Deep Convolutional Neural Network Modeling and Laplace Transformation Algorithm for the Analysis of Surface Quality of Friction Stir Welded Joints
Abstract The quality of Friction Stir Welded joint depends on the input parameters like tool rotational speed, tool traverse speed (mm/min), tool tilt angle, and an axial plunge force. If there is any variation in these input parameters then there will be a chance of formation of various surface defects such as groovy edges, flash formation, and non-homogeneous mixing of alloys. The main objective of the present work is to use machine learning algorithms such as Deep Convolutional Neural Network (DCNN) and Laplace transformation algorithm to detect these surface defects present on the Friction Stir Welded joint. The results showed that the used algorithms can easily detect such surface defects with good accuracy.
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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. https://doi.org/10.14201/ADCAIJ202110199110
Mishra, A. 2020. “Artificial Intelligence Algorithms for the Analysis of Mechanical Property of Friction Stir Welded Joints by using Python Programming”, Weld. Tech. Rev., vol. 92, no. 6, pp. 7-16, Aug. 2020.
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Verma, S., Misra, J.P., Singh, J., Batra, U. and Kumar, Y., 2021. Prediction of tensile behavior of FS welded AA7039 using machine learning. Materials Today Communications, 26, p.101933.
Tawfik, S.A., Isayev, O., Spencer, M.J. and Winkler, D.A., 2020. Predicting thermal properties of crystals using machine learning. Advanced Theory and Simulations, 3(2), p.1900208.
Jiang, X., Jia, B., Zhang, G., Zhang, C., Wang, X., Zhang, R., Yin, H., Qu, X., Song, Y., Su, L. and Mi, Z., 2020. A strategy combining machine learning and multiscale calculation to predict tensile strength for pearlitic steel wires with industrial data. Scripta Materialia, 186, pp.272-277.
Behnood, A. and Golafshani, E.M., 2020. Machine learning study of the mechanical properties of concretes containing waste foundry sand. Construction and Building Materials, 243, p.118152.
Bai, B., Han, X., Zheng, Q., Jia, L., Zhang, C. and Yang, W., 2020. Composition optimization of high strength and ductility ODS alloy based on machine learning. Fusion Engineering and Design, 161, p.111939.
Xie, Q., Suvarna, M., Li, J., Zhu, X., Cai, J. and Wang, X., 2020. Online prediction of mechanical properties of hot rolled steel plate using machine learning. Materials & Design, p.109201.
Xu, X., Wang, L., Zhu, G. and Zeng, X., 2020. Predicting Tensile Properties of AZ31 Magnesium Alloys by Machine Learning. JOM, pp.1-8.
Zhang, J., Sun, Y., Li, G., Wang, Y., Sun, J. and Li, J., 2020. Machine-learning-assisted shear strength prediction of reinforced concrete beams with and without stirrups. Engineering with Computers, pp.1-15.
Mishra, A. (2020). Local binary pattern for the evaluation of surface quality of dissimilar Friction Stir Welded Ultrafine Grained 1050 and 6061-T6 Aluminium Alloys. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 9(2), 69-77. https://doi.org/10.14201/ADCAIJ2020926977
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. https://doi.org/10.14201/ADCAIJ202110199110
Mishra, A. 2020. “Artificial Intelligence Algorithms for the Analysis of Mechanical Property of Friction Stir Welded Joints by using Python Programming”, Weld. Tech. Rev., vol. 92, no. 6, pp. 7-16, Aug. 2020.
Mishra, A., 2020. Discrete Wavelet Transformation Approach for Surface Defects Detection in Friction Stir Welded Joints. Fatigue of Aircraft Structures, 1(ahead-of-print).
Verma, S., Misra, J.P., Singh, J., Batra, U. and Kumar, Y., 2021. Prediction of tensile behavior of FS welded AA7039 using machine learning. Materials Today Communications, 26, p.101933.
Mishra, A., & Patti, A. (2021). Deep Convolutional Neural Network Modeling and Laplace Transformation Algorithm for the Analysis of Surface Quality of Friction Stir Welded Joints. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 10(3), 307–320. https://doi.org/10.14201/ADCAIJ2021103307320
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