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2019 | OriginalPaper | Chapter

Statistical Analysis of Surface Roughness Using RSM in Hard Turning of AISI 4340 Steel with Ceramic Tool

Authors : Asutosh Panda, Sudhansu Ranjan Das, Debabrata Dhupal

Published in: Advances in Industrial and Production Engineering

Publisher: Springer Singapore

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Abstract

The present study concerns the modeling and optimization of surface roughness in dry hard turning of high-strength low-alloy (HSLA) grade AISI 4340 steel (49 HRC) with coated ceramic tool. For parametric study, the turning operations have been established according to Taguchi L27 orthogonal array consisting of an experimental design matrix 3 levels and 3 principal turning parameters (factors) such as, cutting speed, axial feed, and depth of cut. Analysis of sixteen set experimental data with ANOVA showed that axial feed and speed are the most significant controlled cutting parameters for hard turning operation, if the improvement of the machined surface finish is considered. Thereafter, statistical regression model based on response surface methodology has been proposed for correlation of cutting parameters with machined workpiece surface roughness. Finally, optimal cutting conditions with the aim to minimize the surface roughness via desirability function approach of RSM are proposed.

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Literature
1.
Das SR, Panda A, Dhupal D (2017) Analysis of surface roughness in hard turning with coated ceramic inserts: cutting parameters effects, prediction model, cutting conditions optimization and cost analysis. Ciência e Técnica Vitivinícola: Sci Technol J 32:127–154
2.
Bensouilah H, Aouici H, Meddour I, Yallese MA (2016) Performance of coated and uncoated mixed ceramic tools in hard turning process. Measurement 82:1–18CrossRef
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Das SR, Kumar A, Dhupal D (2016) Experimental investigation on cutting force and surface roughness in machining of hardened AISI 52100 steel using CBN tool. Int J Mach Mach Mater 18:501–521
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5.
Nouioua M, Yallese MA, Khettabi R, Belhadi S, Bouhalais ML, Girardin F (2017) Investigation of the performance of the MQL, dry, and wet turning by response surface methodology (RSM) and artificial neural network (ANN). Int J Adv Manuf Technol 93:2485–2504CrossRef
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Das SR, Panda A, Dhupal D (2017) Experimental investigation of surface roughness, flank wear, chip morphology and cost estimation during machining of hardened AISI 4340 steel with coated carbide insert. Mech Adv Mater Mod Processes 3:1–14CrossRef
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Khellaf A, Aouici H, Smaiah S, Boutabba S, Yallese MA, Elbah M (2016) Comparative assessment of two ceramic cutting tools on surface roughness in hard turning of AISI H11 steel: including 2D and 3D surface topography. Int J Adv Manuf Technol 89:333–354CrossRef
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Tang L, Gao C, Huang J (2015) Experimental investigation of surface integrity in finish dry hard turning of hardened tool steel at different hardness levels. Int J Adv Manuf Technol 77:1655–1669CrossRef
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Aouici H, Yallese MA, Chaoui K (2012) Analysis of surface roughness and cutting force components in hard turning with CBN tool: prediction model and cutting conditions optimization. Measurement 45:344–353CrossRef
12.
Naigade DM, Patil DK, Sadaiah M (2013) Some investigations in hard turning of AISI 4340 alloy steel in different cutting environments by CBN insert. Int J Mach Mach Mater 14:165–193
Metadata
Title
Statistical Analysis of Surface Roughness Using RSM in Hard Turning of AISI 4340 Steel with Ceramic Tool
Authors
Asutosh Panda
Sudhansu Ranjan Das
Debabrata Dhupal
Copyright Year
2019
Publisher
Springer Singapore
Book
Advances in Industrial and Production Engineering

Print ISBN: 978-981-13-6411-2

Electronic ISBN: 978-981-13-6412-9

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-981-13-6412-9_3

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