Modeling and Analysis of Machinability Characteristics in PA6 and PA66 GF30 Polyamides through Artificial Neural Network

Abstract

The traditional metallic materials are replaced by some applications for turning process in PA6 and PA66 GF30 polyamides due to excellent properties such as high specific strength and stiffness, wear resistance, dimensional stability, low weight and directional properties. The addition of short fibers to the polyamides improves the properties over the unreinforced polyamides. As a result of these improved properties and potential applications in several fields of engineering, there is a need to understand the machining of unreinforced and reinforced polyamides. Selection of cutting tool and process parameters is important in machining of these composites. This article presents the application of artificial neural network (ANN) modeling to assess the machinability characteristics of unreinforced polyamide (PA6) and reinforced polyamide with 30% of glass fibers (PA66 GF30). The effects of process parameters such as work material, tool material, cutting speed, and feed rate on three aspects of machinability, namely, machining force, power, and specific cutting force have been analyzed through a multilayer feed forward ANN. The input—output patterns required for training are obtained through turning experiments planned as per full factorial design. The model analysis revealed that the minimum machining force results at low feed rate and independent of cutting speed, whereas the power is minimal when both the cutting speed and feed rate are at low levels for PA6 and PA66 GF30 polyamides machining irrespective of the cutting tool. On the other hand, the specific cutting force is minimal at low cutting speed and high feed rate in case of PA6 material, whereas high values of cutting speed and feed rate are essential for minimizing the specific cutting force for PA66 GF30 polyamide machining.

Keywords

PA6 and PA66 GF30 polyamides turning polycrystalline diamond (PCD) tool cemented carbide (K10) tool machining force power specific cutting force artificial neural network (ANN).

References

Palabiyik, M. and Bahadur, S. (2002). Tribological Studies of Polyamide 6 and High-density Polyethylene Blends Filled With PTFE and Copper Oxide and Reinforced with Short Glass Fibers, Wear, 253: 369-376.

Chen, Y.K. , Modi, O.P. , Mhay, A.S. , Chrysanthou, A. and O’Sullivan, J.M. ( 2003). The Effect of Different Metallic Counter Face Materials and Different Surface Treatments on the Wear and Friction of Polyamide 66 and its Composite in Rolling-Sliding Contact, Wear, 255: 714-721.

Chavarria, F. and Paul, D.R. (2004). Comparison of Nanocomposites Based on Nylon 6 and Nylon 66, Polymer, 45: 8501-8515.

Mata, F. , Reis, P. and Davim, J.P. ( 2006). Physical Cutting Model of Polyamide Composites (PA66 GF30), Materials Science Forum, 514-516: 643-647.

Davim, J.P. and Mata, F. ( 2007). A Comparative Evaluation of the Turning of Reinforced and Unreinforced Polyamide, International Journal of Advanced Manufacturing Technology, 33: 911-914.

Franke, R. , Haase, I. , Lehmann, D. , Hupfer, B. and Janke, A. ( 2007). Manufacturing and Tribological Properties of Sandwich Materials with Chemically Bonded PTFE-PA 66 and PA 66/GF, Wear, 262: 958-971.

Pedroso, A.G. , Mei, L.H.I. , Agnelli, J.A.M. and Rosa, D.S. ( 2002). The Influence of the Drying Process Time on the Final Properties of Recycled Glass Fiber Reinforced Polyamide, Polymer Testing, 21: 229-232.

Jordon, J. , Jacob, K.I. , Tannenbaum, R. , Sharaf, M.A. and Jasiuk, I. ( 2005). Experimental Trends in Polymer Nanocomposites - A Review , Materials Science and Engineering - Part A, 393: 1-11.

Davim, J.P. , Reis, P. , Lapa, V. and Antonio, C.C. ( 2003). Machinability Study on Polyetheretherketone (PEEK) Unreinforced and Reinforced (GF30) for Applications in Structural Components , Composite Structures, 62: 67-73.

10.

Davim, J.P. and Reis, P. (2004). Machinability Study on Composite (Polyetheretherketone Reinforced with 30% Glass Fiber - PEEK GF30) Using Polycrystalline Diamond (PCD) and Cemented Carbide (K20) Tools, International Journal of Advanced Manufacturing Technology , 23: 412-418.

11.

Davim, J.P. and Mata, F. (2004). Influence of Cutting Parameters on Surface Roughness in Turning Glass-fiber-reinforced Plastics Using Statistical Analysis, Industrial Lubrication and Tribology , 56(5): 270-274.

12.

Davim, J.P. and Mata, F. (2005). Optimisation of Surface Roughness on Turning Fiber-reinforced Plastics (FRPs) with Diamond Cutting Tools, International Journal of Advanced Manufacturing Technology , 26: 319-323.

13.

EI-Sonbaty, I. , Khashaba, U.A. and Machaly, T. (2004). Factors Affecting the Machinability of GFR/Epoxy Composite, Composite Structures , 63: 329-338.

14.

Groover, M.P. (1996). Fundamentals of Modern Manufacturing Materials: Processes and Systems, Prentice Hall, New Jersey.

15.

Gaitonde, V.N. , Karnik S.R. , Mata , F. and Davim, J.P. (2008). Taguchi Approach for Achieving Better Machinability in Unreinforced and Reinforced Polyamides, Journal of Reinforced Plastics and Composites , 27: 909-924.

16.

Schalkoff, R.B. ( 1997). Artificial Neural Networks, McGraw-Hill , Singapore.

17.

Montgomery, D.C. ( 2003). Design and Analysis of Experiments, John Wiley and Sons, New York.

18.

Math Works Incorporation (2005). MATLAB User Manual, Version 7.1, R 14, Math Works Incorporation , Natick, MA.

19.

Sha, W. and Edwards, K.L. ( 2007). The Use of Artificial Neural Networks in Materials Science Based Research, Materials Design, 28(6): 1747-1752.