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Flank wear regulation using artificial neural networks

Abstract

Tool wear regulation highly influences product quality and the safety and productivity of machining processes. Hence, it is one of the most important elements in the supervisory control of machine tools. The development of this type of machine tool adaptive control is practically at its infancy because there are still no industrial solutions concerning robust, reliable, and highly precise continuous tool wear estimators. Therefore, this paper primarily aims at the determination of a tool wear regulation model that can ensure the maximum allowed amount of tool wear rate within a predefined machining time, while simultaneously maintaining a high level of process productivity. The proposed model is structured using Radial Basis Function Neural Network controller and Modified Dynamical Neural Network filter. It is analysed using an analytical tool wear model with experimentally adjusted parameters.

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Correspondence to Danko Brezak.

Additional information

This paper was recommended for publication in revised form by Associate Editor In-Ha Sung

Danko Brezak received his B.Sc., M.Sc. and Ph.D. in Mechanical Engineering from the Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Croatia in 1998, 2003 and 2007, respectively. He is currently a senior research assistant in the Department of Robotics and Production Systems Automation at the Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb. His research interests include neural networks, fuzzy logic systems, machining process monitoring and control algorithms.

Dubravko Majetic received his B.Sc., M.Sc. and Ph.D. in Mechanical Engineering from the University of Zagreb, Croatia, in 1988, 1992 and 1996, respectively. He is currently a professor in the Department of Robotics and Production Systems Automation at the Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb. His research is oriented towards automatic control and artificial intelligence.

Toma Udiljak finished Mechanical Engineering at the University of Zagreb, Croatia, where he also received his M.Sc. in 1988 and Ph.D. in 1996. He became a professor in 2004. He is currently serving as head of the Machine Tools Chair. His research is oriented towards cutting theory, CAM, and autonomous manufacturing systems.

Josip Kasac received his B.Sc. in Physics from the University of Zagreb in 1995. He then received his M.S. and Ph.D. in Mechanical Engineering from the University of Zagreb in 1998 and 2005, respectively. He is currently an Assistant Professor in the Department of Robotics and Production Systems Automation at the Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Croatia. His research interests include robot control, optimal control, neural network, and fuzzy control.

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Brezak, D., Majetic, D., Udiljak, T. et al. Flank wear regulation using artificial neural networks. J Mech Sci Technol 24, 1041–1052 (2010). https://doi.org/10.1007/s12206-010-0308-5

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Keywords

  • Control
  • Machining
  • Neural network
  • Productivity maximisation
  • Tool wear regulation