Optimization of Tool Wear and Cutting Parameters in SCCO2-MQL Ultrasonic Vibration Milling of SiCp/Al Composites

Silicon carbide particle-reinforced aluminum matrix (SiCp/Al) composites are significant lightweight metal matrix composites extensively utilized in precision instruments and aerospace sectors. Nevertheless, the inclusion of rigid SiC particles exacerbates tool wear in mechanical machining, resulting in a decline in the quality of surface finishes. This work undertakes a comprehensive investigation into the problem of tool wear in SiCp/Al composite materials throughout the machining process. Initially, a comprehensive investigation was conducted to analyze the effects of cutting velocity vc, feed per tooth fz, milling depth ap, and milling width ae on tool wear during high-speed milling under SCCO2-MQL (Supercritical Carbon Dioxide Minimum Quantity Lubrication) ultrasonic vibration conditions. The results show that under the condition of SCCO2-MQL ultrasonic vibration, proper control of milling parameters can significantly reduce tool wear, extend tool service life, improve machining quality, and effectively reduce blade breakage and spalling damage to the tool, reduce abrasive wear and adhesive wear, and thus significantly improve the durability of the tool. Furthermore, a prediction model for tool wear was developed by employing the orthogonal test method and multiple linear regression. The model’s relevance and accuracy were confirmed using F-tests and t-tests. The results show that the model can effectively predict tool wear, among which cutting velocity vc and feed rate fz are the key parameters affecting the prediction accuracy. Finally, a genetic algorithm was used to optimize the milling parameters, and the optimal parameter combination (vc = 60.00 m/min, fz = 0.08 mm/z, ap = 0.20 mm) was determined, and the optimized milling parameters were tested. Empirical findings suggest that the careful selection of milling parameters can significantly mitigate tool wear, extend the lifespan of the tool, and enhance the quality of the surface. This work serves as a significant point of reference for the processing of SiCp/Al composite materials.

Authors

Institutions

Publication Details

Journal
Machines
Published
2024-09-14
DOI
https://doi.org/10.3390/machines12090646
Citations
5
Primary Topic
Advanced machining processes and optimization
Type
article
Field-Weighted Citation Impact
1.06
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimization of Tool Wear and Cutting Parameters in SCCO2-MQL Ultrasonic Vibration Milling of SiCp/Al Composites

Bowen Wang, Xinran Wang, Huiping Zhang, Liqiang Qu
5 citations
Machines
Advanced machining processes and optimization
1.06
article

Optimization of Tool Wear and Cutting Parameters in SCCO2-MQL Ultrasonic Vibration Milling of SiCp/Al Composites

Bowen Wang, Xinran Wang, Huiping Zhang, Liqiang Qu
article en
5 citations

Abstract

Silicon carbide particle-reinforced aluminum matrix (SiCp/Al) composites are significant lightweight metal matrix composites extensively utilized in precision instruments and aerospace sectors. Nevertheless, the inclusion of rigid SiC particles exacerbates tool wear in mechanical machining, resulting in a decline in the quality of surface finishes. This work undertakes a comprehensive investigation into the problem of tool wear in SiCp/Al composite materials throughout the machining process. Initially, a comprehensive investigation was conducted to analyze the effects of cutting velocity vc, feed per tooth fz, milling depth ap, and milling width ae on tool wear during high-speed milling under SCCO2-MQL (Supercritical Carbon Dioxide Minimum Quantity Lubrication) ultrasonic vibration conditions. The results show that under the condition of SCCO2-MQL ultrasonic vibration, proper control of milling parameters can significantly reduce tool wear, extend tool service life, improve machining quality, and effectively reduce blade breakage and spalling damage to the tool, reduce abrasive wear and adhesive wear, and thus significantly improve the durability of the tool. Furthermore, a prediction model for tool wear was developed by employing the orthogonal test method and multiple linear regression. The model’s relevance and accuracy were confirmed using F-tests and t-tests. The results show that the model can effectively predict tool wear, among which cutting velocity vc and feed rate fz are the key parameters affecting the prediction accuracy. Finally, a genetic algorithm was used to optimize the milling parameters, and the optimal parameter combination (vc = 60.00 m/min, fz = 0.08 mm/z, ap = 0.20 mm) was determined, and the optimized milling parameters were tested. Empirical findings suggest that the careful selection of milling parameters can significantly mitigate tool wear, extend the lifespan of the tool, and enhance the quality of the surface. This work serves as a significant point of reference for the processing of SiCp/Al composite materials.

MachinesVol. 12(9)
Harbin University of Science and Technology (CN), Hong Kong Polytechnic University (HK)
Openalex Percentile: Top 26%
Advanced machining processes and optimization
1.06
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.