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Erschienen in:

2023 | OriginalPaper | Buchkapitel

Artificial Neural Network Model for the Evaluation of Tensile Strength of Vibratory-Assisted TIG Welded Aluminium Weldments

verfasst von : M. Vykunta Rao, Kothakota Purushotham, M. V. A. Raju Bahubalendruni

Erschienen in: Recent Trends in Product Design and Intelligent Manufacturing Systems

Verlag: Springer Nature Singapore

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Abstract

Welding is the most prevalent method of joining the components. Several analytical approaches have recently been employed to develop a correlation among welding parameters and weldment quality. Computational intelligence techniques are those that can simulate the relationship among weld process factors and weldment efficiency in a shorter period. The estimation of mechanical properties in this study was carried out utilizing an artificial neural network (ANN). An artificial neural network model developed to understand the relationship between tensile strength (UTS) of vibratory-assisted aluminium weldments to vibromotor voltage input and vibration time during welding. With the available experimental results, a model was created among vibratory aided gas tungsten arc welding (TIG) parameters and tensile strength (UTS) of 5052 H32 aluminium alloy weldments. The created ANN model is tested using results of the experiment. This trained ANN model can also predict the aluminium alloy weld joint ultimate tensile strength with an accuracy of 97.77%.

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Vorheriges Kapitel Study the Influences of Various Input Variables on Material Removal Rate During μEDM Machining of Super Alloy Material

Nächstes Kapitel Process Parameters Optimization in Drilling Using Taguchi Method

Rao, P.G., Rao, P.S., Gopala Krishna, A.: A smart prediction tool for estimating the impact strength of welded joints prepared by vibratory welding process. Proc. Inst. Mech. Eng., Part E: J. Process Mech. Eng. 231(2), 343–346 (2017)

Rao, P.G., Rao, P.S., Krishna, A.G.: Evaluation of bending strength of the vibratory welded joint using regression technique. Int. J. Offshore Polar Eng. 25(03), 227–230 (2015)CrossRef

Panda, B.N., Bahubalendruni, M.R., Biswal, B.B.: A general regression neural network approach for the evaluation of compressive strength of FDM prototypes. Neural Comput. Appl. 26(5), 1129–1136 (2015)CrossRef

Vykunta Rao, M., Raju Bahubalendruni, M.V.A., Chintada, V.B.: General regression neural network-based frame work for the evaluation of ultimate tensile strength of vibratory-assisted welded joints. In: Recent Advances in Manufacturing, Automation, Design and Energy Technologies (pp. 173–180). Springer, Singapore

Dongare, A.D., Kharde, R.R., Kachare, A.D.: Introduction to artificial neural network. Int. J. Eng. Innovative Technol. (IJEIT) 2(1), 189–194 (2012)

Park, Y.S., Lek, S.: Artificial neural networks: multilayer perceptron for ecological modeling. In: Developments in Environmental Modelling (Vol. 28, pp. 123-140). Elsevier, (2016)

Mohamed, Z.E.: Using the artificial neural networks for prediction and validating solar radiation. J. Egypt. Math. Soc. 27(1), 1–13 (2019)MathSciNetCrossRef

Ray, A., Kumar, V., Kumar, A., Rai, R., Khandelwal, M., Singh, T.N.: Stability prediction of Himalayan residual soil slope using artificial neural network. Nat. Hazards 103(3), 3523–3540 (2020)CrossRef

Chakraborty, A., Mukherjee, D., Mitra, S.: Development of pedestrian crash prediction model for a developing country using artificial neural network. Int. J. Inj. Control Saf. Promot. 26(3), 283–293 (2019)CrossRef

10.

Khosravi, A., Koury, R.N.N., Machado, L., Pabon, J.J.G.: Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system. Sustain. Energy Technol. Assessments 25, 146–160 (2018)CrossRef

11.

Elsheikh, A.H., Sharshir, S.W., Abd Elaziz, M., Kabeel, A.E., Guilan, W., Haiou, Z.: Modeling of solar energy systems using artificial neural network: a comprehensive review. Solar Energy 180, 622–639 (2019)

12.

Vykunta Rao, M., Srinivasa Rao, P., Surendra Babu, B.: Vibratory weld conditioning during gas tungsten arc welding of al 5052 alloy on the mechanical and micro-structural behavior. World J. Eng. 17(6), 831–836 (2020)

13.

Rao, M.V., Rao, P.S., Babu, B.S.: Effect of vibratory tungsten inert gas welding on tensile strength of aluminum 5052–H32 alloy weldments. Mater. Focus 6(3), 325–330 (2017)

14.

Fu, X., et al.: Training recurrent neural networks with the Levenberg–Marquardt algorithm for optimal control of a grid-connected converter. 26(9), 1900-1912 (2014)

Titel: Artificial Neural Network Model for the Evaluation of Tensile Strength of Vibratory-Assisted TIG Welded Aluminium Weldments
verfasst von: M. Vykunta Rao
Kothakota Purushotham
M. V. A. Raju Bahubalendruni
Verlag: Springer Nature Singapore
Buch: Recent Trends in Product Design and Intelligent Manufacturing Systems
Print ISBN: 978-981-19-4605-9

Electronic ISBN: 978-981-19-4606-6

Copyright-Jahr: 2023
DOI: https://doi.org/10.1007/978-981-19-4606-6_63

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