Electron Beam Welding (EBW) of Aerospace Alloy (Inconel 825): Optimization and Modeling of Weld Bead Area

Yıl 2021, Cilt: 1 Sayı: 1, 106 - 115, 30.08.2021

Gamze Özakıncı Levent Aydın

Öz

This study investigates the optimum weld area on a popular aerospace alloy (i.e., Inconel 825) made by the electron beam welding technique. Welding speed (S), beam current (I), accelerating voltage (V), and beam oscillation (O) are considered as process parameters to study the weld bead area (WA) of the weldments. An instructive study on multiple non-linear neural regression analyses has been done as a basic introduction to neuro regression modeling with artificial neural network (ANN) philosophy. To do this, the experimental prediction has been modeled with 14 predictive functional structures using fundamental regression modal types to test the accuracy of their predictions. To train the program with the chosen model R^2_training, test it R^2_testing, verify the accuracy R^2_validation is used, and check whether the values are within the engineering limits. Optimization algorithms with three different scenarios have been applied. Only one of the 14 models gave realistic results. It has been seen that the scenario types, selection of different constraints, and different models for design variables affect the optimization results.

Anahtar Kelimeler

Electron beam welding, neuro-regression modeling, optimization, weld bead area

Kaynakça

• [1] B. Choudhury, and M. Chandrasekaran, “Electron beam welding of aerospace alloy (inconel 825): a comparative study of RSM and ANN modeling to predict weld bead area,” Optik - International Journal for Light and Electron Optics, Elsevier GmbH, vol. 219, 2020
• [2] J. W. Sowards, and J. Caron, “Weldability of nickel-base alloys,” Comprehensive Materials Processing, Elsevier, Oxford, vol. - 1, no. 6, pp. 151-179, 2014.
• [3] B. Choudhury, and M. Chandrasekaran, “Investigation on welding characteristics of aerospace materials –a review,” Selection and Peer-review under responsibility of the Committee Members of International Conference on Advancements in Aeromechanical Materials for Manufacturing, Elsevier, vol. 4, pp. 7519–7526, 2017.
• [4] M. Chandrasekaran, M. Muralidhar, C. M. Krishna, and U. S. Dixit, “Application of soft computing techniques in machining performance prediction and optimization: a literature review,” Int J Adv Manuf Technol, Springer-Verlag London Limited, vol. 46, pp. 445–464, 2009.
• [5] F. Khademi, S. M. Jamal, N. Deshpande, and S. Londhe, “Predicting strength of recycled aggregate concrete using artificial neural network, adaptive neuro fuzzy inference system and multiple linear regression,” International Journal of Sustainable Built Environment, Elsevier BV, vol. 5, no. 2, pp. 355-369, 2016.
• [6] R. Palanivel, I. Dinaharan, and R. F. Laubscher, “Application of an artificial neural network model to predict the ultimate tensile strength of friction welded titanium tubes,” J. Braz. Soc. Mech. Sci. Eng., Springer, vol. 41, no. 111, 2019.
• [7] M. F. A. Zaharuddin, D. Kim, and S. Rhee, “An ANFIS based approach for predicting the weld strength of resistance spot welding in artificial intelligence development,” J. Mech. Sci. Technol., Springer, vol. 31, no. 11, pp. 5467–5476, 2017.
• [8] K. Anand, R. Shrivastava, K. Tamilmannan, and P. Sathiya, “A comparative study of artificial neural network and response surface methodology for optimization of friction welding of incoloy 800 H.” Acta Metall. Sin. (Engl. Lett.), Springer, vol. 28, no. 7, pp. 892–902, 2015.
• [9] E. A. Gyasi, P. Kah, H. Wu, and M. A. Kesse, “Modeling of an artificial intelligence system to predict structural integrity in robotic GMAW of UHSS fillet welded joints,” Int. J. Adv. Manuf. Technol., Springer, vol. 93, pp. 1139–1155, 2017.
• [10] M. P. Satpathya, S. B. Mishraa, and S. K. Sahoob, “Ultrasonic spot welding of aluminum-copper dissimilar metals: a study on joint strength by experimentation and machine learning techniques,” J. Manuf. Process., Elsevier, vol. 33, pp. 96–110, 2018.
• [11] H. K. Narang, U. P. Singh, M. M. Mahapatra, and P. K. Jha, “Prediction of the weld pool geometry of TIG arc welding by using fuzzy logic controller,” Int. J. Eng. Sci. Technol., MultiCraft, vol. 3, no. 9, pp. 77–85, 2011.
• [12] N. Sivagurumanikandan, S. Saravanan, G. S. Kumar, S. Raju, and K. Raghukandan, “Prediction and optimization of process parameters to enhance the tensile strength of Nd: YAG laser welded super duplex stainless steel,” Optik, Elsevier Gmbh., vol. 157, pp. 833–840, 2018.
• [13] M. Akbari, S. Saedodin, A. Panjehpour, M. Hassani, M. Afrand, and M. J. Torkamany, “Numerical simulation and designing artificial neural network for estimating melt pool geometry and temperature distribution in laser welding of Ti6Al4V alloy,” Opt.– Int. J. Light Electron. Opt., 2016.
• [14] K. Anand, B. K. Barik, K. Tamilmannan, and P. Sathiya, "Artificial neural network modeling studies to predict the friction welding process parameters of incoloy 800H joints," Eng. Sci. Technol. Int. J., Elsevier BV, vol. 18, pp. 394–407, 2015.
• [15] K. R. Balasubramanian, G. Buvanashekaran, and K. Sankaranarayanasamy, “Modeling of laser beam welding of stainless steel sheet butt joint using neural networks,” CIRP J. Manuf. Sci. Technol., Elsevier, vol. 3, pp. 80–84, 2010.
• [16] I. Polatoglu, and L. Aydin, “A new design strategy with stochastic optimization on the preparation of magnetite cross-linked tyrosinase aggregates (MCLTA),” Process Biochemistry, vol. 99, pp. 131–138, 2020.
• [17] I. Polatoglu, L. Aydin, B. Ç. Nevruz, and S. Ozer, “A novel approach for the optimal design of a biosensor,” Anal. Lett., pp. 1–18, 2020. doi.org/10.1080/ 00032719.2019.1709075.
• [18] A. B. Ceylan, L. Aydin, and M. Nil, H. Mamur, İ. Polatoğlu and H. Sözen. “A new hybrid approach in selection of optimum establishment location of the biogas energy production plant,” Biomass Conv. Bioref, 2021. https://doi.org/10.1007/s13399-021- 01532-8