Estimation of Grain Size Distribution of Friction Stir Welded Joint by using Machine Learning Approach


Machine learning has widely spread in the areas of pattern recognition, prediction or forecasting, cognitive game theory and in bioinformatics. In recent days, machine learning is being introduced into manufacturing and material industries for the development of new materials and simulating the manufacturing of the required products. In the recent paper, machine learning algorithm is developed by using Python programming for the determination of grain size distribution in the microstructure of stir zone seam of Friction Stir Welded magnesium AZ31B alloy plate The grain size parameters such as an equivalent diameter, perimeter, area, orientation etc. were determined. The results showed that the developed algorithm is able to determine various grain size parameters accurately.
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1. Aggour, K.S., Gupta, V.K., Ruscitto, D., Ajdelsztajn, L., Bian, X., Brosnan, K.H., Kumar, N.C., Dheeradhada, V., Hanlon, T., Iyer, N. and Karandikar, J., 2019. Artificial intelligence/machine learning in manufacturing and inspection: A GE perspective. MRS Bulletin, 44(7), pp.545-558.
2. Li, Z., Zhang, Z., Shi, J. and Wu, D., 2019. Prediction of surface roughness in extrusion-based additive manufacturing with machine learning. Robotics and Computer-Integrated Manufacturing, 57, pp.488-495.
3. García-Moreno, A.I., Alvarado-Orozco, J.M., Ibarra-Medina, J. and Martínez-Franco, E., 2020. Image-based porosity classification in Al-alloys by laser metal deposition using random forests. The International Journal of Advanced Manufacturing Technology, pp.1-19.
4. Kopper, A., Karkare, R., Paffenroth, R.C. and Apelian, D., 2020. Model Selection and Evaluation for Machine Learning: Deep Learning in Materials Processing. Integrating Materials and Manufacturing Innovation, pp.1-14.
5. Pothur Hema (October 24th 2019). Experimental Investigations on AA 6061 Alloy Welded Joints by Friction Stir Welding, Aluminium Alloys and Composites, Kavian Omar Cooke, IntechOpen, DOI: 10.5772/intechopen.89797. Available from:
6. Verma, S., Misra, J.P. and Popli, D., 2020. Modeling of friction stir welding of aviation grade aluminium alloy using machine learning approaches. International Journal of Modelling and Simulation, pp.1-8.
7. Hartl, R., Praehofer, B. and Zaeh, M.F., 2020. Prediction of the surface quality of friction stir welds by the analysis of process data using Artificial Neural Networks. Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, 234(5), pp.732-751.
8. Srinivasan, K., Deepa, N. and PM, D.R.V., 2020. Realizing the Resolution Enhancement of Tube-to-Tube Plate Friction Welding Microstructure Images Via Hybrid Sparsity Model for Improved Weld Interface Defects Diagnosis. Journal of Internet Technology, 21(1), pp.61-72.
9. Subramani, V., Jayavel, B., Sengottuvelu, R. and Lazar, P.J.L., 2019. Assessment of microstructure and mechanical properties of stir zone seam of friction stir welded magnesium AZ31B through nano-SiC. Materials, 12(7), p.1044.
Mishra, A., & Pathak, T. (2020). Estimation of Grain Size Distribution of Friction Stir Welded Joint by using Machine Learning Approach. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 10(1), 99–110.


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