Surface Roughness Model for Machining Glass Fiber Reinforced Plastics by PCD Tool using Fuzzy Logics

This article discusses the use of fuzzy logic for modeling machining parameters in machining glass fiber reinforced plastics by poly-crystalline diamond tool. The Taguchi method was used for conducting the experiments, which in turn reduced the number of experiments. An L27 (313) orthogonal array was used to investigate the machining process. The cutting parameters selected were cutting speed, feed, and depth of cut. The output responses considered for the investigation were surface roughness parameters such as arithmetic average height (Ra) and maximum height of the profile (Rt). Fuzzy rule based models were developed for correlating cutting parameters with surface roughness parameters. The model predicted values and measured values were fairly close to each other. The confirmation test results proved the fact that the developed models were effectively representing the surface roughness parameters Ra and Rt in machining of GFRP composites.

[1]  Shigeo Abe,et al.  Neural Networks and Fuzzy Systems , 1996, Springer US.

[2]  K. Palanikumar,et al.  Application of Taguchi and response surface methodologies for surface roughness in machining glass fiber reinforced plastics by PCD tooling , 2008 .

[3]  A. Koplev,et al.  The Cutting Process, Chips and Cutting Forces in Machining CFRP , 1983 .

[4]  Suleyman Yaldiz,et al.  Comparison of experimental results obtained by designed dynamometer to fuzzy model for predicting cutting forces in turning , 2005 .

[5]  R. Krishnamurthy,et al.  Machinability characteristics of fibre reinforced plastics composites , 1988 .

[6]  Madhan Shridhar Phadke,et al.  Quality Engineering Using Robust Design , 1989 .

[7]  W. König,et al.  Machining of Fibre Reinforced Plastics , 1985 .

[8]  K Hashmi,et al.  Fuzzy logic based data selection for the drilling process , 2000 .

[9]  Shuting Lei,et al.  Fuzzy adaptive networks in machining process modeling: surface roughness prediction for turning operations , 2004 .

[10]  Else Eriksen,et al.  Influence from Production Parameters on the Surface Roughness of a Machined Short Fibre Reinforced Thermoplastic , 1999 .

[11]  Nabil Gindy,et al.  A user-friendly fuzzy-based system for the selection of electro discharge machining process parameters , 2006 .

[12]  H. Takeyama,et al.  Machinability of Glassfiber Reinforced Plastics and Application of Ultrasonic Machining , 1988 .

[13]  Peter Cheeseman,et al.  Fuzzy thinking , 1995 .

[14]  Kamlakar P Rajurkar,et al.  Investigations into machining of composites , 1990 .

[15]  J. Paulo Davim,et al.  Multiple regression analysis (MRA) in modelling milling of glass fibre reinforced plastics (GFRP) , 2004, Int. J. Manuf. Technol. Manag..

[16]  J. Ferreira,et al.  Machining optimisation in carbon fibre reinforced composite materials , 1999 .

[17]  James R. Simpson,et al.  Robust Design and Analysis for Quality Engineering , 1998 .

[18]  K. Sakuma,et al.  Tool Wear in Cutting Glass-fiber-reinforced Plastics : The Relation between Fiber Orientation and Tool Wear , 1983 .

[19]  Imtiaz Ahmed Choudhury,et al.  Application of Taguchi method in the optimization of end milling parameters , 2004 .

[20]  Liangchi Zhang,et al.  Surface roughness prediction of ground components using a fuzzy logic approach , 1999 .

[21]  Dwayne Arola,et al.  Orthogonal cutting mechanisms of graphite/epoxy composite. Part II: multi-directional laminate , 1995 .

[22]  K. Palanikumar,et al.  Cutting Parameters Optimization for Surface Roughness in Machining of GFRP Composites using Taguchi’s Method , 2006 .