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Plastic properties determination using virtual dynamic spherical indentation test and machine learning algorithms

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Abstract

Nondestructive experiments are widely employed for determining mechanical properties. In this work, a spherical impact test is virtually performed to determine the plastic properties of a thick aluminum plate. The finite element model is validated experimentally, and several simulations are then performed in accordance with the design of the experiment program. Several algorithms, including support vector machine, Gaussian process regression (GPR), and nonlinear regressions (second- and third-order polynomials) as machine learning techniques, are employed to estimate the material plastic properties. The indentation depth and indentation radius are considered as input variables to predict the tangent modulus (TM) and yield stress (YS). Results reveal that the second-order polynomial and GPR methods realize better performance in terms of determination coefficient and root mean square error criteria in assessing YS.

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Authors and Affiliations

  1. Mechanical Engineering Department, Engineering Faculty, Ayatollah Boroujerdi University, Boroujerd, Iran

    Mohammad Kashfi & Sepehr Goodarzi

  2. Department of Soil Science, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran

    Mostafa Rastgou

Authors
  1. Mohammad Kashfi
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  2. Sepehr Goodarzi
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  3. Mostafa Rastgou
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Corresponding author

Correspondence to Mohammad Kashfi.

Additional information

Mohammad Kashfi has a Ph.D. in Mechanical Engineering. He received his Ph.D. and M.Sc. in mechanical engineering from Bu-Ali Sina University. His research interests include finite element analysis, optimization methods, composite materials, and additive manufacturing.

Sepehr Goodarzi is an Undergraduate Mechanical Engineer. He received his degree from Ayatollah Boroujerdi University in Iran. His interests are solid mechanics and finite element simulation.

Mostafa Rastgou has a Ph.D. in soil physics and conservation. He received his Ph.D. and M.Sc. in soil physics and conservation from Bu-Ali Sina University. His research interests are related to data mining and machine learning.

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Kashfi, M., Goodarzi, S. & Rastgou, M. Plastic properties determination using virtual dynamic spherical indentation test and machine learning algorithms. J Mech Sci Technol 36, 325–331 (2022). https://doi.org/10.1007/s12206-021-1230-8

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  • DOI: https://doi.org/10.1007/s12206-021-1230-8

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