Optimization of the Surface Roughness and Chip Compression Ratio of Duplex Stainless Steel in a Wet Turning Process Using the Taguchi Method

被引:3
作者
Gyliene, Virginija [1 ]
Brasas, Algimantas [1 ]
Ciuplys, Antanas [1 ]
Jablonskyte, Janina [1 ]
机构
[1] Kaunas Univ Technol, Fac Mech Engn & Design, Studentu Str 56, LT-51424 Kaunas, Lithuania
关键词
turning; duplex stainless steel; surface roughness; chip compression ratio; optimization; Taguchi; KEY FACTOR; INTEGRITY; DSS; MICROHARDNESS; PERFORMANCE; WEAR;
D O I
10.3390/machines12070437
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Duplex stainless steels (DSSs) are used in many applications due to their properties, such as high mechanical strength, good corrosion resistance, and relatively low cost. Nevertheless, DSS belongs to the materials group that is difficult to machine. The demand for a total increase in the production requires the optimization of cutting conditions. This paper examines the influence of cutting parameters, namely cutting velocity, feed, and the depth of cut on the surface roughness and chip compression ratio (CCR) after the DSS wet turning process. The study employed Taguchi optimization to determine the ideal cutting parameters for wet turning finishing operations on steel 1.4462. Using the Taguchi design, experiments focused on surface roughness (Ra) and CCR. Utilizing a TiAlN/TiN-PVD coating insert with a 0.4 mm nose radius, cutting velocity of 200 m/min, feed rates of 0.05 mm/rev, and cutting depths of 1 mm yielded the lowest Ra at 0.433 mu m. Meanwhile, a cutting velocity of 200 m/min, feed rate of 0.15 mm/rev, and cutting depth of 0.5 mm resulted in the smallest CCR at 1.39, indicating minimal plastic deformation. The inclusion of additional cooling proved beneficial for surface roughness compared to dry and wet turning methods. The experimental data holds value for training and validating artificial intelligence models, preventing overfitting by ensuring sufficient data collection.
引用
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页数:13
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