ANN prediction of impact toughness of composites produced by explosive welding

  • Yakup KAYA Karabük University
Keywords: ANN, explosive welding/cladding, impact toughness, ship plate, stainless steel.

Abstract

In this study, ship steel-stainless steel composite plates were produced by joining ship steel with stainless steel using explosive welding process. The characterization of the joining interface of the plates was carried out by optical microscopic examination. Charpy impact tests were conducted at different temperatures to determine the impact toughness of the ship steel-stainless steel composites and the effect of the rolling direction on the impact toughness. In addition, after the Charpy impact test, the fractured surfaces of the samples were investigated via scanning electron microscopy (SEM). Using the data obtained as a result of the impact toughness tests, an artificial neural network (ANN) model was improved for the prediction of the impact toughness. Five different material types, two different rolling directions, and eight different temperatures were used as the input parameters of the Charpy impact tests. The impact toughness values obtained at the end of the tests were used as the output parameters of the generated prediction model. The high R2 value obtained in the developed prediction model demonstrated that it could be successfully used for predicting impact toughness. 

References

REFERENCES

Fronczek, D. M., Wojewoda-Budka, J., Chulist, R., Sypien, A., Korneva A., Szulc, Z., Schell, N. & Zieba, P. 2016. Structural properties of Ti/Al clads manufactured by explosive welding and annealing. Materials & Design 91:80-89. https://doi.org/10.1016/j.matdes.2015.11.087

Nieslony, P., Cichosz, P., Krolczyk, G. M., Legutko, S., Smyczek, D. & Kolodziej, M. 2016. Experimental studies of the cutting force and surface morphology of explosively clad Ti-steel plates. Measurement 78:129-137. https://doi.org/10.1016/j.measurement.2015.10.005

Acarer, M., Gülenç, B. & Fındık, F. 2003. Investigation of explosive welding parameters and their effects on microhardness and shear strength. Materials & Design 24:659-664. https://doi.org/10.1016/S0261-3069(03)00066-9

Gülenç, B., Kaya, Y., Durgutlu, A., Gülenç, İ. T., Yıldırım, M. S. & Kahraman, N. 2016. Production of wire reinforced composite materials through explosive welding. Archives Civil Mechanical Engineering 16:1-8. https://doi.org/10.1016/j.acme.2015.09.006

Ma, R., Wang, Y., Wu, J. & Duan, M. 2014. Explosive welding method for manufacturing ITER-grade 316L(N)/CuCrZr hollow structural member. Fusion Engineering and Design 89:3117-3124. https://doi.org/10.1016/j.fusengdes.2014.10.001

Gloc, M., Wachowski, M., Plocinski, T. & Kurzydlowski, K. J. 2016. Microstructural and microanalysis investigations of bond titanium grade1/low alloy steel st52-3N obtained by explosive welding. Journal of Alloys and Compounds 671:446-451. https://doi.org/10.1016/j.jallcom.2016.02.120

Guo, X., Wang, H., Liu, Z., Wang, L., Ma, F. & Tao, J. 2016. Interface and performance of CLAM steel/aluminum clad tube prepared by explosive bonding method. The International Journal of Advanced Manufacturing Technology 82:543-548. https://doi.org/10.1007/s00170-015-7380-z

Shi, C. G., Wang, Y., Zhao, L. S., Hou, H. B. & Ge, Y. H. 2015. Detonation mechanism in double vertical explosive welding of stainless steel/steel. Journal of Iron Steel Reserach, International 22(10):949-953. https://doi.org/10.1016/S1006-706X(15)30095-9

Prasanthi, T. N., Sudha, Ravikirana, C. & Saroja, S. 2016. Explosive cladding and post-weld heat treatment of mild steel and titanium. Materials & Design 93:180-193. https://doi.org/10.1016/j.matdes.2015.12.120

Benyounis, K. Y. & Olabi, A. G. 2008. Optimization of different welding processes using statistical and numerical approaches–A reference guide. Advances in Engineering Software 39:483-496. https://doi.org/10.1016/j.advengsoft.2007.03.012

Nagesh, D. S. & Datta, G. L. 2002. Prediction of weld bead geometry and penetration in shielded metal-arc welding using artificial neural networks. Journal of Materials Processing Technology 123:303-312. https://doi.org/10.1016/S0924-0136(02)00101-2

Chan, B., Pacey, J. & Bibby, M. 1999. Modelling gas metal arc weld geometry using artificial neural network technology. Canadian Metallurgical Quarterly 38: 43-51. https://doi.org/10.1016/S0008-4433(98)00037-8

Mirapeix, J., García-Allende, P. B., Cobo, A., Conde, O. M. & López-Higuera, J. M. 2007. Real-time arc-welding defect detection and classification with principal component analysis and artificial neural networks. NDT & E International 40 :315-323. https://doi.org/10.1016/j.ndteint.2006.12.001

Casalino, G. & Minutolo, F. M. C. 2004. A model for evaluation of laser welding efficiency and quality using an artificial neural network and fuzzy logic. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 218:641-646. https://doi.org/10.1243/0954405041167112

Luo, H., Zeng, H., Hu, L., Hu, X. & Zhou, Z. 2005. Application of artificial neural network in laser welding defect diagnosis. Journal of Materials Processing Technology 170:403-411. https://doi.org/10.1016/j.jmatprotec.2005.06.008

Olabi, A. G., Casalino, G., Benyounis, K. Y. & Hashmi, M. S. J. 2006. An ANN and taguchi algorithms integrated approach to the optimization of CO2 laser welding. Advances in Engineering Software 37:643-648. https://doi.org/10.1016/j.advengsoft.2006.02.002

Martín, O., López, M. & Martín, F. 2007. Artificial neural networks for quality control by ultrasonic testing in resistance spot welding. Journal of Materials Processing Technology 183:226-233. https://doi.org/10.1016/j.jmatprotec.2006.10.011

Dutta, P. & Pratihar, D. K. 2007. Modeling of TIG welding process using conventional regression analysis and neural network-based approaches. Journal of Materials Processing Technology 184:56-68. https://doi.org/10.1016/j.jmatprotec.2006.11.004

Shojaeefard, M. H., Behnagh, R. A., Akbari, M., Givi, M. K. B. & Farhani, F. 2013. Modelling and pareto optimization of mechanical properties of friction stir welded AA7075/AA5083 butt joints using neural network and particle swarm algorithm. Materials & Design 44:190-198. https://doi.org/10.1016/j.matdes.2012.07.025

Balasubramanian, K. R., Buvanashekaran, G. & Sankaranarayanasamy, K. 2010. Modeling of laser beam welding of stainless steel sheet butt joint using neural networks, CIRP Journal of Manufacturing Science and Technology 3:80-84. https://doi.org/10.1016/j.cirpj.2010.07.001

Manikya, Kanti, K. & Srinivasa, Rao, P. 2008. Prediction of bead geometry in pulsed GMA welding using back propagation neural network. Journal of Materials Processing Technology 200:300-305. https://doi.org/10.1016/j.jmatprotec.2007.09.034

Palani. S., & Natarajan, U. 2011. Prediction of surface roughness in CNC end milling by machine vision system using artificial neural network based on 2D fourier transform. The International Journal of Advanced Manufacturing Technology 54:1033-1042. https://doi.org/10.1007/s00170-010-3018-3

Mathew, J., Moat, R. J., Paddea, S., Fitzpatrick, M. E. & Bouchard, P. J. 2017. Prediction of residual stresses in girth welded pipes using an artificial neural network approach. International Journal of Pressure Vessels and Piping 150:89-95. https://doi.org/10.1016/j.ijpvp.2017.01.002

Pouralıakbar, H., Khalaj, M., Nazerfakharı, M. & Khalaj, G. 2015. Artificial neural networks for hardness prediction of HAZ with chemical composition and tensile test of X70 pipeline steels. Journal of Iron and Steel Research, International 22:446-450. https://doi.org/10.1016/S1006-706X(15)30025-X

Palavar, O., Özyürek, D. & Kalyon, A. 2015. Artificial neural network prediction of aging effects on the wear behavior of IN706 superalloy. Materials & Design 82:164-172. https://doi.org/10.1016/j.matdes.2015.05.055

Khorasani, A. & Yazdi, M. R. S. 2017. Development of a dynamic surface roughness monitoring system based on artificial neural networks (ANN) in milling operation. The International Journal of Advanced Manufacturing Technology 93(1-4):141-151. https://doi.org/10.1007/s00170-015-7922-4

Almonacid, F., Fernandez, E. F., Mellit, A. & Kalogirou, S. 2017. Review of techniques based on artificial neural networks for the electrical characterization of concentrator photovoltaic technology. Renewable & Sustainable Energy Reviews 75:938-953. https://doi.org/10.1016/j.rser.2016.11.075

Kaya, Y. 2014. An investigation into the microstructure, mechanical and corrosion properties of Grade A ship steel-stainless steel composites produced by explosive welding method. Ph D Thesis Karabük University.

Akbari, Mousavi, A. A. & Al-Hassani, S. T. S. 2005. Numerical and experimental studies of the mechanism of the wavy interface formations in explosive/impact welding. Journal of the Mechanis and Physics of Solids 53:2501-2528. https://doi.org/10.1016/j.jmps.2005.06.001

Kaya, Y., Kahraman, N., Durgutlu, A. & Gülenç, B. 2017. Investigation of the microstructural, mechanical and corrosion properties of Grade A ship steel-duplex stainless steel composites produced via explosive welding. Metallurgical and Materials Transactions A 48(8):3721-3733. https://doi.org/10.1007/s11661-017-4161-3

Miao, G., Ma, H., Shen, Z. & Yu, Y. 2014. Research on honeycomb structure explosives and double sided explosive cladding. Materials & Design 63:538-543. https://doi.org/10.1016/j.matdes.2014.06.050

Durgutlu, A. Gülenç, B. & Fındık, F. 2005. Examination of copper/stainless steel joints formed by explosive welding. Materials & Design 26:497-507. https://doi.org/10.1016/j.matdes.2004.07.021

Kahraman, N., Gülenç, B. & Fındık, F. 2005. Joining of titanium/stainless steel by explosive welding and effect on interface. Journal of Materials Processing Technology 169:127-133. https://doi.org/10.1016/j.jmatprotec.2005.06.045

Manikandan, P., Hokamoto, K., Deribaş, A. A., Raghukandan, K. & Tomoshige, R. 2006. Explosive welding of titanium/stainless steel by controlling energetic conditions. Materials Transactions 47(8):2049-2055. https://doi.org/10.2320/matertrans.47.2049

Kaya,Y. & Kahraman, N. 2013. An investigation into the explosive welding/cladding of Grade A ship steel/AISI 316L austenitic stainless steel. Materials & Design 52:367-372. https://doi.org/10.1016/j.matdes.2013.05.033

Kaçar, R. & Acarer, M. 2004. An investigation on the explosive cladding of 316L stainless steel-DIN-P355GH steel. Journal of Materials Processing Technology 52:91-96. https://doi.org/10.1016/j.jmatprotec.2004.03.012

Kaçar, R. & Acarer, M. 2003. Microstructure-property relationship in explosively welded duplex stainless steel-steel, Materials Science and Engineering: A 363:290-296. https://doi.org/10.1016/S0921-5093(03)00643-9

Published
2020-05-23
Section
Mechanical Engineering (1)