Study on micro-grinding mechanism and surface quality of high-volume fraction SiCp/Al composites

Gao, Qi; Guo, Guangyan; Wang, Quanzhao

doi:10.1007/s12206-021-0612-2

Cited by 23 publications

(6 citation statements)

References 7 publications

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“…Since the processed aluminum matrix and SiCp particles are irregularly mixed together, it is difficult to distinguish them by SEM observation. Therefore, energy dispersive spectroscope (EDS) analysis was used to realize the differentiation between SiCp particles on the surface and the aluminum matrix [ 30 ]. The EDS surface sweep analysis of sample A after UAG machining with 4 μm is shown in Figure 6 a.…”

Section: Results and Analysismentioning

confidence: 99%

Experimental Studies of the Machinability of SiCp/Al with Different Volume Fractions under Ultrasonic-Assisted Grinding

Hu,

Zhu,

Fan

2024

Materials

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High-volume fraction silicon carbide particle-reinforced aluminum (SiCp/Al) has a promising application for its high specific strength, wear resistance, and thermal conductivity. However, SiCp/Al components with a high-volume fraction are prone to poor surface quality and defects such as fractures, cracks, and micro-pits. It has been reported that ultrasonic-assisted grinding machining (UAG) helps to improve the quality of SiCp/Al machined surfaces. However, the differences between SiCp/Al with different volume fractions obtained by UAG machining are not clear. Therefore, a comparative study of surface roughness, morphology, and cutting force was carried out by UAG machining on SiCp/Al samples with volume fractions of 45% and 60%. Compared to the 45% volume fraction SiCp/Al, the 60% volume fraction SiCp/Al has a higher cutting force and roughness under the same machining parameters. In addition, experiments have shown that cutting forces and surface roughness can be reduced by increasing the tool speed or decreasing the feed rate. UAG machining with an ultrasonic amplitude within 4 μm can also reduce cutting forces and surface roughness. However, more than 6 μm ultrasonic amplitude may lead to an increase in roughness. This study contributes to reasonable parameter settings in ultrasonically-assisted grinding of SiCp/Al with different volume fractions.

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Section: Results and Analysismentioning

confidence: 99%

Experimental Studies of the Machinability of SiCp/Al with Different Volume Fractions under Ultrasonic-Assisted Grinding

Hu,

Zhu,

Fan

2024

Materials

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“…The optimization of grinding parameters can not only improve surface quality but also enhance machining efficiency. The spindle speed has the greatest impact on surface roughness, followed by grinding depth and feed speed [ 139 ]. Furthermore, the grinding force generated during the grinding process decreases with an increase in spindle speed, while it increases with higher feed speed and grinding depth [ 140 ].…”

Section: Grinding Machining Of Sicp/almentioning

confidence: 99%

A Review on Machining SiCp/Al Composite Materials

Chen,

Ding,

Zhang

et al. 2024

Micromachines

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SiCp/Al composite materials are widely used in various industries such as the aerospace and the electronics industries, primarily due to their excellent material properties. However, their machinability is significantly weakened due to their unique characteristics. Consequently, efficient and precise machining technology for SiCp/Al composite materials has become a crucial research area. By conducting a comprehensive analysis of the relevant research literature from both domestic and international sources, this study examines the processing mechanism, as well as the turning, milling, drilling, grinding, special machining, and hybrid machining characteristics, of SiCp/Al composite materials. Moreover, it summarizes the latest research progress in composite material processing while identifying the existing problems and shortcomings in this area. The aim of this review is to enhance the machinability of SiCp/Al composite materials and promote high-quality and efficient processing methods.

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“…Chen et al developed a surface roughness prediction model for Ti-6Al-4V abrasive belt grinding, based on the response surface methodology, and the average error between experimental measurements and model predictions was 6.08% [11]. Gao et al designed microgrinding tests for composites based on the response surface methodology, and their regression equation model (established by regression analysis and removing the insignificant terms) could relatively accurately reflect the effects of cutting depth, feed speed and spindle speed on surface roughness [12]. Bandapalli et al conducted experimental research and estimations on the surface roughness of high-speed micro-end milling of titanium alloys using ANNs, group method data processing (GMDH) and multivariate regression analysis (MRA), and their observed results showed that the prediction accuracy of neural networks was higher than the other techniques [13].…”

Section: Research and Progress Of Surface Roughness Prediction In Met...mentioning

confidence: 99%

Analysis and Prediction of Grind-Hardening Surface Roughness Based on Response Surface Methodology-BP Neural Network

Wang

Shen

2022

Applied Sciences

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Surface morphology and surface roughness are very important properties used to assess the quality of grind-hardening surfaces. In this study, grind-hardening tests for 42CrMo steel were designed using the response surface methodology to reveal the surface morphological characteristics of the grind-hardening surface and the effects of grinding parameters on its roughness. The results showed considerable grinding damage in both the cutting-in and cutting-out areas of the grind-hardened surface, while the middle area was more stable. More specifically, the cutting-in area showed much bonding and damage, while the cutting-out area showed more microcracks. Under the conditions of this test, the surface roughness tended to increase with the increase in cutting depth and workpiece feed speed. The effect of grinding line speed on the grind-hardening surface roughness was not significant. The significance of the effects of grinding parameters on surface roughness ranked as: cutting depth > workpiece feed speed > grinding speed. In turn, a response surface methodology-BP neural network prediction model for the surface roughness of grind-hardening was developed, whose feasibility and validity were confirmed by the experimental results. The model achieved surface roughness prediction of the grind-hardening process with a mean relative error of 2.86%.

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Study on micro-grinding mechanism and surface quality of high-volume fraction SiCp/Al composites

Cited by 23 publications

References 7 publications

Experimental Studies of the Machinability of SiCp/Al with Different Volume Fractions under Ultrasonic-Assisted Grinding

Experimental Studies of the Machinability of SiCp/Al with Different Volume Fractions under Ultrasonic-Assisted Grinding

A Review on Machining SiCp/Al Composite Materials

Analysis and Prediction of Grind-Hardening Surface Roughness Based on Response Surface Methodology-BP Neural Network

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