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Year 2013
Title Prediction of Optimal Design on Tool Wear and Workpiece Surface Temperature in Turning of AISI D2 Steel
Authors Sudhansu Ranjan Das , Amaresh Kumar , Debabrata Dhupal
Broad area Production Engineering
Abstract
 Now-a-days increasing the productivity and the quality of the machined parts are the main challenges of metal cutting industry during turning processes. Optimization methods in turning processes, considered being a vital role for continual improvement of output quality in product and processes include modeling of input-output and in process parameters relationship and determination of optimal cutting conditions. This paper presents an optimization method of the cutting parameters (cutting speed, depth of cut and feed) in dry turning of AISI D2 steel to achieve minimum tool wear and low workpiece surface temperature. The experimental layout was designed based on the Taguchi’s L9 (34

Keywords: AISI D2 steel, tool wear, workpiece surface temperature, Taguchi method, regression analysis. ) Orthogonal array technique and analysis of variance (ANOVA) was performed to identify the effect of the cutting parameters on the response variables. The results showed that depth of cut and cutting speed are the most important parameter influencing the tool wear. The minimum tool wear was found at cutting speed of 150 m/min, depth of cut of 0.5 mm and feed of 0.25 mm/rev. Similarly low workpiece surface temperature was obtained at cutting speed of 150 m/min, depth of cut of 0.5 mm and feed of 0.25 mm/rev. Thereafter, optimal ranges of tool wear and workpiece surface temperature values were predicted. Finally, the relationship between factors and the performance measures were developed by using multiple regression analysis.

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