Modelling of Flank wear, Surface roughness and Cutting Temperature in Sustainable Hard Turning of AISI D2 Steel

Abstract Productivity and quality of products are major concern for industries aspects. However present paper focused on the investigation of flank wear, average roughness of the surface and chip-tool interface temperature in the machine turning of heat-treated AISI D2 grade tool steel using indexable multi-layer coated carbide inserts. Abrasion, diffusion, chipping and catastrophic breakage are major tool failure mechanisms involved. Response surface methodology (RSM) based models and Artificial-Neural-Network (ANN) models are implemented for forecasting the responses in hard-turning. Comparative assessment between actual and predicted results has been carried. ANN model for flank wear generated more accurate results compare to RSM Model whereas for surface finish and chip-tool interface temperature, the accuracy of RSM based prediction is more precise compared to ANN.

[1]  Gérard Poulachon,et al.  Wear behavior of CBN tools while turning various hardened steels , 2004 .

[2]  Guicai Zhang,et al.  Modeling Flank Wear Progression Based on Cutting Force and Energy Prediction in Turning Process , 2016 .

[3]  Ashok Kumar Sahoo,et al.  Performance studies of multilayer hard surface coatings (TiN/TiCN/Al2O3/TiN) of indexable carbide inserts in hard machining: Part-II (RSM, grey relational and techno economical approach) , 2013 .

[4]  Lakhdar Boulanouar,et al.  Modeling and multi-objective optimization of surface roughness and productivity in dry turning of AISI 52100 steel using (TiCN-TiN) coating cermet tools , 2017 .

[5]  J. Paulo Davim,et al.  Modelling of surface finish and tool flank wear in turning of AISI D2 steel with ceramic wiper inserts , 2007 .

[6]  E. Kirubakaran,et al.  Surface Roughness Prediction using Artificial Neural Network in Hard Turning of AISI H13 Steel with Minimal Cutting Fluid Application , 2014 .

[7]  N. Sidik,et al.  An overview of current status of cutting fluids and cooling techniques of turning hard steel , 2017 .

[8]  N. Senthilkumar,et al.  Flank wear and surface roughness prediction in hard turning via artificial neural network and multiple regressions , 2015 .

[9]  Sören Hägglund,et al.  Assessment of Commonly used Tool Life Models in Metal Cutting , 2017 .

[10]  Salim Belhadi,et al.  Investigation of the performance of the MQL, dry, and wet turning by response surface methodology (RSM) and artificial neural network (ANN) , 2017 .

[11]  R. Komanduri,et al.  Machining of Hard Materials , 1984 .