PRASHANT KAMBLE - Academia.edu (original) (raw)

PRASHANT KAMBLE

Uploads

Papers by PRASHANT KAMBLE

Research paper thumbnail of Experimental Investigation to Maximize Material Removal Rate for Turning en 24 Steel by Taguchi Approach

The main aim of this paper is to maximize the material removal rate (MRR) in turning process. Tag... more The main aim of this paper is to maximize the material removal rate (MRR) in turning process. Taguchi methodology is used as an optimization tool to optimize the turning process. The input parameters for turning process are Cutting Environment, feed rate, nose radius, depth of cut and tool type. The response variable is MRR. Consideration of noise factor (uncontrollable factor) makes the design robust. Hence machine tool condition in terms of spindle vibration is taken. Cutting speed is kept constant (210 m/min). The results of ANOVA and mean S/N ratio indicate that depth of cut (85.4 % contribution) is the most significant machining parameters followed by feed rate, cutting environment, nose radius and tool type. MRR is optimized up to 11.056 mm 3 /s. Conformity test revealed that the predicted and experimental values of MRR are within the range given by confidence interval.

Research paper thumbnail of Optimization of End Milling Process for Al2024-T4 Aluminum by Combined Taguchi and Artificial Neural Network Process

Advances in Intelligent Systems and Computing

Research paper thumbnail of Experimental investigation of end milling operation on Al2024

Materials Today: Proceedings

Research paper thumbnail of Multi objective optimization of turning parameters considering spindle vibration by Hybrid Taguchi Principal component analysis (HTPCA)

Materials Today: Proceedings

Research paper thumbnail of Multi Objective Optimization of Turning AISI 4340 Steel Considering Spindle Vibration Using Taguchi- Fuzzy Inference System

Materials Today: Proceedings, 2015

Research paper thumbnail of Taguchi Approach for Experimental Investigation to Minimize Surface Roughness for Turning EN 24 Steel

International Journal for Research in Applied Science and Engineering Technology

The main aim of this paper is to optimize the material removal rate (MRR) in turning process. Tag... more The main aim of this paper is to optimize the material removal rate (MRR) in turning process. Taguchi methodology is used as an optimization tool to optimize the turning process. The input parameters for turning process are Cutting Environment, feed rate, nose radius, depth of cut and tool type. The response variable is Surface Roughness. Consideration of noise factor (uncontrollable factor) makes the design robust. Hence machine tool condition in terms of spindle vibration is taken. Cutting speed is kept constant (210 m/min). The results of ANOVA indicate that feed rate is the most significant machining parameters followed by nose radius, cutting environment, depth of cut and tool type. Based on the main effect plot of S/N ratio, the optimal machining parameters are the cutting environment at level 3 (A 3 = MQL), nose radius at level 3 (B 3 = 1.2 mm), feed rate at level3 (C 3 =0.35 mm/rev), depth of cut at level 2 (D 2 = 1.0 mm), and tool type at level 1 (E 1 = Uncoated or A 3 B 3 C 3 D 2 E 1 in short. Surface roughness is optimized up to 0.98 um. Conformity test revealed that the predicted and experimental values of Surface roughness are within the range given by confidence interval.

Research paper thumbnail of Experimental Investigation to Maximize Material Removal Rate for Turning en 24 Steel by Taguchi Approach

The main aim of this paper is to maximize the material removal rate (MRR) in turning process. Tag... more The main aim of this paper is to maximize the material removal rate (MRR) in turning process. Taguchi methodology is used as an optimization tool to optimize the turning process. The input parameters for turning process are Cutting Environment, feed rate, nose radius, depth of cut and tool type. The response variable is MRR. Consideration of noise factor (uncontrollable factor) makes the design robust. Hence machine tool condition in terms of spindle vibration is taken. Cutting speed is kept constant (210 m/min). The results of ANOVA and mean S/N ratio indicate that depth of cut (85.4 % contribution) is the most significant machining parameters followed by feed rate, cutting environment, nose radius and tool type. MRR is optimized up to 11.056 mm 3 /s. Conformity test revealed that the predicted and experimental values of MRR are within the range given by confidence interval.

Research paper thumbnail of Optimization of End Milling Process for Al2024-T4 Aluminum by Combined Taguchi and Artificial Neural Network Process

Advances in Intelligent Systems and Computing

Research paper thumbnail of Experimental investigation of end milling operation on Al2024

Materials Today: Proceedings

Research paper thumbnail of Multi objective optimization of turning parameters considering spindle vibration by Hybrid Taguchi Principal component analysis (HTPCA)

Materials Today: Proceedings

Research paper thumbnail of Multi Objective Optimization of Turning AISI 4340 Steel Considering Spindle Vibration Using Taguchi- Fuzzy Inference System

Materials Today: Proceedings, 2015

Research paper thumbnail of Taguchi Approach for Experimental Investigation to Minimize Surface Roughness for Turning EN 24 Steel

International Journal for Research in Applied Science and Engineering Technology

The main aim of this paper is to optimize the material removal rate (MRR) in turning process. Tag... more The main aim of this paper is to optimize the material removal rate (MRR) in turning process. Taguchi methodology is used as an optimization tool to optimize the turning process. The input parameters for turning process are Cutting Environment, feed rate, nose radius, depth of cut and tool type. The response variable is Surface Roughness. Consideration of noise factor (uncontrollable factor) makes the design robust. Hence machine tool condition in terms of spindle vibration is taken. Cutting speed is kept constant (210 m/min). The results of ANOVA indicate that feed rate is the most significant machining parameters followed by nose radius, cutting environment, depth of cut and tool type. Based on the main effect plot of S/N ratio, the optimal machining parameters are the cutting environment at level 3 (A 3 = MQL), nose radius at level 3 (B 3 = 1.2 mm), feed rate at level3 (C 3 =0.35 mm/rev), depth of cut at level 2 (D 2 = 1.0 mm), and tool type at level 1 (E 1 = Uncoated or A 3 B 3 C 3 D 2 E 1 in short. Surface roughness is optimized up to 0.98 um. Conformity test revealed that the predicted and experimental values of Surface roughness are within the range given by confidence interval.

Log In