Grey-fuzzy algorithm to optimise machining parameters in drilling of hybrid metal matrix composites

Abstract Metal matrix composites (MMCs) are difficult to machine due to their abrasive properties. With the projected widespread application of MMCs, it is necessary to develop an appropriate technology for their effective machining. The present investigation focuses on finding the optimal machining parameters setting in drilling of hybrid aluminium metal matrix composites using the grey-fuzzy algorithm. This proposed algorithm, coupling the grey relational analysis with the fuzzy logic, obtains a grey-fuzzy reasoning grade to evaluate the multiple performance characteristics according to the grey relational coefficient of each performance characteristics. The Taguchi method of experimental design is a widely accepted technique used for producing high quality products at low cost, therefore a L27 3-level orthogonal array is used for the experiments. The optimisation of multiple responses in complex processes is common; therefore, to reduce the degree of uncertainty during the decision making, fuzzy rule-based reasoning is integrated with the Taguchi’s method. The response table, response graph and analysis of variance (ANOVA) are used to find the optimal setting and the influence of machining parameters on the multiple performance characteristics. Experimental results have shown that the required performance characteristics in the drilling process are improved by using this approach.

[1]  B. Latha,et al.  Modeling and Analysis of Surface Roughness Parameters in Drilling GFRP Composites Using Fuzzy Logic , 2010 .

[2]  Ko-Ta Chiang,et al.  The method of grey-fuzzy logic for optimizing multi-response problems during the manufacturing process: a case study of the light guide plate printing process , 2009 .

[3]  K. Palanikumar,et al.  Experimental investigation and optimisation in drilling of GFRP composites , 2011 .

[4]  A. Haq,et al.  Multi response optimization of machining parameters of drilling Al/SiC metal matrix composite using grey relational analysis in the Taguchi method , 2008 .

[5]  Andrés Bustillo,et al.  A soft computing system using intelligent imputation strategies for roughness prediction in deep drilling , 2012, J. Intell. Manuf..

[6]  N Tosun,et al.  Determination of optimum parameters for multi-performance characteristics in drilling by using grey relational analysis , 2006 .

[7]  Ful-Chiang Wu,et al.  Optimising robust design for correlated quality characteristics , 2003 .

[8]  Chang Ching-Kao,et al.  The optimal cutting-parameter selection of heavy cutting process in side milling for SUS304 stainless steel , 2007 .

[9]  C. S. Chen,et al.  Free abrasive wire saw machining of ceramics , 2009 .

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

[11]  Chih-Chung Chou,et al.  Machining parameters optimization on the die casting process of magnesium alloy using the grey-based fuzzy algorithm , 2008 .

[12]  P. Asokan,et al.  Development of multi-objective optimization models for electrochemical machining process , 2008 .

[13]  J. Paulo Davim,et al.  Some studies on drilling of hybrid metal matrix composites based on Taguchi techniques , 2008 .

[14]  K. Palanikumar,et al.  Multiple Performance Optimization of Machining Parameters on the Machining of GFRP Composites Using Carbide (K10) Tool , 2006 .

[15]  P. Asokan,et al.  Drilling of hybrid Al-5%SiCp-5%B4Cp metal matrix composites , 2010 .

[16]  Vladimir Pucovsky,et al.  Application of fuzzy logic and regression analysis for modeling surface roughness in face milliing , 2013, J. Intell. Manuf..

[17]  Andrés Bustillo,et al.  Using artificial intelligence to predict surface roughness in deep drilling of steel components , 2011, Journal of Intelligent Manufacturing.

[18]  K. Palanikumar,et al.  Application of grey fuzzy logic for the optimization of drilling parameters for CFRP composites with multiple performance characteristics , 2012 .

[19]  U. Zuperl,et al.  Intelligent system for machining and optimization of 3D sculptured surfaces with ball-end milling , 2006 .

[20]  Franc Cus,et al.  Modeling and adaptive force control of milling by using artificial techniques , 2012, J. Intell. Manuf..

[21]  Tatjana V. Sibalija,et al.  An integrated approach to optimise parameter design of multi-response processes based on Taguchi method and artificial intelligence , 2012, J. Intell. Manuf..

[22]  T. Rajmohan,et al.  Application of the central composite design in optimization of machining parameters in drilling hybrid metal matrix composites , 2013 .

[23]  Hari Singh,et al.  Optimization of machining techniques — A retrospective and literature review , 2005 .

[24]  Ahmad Mayyas,et al.  Artificial neural network modeling of the drilling process of self-lubricated aluminum/alumina/graphite hybrid composites synthesized by powder metallurgy technique , 2009 .

[25]  T. Rajmohan,et al.  Optimization of machining parameters in drilling hybrid aluminium metal matrix composites , 2012 .

[26]  Gautam Majumdar,et al.  Optimization of bead geometry of submerged arc weld using fuzzy based desirability function approach , 2013, J. Intell. Manuf..

[27]  C. L. Lin,et al.  Optimisation of the EDM Process Based on the Orthogonal Array with Fuzzy Logic and Grey Relational Analysis Method , 2002 .

[28]  Yan-Cherng Lin,et al.  Optimization of machining parameters using magnetic-force-assisted EDM based on gray relational analysis , 2009 .

[29]  S. Aslan,et al.  Drilling of a hybrid Al/SiC/Gr metal matrix composites , 2012 .

[30]  Susana Ferreiro,et al.  A Bayesian network for burr detection in the drilling process , 2012, J. Intell. Manuf..

[31]  Surjya K. Pal,et al.  Optimization of quality characteristics parameters in a pulsed metal inert gas welding process using grey-based Taguchi method , 2009 .

[32]  Nihat Tosun,et al.  Gray relational analysis of performance characteristics in MQL milling of 7075 Al alloy , 2010 .

[33]  Bala Murugan Gopalsamy,et al.  Optimisation of machining parameters for hard machining: grey relational theory approach and ANOVA , 2009 .

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