IJAT Vol.12 No.1 pp. 105-112
doi: 10.20965/ijat.2018.p0105


An Evaluation Criterion to Select Temperature Measurement Positions in End-Milling

Dongjin Wu and Koji Teramoto

Division of Engineering, Muroran Institute of Technology
27-1 Mizumoto, Muroran, Hokkaido 050-8585, Japan

Corresponding author

January 30, 2017
October 19, 2017
January 5, 2018
temperature measurement, point selection, FEM analysis, sensitive points, end-milling

The objective of this study is to utilize measured temperatures for process monitoring in precision end-milling. Thermal expansion of machining workpiece deteriorates machining accuracy and is considered as an important phenomenon to achieve accurate end-milling process. Thermo-couples are typically employed to measure the temperatures of machining workpiece. This study proposes a method to select appropriate temperature measurement positions based on variations in conscious machining evaluation. The variations in the conscious evaluation of temperature distributions on the workpiece are calculated by extending a conventional nominal machining simulation. Variations in the machining process are generated by using different combinations of model parameters for process simulations. An orthogonal array is employed to assign the parameters to reduce the combination number. An evaluation criterion to select measuring points is calculated given the temperature distributions corresponding to the parameter combinations. Feasibility of the proposed criterion is investigated by evaluating a reported temperature estimation case study. Furthermore, an adaptability of the proposed criterion to different machining situations is evaluated by comparing selected measuring points corresponding to different cutter paths.

Cite this article as:
D. Wu and K. Teramoto, “An Evaluation Criterion to Select Temperature Measurement Positions in End-Milling,” Int. J. Automation Technol., Vol.12 No.1, pp. 105-112, 2018.
Data files:
  1. [1] J. Rech, J. L. Battaglia, and A. Moisan, “Thermal influence of cutting tool coatings,” J. of Materials Processing Technology, Vol.159, Issue 1, pp. 119-124, 2005.
  2. [2] M. Puts, G. Schmidta, and U. Semmlera, “Heat Flux in Cutting: Importance, Simulation and Validation,” Procedia CIRP Vol.31, pp. 334-339, 2015.
  3. [3] L.-J. Xie, J. Schmidt, C. Schmidt, and F. Biesinger, “2D FEM estimate of tool wear in turning operation,” Wear, Vol.258, No.10, pp. 1479-1490, 2004.
  4. [4] K. Teramoto, D. Wu, K. Ota, and R. Hayashi, “A Framework of Accuracy Assured Machining for Smart Manufacturing,” Mem. Muroran Inst. Tech., Vol.65, pp. 35-39, 2015.
  5. [5] J. E. Franke, T. Maier, F. Schäfer, and M. F. Zaeh, “Experimental Evaluation of the Thermal Machine Tool Behavior for Model Updating,” Int. J. of Automation Technology, Vol.6, No.2, pp. 125-136, 2012.
  6. [6] T. Moriwaki, Emwardy and L. Wang, “Study on Machining Error Due to Cutting Heat in End milling,” Mem. Grad. School Sci. & Technol., Kobe Univ., Vol.13, No.A, pp. 130-140, 1995.
  7. [7] T. Matsumura and E. Usui, “Temperature analysis of tool and workpiece in milling process,” Proc. of the 2000 Japan-USA Flexible Automation Conf., Vol.1, pp. 515-520, 2000.
  8. [8] R. Joliet, A. Byfut, P. Kersting, A. Schroeder, and A. Zabel, “Validation of a Heat Input Model for the Prediciton of Thermomechanical Deformation during NC Milling,” Proceedia CIRP, Vol.8, pp. 403-408, 2013.
  9. [9] B. Denkena, A. Schmidt, P. Maass, D. Niederwestberg, C. Niebuhr, and J. Vehmeyer, “Prediction of temperature induced shape deviations in dry milling,” Proceedia CIRP, Vol.31, pp. 340-345, 2015.
  10. [10] T. Moriwaki, N. Sugimura, and L. Wang, “Development of Modeling System for CAD/CAE of Machine Tools (2nd Report) Application of Thermal Analysis for Moving Parts,” J. of the Japan Society for Precision Engineering, Vol.60, No.7, pp. 959-963, 1994 (in Japanese).
  11. [11] Z. Haitao, Y. Jianguo, and S. Jinhua, “Simulation of thermal behavior of a CNC machine tool spindle,” Int. J. of Machine Tools and Manufacture, Vol.47, pp. 1003-1010, 2007.
  12. [12] D. Freiburg, S. Odendahl, T. Siebrecht, M. Steiner, T. Wagner, and A. Zabel, “Simulation based Process Optimization for the Milling of Light Weight Component,” Proceedia CIRP, Vol.18, pp. 132-137, 2014.
  13. [13] I. Kadashevicha, M. Beutnerb, B. Karpuschewskib, and T. Hallea, “A novel simulation approach to determine thermally induced geometric deviations in dry gear hobbing,” Procedia CIRP, Vol.31, pp. 483-488, 2015.
  14. [14] B. Haddag, S. Atlati, M. Nouari, and M. Zenasni, “Analysis of the heat transfer at the tool-workpiece interface in machining: determination of heat generation and heat transfer coefficients,” Heat and Mass Transfer, Vol.51, No.10, pp. 1355-1370, 2014.
  15. [15] H. Wernsing and C. Bueskens, “Parameter identification for finite element based models in dry machining,” Proceedia CIRP, Vol.31, pp. 328-333, 2015.
  16. [16] M. A. Davies, T. Ueda, R. M’Saoubi, B. Mullany, and A. L. Cooke, “On the Measurement of Temperature in Material Removal Processes,” Annals of CIRP, Vol.56, No.2, pp. 581-604, 2007.
  17. [17] Y. Shimizu, W. Lu, Y. Ohba, and W. Gao, “Development of a Micro-Sized Thermal Contact Sensor for Inspection of Surface Defects,” Int. J. of Automation Technology, Vol.7, No.6, pp. 708-713, 2013.
  18. [18] S. Takata, “Generation of a Machining Scenario and Its Applications to Intelligent Machining Operations,” CIRP Annals – Manufacturing Technology, Vol.42, No.1, pp. 531-534, 1993.
  19. [19] K. Teramoto, R. Tanaka, T. Ishida, and Y. Takeuchi, “Thermal State Visualization of Machining Workpiece by Means of a Sensor-Configured Heat Conduction Simulation,” JSME Int. J. Series C Mechanical Systems, Machine Elements and Manufacturing, Vol.49. No.2, pp. 287-292, 2006.
  20. [20] M. Baeker, “A new method to determine material parameters from machining simulations using inverse identification,” Proceedia CIRP, Vol.31, pp. 399-404, 2015.
  21. [21] M. Gulpak and J. Soelter, “Development and validation of a hybrid model for the prediction of shape deviations in dry machining process,” Proceedia CIRP, Vol.31, pp. 346-351, 2015.
  22. [22] J. C. Outeiro, A. M. Dias, and J. L. Lebrun, “Experimental assessment of temperature distribution in three-dimensional cutting process,” Machining Science and Technology, Vol.8, No.3, pp. 357-376, 2004.
  23. [23] A. Kus, Y. Isik, M. C. Cakir, S. Coskun, and K. Ozdemir, “Thermocouple and Infrared Sensor-Based Measurement of Temperature Distribution in Metal Cutting,” Sensors, Vol.15, No.1, pp. 1274-1291, 2015.
  24. [24] N. A. Abukhshim, P. T. Mativenga, and M. A. Sheikh, “Heat generation and temperature prediction in metal cutting: A review and implications for high speed machining,” I. J. of Machine Tools and Manufacture, Vol.46, Issues 7–8, pp. 782-800, 2006.
  25. [25] S. A. Tsirkasa, P. Papanikosb, and Th. Kermanidisa, “Numerical simulation of the laser welding process in butt-joint specimens,” J. of Materials Processing Technology. Vol.134, Issue 1, pp. 59-69, 2003.
  26. [26] F. Klocke, D. Lung, and H. Puls, “FEM-Modelling of the thermal workpiece deformation in dry turning,” Procedia CIRP, Vol.8, pp. 240-245, 2013.
  27. [27] F. Salvatore, S. Saad, and H. Hamdi, “Modeling and Simulation of Tool Wear During the Cutting Process,” Procedia CIRP, Vol.8, pp. 305-310, 2013.
  28. [28] T. Özel and E. Zeren, “Determination of work material flow stress and friction for FEA of machining using orthogonal cutting tests,” J. of Materials Processing Technology, pp. 153-154, pp. 1019-1025, 2004.

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Last updated on Jun. 03, 2024