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International Journal on Interactive Design and Manufacturing (IJIDeM)

Erschienen in:

28.05.2022 | Short Original Paper

Performance evaluation of machine learning based-classifiers in friction stir welding of Aa6061-T6 alloy

verfasst von: A. Kiran Kumar, Mulugundam Siva Surya, P. Venkataramaiah

Erschienen in: International Journal on Interactive Design and Manufacturing (IJIDeM) | Ausgabe 1/2023

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Abstract

Machine learning (ML) in the manufacturing sector shows promising results that help lower costs, time, and complexity in identifying the best parameters out of the wide range. This work studied three machine learning classification models' (decision tree, random forest, and XGBoost models) performance for a friction stir welded AA 6061-T6 aluminium alloy to determine the best classifier model. The models were created by training and testing the experimental data to check the influence of input parameters on the yield strength of the FSW AA6061-T6 alloy. The XGBoost model showed a maximum accuracy of 95.24% among the three models. The classifier models performance is evaluated using AUC (area under the curve metric) and confusion matrix. Finally, the model can be used to predict the combination of parameters that produce the required weld strength, which helps reduce the experimental cost and material wastage.

Vorheriger Artikel Experimental investigation and parametric optimization of a milling process using multi-criteria decision making methods: a comparative analysis

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Okuyucu, H., Kurt, A., Arcaklioglu, E.: Artificial neural network application to the friction stir welding of aluminum plates. Mater. Des. 28(1), 78–84 (2007)CrossRef

Shojaeefard, M.H., Khalkhali, A., Akbari, M., et al.: Investigation of friction stir welding tool parameters using FEM and neural network. Proc. Inst. Mech. Eng. L J. Mat. Des. Appl. 229(3), 209–217 (2015)

Tansel, I.N., Demetgul, M., Okuyucu, H., et al.: Optimizations of friction stir welding of aluminum alloy by using genetically optimized neural network. Int. J. Adv. Manuf. Technol. 48(1–4), 95–101 (2010)CrossRef

Ghetiya, N.D., Patel, K.M., Kavar, A.J.: Multi-objective opti- mization of FSW process parameters of aluminium alloy using Taguchi-based grey relational analysis. Trans. Indian Inst. Met. 69(4), 917–923 (2016)CrossRef

Na, M.G., Kim, J.W., Lim, D.H., et al.: Residual stress predic- tion of dissimilar metals welding at NPPs using support vector regression. Nucl. Eng. Des. 238(7), 1503–1510 (2008)CrossRef

Verma, S., Gupta, M., Misra, J.P.: Performance evaluation of friction stir welding using machine learning approaches. MethodsX. 5, 1048–1058 (2018)CrossRef

Chan, J.C.W., Paelinckx, D.: Evaluation of random forest and adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using air- borne hyperspectral imagery. Remote Sens. Environ. 112(6), 2999–3011 (2008)CrossRef

Sesnie, S.E., Gessler, P.E., Finegan, B., et al.: Integrating Landsat TM and SRTM-DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments. Remote Sens. Environ. 112(5), 2145–2159 (2008)CrossRef

Waske, B., Braun, M.: Classifier ensembles for land cover mapping using multitemporal SAR imagery. ISPRS J. Photogr. Remote Sens. 64(5), 450–457 (2009)CrossRef

10.

Astuti, W., Armansyah, J.: Saedon, Development of prediction system model for mechanical property in friction stir welding using support vector machine (SVM). J. Mech. Eng. 5, 216–225 (2018)

11.

Berkay, E., Mehmet Ali, G., Selcuk, M.: Artificial intelligence applications for friction stir welding: a review, metals and materials international (2020). https://doi.org/10.1007/s12540-020-00854-y

12.

Kalinenko, A., Vysotskii, I., Malopheyev, S., Mironov, S., Kaibyshev, R.: Relationship between welding conditions, abnormal grain growth and mechanical performance in friction-stir welded 6061–T6 aluminum alloy. Mater. Sci. Eng. A (2021). https://doi.org/10.1016/j.msea.2021.141409CrossRef

13.

Zhang, L., Zhong, H., Li, S., Zhao, H., Chen, J., Qi, L.: Microstructure, mechanical properties and fatigue crack growth behavior of friction stir welded joint of 6061–T6 aluminum alloy. Int. J. Fatigue (2020). https://doi.org/10.1016/j.ijfatigue.2020.105556CrossRef

14.

Sergey, M., Igor, V., Vladislav, K., Sergey, M., Rustam, K.: Optimization of processing-microstructure-properties relationship in friction-stir welded 6061-T6 aluminum alloy. Mater. Sci. Eng. A 136–143 (2016). https://doi.org/10.1016/j.msea.2016.03.063

15.

Kalinenko, A., Kim, K., Vysotskiy, I., Zuiko, I., Malopheyev, S., Mironov, S., Kaibyshev, R.: Microstructure-strength relationship in friction-stir welded 6061–T6 aluminum alloy. Mater. Sci. Eng. A (2020). https://doi.org/10.1016/j.msea.2020.139858CrossRef

16.

Shivraman, T., Akshansh, M.: Machine learning classification-based approach for mechanical properties of friction stir welding of copper. Manuf. Lett. 52–55 (2010). https://doi.org/10.1016/j.mfglet.2021.05.010

17.

Salloomi, K.N.: Fully coupled thermomechanical simulation of friction stir welding of aluminum 6061-T6 alloy T-joint. J. Manuf. Process. 746–754 (2019). https://doi.org/10.1016/j.jmapro.2019.06.030.

18.

Asmare, A., Al-Sabur, R., Messele, E.: Experimental investigation of friction stir welding on 6061–T6 aluminum alloy using Taguchi-based GRA. Metals (2020). https://doi.org/10.3390/met10111480CrossRef

19.

Kishan, F., Vishvesh, B.: Effect of shoulder diameter on bobbin tool friction stir welding of AA 6061-T6 alloy. Mater. Today Proc. 810–815 (2021). https://doi.org/10.1016/j.matpr.2020.11.366

20.

Liu, H.J., Hou, J.C., Guo, H.: Effect of welding speed on microstructure and mechanical properties of self-reacting friction stir welded 6061-T6 aluminum alloy, Mater. Des. 872–878 (2013). https://doi.org/10.1016/j.matdes.2013.03.105

21.

Jingqing, Z., Yifu, S., Bo, L., Haisheng, X., Xin, Y., Binbin, K., Jicheng, G.: Numerical simulation and experimental investigation on friction stir welding of 6061-T6 aluminum alloy. Mater. Des. 94–101 (2014). https://doi.org/10.1016/j.matdes.2014.03.043

22.

Rajkumar, S., Muralidharan, C., Balasubramanian, V.: Establishing empirical relationships to predict grain size and tensile strength of friction stir welded AA 6061-T6 aluminium alloy joints. Trans. Nonferrous Metals Soc. China 1863–1872 (2010).

23.

Elatharasan, G., Senthil Kumar, V.S.: An experimental analysis and optimization of process parameter on friction stir welding of AA 6061–T6 aluminum alloy using RSM. Proc. Eng. 64, 1227–1234 (2013)CrossRef

24.

Maneiah, D., Debashis Mishra, K., Prahlada Rao, K., Raju, B.: Process parameters optimization of friction stir welding for optimum tensile strength in Al 6061–T6 alloy butt welded joints. Mater. Today Proc. 27, 904–908 (2020). https://doi.org/10.1016/j.matpr.2020.01.215CrossRef

Titel: Performance evaluation of machine learning based-classifiers in friction stir welding of Aa6061-T6 alloy
verfasst von: A. Kiran Kumar
Mulugundam Siva Surya
P. Venkataramaiah
Publikationsdatum: 28.05.2022
Verlag: Springer Paris
Erschienen in: International Journal on Interactive Design and Manufacturing (IJIDeM) / Ausgabe 1/2023
Print ISSN: 1955-2513
Elektronische ISSN: 1955-2505
DOI: https://doi.org/10.1007/s12008-022-00904-2

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