Taguchi-Based Grey Relation Optimization of Machining Parameters and Cutting Path Strategies in CNC Pocket Milling Operations

Abstract This study has focused on the Taguchi-based multi-response optimization of the pocket milling process for an optimal parametric combination to yield minimum surface roughness within a minimum machining time using a combination of Grey relational analysis (GRA) and the Taguchi method on the CNC process of DIN 1.0038 medium carbon steel. Sixteen experimental runs based on an orthogonal array of the Taguchi method were performed to derive multi-objective functions to be optimized within the experimental range. The objective functions have been selected in relation to parameters of the pocket milling process, i. e., surface roughness and machining time. The Taguchi approach was followed by Grey relational analysis to solve the multi-response optimization problem. The significance of these factors on overall quality characteristics of the pocket milling process has also been evaluated quantitatively by a variance analysis method (ANOVA). Optimal results have been verified by validation experiments to calculate the effectiveness of the method. The application of this method showed that proper selection of the milling parameters produces better surface roughness within the minimum machining time.

[1]  George Chryssolouris,et al.  A Comparison of Statistical and AI Approaches to the Selection of Process Parameters in Intelligent Machining , 1990 .

[2]  P. Oxley.  Modelling machining processes with a view to their optimization and to the adaptive control of metal cutting machine tools , 1988 .

[3]  Y. S. Tarng,et al.  Design optimization of cutting parameters for turning operations based on the Taguchi method , 1998 .

[4]  Tuğrul Özel,et al.  Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks , 2005 .

[5]  A. Bendell,et al.  Taguchi methods : applications in world industry , 1989 .

[6]  George-Christopher Vosniakos,et al.  Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments , 2002 .

[7]  Hossam A. Kishawy,et al.  Effect of coolant strategy on tool performance, chip morphology and surface quality during high-speed machining of A356 aluminum alloy , 2005 .

[8]  Tuğrul Özel,et al.  Multi-objective optimization for turning processes using neural network modeling and dynamic-neighborhood particle swarm optimization , 2007 .

[9]  Ugur Esme,et al.  OPTIMIZATION OF WELD BEAD GEOMETRY IN TIG WELDING PROCESS USING GREY RELATION ANALYSIS AND TAGUCHI METHOD OPTIMIZACIJA GEOMETRIJE TIG-VARKOV Z GREYJEVO ANALIZO IN TAGUCHIJEVO METODO , 2009 .

[10]  Ming-Yung Wang,et al.  Experimental study of surface roughness in slot end milling AL2014-T6 , 2004 .

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

[13]  Tae Jo Ko,et al.  A dynamic surface roughness model for face milling , 1997 .

[14]  Yalcin M. Ertekin,et al.  Identification of common sensory features for the control of CNC milling operations under varying cutting conditions , 2003 .

[15]  René Mayer,et al.  Surface shape prediction in high speed milling , 2004 .

[16]  Shi Hyoung Ryu,et al.  Roughness and texture generation on end milled surfaces , 2006 .

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

[18]  M. Estrems,et al.  Influence of radial and axial runouts on surface roughness in face milling with round insert cutting tools , 2004 .

[19]  Y. L. Yao,et al.  Modelling of multivariate time series for tool wear estimation in finish-turning , 1992 .

[20]  A. R. Ab-Kadir,et al.  A Comparison of Milling Cutting Path Strategies for Thin-Walled Aluminium Alloys Fabrication , 2013 .

[21]  David K. Aspinwall,et al.  Surface integrity of a high speed milled gamma titanium aluminide , 2001 .

[22]  Cevdet Gologlu,et al.  The effects of cutter path strategies on surface roughness of pocket milling of 1.2738 steel based on Taguchi method , 2008 .

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

[24]  Mustafa Kemal Kulekci,et al.  Use of Grey-Taguchi Method for the Optimization of Oblique Turning Process of AZ91D Magnesium Alloy , 2012 .

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

[26]  Álisson Rocha Machado,et al.  Performance of single Si3N4 and mixed Si3N4+PCBN wiper cutting tools applied to high speed face milling of cast iron , 2005 .

[27]  D. Montgomery,et al.  GAUGE CAPABILITY ANALYSIS AND DESIGNED EXPERIMENTS. PART II: EXPERIMENTAL DESIGN MODELS AND VARIANCE COMPONENT ESTIMATION , 1993 .

[28]  Pedro Paulo Balestrassi,et al.  Optimization of Radial Basis Function neural network employed for prediction of surface roughness in hard turning process using Taguchi's orthogonal arrays , 2012, Expert Syst. Appl..

[29]  Babur Ozcelik,et al.  The statistical modeling of surface roughness in high-speed flat end milling , 2006 .

[30]  Shi-Jer Lou,et al.  In-Process Surface Roughness Recognition (ISRR) System in End-Milling Operations , 1999 .

[31]  S. Joshi,et al.  Modeling of surface roughness in precision machining of metal matrix composites using ANN , 2008 .

[32]  Richard A. Wysk,et al.  An integrated system for selecting optimum cutting speeds and tool replacement times , 1992 .

[33]  Naresh Kumar Reddy,et al.  Determination of Optimal Cutting Conditions Using Design of Experiments And Optimization Techniques , 2012 .

[34]  Margaret J. Robertson,et al.  Design and Analysis of Experiments , 2006, Handbook of statistics.