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MACHINE LEARNING CLASSIFICATION MODELS FOR DETECTION OF THE FRACTURE LOCATION IN DISSIMILAR FRICTION STIR WELDED JOINT
Authors:
Akshansh Mishra1
1Department of Mechanical Engineering, Politecnico Di Milano, Italy
Received: 26.07.2020.
Accepted: 17.08.2020.
Available: 30.09.2020.
Abstract:
Data analysis is divided into two categories i.e. classification and prediction. These two categories can be used for extraction of models from the dataset and further determine future data trends or important set of classes available in the dataset. The aim of the present work is to determine location of the fracture failure in dissimilar friction stir welded joint by using various machine learning classification models such as Decision Tree, Support Vector Machine (SVM), Random Forest, Naïve Bayes and Artificial Neural Network (ANN). It is observed that out of these classification algorithms, Artificial Neural Network results have the best accuracy score of 0.95.
Keywords:
Machine Learning, artificial neural network, artificial intelligence, friction stir welding
References:
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[2] A. McCallum, K. Nigam, J. Rennie, K. Seymore, A machine learning approach to building domain-specific search engines. Proceedings of the 16th international joint conference on Artificial intelligence -IJCAI’ 99, Vol.2, Morgan Kaufmann Publishers Inc., San Francisco, United States, 1999, pp.662-667.
[3] T.G. Dietterich, Machine-learning research. AI magazine, 18(4), 1997: 97-97. https://doi.org/10.1609/aimag.v18i4.1324
[4] C.M. Bishop, Pattern recognition and machine learning. Springer, 2006.
[5] M.E. Zaghloul, D. Linholm, C.P. Reeve, A machine-learning classification approach for IC manufacturing control based on test structure measurements. IEEE Transactions on Semiconductor Manufacturing, 2 (2), 1989. 47-53.
https://doi.org/10.1109/66.24928
[6] D. Kim, P. Kang, S. Cho, H.J. Lee, S. Doh, Machine learning-based novelty detection for faulty wafer detection in semiconductor manufacturing. Expert Systems with Applications, 39 (4), 2012: 4075-4083.
https://doi.org/10.1016/j.eswa.2011.09.088
[7] S. Sudhagar, M. Sakthivel, P. Ganeshkumar, Monitoring of friction stir welding based on vision system coupled with Machine learning algorithm. Measurement, 144, 2019: 135-143. https://doi.org/10.1016/j.measurement.2019.05.018
[8] Y. Du, T. Mukherjee, T. DebRoy, Conditions for void formation in friction stir welding from machine learning. npj Computational Materials, 5 (68), 2019: 1-8. https://doi.org/10.1038/s41524-019-0207-y
[9] S. Chinnakannan, Friction Welding of Austenitic Stainless Steel with Copper Material. Austenitic Stainless Steels: New Aspects, 2017. p.171.
[10] https://clearpredictions.com/Home/DecisionTreew
[11] https://builtin.com/data-science/randomforest-algorithm
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)
Volume 10
Number 3
September 2025
Last Edition
Volume 10
Number 3
September 2025
How to Cite
A. Mishra, Machine Learning Classification Models for Detection of the Fracture Location in Dissimilar Friction Stir Welded Joint. Applied Engineering Letters, 5(3), 2020: 87-93.
https://doi.org/10.18485/aeletters.2020.5.3.3
More Citation Formats
Mishra, A. (2020). Machine Learning Classification Models for Detection of the Fracture Location in Dissimilar Friction Stir Welded Joint. Applied Engineering Letters, 5(3), 87–93. https://doi.org/10.18485/aeletters.2020.5.3.3
Mishra, Akshansh. “Machine Learning Classification Models for Detection of the Fracture Location in Dissimilar Friction Stir Welded Joint.” Applied Engineering Letters, vol. 5, no. 3, 2020, pp. 87–93, https://doi.org/10.18485/aeletters.2020.5.3.3.
Mishra, Akshansh. 2020. “Machine Learning Classification Models for Detection of the Fracture Location in Dissimilar Friction Stir Welded Joint.” Applied Engineering Letters 5 (3): 87–93. https://doi.org/10.18485/aeletters.2020.5.3.3.
Mishra, A. (2020). Machine Learning Classification Models for Detection of the Fracture Location in Dissimilar Friction Stir Welded Joint. Applied Engineering Letters, 5(3), pp.87–93. doi:10.18485/aeletters.2020.5.3.3.
Journal Menu
Archive
MACHINE LEARNING CLASSIFICATION MODELS FOR DETECTION OF THE FRACTURE LOCATION IN DISSIMILAR FRICTION STIR WELDED JOINT
Authors:
Akshansh Mishra1
1Department of Mechanical Engineering, Politecnico Di Milano, Italy
Received: 26.07.2020.
Accepted: 17.08.2020.
Available: 30.09.2020.
Abstract:
Data analysis is divided into two categories i.e. classification and prediction. These two categories can be used for extraction of models from the dataset and further determine future data trends or important set of classes available in the dataset. The aim of the present work is to determine location of the fracture failure in dissimilar friction stir welded joint by using various machine learning classification models such as Decision Tree, Support Vector Machine (SVM), Random Forest, Naïve Bayes and Artificial Neural Network (ANN). It is observed that out of these classification algorithms, Artificial Neural Network results have the best accuracy score of 0.95.
Keywords:
Machine Learning, artificial neural network, artificial intelligence, friction stir welding
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)
Volume 10
Number 3
September 2025
Last Edition
Volume 10
Number 3
September 2025