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While autonomous driving has come close to reality over the recent years, machining is still characterised by many manual tasks and prone to costly errors. In this article, an overview is given about the potential of autonomous machining and uprising technologies that support this vision. For that purpose, a definition of autonomous machine tools and the required elements is presented. Next, selected elements of an autonomous machine tool, e.g. sensory machine components and control loops, are discussed. Finally, some insights into ongoing research projects regarding the use of machine learning for process planning and control are given.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
28--37
Opis fizyczny
Bibliogr. 19 poz., rys.
Twórcy
autor
- Institute of Production Engineering and Machine Tools, Leibniz Universität Hannover, Hannover, Germany
autor
- Institute of Production Engineering and Machine Tools, Leibniz Universität Hannover, Hannover, Germany
autor
- Institute of Production Engineering and Machine Tools, Leibniz Universität Hannover, Hannover, Germany
autor
- Institute of Production Engineering and Machine Tools, Leibniz Universität Hannover, Hannover, Germany
Bibliografia
- [1] KAGERMANN H., et al., 2017, Fachforum autonome systeme im hightech-forum: autonome systeme – chancen und risiken für wirtschaft, wissenschaft und gesellschaft, Final Report, Berlin, April 2017.
- [2] HASSAN M., et al., 2018, Intelligent Machining: Real-Time Tool Condition Monitoring and Intelligent Adaptive Control Systems, Journal of Machine Engineering 18/1, 5–17.
- [3] JEDRZEJEWSKI J., KWASNY W., 2015, Discussion of machine tool intelligence, based on selected concepts and research, Journal of Machine Engineering, 15/4, 5–26.
- [4] TÖNSHOFF H.K., 1995, Werkzeugmaschinen – Grundlagen, Springer, Berlin, Heidelberg, New York.
- [5] DENKENA B., et al., 2018, Process parallel simulation of workpiece temperatures using sensory workpieces, CIRP Journal of Manufacturing Science and Technology, 21, 140–149.
- [6] MONOSTORI L., et al., 2016, Cyber-physical systems in manufacturing, CIRP Annals – Manufacturing Technology, 65/2, 621–641.
- [7] DENKENA B., KIESNER J., 2016, Strain gauge based sensing hydraulic fixtures, Mechatronics, 34, 111–118.
- [8] DENKENA B., BOUJNAH H., 2018, Feeling machines for online detection and compensation of tool deflection in milling, CIRP Annals – Manufacturing Technology, 67/1, 423–426.
- [9] TETI R., et al., 2010, Advanced monitoring of machining operations, CIRP Annals – Manufacturing Technology, 32/2, 563–572.
- [10] DENKENA B., et al., 2013, Design and analysis of a prototypical sensory Z-slide for machine tools, Production Engineering Research and Development (WGP), 7/1, 9–14.
- [11] DENKENA B., et al., 2016, Detection of tool deflection in milling by a sensory axis slide for machine tools, Mechatronics, 34, 95–99.
- [12] GRIESBACH T., et al., 2011, Application of sacrificial layers for the modular sensor fabrication on a flexible polymer substrate, Proceedings of the Sensors and Test Conference, Nuremberg, Germany, 355–360.
- [13] OVERMEYER L., et al., 2011, Laser patterning of thin film sensors on 3-D surfaces, CIRP Annals – Manufacturing Technology, 61/1, 215–218.
- [14] DESAI K.A, RAO P.V.M., 2012, On cutter deflection surface errors in peripheral milling, Journal of Materials Processing Technology, 212, 2443–2454.
- [15] SENCER B., et al., 2008, Feed optimization for five-axis CNC machine tools with drive constraints, International Journal of Machine Tools and Manufacture, 48/7-8, 733–745.
- [16] DITTRICH M.-A., et al., 2018, Self-optimizing tool path generation for 5-axis machining processes, CIRP Journal of Manufacturing Science and Technology, DOI: 10.1016/j.cirpj.2018.11.005.
- [17] CORTES C., VAPNIK V., 1995, Support-vector networks, Machine Learning, 20/3, 273–297.
- [18] DRUCKER H., et al., 1996, Support vector regression machines, Advances in Neural Information Processing Systems, 9, 155–161.
- [19] CLARKE S.M., et al., 2005, Analysis of support vector regression for approximation of complex engineering analyses, Journal of Mechanical Design, 127/6, 1077–1087.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-fe8ad3eb-ea63-4da7-bec2-fef51d2424b1