Gene Expression Programming Model for Tribological Behavior of Novel SiC-ZrO2-Al Hybrid Composites

Hossein Abbasi; Malihe Zeraati; Reza Fallah Moghaddam; Narendra Pal Singh Chauhan; Ghasem Sargazi; Ritamaria Di Lorenzo

doi:10.3390/ma15238593

Gene Expression Programming Model for Tribological Behavior of Novel SiC-ZrO₂-Al Hybrid Composites

Materials (Basel). 2022 Dec 2;15(23):8593. doi: 10.3390/ma15238593.

Authors

Hossein Abbasi¹, Malihe Zeraati², Reza Fallah Moghaddam³, Narendra Pal Singh Chauhan⁴, Ghasem Sargazi⁵, Ritamaria Di Lorenzo⁶

Affiliations

¹ Department of Computer Engineering, Tangestan Branch, Islamic Azad University, Ahram 75541, Iran.
² Department of Metallurgy and Materials Science, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman 76169-14111, Iran.
³ Faculty of Engineering, University of Garmsar, Garmsar 59146-33817, Iran.
⁴ Department of Chemistry, Faculty of Science, Bhupal Nobles' University, Udaipur 313002, Rajasthan, India.
⁵ Noncommunicable Diseases Research Center, Bam University of Medical Sciences, Bam 76617-71967, Iran.
⁶ Facoltà di Medicina e Chirurgia, Università degli Studi di Napoli Federico II, 80138 Naples, Italy.

Abstract

In order to improve product format quality and material flexibility, variety of application, and cost-effectiveness, SiC, ZrO₂, and Al hybrid composites were manufactured in the research utilizing the powder metallurgy (PM) technique. A model was created to predict the tribological behavior of SiC-ZrO₂-Al hybrid composites using statistical data analysis and gene expression programming (GEP) based on artificial intelligence. For the purpose of examining the impact of zirconia concentration, sliding distance, and applied stress on the wear behavior of hybrid composites, a comprehensive factor design of experiments was used. The developed GEP model was sufficiently robust to achieve extremely high accuracy in the prediction of the determine coefficient (R²), the root mean square error (RMSE), and the root relative square error (RRSE). The maximum state of the RMSE was 0.4357 for the GEP-1 (w1) model and the lowest state was 0.7591 for the GEP-4 (w1) model, while the maximum state of the RRSE was 0.4357 for the GEP-1 (w1) model and the minimum state was 0.3115 for the GEP-3 model (w1).

Keywords: composite; gene expression programming; hybrid; tribological; zirconia.

Grants and funding

This research received no external funding.