Adaptive control simulation to optimize metal removal for rough turning

A. M. Alaskari, S. E. Oraby


In conventional numerical control machining, it is not possible to change the preset operating parameters in the program during the machining cycle. By contrast, the adaptive control technique uses real-time sensing to continuously and instantaneously adjust the operative feed and/or the speed parameters to their optimal levels in order to ensure a more productive operation. In this study, a model-based adaptive control simulation strategy is proposed to optimize metal removal during rough turning by efficiently utilizing the available power resources within a safe machining environment. The approach is based on recursive continuous iterations to predict the instantaneous level of edge wear, together with the corresponding cutting forces and the consumed power, by considering the relevant models. The best speed-feed pair that provides the maximum metal removal rate without violating the imposed forces and power constraints is selected. Procedures are repeated for subsequent cut intervals by considering cumulative edge wear from preceding intervals until the accumulated edge wear level reaches the specified criterion value. The performance of the proposed model-based method was verified through comparisons with several conventional fixed-parameter wear-time methods. Results proved the superiority of the proposed AC disparate-parameter procedures in terms of noticeable greater productivity as the entire available machine power was exploited with a safe machining environment with reduced cost of the replacement cycle.


Adaptive control; consumed power; cutting forces; cutting tool wear; metal removal rate.

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Alajmi, M.S. & Oraby, S.E. 2014. On the influence of the speed-feed interaction on the wear rate and life of multiple coated carbide inserts considering rough turning process. Applied Mechanics and Materials. Trans Tech Publications 575: 431–6.

Alaskari, A. M. Oraby, S. E. & Al-Khalid, H. K. 2011. Mathematical Modeling Experimental Approach of the Friction on the Tool-Chip Interface of Multicoated Carbide Turning Inserts. International Journal of Advances in Machining and Forming Operations 3(2): 167-83.

Albert, M. 2014. Tool monitoring for multitasking machines. Modern Machine Shop., [Sep. 1, 2016].

Altintas Y. 2012. Manufacturing Automation: metal cutting mechanics, machine tool vibrations, and CNC design. Second edition. Cambridge University Press. Cambridge UK.

Astakhov, V.P. 2004. The assessment of cutting tool wear. International Journal of Machine Tools and Manufacture 44(6): 637–47.

Astakhov, V.P. 2006. Tribology of Metal Cutting. Elsevier. San Diego CA USA.

CARON™, 2016. Tool monitoring adaptive control. Caron Engineering,, [Sep. 1, 2016].

Chiang, S.T., Liu, D.I., Lee, A.C. & Chieng, W.H. 1995. Adaptive control optimization in end milling using neural networks. International Journal of Machine Tools and Manufacture 35(4): 637–60.

Cus, F., Zuperl, U., Kiker, E. & Mllfelener, M. 2006. Adaptive controller design for feedrate maximization of machining process. Journal of Achievements in Materials and Manufacturing Engineering 17(1-2): 237–40.

Eitel, L. 2011. Adaptive control in machining-motion system design. Machine Design., [Sep. 1, 2016].

FANUC™, 2011. FANUC integrates iAdaptS controller into CNC. Automation., [Sep. 1, 2016].

Goldsberry, C. 2012. Fanuc FA America awarded new patent. Plastics Today., [Sep. 1, 2016].

Haftel, L. 2007. Adaptive controls save tools and time—Technology advances supercharge an old process. American Machinist., [Sep. 1, 2016].

Hanson, K. 2014. Adapt and conquer. Cutting Tool Engineering., [Sep. 1, 2016].

Huang, Y. & Yuan, J .2014. High-speed constant force milling based on fuzzy controller and BP neural network. International Journal of Control & Automation 7(5): 143–52.

OKUMA™, 2016. Software automates tracking of tool wear and reduces operator costs. Okuma the Americas. [Sep. 1, 2016].

OMATIVE™, 2016. Adaptive control and monitoring. OMATIVE Systems, Internet: [Sep. 1, 2016].

Oraby, S.E. & Hayhurst, D.R. 1991. Development of models for tool wear force relationships in metal cutting. International Journal of Mechanical Sciences 33(2): 125–38.

Oraby, S.E. & Hayhurst, D.R. .1990. High-capacity compact three-component cutting force dynamometer. International Journal of Machine Tools and Manufacture, 30(4): 549–59.

Oraby, S.E. & Alaskari A.M. 2008. On the variability of tool wear and life at disparate operating parameters. Kuwait Journal of Science &Engineering, 35(1B): 123–50.

Oraby, S.E., Almeshaiei, E.A. & Alaskari, A. 2003. An adaptive control simulation approach based on a mathematical model optimization algorithm for rough turning. Kuwait Journal of Science & Engineering 30(2): 213–34.

Prasad, B.S., Prasad D.S., Sandeep A. & Veeraiah, G. 2013. Condition monitoring of CNC machining using adaptive control. International Journal of Automation and Computing 10(3): 202–9.

Ralston, P.A.S.& Wards T.L. 1988. Mathematical models used for adaptive control of machine tools. Mathematical and Computer Modelling 11: 1151–55.

Sun, Y., Zhao, Y., Bao, Y.& Guo, D. 2014. A novel adaptive-feedrate interpolation method for NURBS tool path with drive constraints. International Journal of Machine Tools and Manufacture 77: 74–81.

Taylor, F.W. 1907. On the art of metal cutting. Transactions American Society of Mechanical Engineers 28: 31–50.

Ulsoy, A.G., Koren, Y. & Rasmussen, F. 1983. Principal developments in the adaptive control of machine tools. Journal of Dynamic Systems, Measurement and Control 105(2): 107–12.


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