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Crack Growth Prediction Models for a Pre-defined Semi-elliptical Crack Embedded in a Cantilever Bar Using Supervised Machine Learning Algorithms

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Advances in Mechanical Engineering and Material Science (ICAMEMS 2023)

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

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Abstract

Any machine component or structure can fracture due to the presence of cracks. With the assistance of finite element tools, we can only dissect the stable crack growth that requires much computational time and is vulnerable. This work developed several ML models using supervised machine learning algorithms and compared their performance. These models have shown decent precision in detecting the crack growth behavior of a pre-defined semi-elliptical crack embedded in a cantilever bar. The correlation coefficient R squared (R2), mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) were used to evaluate and compare the performance of the developed ML models. The accuracy of the crack growth forecast is found to be ~ 86.47%, ~ 93.68%, ~ 91.50%, ~ 92.04%, and ~ 94.64% for linear regression (LR), quadratic polynomial regression (QPR), decision tree (DT), random forest (RF), and k-nearest neighbor (KNN), respectively; among them, KNN had the best prediction accuracy.

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Acknowledgements

I thank my research supervisor, Prof. Mukul Shukla, for productive conversations and feedback.

Data Availability Statement

The researchers can access the training data and algorithms. This work uses upon reasonable request.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, India

    Harsh Kumar Bhardwaj & Mukul Shukla

  2. Department of Mechanical and Industrial Engineering Technology, University of Johannesburg, Johannesburg, South Africa

    Mukul Shukla

Authors
  1. Harsh Kumar Bhardwaj
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  2. Mukul Shukla
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Corresponding author

Correspondence to Mukul Shukla .

Editor information

Editors and Affiliations

  1. School of Mechanical Engineering (SMEC), VIT-AP University, Amaravati, Andhra Pradesh, India

    Pankaj Tambe

  2. Mechanical Engineering, Binghamton University, Binghamton, NY, USA

    Peter Huang

  3. School of Mechanical Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India

    Suyog Jhavar

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Bhardwaj, H.K., Shukla, M. (2024). Crack Growth Prediction Models for a Pre-defined Semi-elliptical Crack Embedded in a Cantilever Bar Using Supervised Machine Learning Algorithms. In: Tambe, P., Huang, P., Jhavar, S. (eds) Advances in Mechanical Engineering and Material Science. ICAMEMS 2023. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-99-5613-5_11

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  • DOI: https://doi.org/10.1007/978-981-99-5613-5_11

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-5612-8

  • Online ISBN: 978-981-99-5613-5

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