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A FEM-guided data-driven machine learning model for residual stress characterization in ultrasonic surface rolling of lightweight alloys

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Abstract

This work proposes the development of a Bayesian-regularized machine learning (ML) model tailored for predicting residual stresses in compressive and tensile modes during ultrasonic rolling of lightweight alloys. Training data were acquired through finite element method (FEM) simulations covering various parameters and alloys. The model exhibited high performance, with R2 values of 0.973 and 0.926, and corresponding RMSE values of 0.038 and 0.070 for compressive and tensile modes, respectively. The successful outcome was attributed to the effective implementation of Bayesian optimization, showcasing its proficiency in scenarios with limited data volumes. Furthermore, a delicate balance between the relevance scores of material properties and rolling processing parameters was identified for optimal prediction performance. Specifically, higher tensile stress values correlated with elevated relevance scores of static pressure, frequency, amplitude of ultrasonic vibration, compressive strength, Poisson ratio, and material density. In contrast, higher compressive stress values were well-predicted with increased relevance scores of rolling depth, amplitude of ultrasonic vibration, yield stress, and Poisson ratio. The study also elucidates the rationale behind these relevance scores and provides a compelling case study demonstrating the fine-tuning of input parameters to achieve target residual stress levels.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. Department of Computer Engineering & Applications, GLA University, Mathura, India

    Rahul Pradhan

  2. Department of Chemistry, University College of Duba, University of Tabuk, Tabuk, Saudi Arabia

    Farag M. A. Altalbawy

  3. Department of Industrial and Manufacturing Engineering, University of Engineering and Technology (Rachna Campus) Lahore, Lahore, Pakistan

    Ahmed Raza Khan

  4. Departamento de Ingeniería Civil, Universidad César Vallejo, Chimbote, Perú

    Carlos Rodriguez-Benites

  5. Department of Mathematics, Chaudhary Charan Singh University, Meerut, Uttar Pradesh, 250004, India

    M. K. Sharma

  6. Department of Computer Science, Nawroz University, Duhok, Iraq

    Renas Rajab Asaad

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  1. Rahul Pradhan
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  2. Farag M. A. Altalbawy
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Contributions

Rahul Pradhan played a key role in conceptualization, modeling, and simulation, as well as contributing to data analysis. Farag M. A. Altalbawy contributed significantly to investigation, literature review, and data analysis. Renas Rajab Asaad’s expertise was evident in modeling and simulation, investigation, and writing. Carlos Rodriguez-Benites made contributions to data analysis and literature review. Ahmed Raza Khan was involved in investigation, and numerical modeling. Finally, M. K. Sharma played a crucial role in conceptualization and data analysis. All authors contributed to the manuscript preparation and writing.

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Correspondence to Farag M. A. Altalbawy.

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Pradhan, R., Altalbawy, F.M.A., Khan, A.R. et al. A FEM-guided data-driven machine learning model for residual stress characterization in ultrasonic surface rolling of lightweight alloys. Appl. Phys. A 130, 400 (2024). https://doi.org/10.1007/s00339-024-07577-6

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