Performance evaluation in CNC turning of AA6063-T6 alloy using WASPAS approach

PurposeSurface roughness and vibration during machining are inevitable which critically affect the product quality characteristics. This paper aims to suggest the implementation of a multi-objective optimization technique to obtain the favorable parametric conditions which lead to minimum tool vibration and surface roughness of 6063-T6 aluminum alloy in computer numerically controlled (CNC) turning.Design/methodology/approachThe case study has been accomplished according to response surface methodology RSM’s Box–Behnken design (BBD) matrix using Titanium Nitride-coated Tungsten Carbide insert in a dry environment. As the experimental results are quite nonlinear, a second-order regression model has been developed for the responses (surface roughness and tool vibration) in terms of input cutting parameters (spindle speed, feed rate and depth of cut). The goodness of fit of the models has also been verified with analysis of variance (ANOVA) results.FindingsThe significance efficacy of input parameters on surface roughness and tool vibrations has been illustrated through multi-objective overlaid 3D surface plots and contour plots. Finally, parametric optimization has been performed to get the desired response values under the umbrella of weighted aggregate sum product assessment (WASPAS) method and verified confidently with confirmatory test results.Originality/valueThe results of this study reveals that hybrid RSM with WASPAS method can be readily applicable to optimize multi-response problems in the manufacturing field with higher confidence.

[1]  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 .

[2]  Mehmet Çunkas,et al.  Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method , 2011, Expert Syst. Appl..

[3]  Jurgita Antucheviciene,et al.  Measuring Congruence of Ranking Results Applying Particular MCDM Methods , 2011, Informatica.

[4]  J. I. Nanavati,et al.  Optimisation of machining parameters for turning operations based on response surface methodology , 2013 .

[5]  S. A. Tobias.  Machine tool vibration research , 1961 .

[6]  Pradeep Kumar,et al.  Taguchi’s optimization of process parameters for production accuracy in ultrasonic drilling of engineering ceramics , 2009, Prod. Eng..

[7]  M. K. Starr,et al.  Executive decisions and operations research , 1970 .

[8]  Zhongyu Wang,et al.  Novel method for evaluating surface roughness by grey dynamic filtering , 2010 .

[9]  E. Zavadskas,et al.  Optimization of Weighted Aggregated Sum Product Assessment , 2012 .

[10]  Siba Sankar Mahapatra,et al.  A thermal-structural model for process performance analysis , 2015 .

[11]  Siba Sankar Mahapatra,et al.  An intelligent approach to optimize the EDM process parameters using utility concept and QPSO algorithm , 2017 .

[12]  Oktay Celep,et al.  Optimization of some parameters of stirred mill for ultra-fine grinding of refractory Au/Ag ores , 2011 .

[13]  Ghorbani Siamak,et al.  Optimization of Surface Roughness and Vibration in Turning of Aluminum Alloy AA2024 Using Taguchi Technique , 2014 .

[14]  Ravinder Kumar,et al.  Study on surface roughness measurement for turning of Al 7075/10/SiCp and Al 7075 hybrid composites by using response surface methodology (RSM) and artificial neural networking (ANN) , 2015 .

[15]  R. Gandhinathan,et al.  Optimization of surface roughness and tool wear in hard turning of austempered ductile iron (grade 3) using Taguchi method , 2016 .

[16]  Ahmet Murat Pinar,et al.  Optimization of Process Parameters with Minimum Surface Roughness in the Pocket Machining of AA5083 Aluminum Alloy via Taguchi Method , 2013 .

[17]  Siba Sankar Mahapatra,et al.  A particle swarm approach for multi-objective optimization of electrical discharge machining process , 2016, J. Intell. Manuf..

[18]  S. S. Dhami,et al.  Minimization of Surface Roughness and Tool Vibration in CNC Milling Operation , 2015 .

[19]  Asish Bandyopadhyay,et al.  Optimization of process parameters in CNC turning of aluminium alloy using hybrid RSM cum TLBO approach , 2016 .

[20]  K. Palanikumar,et al.  Modeling and analysis for surface roughness in machining glass fibre reinforced plastics using response surface methodology , 2007 .

[21]  Carmita Camposeco-Negrete,et al.  Optimization of cutting parameters using Response Surface Method for minimizing energy consumption and maximizing cutting quality in turning of AISI 6061 T6 aluminum , 2015 .

[22]  M. Siddhpura,et al.  A review of chatter vibration research in turning , 2012 .

[23]  Shankar Chakraborty,et al.  Applications of WASPAS Method in Manufacturing Decision Making , 2014, Informatica.

[24]  S. Mahapatra,et al.  A hybrid approach for multi-response optimization of non-conventional machining on AlSiCp MMC , 2013 .

[25]  B. Bhattacharyya,et al.  Parametric analysis on electrochemical discharge machining of silicon nitride ceramics , 2006 .

[26]  S. K. Sarangi,et al.  Machinability study of pure aluminium and Al–12% Si alloys against uncoated and coated carbide inserts , 2009 .

[27]  K. Taraman,et al.  Multi machining output—multi independent variable turning research by response surface methodology , 1974 .

[28]  Evangelos Triantaphyllou,et al.  An examination of the effectiveness of multi-dimensional decision-making methods: A decision-making paradox , 1989, Decis. Support Syst..

[29]  Jurgita Antucheviciene,et al.  Evaluation of Ranking Accuracy in Multi-Criteria Decisions , 2006, Informatica.

[30]  Xinmin Lai,et al.  Optimization of laser welding thin-gage galvanized steel via response surface methodology , 2012 .