Optimization of machining parameters of Al/SiC-MMC with ANOVA and ANN analysis

In recent years, the utilization of metal matrix composites (MMC) materials in many engineering fields has increased tremendously. Accordingly the need for accurate machining of composites has also increased enormously. Despite the recent developments in the near net shape manufacture, composite parts often require post-mold machining to meet dimensional tolerances, surface quality and other functional requirements. In the present work, the surface roughness of Al–SiC (20 p) has been studied in this paper by turning the composite bars using coarse grade polycrystalline diamond (PCD) insert under different cutting conditions. Experimental data collected are tested with analysis of variance (ANOVA) and artificial neural network (ANN) techniques. Multilayer perceptron model has been constructed with back-propagation algorithm using the input parameters of depth of cut, cutting speed and feed. Output parameter is surface finish of the machined component. On completion of the experimental test, ANOVA and an ANN are used to validate the results obtained and also to predict the behavior of the system under any condition within the operating range.

[1]  K. Palanikumar,et al.  OPTIMAL MACHINING CONDITIONS FOR TURNING OF PARTICULATE METAL MATRIX COMPOSITES USING TAGUCHI AND RESPONSE SURFACE METHODOLOGIES , 2006 .

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

[3]  Liangchi Zhang,et al.  Prediction of cutting forces in machining of Metal Matrix Composites , 2006 .

[4]  M. Murugan,et al.  Machinability issues in turning of Al-SiC (10p) metal matrix composites , 2008 .

[5]  D. Bhattacharyya,et al.  Multiple regression and neural networks analyses in composites machining , 2003 .

[6]  Gerald S. Cole,et al.  Metal-matrix composites in the automotive industry: Opportunities and challenges , 1993 .

[7]  M. Surappa,et al.  Effect of sliding speed on wear behaviour of A356 Al-30 wt.% SiCp MMC , 1997 .

[8]  Johari Muhammad Al Firdausi.  Design optimization of cutting parameters for truning operation based on Taguchi method , 2008 .

[9]  A. Chattopadhyay,et al.  Neural-networks-based tool wear monitoring in turning medium carbon steel using a coated carbide tool , 1997 .

[10]  Liangchi Zhang,et al.  An FEM investigation into the behavior of metal matrix composites: tool-particle interaction during orthogonal cutting , 2007 .

[11]  Xiaoping Li,et al.  Tool wear acceleration in relation to workpiece reinforcement percentage in cutting of metal matrix composites , 2001 .

[12]  Y. G. Srinivasa,et al.  A back-propagation algorithm applied to tool wear monitoring , 1994 .

[13]  A. Köpf,et al.  Diamond coated cutting tools for machining of non-ferrous metals and fibre reinforced polymers , 2006 .

[14]  F. O. Rasch,et al.  Machinability of Particulate Aluminium Matrix Composites , 1992 .

[15]  R. J. Kuo,et al.  Intelligent tool wear estimation system through artificial neural networks and fuzzy modeling , 1998, Artif. Intell. Eng..

[16]  F. Kennedy,et al.  The friction and wear of Cu-based silicon carbide particulate metaal matrix composites for brake applications , 1997 .

[17]  J. Davim,et al.  Experimental cutting model of metal matrix composites (MMCs) , 2007 .

[18]  M. F. DeVries,et al.  Neural Network Sensor Fusion for Tool Condition Monitoring , 1990 .

[19]  J. Davim.  Application of Merchant theory in machining particulate metal matrix composites , 2007 .

[20]  J. Davim.  Design of optimisation of cutting parameters for turning metal matrix composites based on the orthogonal arrays , 2003 .