Optimization in Milling of Polymer Materials for High Quality Surfaces
Ryota Uchiyama*,, Yuki Inoue*, Fumihiro Uchiyama*, and Takashi Matsumura**
*Uchiyama Hamono Co., Ltd.
3-8-1 Ryoke, Naka-ku, Hamamatsu, Shizuoka 430-0852, Japan
**Tokyo Denki University, Tokyo, Japan
High quality surfaces with transparency are required for manufacturing of plastic products. In cutting of polymer materials, surface quality is sometimes deteriorated by tarnish and/or unequal spaces of area on a surface. The cutting parameters should be determined through understanding of surface finish characteristics. This paper presents an optimization approach in milling of polycarbonate with polycrystal diamond tools in terms of the surface finish. Surfaces are finished with changing the feed rate and the clearance angle of the tool. The surface finishes, then, were observed to classify the deterioration type into welding, adhesion, and the unequal space of cutter marks with measurement of the surface profiles. The measured surface roughnesses are decomposed into the theoretical/geometrical term and the irregular term induced by the thermal and the dynamic effects. A map is presented to characterize the irregular term for the feed rates and the clearance angles. Because the surface roughnesses are measured at discrete sets of the cutting parameters in the actual cutting tests, the process design cannot be conducted to optimize the operation parameters. Therefore, a neural network is applied to associate the cutting parameters with the irregular term in the map. An approach is presented to determine the number of hidden nodes/units in the design of the neural network. Three prominent areas of welding, adhesion, and unequal spaces of the cutter marks, appear in the map of irregular roughness. The map of the surface roughness is made to optimize the cutting process. The applicable feed rates and clearance angles are determined for the tolerable surface roughnesses. The gradient information in the map is used to evaluate the stability/robustness of the surface quality for changing the parameters. The optimum parameters were determined to minimize the gradient information in the applicable feed rates and clearance angles.
-  M. Alauddin, I. A. Choudhury, M. A. El Baradie, and M. S. J. Hashmi, “Plastics and their machining: A review,” J. of Materials Processing Technology, Vol.54, Issues 1-4, pp. 40-46, 1995.
-  J. W. Carr and C. Feger, “Ultraprecision machining of polymers,” Precision Engineering, Vol.15, Issue 4, pp. 221-237, 1993.
-  V. Mishra, R. Sharma, N. Khatri, H. Garg, V. Karar, G. S. Khan, and R. V. Sarepaka, “Processing of polycarbonate by ultra-precision machining for optical applications,” Materials Today: Proc., Vol.5, Issue 11, Part 3, pp. 25130-25138, 2018.
-  N. Khatri, V. Mishra, and R. G. Sarepaka, “Optimization of process parameters to achieve nano level surface quality on polycarbonate,” Int. J. of Computer Applications, Vol.48, No.13, pp. 39-44, 2012.
-  P. G. Benardos and G. C. Vosniakos, “Predicting surface roughness in machining: a review,” Int. J. of Machine Tools and Manufacture, Vol.43, Issue 8, pp. 833-844, 2003.
-  D. K. Baek, T. J. Ko, and H. S. Kim, “Optimization of feed rate in a face milling operation using a surface roughness model,” Int. J. of Machine Tools and Manufacture, Vol.41, Issue 3, pp. 451-462, 2001.
-  C. Felho, B. Karpuschewski, and J. Kundrak, “Surface roughness modelling in face milling,” Procedia CIRP, Vol.31, pp. 136-141, 2015.
-  K. Y. Lee, M. C. Kang, Y. H. Jeong, D. W. Lee, and J. S. Kim, “Simulation of the surface roughness and profile in high speed end milling,” J. of Materials Processing Technology, Vol.113, Issues 1-3, pp. 410-415, 2001.
-  T. Surmann and D. Biermann, “The effect of tool vibrations on the flank surface created by peripheral milling,” CIRP Annals, Vol.57, Issue 1, pp. 375-378, 2008.
-  C. F. Cheung and W. B. Lee, “A theoretical and experimental investigation of surface roughness formation in ultra-precision diamond turning,” Int. J. of Machine Tools and Manufacture, Vol.40, Issue 7, pp. 979-1002, 2000.
-  B. Denkena, V. Böß, and D. Nespor, “Kinematic and stochastic surface topography of machined TiAl6V4-parts by means of ball nose end milling,” Procedia Engineering, Vol.19, pp. 81-87, 2011.
-  B. Denkena, V. Böß, D. Nespor, P. Gilge, S. Hohenstein, and J. Seume, “Prediction of the 3D surface topography after ball end milling and its influence on aerodynamics,” Procedia CIRP, Vol.31, pp. 221-227, 2015.
-  F. Uchiyama, A. Tsuboi, and T. Matsumura, “Surface profile analysis in milling with structured tool,” Int. J. Automation Technol., Vol.13, No.1, 2019.
-  C. Bruni, L. d’Apolito, A. Forcellese, F. Gabrielli, and M. Simoncini, “Surface roughness modelling in finish face milling under MQL and dry cutting conditions,” Int. J. of Material Forming, Vol.1, pp. 503-506, 2008.
-  B. A. Beatrice, E. Kirubakaran, P. R. J. Thangaiah, and K. L. D. Wins, “Surface roughness prediction using artificial neural network in hard turning of AISI H13 steel with minimal cutting fluid application,” Procedia Engineering, Vol.97, pp. 205-211, 2014.
-  T. Matsumura, T. Obikawa, T. Shirakashi, and E. Usui, “Autonomous turning operation planning with adaptive prediction of tool wear and surface roughness,” J. of Manufacturing Systems, Vol.12, No.3, pp. 253-262, 1993.
-  K. V. Rao, B. S. N. Murthy, and N. M. Rao, “Prediction of cutting tool wear, surface roughness and vibration of work piece in boring of AISI 316 steel with artificial neural network,” Measurement, Vol.51, pp. 63-70, 2014.
-  P. G. Benardos and G. C. Vosniakos, “Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments,” Robotics and Computer Integrated Manufacturing, Vol.18, Issues 5-6, pp. 343-354, 2002.
-  B. P. Huang, J. C. Chen, and Y. Li, “Artificial-neural-networks- based surface roughness Pokayoke system for end-milling operations,” Neurocomputing, Vol.71, Issues 4-6, pp. 544-549, 2008.
-  G. E. Hinton, S. Osindero, and Y. W. Teh, “A fast learning algorithm for deep belief nets,” Neural Computation, Vol.18, Issue 7, pp. 1527-1554, 2006.
-  G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science, Vol.313, Issue 5786, pp. 504-507, 2006.
-  D. E. Rumelhart, J. L. McClelland, and the PDP Research Group, “Parallel Distributed Processing,” MIT Press, 1986.
-  T. Matsumura, S. Arakawa, and S. Tamura, “Study on adhesion with stress and temperature in drilling of aluminum alloy,” Proc. of the ASME 2020 Int. Symp. on Flexible Automation, ISFA2020-9633, 2020.
-  L. T. Tunç and E. Budak, “Effect of cutting conditions and tool geometry on process damping in machining,” Int. J. of Machine Tools and Manufacture, Vol.57, pp. 10-19, 2012.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 International License.