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Title: | ANALYSIS OF CUTTING PARAMETERS ON SURFACE ROUGHNESS IN TURNING OF MILD STEEL |
Authors: | KOLI, YASHWANT |
Keywords: | SURFACE ROUGHNESS REGRESSION ANALYSIS TAGUCHI ANOVA TAYLOR HOBSON'S SURTRONIC 3++ |
Issue Date: | Jul-2017 |
Series/Report no.: | TD-2828; |
Abstract: | In metal cutting and manufacturing industries, surface finish of a product is very crucial in determining the quality. Good surface finish not only assures quality, but also reduces manufacturing cost. Surface finish is important in terms of tolerances, it reduces assembly time and avoids the need for secondary operation, thus reduces operation time and leads to overall cost reduction. Besides, good-quality turned surface is significant in improving fatigue strength, corrosion resistance, and creep life. This study focuses on optimizing cutting parameters based on the Taguchi method, a powerful tool to design optimization for quality, is used to minimize surface roughness. A full factorial 27 experiments, the signal-to-noise (S:N) ratio, analysis of variance (ANOVA) and regression analysis are employed to investigate the cutting characteristics of mild steel bars using carbide cutting tools. The main objective is to study the effect of cutting speed, feed and depth of cut on surface roughness of mild steel in turning operation using carbide tool. Different cutting parameters have different influential on the surface finish. The cutting speed, feed and depth of cut were decide using the suitable range recommended; which were 71.628m/min, 95.504m/min and 119.380m/min for cutting speed, 0.04mm/rev, 0.14mm/rev and 0.24mm/rev for feed and lastly 0.2mm, 0.4mm and 0.6mm for depth of cut. The specimen was turned under different level of parameters and was measured the surface roughness using a Taylor Hobson’s Surtronic 3+. From the result, it is concluded that higher cutting speed and lower feed produce better surface finish. The optimum cutting parameters were 119.380m/min, 0.04mm/rev and 0.6mm, which produced minimum surface roughness of 1.33μm. According to the ANOVA analysis, feed is the dominant factor by 85.82%. |
URI: | http://dspace.dtu.ac.in:8080/jspui/handle/repository/15855 |
Appears in Collections: | M.E./M.Tech. Production Engineering |
Files in This Item:
File | Description | Size | Format | |
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thesis 2k15pie21.pdf | 3.5 MB | Adobe PDF | View/Open |
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