Response surface and neural network based predictive models of cutting temperature in hard turning

Graphical abstract

[1]  Soumitra Paul,et al.  Role of Cryogenic Cooling on Cutting Temperature in Turning Steel , 2002 .

[2]  Jaber Abu Qudeiri,et al.  Multi-objective optimization of oblique turning operations using finite element model and genetic algorithm , 2014 .

[3]  Tuğrul Özel,et al.  Predictive Analytical and Thermal Modeling of Orthogonal Cutting Process—Part I: Predictions of Tool Forces, Stresses, and Temperature Distributions , 2006 .

[4]  Mozammel Mia,et al.  Optimization of surface roughness and cutting temperature in high-pressure coolant-assisted hard turning using Taguchi method , 2016, The International Journal of Advanced Manufacturing Technology.

[5]  N. R. Dhar,et al.  An experimental investigation on effect of minimum quantity lubrication in machining AISI 1040 steel , 2007 .

[6]  Álisson Rocha Machado,et al.  The effect of application of cutting fluid with solid lubricant in suspension during cutting of Ti-6Al-4V alloy , 2015 .

[7]  A. Senthil Kumar,et al.  Effect of High-Pressure Coolant on Machining Performance , 2002 .

[8]  Behnam Davoodi,et al.  Experimental investigation and optimization of cutting parameters in dry and wet machining of aluminum alloy 5083 in order to remove cutting fluid , 2014 .

[9]  N. R. Dhar,et al.  GA Based Multi Objective Optimization of the Predicted Models of Cutting Temperature, Chip Reduction Co-Efficient and Surface Roughness in Turning AISI 4320 Steel by Uncoated Carbide Insert under HPC Condition , 2010 .

[10]  Sharath Chandra Guntuku,et al.  Optimisation of turning parameters by integrating genetic algorithm with support vector regression and artificial neural networks , 2014, The International Journal of Advanced Manufacturing Technology.

[11]  Hao Xu,et al.  An improved three-dimensional inverse heat conduction procedure to determine the tool-chip interface temperature in dry turning , 2013 .

[12]  N. R. Dhar,et al.  Effects of minimum quantity lubrication on turning AISI 9310 alloy steel using vegetable oil­based cutting fluid , 2009 .

[13]  Richard F. Gunst,et al.  Applied Regression Analysis , 1999, Technometrics.

[14]  N. R. Dhar,et al.  Cutting temperature, tool wear, surface roughness and dimensional deviation in turning AISI-4037 steel under cryogenic condition , 2007 .

[15]  Vishal S. Sharma,et al.  Cooling techniques for improved productivity in turning , 2009 .

[16]  Mohammed Nouari,et al.  Modeling of velocity-dependent chip flow angle and experimental analysis when machining 304L austenitic stainless steel with groove coated-carbide tools , 2013 .

[17]  David J. C. MacKay,et al.  A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.

[18]  N. R. Dhar,et al.  Beneficial effects of cryogenic cooling over dry and wet machining on tool wear and surface finish in turning AISI 1060 steel , 2001 .

[19]  N. R. Dhar,et al.  THE INFLUENCE OF HIGH PRESSURE COOLANT ON TEMPERATURE TOOL WEAR AND SURFACE FINISH IN TURNING 17CrNiMo6 AND 42CrMo4 STEELS , 2009 .

[20]  Puneet Sharma,et al.  Investigation of effects of nanofluids on turning of AISI D2 steel using minimum quantity lubrication , 2015 .

[21]  A. Molinari,et al.  Modeling of tool wear by diffusion in metal cutting , 2002 .

[22]  Cornel Mihai Nicolescu,et al.  A novel numerical modeling approach to determine the temperature distribution in the cutting tool using conjugate heat transfer (CHT) analysis , 2015, The International Journal of Advanced Manufacturing Technology.

[23]  Syed Mithun Ali Modeling of Chip Tool Interface Temperature in Machining Steel- An Artificial Intelligence (AI) Approach , 2011 .

[24]  David J. C. MacKay,et al.  Bayesian Interpolation , 1992, Neural Computation.

[25]  Martin T. Hagan,et al.  Gauss-Newton approximation to Bayesian learning , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[26]  Mozammel Mia,et al.  EFFECT OF HIGH PRESSURE COOLANT JET ON CUTTING TEMPERATURE, TOOL WEAR AND SURFACE FINISH IN TURNING HARDENED (HRC 48) STEEL , 2015 .

[27]  Seung-Han Yang,et al.  Multi-objective optimization of cutting parameters in turning process using differential evolution and non-dominated sorting genetic algorithm-II approaches , 2010 .

[28]  James Wallbank,et al.  Cutting temperature: prediction and measurement methods—a review , 1999 .

[29]  S. Karsoliya,et al.  Approximating Number of Hidden layer neurons in Multiple Hidden Layer BPNN Architecture , 2012 .