Optimization of end milling parameters under minimum quantity lubrication using principal component analysis and grey relational analysis

Machining is the major reliable practice in accomplishment of metal cutting industries. The accelerated growing competition demands top superior and large quantity with low cost products. Metal working fluids have significant fragment of manufacturing cost and causes ecological impacts and health problems. This work attempts to advance a competent machining alignment with no ecological impacts. The prediction of quality characteristics and enhancement of machining field are consistently accepting great interest in machining sectors to compress the accomplishment costs. In this paper, GA based ANN prediction model proposes to envisage the quality characteristics of surface roughness and tool wear. The comparison of predicted and experimental values acknowledges the precision of the model. The end milling experiments are conducted beneath minimum quantity lubrication. This paper as well deals with the multiple objective optimization with principal component analysis, grey relational analysis and Taguchi method. ANOVA was carried out to determine each parameter contribution percentage on quality characteristics. The results show that cutting speed is the most influencing parameter followed by feed velocity, lubricant flow rate and depth of cut. The confirmation tests acknowledge that the proposed multiple-objective methodology is able in determining optimum machining parameters for minimum surface roughness and tool wear.

[1]  Yoke San Wong,et al.  Multi-objective optimization of high-speed milling with parallel genetic simulated annealing , 2006 .

[2]  David Kerr,et al.  Assessment and visualisation of machine tool wear using computer vision , 2006 .

[3]  Asa Prateepasen,et al.  Coherence function model for tool wear monitoring , 2008 .

[4]  Ibrahim N. Tansel,et al.  Tool wear estimation in micro-machining.: Part I: tool usage–cutting force relationship , 2000 .

[5]  Y. K. Chou,et al.  CBN tool wear in hard turning: a survey on research progresses , 2007 .

[6]  Janez Kopac,et al.  New approach in tool wear measuring technique using CCD vision system , 2005 .

[7]  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 .

[8]  Sulaiman Hasan,et al.  Tool flank wear analyses on martensitic stainless steel by turning , 2008 .

[9]  W. Bouzid Saï,et al.  An investigation of tool wear in high-speed turning of AISI 4340 steel , 2005 .

[10]  J. Lorentzon,et al.  Computational Materials Science and Surface Engineering Modelling the influence of carbides on tool wear , 2009 .

[11]  N. Tosun,et al.  Optimisation for hot turning operations with multiple performance characteristics , 2004 .

[12]  Mohammed Nouari,et al.  Experimental verification of a diffusion tool wear model using a 42CrMo4 steel with an uncoated cemented tungsten carbide at various cutting speeds , 2005 .

[13]  P. K. Venuvinod,et al.  Hybrid Learning for Tool Wear Monitoring , 2000 .

[14]  Surjya K. Pal,et al.  Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-II , 2007 .

[15]  S. K. Choudhury,et al.  Role of temperature and surface finish in predicting tool wear using neural network and design of experiments , 2003 .

[16]  C. Kuo,et al.  Tool life and surface integrity when milling inconel 718 with coated cemented carbide tools , 2010 .

[17]  Fu-Chen Chen,et al.  Multi-objective optimisation of high-speed electrical discharge machining process using a Taguchi fuzzy-based approach , 2007 .

[18]  I. Rajendran,et al.  A study on optimisation of cutting parameters and prediction of surface roughness in end milling of aluminium under MQL machining , 2010 .

[19]  N. R. Abburi,et al.  Multi-objective optimization of multipass turning processes , 2007 .

[20]  Joseph C. Chen,et al.  An artificial-neural-networks-based in-process tool wear prediction system in milling operations , 2005 .

[21]  Tian-Syung Lan,et al.  TAGUCHI OPTIMIZATION OF MULTI-OBJECTIVE CNC MACHINING USING TOPSIS , 2009 .

[22]  N. Ramakrishnan,et al.  Wear of rotary carbide tools in machining of Al/SiCp composites , 1999 .

[23]  Che Hassan Che Haron,et al.  High speed milling of Ti-6Al-4V using coated carbide tools , 2008 .

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

[25]  S. Shanmugasundaram,et al.  Prediction of tool wear using regression and ANN models in end-milling operation , 2008 .

[26]  Anselmo Eduardo Diniz,et al.  Influence of Cutting Conditions on Tool Life, Tool Wear and Surface Finish in the Face Milling Process , 2002 .

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

[28]  Jacob Chen,et al.  Fuzzy Logic Based In-Process Tool-Wear Monitoring System in Face Milling Operations , 2002 .

[29]  Rodolfo E. Haber,et al.  Transductive-Weighted Neuro-Fuzzy Inference System for Tool Wear Prediction in a Turning Process , 2009, HAIS.

[30]  Snr. D. E. Dimla.  The Correlation of Vibration Signal Features to Cutting Tool Wear in a Metal Turning Operation , 2002 .