Optimization of drilling parameters on surface roughness in drilling of AISI 1045 using response surface methodology and genetic algorithm

Modeling and optimization of cutting parameters are one of the most important elements in machining processes. The present study focused on the influence machining parameters on the surface roughness obtained in drilling of AISI 1045. The matrices of test conditions consisted of cutting speed, feed rate, and cutting environment. A mathematical prediction model of the surface roughness was developed using response surface methodology (RSM). The effects of drilling parameters on the surface roughness were evaluated and optimum machining conditions for minimizing the surface roughness were determined using RSM and genetic algorithm. As a result, the predicted and measured values were quite close, which indicates that the developed model can be effectively used to predict the surface roughness. The given model could be utilized to select the level of drilling parameters. A noticeable saving in machining time and product cost can be obtained by using this model.

[1]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[2]  Anil Mital,et al.  Surface finish prediction models for fine turning , 1988 .

[3]  Douglas C. Montgomery,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[4]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[5]  Mitsuo Gen,et al.  Genetic algorithms and engineering design , 1997 .

[6]  Fritz Klocke,et al.  Present Situation and Future Trends in Modelling of Machining Operations Progress Report of the CIRP Working Group ‘Modelling of Machining Operations’ , 1998 .

[7]  Michel Guillot,et al.  On-Line Optimization of the Turning Process Using an Inverse Process Neurocontroller , 1998 .

[8]  Anselmo Eduardo Diniz,et al.  Using a minimum quantity of lubricant (MQL) and a diamond coated tool in the drilling of aluminum–silicon alloys , 2002 .

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

[10]  George-Christopher Vosniakos,et al.  Predicting surface roughness in machining: a review , 2003 .

[11]  V. C. Venkatesh,et al.  Application of response surface methodology in describing the performance of coated carbide tools when turning AISI 1045 steel , 2004 .

[12]  Nihat Tosun,et al.  A study on kerf and material removal rate in wire electrical discharge machining based on Taguchi method , 2004 .

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

[14]  Eduardo Carlos Bianchi,et al.  Study on the behavior of the minimum quantity lubricant - MQL technique under different lubricating and cooling conditions when grinding ABNT 4340 steel , 2005 .

[15]  J. Paulo Davim,et al.  The performance of cutting fluids when machining aluminium alloys , 2006 .

[16]  Cihan Özel,et al.  Optimisation of surface roughness with GA approach in turning 15% SiCp reinforced AlSi7Mg2 MMC material , 2006 .

[17]  N. R. Dhar,et al.  Performance evaluation of minimum quantity lubrication by vegetable oil in terms of cutting force, cutting zone temperature, tool wear, job dimension and surface finish in turning AISI-1060 steel , 2006 .

[18]  Robert Heinemann,et al.  Effect of MQL on the tool life of small twist drills in deep-hole drilling , 2006 .

[19]  Walter Lindolfo Weingaertner,et al.  Analysis of temperature during drilling of Ti6Al4V with minimal quantity of lubricant , 2006 .

[20]  K. Palanikumar,et al.  OPTIMAL MACHINING CONDITIONS FOR TURNING OF PARTICULATE METAL MATRIX COMPOSITES USING TAGUCHI AND RESPONSE SURFACE METHODOLOGIES , 2006 .

[21]  Samir Ali Alrabii,et al.  Chip Thickness and Microhardness Prediction Models during Turning of Medium Carbon Steel , 2007, J. Appl. Math..

[22]  Anselmo Eduardo Diniz,et al.  Influence of the direction and flow rate of the cutting fluid on tool life in turning process of AISI 1045 steel , 2007 .

[23]  V. N. Gaitonde,et al.  Integrating Box-Behnken design with genetic algorithm to determine the optimal parametric combination for minimizing burr size in drilling of AISI 316L stainless steel , 2008 .

[24]  Mohan Kumar Pradhan,et al.  Modelling of machining parameters for MRR in EDM using response surface methodology , 2008 .

[25]  M. Davidson,et al.  Surface roughness prediction of flow-formed AA6061 alloy by design of experiments , 2008 .

[26]  Prasanta Sahoo,et al.  Fractal dimension modelling of surface profile and optimisation in CNC end milling using Response Surface Method , 2008, Int. J. Manuf. Res..

[27]  K. Palanikumar,et al.  Application of Taguchi and response surface methodologies for surface roughness in machining glass fiber reinforced plastics by PCD tooling , 2008 .

[28]  Ahmet T. Alpas,et al.  Minimum quantity lubrication drilling of aluminium–silicon alloys in water using diamond-like carbon coated drills , 2008 .

[29]  J. Paulo Davim,et al.  A study of drilling performances with minimum quantity of lubricant using fuzzy logic rules , 2009 .