Optimization of process parameters in CNC turning of aluminium alloy using hybrid RSM cum TLBO approach

The main aim of the present work is to analyse the significance of turning parameters on surface roughness in computer numerically controlled (CNC) turning operation while machining of aluminium alloy material. Spindle speed, feed rate and depth of cut have been considered as machining parameters. Experimental runs have been conducted as per Box-Behnken design method. After experimentation, surface roughness is measured by using stylus profile meter. Factor effects have been studied through analysis of variance. Mathematical modelling has been done by response surface methodology, to made relationships between the input parameters and output response. Finally, process optimization has been made by teaching learning based optimization (TLBO) algorithm. Predicted turning condition has been validated through confirmatory experiment.

[1]  Kumar Abhishek,et al.  Parametric appraisal and optimization in machining of CFRP composites by using TLBO (teaching–learning based optimization algorithm) , 2017, J. Intell. Manuf..

[2]  Toshimichi Moriwaki,et al.  Intelligent monitoring and identification of cutting states of chips and chatter on CNC turning machine , 2008 .

[3]  Ossama B. Abouelatta,et al.  Surface roughness prediction based on cutting parameters and tool vibrations in turning operations , 2001 .

[4]  Asish Bandyopadhyay,et al.  INVESTIGATION ON SURFACE ROUGHNESS IN CYLINDRICAL GRINDING , 2011 .

[5]  Pramod Kumar Jain,et al.  In-process prediction of surface roughness in turning of Ti–6Al–4V alloy using cutting parameters and vibration signals , 2013 .

[6]  M. Nalbant,et al.  The experimental investigation of the effects of uncoated, PVD- and CVD-coated cemented carbide inserts and cutting parameters on surface roughness in CNC turning and its prediction using artificial neural networks , 2009 .

[7]  John Edwin Raja Dhas,et al.  Modeling and prediction of machining quality in CNC turning process using intelligent hybrid decision making tools , 2013, Appl. Soft Comput..

[9]  Ali R. Yildiz,et al.  Optimization of multi-pass turning operations using hybrid teaching learning-based approach , 2013 .

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

[11]  R. Venkata Rao,et al.  Review of applications of TLBO algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems , 2016 .

[12]  T. P. Singh,et al.  Multi-objective Optimization of Turning Process During Machining of AlMg1SiCu Using Non-dominated Sorted Genetic Algorithm , 2014 .

[13]  Ágota Drégelyi-Kiss,et al.  Analysis of surface roughness of aluminum alloys fine turned: United phenomenological models and multi-performance optimization , 2015 .

[15]  P. Jayaraman,et al.  Multi-response Optimization of Machining Parameters of Turning AA6063 T6 Aluminium Alloy using Grey Relational Analysis in Taguchi Method☆ , 2014 .

[16]  Asish Bandyopadhyay,et al.  Modeling and optimization of machining parameters in cylindrical grinding process , 2016 .

[17]  R. Venkata Rao,et al.  Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems , 2011, Comput. Aided Des..

[18]  D. R. Salgado,et al.  Surface Finish Monitoring in Taper Turning CNC Using Artificial Neural Network and Multiple Regression Methods , 2013 .

[20]  R. Venkata Rao,et al.  Parameter optimization of modern machining processes using teaching-learning-based optimization algorithm , 2013, Eng. Appl. Artif. Intell..

[21]  R. Venkata Rao,et al.  Teaching-Learning-Based Optimization: An optimization method for continuous non-linear large scale problems , 2012, Inf. Sci..

[22]  Chaoyong Zhang,et al.  Multi-objective teaching–learning-based optimization algorithm for reducing carbon emissions and operation time in turning operations , 2015 .

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