Yttrium oxide reinforced AZ31B processed by FSP: Predictive modeling of mechanical, tribological, and corrosion properties

Magnesium alloy AZ31B was subjected to friction stir processing with yttrium oxide reinforcement, resulting in substantial enhancement of its mechanical properties. FSP decreased the grain size from 10 µm to 3.19 µm, concurrently elevating microhardness from 83 HV to 129.6 HV, for a tool rotational speed of 1200 rpm, a traverse speed of 13 mm/min, and an 18 mm shoulder diameter. The wear rate decreased from 9.4 × 10 -7 g/Nm to 6.9 × 10 -7 g/Nm, and the corrosion rate decreased from 9 mm/year to 2.06 mm/year. Predictive models developed using Deep Neural Networks, 1D Convolutional Neural Networks, and Gaussian Process Regression, proficiently assessed hardness, wear, and corrosion resistance. The results underscore the beneficial effects of yttrium oxide and refined FSP.

Authors

Institutions

Publication Details

Journal
Materials Science and Technology
Published
2025-02-16
DOI
https://doi.org/10.1177/02670836251316550
Citations
5
Primary Topic
Aluminum Alloys Composites Properties
Type
article
Field-Weighted Citation Impact
3.57
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Yttrium oxide reinforced AZ31B processed by FSP: Predictive modeling of mechanical, tribological, and corrosion properties

R. Vaira Vignesh, Padmanaban Ramasamy, V. Balasubramanian, B Subi
5 citations
Materials Science and Technology
Aluminum Alloys Composites Properties
3.57
article

Yttrium oxide reinforced AZ31B processed by FSP: Predictive modeling of mechanical, tribological, and corrosion properties

R. Vaira Vignesh, Padmanaban Ramasamy, V. Balasubramanian, B Subi
article en
5 citations

Abstract

Magnesium alloy AZ31B was subjected to friction stir processing with yttrium oxide reinforcement, resulting in substantial enhancement of its mechanical properties. FSP decreased the grain size from 10 µm to 3.19 µm, concurrently elevating microhardness from 83 HV to 129.6 HV, for a tool rotational speed of 1200 rpm, a traverse speed of 13 mm/min, and an 18 mm shoulder diameter. The wear rate decreased from 9.4 × 10 -7 g/Nm to 6.9 × 10 -7 g/Nm, and the corrosion rate decreased from 9 mm/year to 2.06 mm/year. Predictive models developed using Deep Neural Networks, 1D Convolutional Neural Networks, and Gaussian Process Regression, proficiently assessed hardness, wear, and corrosion resistance. The results underscore the beneficial effects of yttrium oxide and refined FSP.

Materials Science and Technology
Annamalai University (IN), Amrita Vishwa Vidyapeetham (IN)
Openalex Percentile: Top 8%
Aluminum Alloys Composites Properties
3.57
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Yttrium oxide reinforced AZ31B processed by FSP: Predictive modeling of mechanical, tribological, and corrosion properties — R. Vaira Vignesh, Padmanaban Ramasamy, et al. · Materials Science and Technology (2025) | TGRS Research Map | TGRS