Optimization of Machining Performance in High-Pressure Assisted Turning of Ti6Al4V Alloy

In this study, a genetic algorithm has been employed to determine optimum cutting parameters in the turning of Ti6Al4V alloy under conventional and high pressure cooling conditions. Three machining performance measures, i.e. surface roughness, material removal rate and cutting power, are considered as optimization criteria. First, with multi-regression analysis of experimental responses, empirical equations are defined and, by using these equations, objective functions are constructed for each pressure level, based on a hybrid model. Objective functions are maximized by means of a genetic algorithm and optimum machining parameters are determined. Moreover, tool wear tests are carried out at a cutting condition that is close to the optimum machining parameters. Optimization results show that optimum cutting parameters and their responses, particularly in P = 6 and 150 bar cooling conditions, are quite similar, but tool life is significantly different. Maximum tool life is achieved in the highest pressure level (P = 300 bar).

[1]  Shane Y. Hong,et al.  New cooling approach and tool life improvement in cryogenic machining of titanium alloy Ti-6Al-4V , 2001 .

[2]  S. Palanisamy,et al.  Effects of coolant pressure on chip formation while turning Ti6Al4V alloy , 2009 .

[3]  Peter Krajnik,et al.  Capability of high pressure cooling in the turning of surface hardened piston rods , 2010 .

[4]  I. S. Jawahir,et al.  Development of hybrid predictive models and optimization techniques for machining operations , 2007 .

[5]  I. S. Jawahir,et al.  Contour finish turning operations with coated grooved tools : Optimization of machining performance , 2009 .

[6]  N. Baskar,et al.  Application of Particle Swarm Optimization technique for achieving desired milled surface roughness in minimum machining time , 2012, Expert Syst. Appl..

[7]  Emmanuel O. Ezugwu,et al.  Effect of high-pressure coolant supply when machining nickel-base, Inconel 718, alloy with coated carbide tools , 2004 .

[8]  D. Biermann,et al.  Machining of β-titanium-alloy Ti–10V–2Fe–3Al under cryogenic conditions: Cooling with carbon dioxide snow , 2011 .

[9]  Peter Krajnik,et al.  Investigation of machining performance in high-pressure jet assisted turning of Inconel 718: An experimental study , 2009 .

[10]  Kai Yang,et al.  Design for Six Sigma , 2005 .

[11]  Abhijit Dilip Kardekar,et al.  MODELING AND OPTIMIZATION OF MACHINING PERFORMANCE MEASURES IN FACE MILLING OF AUTOMOTIVE ALUMINUM ALLOY A380 UNDER DIFFERENT LUBRICATION/COOLING CONDITIONS FOR SUSTAINABLE MANUFACTURING , 2005 .

[12]  Shane Y. Hong,et al.  Cooling approaches and cutting temperatures in cryogenic machining of Ti-6Al-4V , 2001 .

[13]  Oğuz Çolak,et al.  Investigation on Machining Performance of Inconel 718 in High Pressure Cooling Conditions , 2012 .

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

[15]  R. Venkata Rao,et al.  Parameter optimization of modern machining processes using teaching-learning-based optimization algorithm , 2013, Eng. Appl. Artif. Intell..

[16]  S. Paul,et al.  Some studies on high-pressure cooling in turning of Ti–6Al–4V , 2009 .

[17]  Rosemar Batista da Silva,et al.  Surface integrity of finished turned Ti–6Al–4V alloy with PCD tools using conventional and high pressure coolant supplies , 2007 .

[18]  Franci Cus,et al.  Optimization of cutting process by GA approach , 2003 .

[19]  J. Kaminski,et al.  Temperature reduction in the cutting zone in water-jet assisted turning , 2000 .

[20]  A. K. Balaji,et al.  Performance-Based Predictive Models and Optimization Methods for Turning Operations and Applications: Part 3—Optimum Cutting Conditions and Selection of Cutting Tools , 2007 .

[21]  J. Bonney,et al.  High Productivity Rough Turning of Ti-6Al-4V Alloy, with Flood and High-Pressure Cooling , 2009 .