Performances of cryo-treated and untreated cutting tools in machining of AA7075 aerospace aluminium alloy

航空航天 机械加工 刀具 冶金 材料科学 碳化物 铝合金 微观结构 陶瓷 合金 表面完整性 工程类 航空航天工程
作者
Mehmet Şükrü Adin
出处
期刊:European mechanical science [European Mechanical Science]
卷期号:7 (2): 70-81 被引量:32
标识
DOI:10.26701/ems.1270937
摘要

The quality of drilled holes in aluminium alloys used in the aerospace industry is vital to ensure high-precision structural integrity. For this reason, optimum selection of cost-effective cutting tools and cutting parameters is of great importance. Nowadays, due to their high cost and supply difficulties, there is a great interest in improving the performance of traditional HSS cutting tools as an alternative to ceramic, carbide and coated cutting tools. HSS cutting tools are widely used in different industries due to their cost-effectiveness and suitability to improve tool performance. In this research, the performances of cryo-treated (DC&T) and untreated (UT) HSS cutting tools used in dry machining of AA7075 aluminium alloys were investigated. Thanks to DC&T processes applied to HSS cutting tool, improvements have occurred in its microstructure. The hardness value of HSS cutting tool increased by 6.89% with the effect of DC&T processes applied. When the highest and lowest Ra values obtained using DC&T and UT HSS cutting tools were compared, it was seen that DC&T HSS cutting tool performed better by 11.05% and 25.91%, respectively. It has been determined that the hole surface quality of the aluminium workpiece machined with DC&T and UT HSS drills is negatively affected by the increase in spindle speed and feed rate. The highest S/N ratios calculated according to Ra values of holes drilled on aluminium workpieces using DC&T and UT HSS cutting tools were found to be -7.12 dB (2.27 μm) and -9.62 dB (3.03 μm), respectively. In the ANOVA analysis, it was determined that the most effective parameters on Ra values were spindle speed (70.62%), tools (18.19%) and feed rate (9.98%), respectively. In the regression analysis, R2 value for Ra values was calculated as 98.30%. High R2 value result shows that the model developed is quite successful in estimating Ra values.

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