Analysis and optimization of hard turning operation using cubic boron nitride tool

Abstract The present work concerns an experimental study of hard turning of AISI 52100 bearing steel, with CBN tool. The combined effects of process parameters (cutting speed, feed rate, depth of cut and cutting time) on performance characteristics (tool wear, surface roughness, cutting forces and metal volume removed) are investigated using ANOVA analysis. The relationship between process parameters and performance characteristics through the response surface methodology (RSM) are modeled. Moreover, Grey-Taguchi method, composite desirability function and genetic algorithm are used as multi-objective optimization approaches to find the process parameters values that optimize simultaneously the performance characteristics. The results show that the cutting speed exhibits maximum influence on abrasive tool wear. The depth of cut affects strongly the cutting forces; however, it has a negligible influence on surface roughness. The cutting time has a considerable effect on all performance characteristics. Though the optimization approaches predicted near similar results, the GA technique seems to be the most advantageous approach. Finally, the proposed experimental and integrated approaches bring reliable methodologies to model, to optimize and to improve the hard turning process. They can be extended efficiently to study other machining processes.

[1]  Sam Kwong,et al.  Genetic algorithms and their applications , 1996, IEEE Signal Process. Mag..

[2]  J. Davim.  Design of optimisation of cutting parameters for turning metal matrix composites based on the orthogonal arrays , 2003 .

[3]  Saurav Datta,et al.  Grey-based taguchi method for optimization of bead geometry in submerged arc bead-on-plate welding , 2008 .

[4]  Yunn-Shiuan Liao,et al.  Optimization of machining parameters of Wire-EDM based on Grey relational and statistical analyses , 2003 .

[5]  C. C. Tsao,et al.  Grey–Taguchi method to optimize the milling parameters of aluminum alloy , 2009 .

[6]  J. Ferreira,et al.  A multivariate hybrid approach applied to AISI 52100 hardened steel turning optimization , 2007 .

[7]  Hari Singh,et al.  Optimization of multiple quality characteristics for CNC turning under cryogenic cutting environment using desirability function , 2008 .

[8]  Ramón Quiza Sardiñas,et al.  Genetic algorithm-based multi-objective optimization of cutting parameters in turning processes , 2006, Eng. Appl. Artif. Intell..

[9]  M. Yallese,et al.  Statistical analysis of surface roughness and cutting forces using response surface methodology in hard turning of AISI 52100 bearing steel with CBN tool , 2010 .

[10]  Imtiaz Ahmed Choudhury,et al.  Tool-life prediction model by design of experiments for turning high strength steel (290 BHN) , 1998 .

[11]  Sue J. Lin,et al.  Grey relation performance correlations among economics, energy use and carbon dioxide emission in Taiwan , 2007 .

[12]  Ming-Jong Tsai,et al.  The use of grey relational analysis to determine laser cutting parameters for QFN packages with multiple performance characteristics , 2009 .

[13]  G. Derringer,et al.  Simultaneous Optimization of Several Response Variables , 1980 .

[14]  Hans Kurt Tönshoff,et al.  Cutting of Hardened Steel , 2000 .

[15]  I. Jawahir,et al.  Tool-wear mechanisms in hard turning with polycrystalline cubic boron nitride tools , 2001 .

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

[17]  Chia-Ming Chang,et al.  The use of grey-based Taguchi methods to determine submerged arc welding process parameters in hardfacing , 2002 .

[18]  Taho Yang,et al.  The use of a grey-based Taguchi method for optimizing multi-response simulation problems , 2008 .

[19]  J. Paulo Davim,et al.  Machinability investigations in hard turning of AISI D2 cold work tool steel with conventional and wiper ceramic inserts , 2009 .

[20]  Ekkard Brinksmeier,et al.  Capability Profile of Hard Cutting and Grinding Processes , 2005 .

[21]  J. Paulo Davim,et al.  Analysis of Machinability During Hard Turning of Cold Work Tool Steel (Type: AISI D2) , 2009 .

[22]  Deng Ju-Long,et al.  Control problems of grey systems , 1982 .

[23]  Amar Patnaik,et al.  Optimization of wire electrical discharge machining (WEDM) process parameters using Taguchi method , 2007 .

[24]  A. K. Dubey,et al.  Multi-response optimization of electro-chemical honing using utility-based Taguchi approach , 2009 .

[25]  Suleyman Neseli,et al.  Optimization of tool geometry parameters for turning operations based on the response surface methodology , 2011 .

[26]  J. L Lin,et al.  The use of the orthogonal array with grey relational analysis to optimize the electrical discharge machining process with multiple performance characteristics , 2002 .

[27]  Bernard C. Jiang,et al.  Machine vision-based gray relational theory applied to IC marking inspection , 2002 .

[28]  J. Deng,et al.  Introduction to Grey system theory , 1989 .

[29]  J. Paulo Davim,et al.  Machining of Hard Materials , 2011 .

[30]  S. G. Deshmukh,et al.  A genetic algorithmic approach for optimization of surface roughness prediction model , 2002 .

[31]  C. Fung.  Manufacturing process optimization for wear property of fiber-reinforced polybutylene terephthalate composites with grey relational analysis , 2003 .

[32]  P. V. Rao,et al.  Selection of optimum tool geometry and cutting conditionsusing a surface roughness prediction model for end milling , 2005 .

[33]  Muammer Nalbant,et al.  Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning , 2007 .

[34]  Ming-Chang Jeng,et al.  Optimization of turning operations with multiple performance characteristics using the Taguchi method and Grey relational analysis , 2009 .

[35]  J. Horng,et al.  Investigating the machinability evaluation of Hadfield steel in the hard turning with Al2O3/TiC mixed ceramic tool based on the response surface methodology , 2008 .

[36]  Mahmudur Rahman,et al.  Determination of optimal cutting conditions using design of experiments and optimization techniques , 1993 .

[37]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[38]  Vishal S. Sharma,et al.  Estimation of cutting forces and surface roughness for hard turning using neural networks , 2008, J. Intell. Manuf..

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

[40]  S. K. Choudhury,et al.  Investigation of tool wear and cutting force in cryogenic machining using design of experiments , 2008 .

[41]  J. Paulo Davim,et al.  Selection of optimal MQL and cutting conditions for enhancing machinability in turning of brass , 2008 .

[42]  Ulaş Çaydaş,et al.  Optimization of turning parameters for surface roughness and tool life based on the Taguchi method , 2008 .