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Erschienen in:

2022 | OriginalPaper | Buchkapitel

General Regression Neural Network-Based Frame Work for the Evaluation of Ultimate Tensile Strength of Vibratory-Assisted Welded Joints

verfasst von : M. Vykunta Rao, M. V. A. Raju Bahubalendruni, Vinod Babu Chintada

Erschienen in: Recent Advances in Manufacturing, Automation, Design and Energy Technologies

Verlag: Springer Singapore

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Abstract

Welding is a widely used process to join the components. In recent years, several analytical methods are used to build the relationship between parameters of welding process and the quality of weldments. Soft computing tools are the one which can model the relationship between parameters of welding process and the quality of weldments at a shorter interval of time. In the present work, assessment of weld quality has been carried using general regression neural network (GRNN). A model is developed between vibratory tungsten inert gas welding process parameters and ultimate tensile strength of aluminum 5052 alloy weldments using experimental data. The developed GRNN model is validated with the experimental data. The predicted GRNN values closely matched with experimental values. This trained GRNN model can also predict the ultimate tensile strength of welded joints with an accuracy of 98.94%.

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Vorheriges Kapitel The Effect of HSS and Carbide Tools on the Mechanism of Chip Formation During Turning of Medium Carbon Steel (AISI1030)

Nächstes Kapitel Experimental Studies on Material Removal Rate of Die Steel in Electrochemical Micromachining Process Using Taguchi Method

Rao, P.G., Rao, P.S., Krishna, A.G.: Mechanical properties improvement of weldments using vibratory welding system. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 229(5), 776–784 (2015)CrossRef

Bade, V.S., Srinivasa Rao, P., Govinda Rao, P.: The effect of vibratory conditioning on tensile strength and microstructure of 1018 mild steel. World J. Eng.17(6), 837–844 (2020)

Specht, D.F.: A general regression neural network. IEEE Trans. Neural Netw. 2(6), 568–576 (1991)

Seela, C.R., Ravisankar, B., Raju, B.M.V.A.: A GRNN based frame work to test the influence of nano zinc additive biodiesel blends on CI engine performance and emissions. Egypt. J. Pet. 27(4), 641–647 (2018)

Bendu, H., Deepak, B.B.V.L., Murugan, S.: Application of GRNN for the prediction of performance and exhaust emissions in HCCI engine using ethanol. Energy Convers. Manage. 122, 165–173 (2016)

Majumder, H., Maity, K.P.: Predictive analysis on responses in WEDM of titanium grade 6 using general regression neural network (GRNN) and multiple regression analysis (MRA). SILICON 10(4), 1763–1776 (2018)

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)

Kalpana, J., Ramana, S.V., Rao, P.G., ChittiBabu, V., Santa Rao, K.: Implementation of generalized regression neural network to establish a relation between vibration parameters and time of vibration for welded joints. IOSR J. Mech. Civ. Eng. 6(4), 36–42 (2013)

10.

Rooki, R.: Application of general regression neural network (GRNN) for indirect measuring pressure loss of Herschel-Bulkley drilling fluids in oil drilling. Measurement 85, 184–191 (2016)

11.

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)

12.

Panda, B.N., Bahubalendruni, M.R., Biswal, B.B.: Optimization of resistance spot welding parameters using differential evolution algorithm and GRNN. In: 2014 IEEE 8th International Conference on Intelligent Systems and Control (ISCO), pp. 50–55 (2014)

13.

Wei, W., Shaoyi, B., Lanchun, Z., Kai, Z., Yongzhi, W., Weixing, H.: Vehicle sideslip angle estimation based on general regression neural network. Math. Prob. Eng. (2016)

14.

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)

15.

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)

16.

Rao, M.V., Rao, P.S., Babu, B.S.: Vibration effect on the impact strength of Al 5052–H32 weldments. J. Mech. Eng. Res. Dev. 40(1), 134–139 (2017)

Titel: General Regression Neural Network-Based Frame Work for the Evaluation of Ultimate Tensile Strength of Vibratory-Assisted Welded Joints
verfasst von: M. Vykunta Rao
M. V. A. Raju Bahubalendruni
Vinod Babu Chintada
Verlag: Springer Singapore
Buch: Recent Advances in Manufacturing, Automation, Design and Energy Technologies
Print ISBN: 978-981-16-4221-0

Electronic ISBN: 978-981-16-4222-7

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-981-16-4222-7_20

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