IJAT Vol.16 No.2 pp. 138-148
doi: 10.20965/ijat.2022.p0138


Prediction of Gloss in Plastic Injection Parts Based on 3D Surface Roughness from Virtual Machining with Artificial Neural Networks

Wiroj Thasana*,† and Weerachart Wetchakama**

*Department of Mechanical Engineering, Rajamangala University of Technology Isan (RMUTI) Surin Campus
145 Moo 15 Surin-Prasat Rd., Nokmuang, Muang District, Surin 32000, Thailand

Corresponding author

**Department of Certification Division, Thai Industrial Standards Institute (TISI), Bangkok, Thailand

September 1, 2021
November 24, 2021
March 5, 2022
virtual machining, kinematic motion deviations, three-dimensional surface roughness, machining process simulations, artificial neural network

The gloss is one of the most important characteristics of plastic injection molding parts, whereby the molding process needs to consider the influence of the three main factors such as the surface roughness of the cavity, chemical properties of the plastic, and injection parameters. The surface roughness of the plastic injection mold was considered for the gloss that occurs with the parts of the plastic injection processes. Therefore, the objective of this research is to predict the gloss for plastic injection parts based on the artificial neural network method from the input parameters of 3D surface roughness from virtual machining on a 3-axis CNC machining center, and plastic injection parameters. The shape generation motions were mathematically described by combining 4 × 4 transformation matrices including the kinematic motion deviations, machining parameters, end milling ball nose geometries, and cutting force. The results of the research showed the prediction of gloss in plastic injection parts from a neural network model compared to the gloss value of the actually measured workpiece and response surface methodology combined with central composite design. It was found that the average error of the gloss was 2.36%. The proposed method provides us with a systematic method to estimate the gloss for plastic injection parts before producing the actual cavity mold, which leads to increased accuracy as well as efficiency in manufacturing plastic injection parts.

Cite this article as:
W. Thasana and W. Wetchakama, “Prediction of Gloss in Plastic Injection Parts Based on 3D Surface Roughness from Virtual Machining with Artificial Neural Networks,” Int. J. Automation Technol., Vol.16 No.2, pp. 138-148, 2022.
Data files:
  1. [1] K. Chaisuriyakul, W. Thasana, and T. Khajifa, “Study on influence of heating and cooling temperature in injection molding to effect on glossy of plastic injection parts,” Proc. of 8th TSME-ICOME 2017, pp. 1238-1246, 2017.
  2. [2] A. A. Kadir, X. Xu, and E. Hämmerle, “Virtual machine tools and virtual machining – A technological review,” Robot. Comput.-Integr. Manuf., Vol.27, pp. 494-508. 2011.
  3. [3] A. Takahashi, A. Yoshida, W. Thasana, N. Sugimura, K. Iwamura, and Y. Tanimizu, “Analysis of kinematic motion deviations of machining centers based on geometric tolerances,” J. Adv. Mech. Des. Syst. Manuf., Vol.8, No.4. pp. 1-12, 2014.
  4. [4] R. Takematsu, N. Satonaka, W. Thasana, K. Iwamura, and N. Sugimura, “A study on tolerance design of parallel link robots based on mathematical models,” J. Adv. Mech. Des. Syst. Manuf., Vol.12, No.1, pp. 1-14, 2018.
  5. [5] W. Thasana and S. Chianrabutra, “A comparison between simulation and experiment of virtual machining in CNC turning machine considering kinematic motion deviations, tool wear and workpiece deflection errors,” J. Adv. Mech. Des. Syst. Manuf., Vol.13, No.1, pp. 1-14, 2019.
  6. [6] M. Soori, B. Arezoo, and M. Habibi, “Accuracy analysis of tool deflection error modelling in prediction of milled surfaces by a virtual machining system,” Int. J. Computer Applications in Technology, Vol.55, No.4, pp. 308-321, 2017.
  7. [7] M. Soori, B. Arezoo, and M. Habibi, “Dimensional and geometrical errors of three-axis CNC milling machines in a virtual machining system,” Comput. Aided Des., Vol.45, pp. 1306-1313, 2013.
  8. [8] M. Soori, B. Arezoo, and M. Habibi, “Virtual machining considering dimensional, geometrical and tool deflection errors in three-axis CNC milling machines,” J. Manuf. Syst., Vol.33, pp. 498-507, 2014.
  9. [9] H. Narita, “A Method for using a virtual machining simulation to consider both equivalent CO2 emissions and machining costs in determining cutting conditions,” Int. J. Automation Technol., Vol.9, No.2, pp. 115-121, 2015.
  10. [10] Y. Altintas, P. Kersting, D. Biermann, E. Budak, B. Denkena, and I. Lazoglu, “Virtual process systems for part machining operations,” CIRP Ann. – Manuf. Technol, Vol.63, pp. 585-605, 2014.
  11. [11] Y. Altintas, “Virtual High Performance Machining,” Procedia CIRP, Vol.46, pp. 372-378, 2016.
  12. [12] Y. Altintas, J. Yang, and Z. M. Kilic, “Virtual prediction and constraint of contour errors induced by cutting force disturbances on multi-axis CNC machine tools,” CIRP Ann. – Manuf. Technol, Vol.68, pp. 377-380, 2019.
  13. [13] K, Morishige and S, Mori, “Tool Path Generation for 5-Axis Rough Cutting Using Haptic Device,” Int. J. Automation Technol., Vol.14, No.5, pp. 808-815, 2020.
  14. [14] A. Caggiano and L. Nele, “Artificial neural network for tool wear prediction based on sensor fusion monitoring of CFRP/CFRP stack drilling,” Int. J. Automation Technol., Vol.12, No.3, pp. 275-281, 2018.
  15. [15] E. Wenkler, F. Arnold, A. Hanel, A. Nestler, and A. Brosius, “Intelligent characteristic value determination for cutting processes based on machine learning,” Procedia CIRP, Vol.79, pp. 9-14, 2019.
  16. [16] J. Herwan, S. Kano, O. Ryabov, H. Sawada, N. Kasashima, and T. Misaka, “Predicting surface roughness of dry cut grey cast iron based on cutting parameters and vibration signals from different sensor positions in CNC turning,” Int. J. Automation Technol., Vol.14, No.2, pp. 217-228, 2020.
  17. [17] ISO 230-1:2012, “Test code for machine tools – Part 1: Geometric accuracy of machines operating under no-load or quasi-static conditions,” pp. 107-109. 2012.
  18. [18] S. Engin and Y. Altintas, “Mechanics and dynamics of general milling cutters part I: Helical end mills,” Int. J. Mach. Tools Manuf., Vol.41, pp. 2195-2112, 2001.
  19. [19] Y. Altintas, “Manufacturing Automation: Metal Cutting Mechanics, Machine Tool Vibrations, and CNC Design (2nd ed.),” Cambridge University Press, 2012.
  20. [20] W. Zhang and M. Wan, “Milling Simulation: Metal Milling Mechanics, Dynamics and Clamping Principles,” ISTE Ltd. and John Wiley & Sons, 2016.
  21. [21] W. Thasana, N. Sugimura, K. Iwamura, and Y. Tanimizu, “A study on estimation of 3-dimensional surface roughness of boring processes including kinematic motion deviations,” J. Adv. Mech. Des. Syst. Manuf., Vol.8, No.4, pp. 1-12, 2014.
  22. [22] W. Thasana, N. Sugimura, K. Iwamura, and Y. Tanimizu, “A study on estimation of three-dimensional tolerances based on simulation of virtual machining in turning processes including kinematic motion deviations,” J. Adv. Mech. Des. Syst. Manuf., Vol.9, No.1, pp. 1-13, 2015.
  23. [23] A. M. Zain, H. Haron, and S. Sharif, “Prediction of surface roughness in the end milling machining using Artificial Neural Network,” Expert Syst. Appl.,Vol.37, pp. 1755-1768, 2010.
  24. [24] 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 Eng., Vol.97, pp. 205-211, 2014.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jun. 03, 2024