Department of Mechanical Engineering

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Browsing Department of Mechanical Engineering by Author "Adjattor, A.A."

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    Open Access
    Experimental prediction and optimization of material removal rate during hard turning of austenitic 304L stanless steel
    (Kwame Nkrumah University of Science and Technology, 2016-08) Ojolo, S.J.; Adjattor, A.A.; Olatunji, R.S.
    This work involves a predictive model for material removal rate (MRR). It investigates the influence of machining process parameters such as cutting speed, feed rate and depth of cut on the material removal rate (output parameter) during hard turning of AISI 304L austenitic stainless steel (0.03 wt. % C (max)). A total of 27 experiments were conducted using a MORI SEIKI SL-253B CNC machine with cemented carbide cutting tool under three different spindle speeds (1000, 1200, 1400rev/min), feed rates (0.05, 0.10, 0.15mm/rev) and depths of cut (0.4, 0.8, 1.2mm). The machining parameter settings were determined using the Taguchi experimental design method. The Taguchi method and relationship between MRR and input parameters were arrived at through MINITAB16 software package. The optimum machining parameters combination was obtained by using larger-the-better analysis of signal-to-noise (S/N) ratio. The optimal cutting condition is at spindle speed level 2 (1200 rpm); feed rate at level 3 (0.15mm/rev) and Depth of cut at level 3 (1.2 mm) which gave an optimum MRR of 77.80243mm3/min. The S/ N ratio response table, main effect plots and the relationship between cutting parameters and the MRR was obtained. A mathematical model was developed using multiple regression analysis to predict MRR during hard turning of AISI 304L austenitic stainless steel. The level of importance and performance characteristics of the machining parameters on MRR was determined by using analysis of variance (ANOVA). From the results, the feed rate had the most significant effects on the MRR followed by depth of cut.The spindle speed has the least effect on MRR. It was also revealed that the predicted results found a good correlation with the experimental results as the regression line fits well for both results data at 95% confidence interval.