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Supervised and Unsupervised Machine Learning Algorithms for Forecasting the Fracture Location in Dissimilar Friction-Stir-Welded Joints

Author

Listed:
  • Akshansh Mishra

    (School of Industrial and Information Engineering, Politecnico Di Milano, 20156 Milan, Italy)

  • Anish Dasgupta

    (Artificial Intelligence Analytics, Cognizant Technology Solutions, Kolkata 700106, India)

Abstract

Artificial-intelligence-based algorithms are used in manufacturing to automate difficult activities and discover workflow or process patterns that had never been noticed before. Recent studies deal with the forecasting of the fracture location in dissimilar friction-stir-welded AA5754–C11000 alloys. Four types of supervised machine-learning-based classification algorithms i.e., decision tree, logistic classification, random forest, and AdaBoost were implemented. Additionally, in the present work, for the first time, a neurobiological-based unsupervised machine learning algorithm, i.e., self-organizing map (SOM) neural network, is implemented for determining the fracture location in dissimilar friction-stir-welded AA5754–C11000 alloys. Tool shoulder diameter (mm), tool rotational speed (RPM), and tool traverse speed (mm/min) are input parameters, while the fracture location, i.e., whether the specimen’s fracture is in the thermo-mechanically affected zone (TMAZ) of copper, or if it fractures in the TMAZ of aluminium. The results show that out of all implemented algorithms, the SOM algorithm is able to predict the fracture location with the highest accuracy of 96.92%.

Suggested Citation

  • Akshansh Mishra & Anish Dasgupta, 2022. "Supervised and Unsupervised Machine Learning Algorithms for Forecasting the Fracture Location in Dissimilar Friction-Stir-Welded Joints," Forecasting, MDPI, vol. 4(4), pages 1-11, September.
    • Handle: RePEc:gam:jforec:v:4:y:2022:i:4:p:43-797:d:929320
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    References listed on IDEAS

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    1. Yuo-Hsien Shiau & Su-Fen Yang & Rishan Adha & Syamsiyatul Muzayyanah, 2022. "Modeling Industrial Energy Demand in Relation to Subsector Manufacturing Output and Climate Change: Artificial Neural Network Insights," Sustainability, MDPI, vol. 14(5), pages 1-18, March.
    2. Peng Zhan & Shaokun Wang & Jun Wang & Leigang Qu & Kun Wang & Yupeng Hu & Xueqing Li, 2021. "Temporal anomaly detection on IIoT-enabled manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1669-1678, August.
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