Prediction of machined surface roughness during the high-speed machining of Zr-based bulk metallic glasses

A theoretical model of surface roughness prediction for high-speed machining Zr-based bulk metallic glasses was established. The roughness components of the prediction model, including plastic side flow, material recovery, cutting vibration and kinematics were discussed. The cutting forces and machined surface morphology were studied to characterize the cutting vibration and plastic side flow, respectively. Specially, the cutting vibration during machining was characterized with radial force amplitude. The surface morphology of the Zr-based bulk metallic glass (BMG) was mainly characterized with plastic flow traces, molten droplets, dimples, adhensions and ploughing marks. The presence of material recovery would effectively improve the surface quality and, on the contrary, the presence of plastic side flow would deteriorate it. Finally, the surface roughness prediction model was validated with turning experiments under varying tool geometric parameters, and the calculated surface roughness showed good agreement with the measured results. The present results are beneficial to revealing the underlying mechanisms for machined surface roughness, and can provide basis for optimizing cutting parameters and surface quality for industry.

Authors

Institutions

Publication Details

Journal
Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture
Published
2025-04-22
DOI
https://doi.org/10.1177/09544054251333501
Primary Topic
Metallic Glasses and Amorphous Alloys
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Prediction of machined surface roughness during the high-speed machining of Zr-based bulk metallic glasses

Yongsen Shang, Shunhua Chen, Haidong Yang, Junsheng Zhang et al.
Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture
Metallic Glasses and Amorphous Alloys
article

Prediction of machined surface roughness during the high-speed machining of Zr-based bulk metallic glasses

Yongsen Shang, Shunhua Chen, Haidong Yang, Junsheng Zhang, Zhenhua Qing
article en

Abstract

A theoretical model of surface roughness prediction for high-speed machining Zr-based bulk metallic glasses was established. The roughness components of the prediction model, including plastic side flow, material recovery, cutting vibration and kinematics were discussed. The cutting forces and machined surface morphology were studied to characterize the cutting vibration and plastic side flow, respectively. Specially, the cutting vibration during machining was characterized with radial force amplitude. The surface morphology of the Zr-based bulk metallic glass (BMG) was mainly characterized with plastic flow traces, molten droplets, dimples, adhensions and ploughing marks. The presence of material recovery would effectively improve the surface quality and, on the contrary, the presence of plastic side flow would deteriorate it. Finally, the surface roughness prediction model was validated with turning experiments under varying tool geometric parameters, and the calculated surface roughness showed good agreement with the measured results. The present results are beneficial to revealing the underlying mechanisms for machined surface roughness, and can provide basis for optimizing cutting parameters and surface quality for industry.

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture
Hefei University of Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 90%
Metallic Glasses and Amorphous Alloys
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.