welding,” Advances in Mechanical Engineering, Vol. 10, No. 8, pp. 1-9, Aug. 2018.

[23] R.H. Myers, D.C Montgomery, Response Surface Methodology Process and Product Optimization using Designed Experiments, 2nd ed., Hoboken, New Jersey: John Wiley and Sons, 2002.

[24] D. Hauserova, J. Dlouh, and M. Kover “Pearlite Lamellae Spheroidisation During Austenitization and Subsequent Temperature Hold”, Archives of Metallurgy and Materials, Vol. 62, No. 1, pp. 201-204, 2017.

[25] X. Bai, S. Wu, and P. K. Liaw, “Influence of thermo-mechanical embrittlement processing on microstructure and mechanical behavior of a pressure vessel steel,” Materials & Design, Vol. 89, pp. 759–769, Jan. 2016.

[26] W. Hui, Y. Zhang, X. Zhao, N. Xiao, and F. Hu, “High cycle fatigue behavior of V-microalloyed medium carbon steels: A comparison between bainitic and ferritic-pearlitic microstructures,” International Journal of Fatigue, Vol. 91, pp. 232–241, Oct. 2016.

[27] W. Yun, B. Philip, X. Zhenying, and W. Junfeng, “Study on fatigue crack growth performance of EH36 weldments by laser shock processing,” Surfaces and Interfaces, Vol. 15, pp. 199–204, Jun. 2019.

[28] J. Toribio, B. González, J.-C. Matos, and F.-J. Ayaso, “Influence of Microstructure on Strength and Ductility in Fully Pearlitic Steels,” Metals, Vol. 6, No. 12, p. 318, Dec. 2016.

[29] M. Saadati, A. K. Edalat Nobarzad, and M. Jahazi, “On the hot cracking of HSLA steel welds: Role of epitaxial growth and HAZ grain size,” Journal of Manufacturing Processes, Vol. 41, pp. 242–251, May 2019.