Analytical method of designing a comparable milling machine model based on Matlab/Simulink
Bashir Osman1, Haitao Zhu2
1Harbin Engineering University, College of Mechanical and Electrical Engineering, Harbin, China
2Harbin Engineering University, College of Ship Building Engineering, Harbin, China
1Nile Valley University, Faculty of Engineering and Technology, Mechanical Engineering, Atbara, Sudan
E-mail: firstname.lastname@example.org, email@example.com
Abstract. Because of time-varying, nonlinearity and complexity of the machining process, the traditional PID control has been unable to meet the requirements, which are being high-speed and high-precision. However, an advanced control methods can be a good solution for this kind of control system, a prefect simulation results depends on the accuracy of the modeling process, such models can be used to develop more precise and formalized description of process activities, on modeling process, and now a days a lot of software had been used do this issue. This paper aims to create a milling machine process model using the Simulink modeling method, which use the logic of cutting process as a mathematical terms indicates areal milling machining equations, and give it appropriate processing parameters to ensure the simulating results are comparable, the step response method will be used as good indicator to compare the Model process, using Matlab analysis, first the process will be modelled in the Matlab software to test the step response under various parameters and then compare the results.
Keywords: milling machining, servo controller, Matlab/Simulink, linear and nonlinear model.
 Chryssolouris G., Domroese M. An experimental study of strategies for integrating sensor information in machining. CIRP Annals, Vol. 38, Issue 1, 1989, p. 425‑428.
 Chryssolouris G., Domroese M., Zsoldos L. A decision-making strategy for machining control. CIRP Annals, Vol. 39, Issue 1, 1990, p. 501‑504.
 Chryssolouris G., Guillot M., A comparison of statistical and AI approaches to the selection of process parameters in intelligent machining. Journal of Engineering for Industry, Vol. 112, 1990, p. 122‑134.
 Tlusty J. Manufacturing Processes and Equipment. 1st Edition, Upper Saddle River, Prentice-Hall, NJ, 2000.
 Lasitzhan L. G. Mechanical Vibrations and Industrial Noise Control. Prentice-Hall of India Pvt. Ltd, 2013.
 Alok Sinha Vibration of Mechanical System. Cambridge University Press, 2012.
 Crandall S. H., Dahl N. C., Lardner T. J. An Introduction to Mechanics of Solids. Mcgraw-Hill, New York, 1999.
 Huang Y., Yuan J. High speed constant force milling based on fuzzy controller and BP neural network. International Journal of Control and Automation, Vol. 7, Issue 5, 2014, p. 143‑152.
 Scacchji W. Process Models in Software Engineering. Encyclopedia of Software Engineering, 2011.
 Elke L. Servo Control Systems 1: Dc Servo Mechanisms. White Paper, Control Systems Principles.
 Tlusty Ed. J. Manufacturing Processes and Equipment. Prentice Hall, Upper Saddle River, NJ, 2002.
 Armarego E. J., Epp C. J. An investigation of zero helix peripheral up-milling. International Journal of Machine Tool Design and Research, Vol. 10, Issue 2, 1970, p. 273‑291.
 Oenigsberger F., Sabberwal A. J. P. An investigation into the cutting force pulsations during milling operations. International Journal of Machine Tool Design and Research, Vol. 1, 1961, p. 15‑33.
 Saglam H., Suleyman Y., Unsacar F. The effect of tool geometry and cutting speed on main cutting force and tool tip temperature. Materials and Design, Vol. 28, Issue 1, 2007, p. 101‑111.
Cite this article
Osman Bashir, Zhu Haitao Analytical method of designing a comparable milling machine model based on Matlab/Simulink. Vibroengineering PROCEDIA, Vol. 14, 2017, p. 300‑305.
© JVE International Ltd. Vibroengineering PROCEDIA. Oct 2017, Vol. 14. ISSN 2345-0533