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Authors
Affiliations
1 SCSVMV University, Kanchipuram, IN
2 Geethanjali Institute of Science and Technology, Nellore, IN
3 Mechanical Engg Dept., SCSVMV University, Kanchipuram, IN
4 Mechanical Engg Dept., AITS, Tirupati, IN
Source
Manufacturing Technology Today, Vol 15, No 3 (2016), Pagination: 3-11
Abstract
The paper study is carried out on orthogonal cutting of AISI 1042(EN 41B) carbon steel under dry turning conditions with PVD coated cemented carbide inserts. This paper presents the cutting parameters which influences the Surface roughness (Ra) and Material Removal Rate (MRR) on AISI 1042. Experiments are carried out by conducting experiments based on Taguchi technique considering L27 orthogonal array on a lathe (PSG A141). The process parameters considered for the study are cutting speed, feed rate, and depth of cut and the output parameters are MRR and Ra. A Model has been developed using regression technique and the optimal cutting parameters for minimum surface roughness, and maximum MRR were obtained using Taguchi technique. For predicting the surface roughness, an Artificial Neural Network (ANN) model was designed through back propagation network using MATLAB software for the data obtained. Comparison of the experimental data and ANN results shows that there is no significant difference and ANN was used confidently. Optimal machining parameters were determined by the grey relation grade obtained from the grey relation analysis for multi-performance characteristics (the surface roughness and Material removal rate). The results of confirmation experiments reveal that grey relation analysis coupled with factorial design can effectively be used to obtain the optimal combination of turning parameters. Experimental results have shown that the surface roughness and material removal rate in the turning process can be improved effectively through the new approach.
Keywords
AISI No1042 Alloy Steel, PVD Coated Tool, Taguchi Technique, ANN and Grey Relational Analysis.
