What question did this study set out to answer?

This work aims to investigate the wear behavior of AZ31 Mg alloy reinforced with TiC under varying loads using innovative processing techniques and machine learning models.

March 26, 2026Scientific ReportsOpen Access

Predicting wear behavior of AZ31/TiC composites produced via ultrasonic vibration assisted friction stir processing using machine learning models

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Authors

TKT. Satish Kumar SSS. Sri Shalini JPJana Petrů

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Overview

Experimental analysis reveals improved wear resistance in AZ31/TiC composites via innovative processing methods, indicating significant advances in material performance.

Key Points

This work aims to investigate the wear behavior of AZ31 Mg alloy reinforced with TiC under varying loads using innovative processing techniques and machine learning models.
Produced AZ31 Mg alloy reinforced with 15 vol.% TiC using ultrasonic vibration assisted friction stir processing (FSVP).
Assessed wear characteristics using a pin-on-disc tribometer under different applied loads.
Compared performance of FSVP and conventional friction stir processing (FSP) methods using tribological testing.
Employed Gradient Boosting machine learning model for predictive analysis of wear behavior.
FSVP reduced the coefficient of friction and improved wear resistance significantly.
Wear rate decreased by approximately 25% at moderate loads and up to 50% at elevated loads with TiC incorporation.
Identified a transition in wear mechanisms from mild oxidative and abrasive wear at lower loads to severe plastic deformation at higher loads.
Achieved high predictive accuracy with R² value of 0.9925 using Gradient Boosting model.

Cite This Study

Kumar et al. (2026) studied this question.

synapsesocial.com/papers/69c4cd80fdc3bde448919df9 https://doi.org/10.1038/s41598-026-44372-0

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March 26, 2026Scientific ReportsOpen Access

Predicting wear behavior of AZ31/TiC composites produced via ultrasonic vibration assisted friction stir processing using machine learning models

View Full Paper

Ask AI

Bookmark

Authors

TKT. Satish Kumar SSS. Sri Shalini JPJana Petrů

Discussion

Member takes

Overview

Experimental analysis reveals improved wear resistance in AZ31/TiC composites via innovative processing methods, indicating significant advances in material performance.

Key Points

This work aims to investigate the wear behavior of AZ31 Mg alloy reinforced with TiC under varying loads using innovative processing techniques and machine learning models.
Produced AZ31 Mg alloy reinforced with 15 vol.% TiC using ultrasonic vibration assisted friction stir processing (FSVP).
Assessed wear characteristics using a pin-on-disc tribometer under different applied loads.
Compared performance of FSVP and conventional friction stir processing (FSP) methods using tribological testing.
Employed Gradient Boosting machine learning model for predictive analysis of wear behavior.
FSVP reduced the coefficient of friction and improved wear resistance significantly.
Wear rate decreased by approximately 25% at moderate loads and up to 50% at elevated loads with TiC incorporation.
Identified a transition in wear mechanisms from mild oxidative and abrasive wear at lower loads to severe plastic deformation at higher loads.
Achieved high predictive accuracy with R² value of 0.9925 using Gradient Boosting model.

Cite This Study

Kumar et al. (2026) studied this question.

synapsesocial.com/papers/69c4cd80fdc3bde448919df9 https://doi.org/10.1038/s41598-026-44372-0

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