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Software-defined workpiece positioning for resource-optimized machine tool utilization

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Advancing climate change, tense world markets, and political pressure steadily increase the demand for resource-optimized production solutions. Herby, the positioning of the raw material in the machine tool is an important factor that has received little attention. Traditionally, this is done centrally on the machine table, which leads to locally increased wear of the feed axis. Furthermore, positioning directly influences energy consumption during machining. Consequently, the longest possible component utilization through optimum wear and energy optimization creates a direct conflict of objectives. To solve this conflict, this paper presents an automated approach for software-defined workpiece positioning and NC-Code optimization regarding the axis-specific energy consumption and the spindle condition of ball screws. An approach for mapping the energy consumption and the directly measured spindle condition is presented. Both represent input variables of the cost function. Approaches for the optimization of the position as well as for the practical implementation are proposed.
Rocznik
Strony
71--84
Opis fizyczny
Bibliogr. 39 poz., rys., tab.
Twórcy
  • wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Germany
  • wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Germany
autor
  • wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Germany
  • wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Germany
Bibliografia
  • [1] https://de.statista.com/statistik/daten/studie/252029/umfrage/industriestrompreise-inkl-stromsteuer-in-deutschland/, accessed 13 January 2023.
  • [2] https://www.destatis.de/DE/Themen/Branchen-Unternehmen/Industrie-Verarbeitendes-Gewerbe/materialknappheit-industrieaktivitaet.html, accessed 13 Jannuary 2023.
  • [3] https://de.statista.com/statistik/daten/studie/1228159/umfrage/prognose-zum-fachkraefteangebot-2040-bei-mittlerer-zuwanderung/, accessed 13 Jannuary 2023.
  • [4] GÖNNHEIMER P., et. al., 2022, Datenaufnahme und -verarbeitung in der Brownfield-Produktion, Zeitschrift für wirtschaftlichen Fabrikbetrieb, 117/5, 317–320.
  • [5] SCHOPP M., 2009, Sensorbasierte Zustandsdiagnose und -prognose von Kugelgewindetrieben. Dissertation, Karlsruher Institut für Technologie, Shaker, ISBN: 978–3–8322–8733–7.
  • [6] EDEM I.F., MATIVENGA P.T., 2017, Energy Demand Reduction in Milling Based on Component and Toolpath Orientations, Procedia Manufacturing, 7, 253–261.
  • [7] IMANI ASRAI R., 2014, Mechanistic Modelling of Energy Consumption in CNC Machining, Dissertation, University of Bath, ISNI: 0000 0004 7425 2083.
  • [8] FRIGERIO N., MATTA A., 2014, Energy Efficient Control Strategy for Machine Tools with Stochastic Arrivals and Time Dependent Warm-up, Procedia CIRP, 15, 56–61.
  • [9] PENG T., XU X., 2014, Energy-Efficient Machining Systems: A Critical Review, Int. J. Adv. Manuf. Technol., 72/9–12, 1389–1406.
  • [10] PAVANASKAR S., MCMAINS S., 2015, Machine Specific Energy Consumption Analysis for CNC-Milling Toolpaths, ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Boston, Massachusetts, USA, 02.08.2015–05.08.2015.
  • [11] PAVANASKAR S., et al., 2015, Energy-Efficient Vector Field Based Toolpaths for CNC Pocketmachining, Journal of Manufacturing Processes, 20, 314–320.
  • [12] EDEM I.F., MATIVENGA P.T., 2016, Impact of Feed Axis on Electrical Energy Demand in Mechanical Machining Processes, Journal of Cleaner Production, 137, 230–240.
  • [13] EDEM I.F., MATIVENGA P.T., 2017, Modelling of Energy Demand from Computer Numerical Control (CNC) Toolpaths, Journal of Cleaner Production, 157, 310–321.
  • [14] KARANJKAR N., et al., 2018, Digital Twin for Energy Optimization in an SMT-PCB Assembly Line, 2018 IEEE IOTAIS Bali, 01.11.2018–03.11.2018, 85–89.
  • [15] RODRIGUES G.S., et al., 2018, A Novel Method for Analysis and Optimization of Electric Energy Consumption in Manufacturing Processes, Procedia Manufacturing, 17, 1073–1081.
  • [16] DENKENA B., et al., 2020, Energy Efficient Machine Tools, CIRP Annals, 69/2, 646–667.
  • [17] MOSE C., 2021, Berücksichtigung der Energieeffizienz der Fertigung in Konstruktion und Planung, Dissertation, Dresdner fügetechnische Berichte, TUDpress, Dresden.
  • [18] BRILLINGER M., et al.., 2021, Energy Prediction for CNC Machining with Machine Learning, CIRP Journal of Manufacturing Science and Technology, 35, 715–723.
  • [19] CAO J., et al., 2021, A Novel CNC Milling Energy Consumption Prediction Method Based on Program Parsing and Parallel Neural Network, Sustainability, 13/24, 13918.
  • [20] VERL A., et al., 2009, Sensorless Automated Condition Monitoring for the Control of the Predictive Maintenance of Machine Tools, CIRP Annals, 58/1, 375–378.
  • [21] SCHMID J., et al., 2010, A Wireless MEMS-Sensor Network Concept for the Condition Monitoring of Ball Screw Drives in Industrial Plants, Proceedings of the 8th ACM SenSys '10, Association for Computing Machinery, 425–426.
  • [22] VERL A., FREY S., 2010, Correlation Between Feed Velocity and Preloading in Ball Screw Drives, CIRP Annals, 59/1, 429–432.
  • [23] WALTHER M., 2011, Antriebsbasierte Zustandsdiagnose von Vorschubantrieben, Zugl., Stuttgart, Univ., Diss., 2011.
  • [24] MÖHRING H., BERTRAM O., 2012, Integrated Autonomous Monitoring of Ball Screw Drives, CIRP Annals, 61/1, 355–358.
  • [25] HELWIG N., 2018, Zustandsbewertung Industrieller Prozesse Mittels Multivariater Sensordatenanalyse am Beispiel Hydraulischer und Elektromechanischer Antriebssysteme, Dissertation, Universität des Saarlandes, Shaker, Düren., ISBN: 9783844064940.
  • [26] BENKER M., et al., 2019, Estimating Remaining Useful Life of Machine Tool Ball Screws Via Probabilistic Classification, 2019 IEEE International Conference on Prognostics and Health Management (ICPHM), https://doi.org/10.1109/ICPHM.2019.8819445.
  • [27] RIAZ N., et al., 2020, A Novel 2-D Current Signal-Based Residual Learning with Optimized Softmax to Identify Faults in Ball Screw Actuators, IEEE Access 8, 115299–115313.
  • [28] VEITH M., et al., 2020, Detektion des Vorspannungsverlusts in Kugelgewindetrieben, wt 110/(07–08), 485–490.
  • [29] XI T., et al., 2020, Condition Monitoring of Ball-Screw Drives Based on Frequency Shift, IEEE/ASME Trans. Mechatron., 25/3, 1211–1219.
  • [30] RIAZ N., et al., 2021, An Intelligent Hybrid Scheme for Identification of Faults in Industrial Ball Screw Linear Motion Systems, IEEE, Access 9, 35136–35150.
  • [31] SCHLAGENHAUF T., et al., 2019, Integration von Machine Vision in Kugelgewindespindeln, WT Werkstat-technik Online, 7/8, 605–610.
  • [32] SCHLAGENHAUF T., BURGHARDT N., 2021, Intelligent Vision Based Wear Forecasting on Surfaces of Machine Tool Elements, SN Appl. Sci., 3/12, 1–13, https://doi.org/ 10.1007/s42452-021-04839-3.
  • [33] SCHLAGENHAUF T., 2022, Bildbasierte Quantifizierung und Prognose des Verschleißes an Kugelgewin-Detriebspindeln, Dissertation, Karlsruher Institut für Technologie (KIT), Shaker, Düren., ISBN: 978-3-8440-8875-5.
  • [34] LI B., MELKOTE S.N., 1999, Improved Workpiece Location Accuracy Through Fixture Layout Optimization, International Journal of Machine Tools and Manufacture, 39/6, 871–883.
  • [35] KAYA N., 2006, Machining Fixture Locating and Clamping Position Optimization Using Genetic Algorithms, Computers in Industry, 57/2, 112–120.
  • [36] LIU S. et al., 2017, Optimization of the Number and Positions of Fixture Locators in the Peripheral Milling of a Low-Rigidity Workpiece, Int. J. Adv. Manuf. Technol., 33, 668–676.
  • [37] WEBER J., 2017, Modellbasierte Werkstück- und Werkzeugpositionierung zur Reduzierung der Zykluszeit in NC-Programmen, Dissertation, Universität Paderborn.
  • [38] WEBER J., et al., 2018, Workpiece Positioning Based on Supervised Learning Methods for Simulation-Based Optimization of Virtual Tooling Processes, 2018 Winter Simulation Conference: December 9–12, NJ: IEEE, https://doi.org/10.1109/WSC.2018.8632523.
  • [39] GÖNNHEIMER P., et. al., 2022, Generation of Identifiable CNC Reference Runs with High Information Content for Machine Learning and Analytic Approaches to Parameter Identification, Procedia CIRP, 107, 734–739.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5f243ba5-a49c-4a6b-b6e2-37d679df17f7
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