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Tytuł artykułu

On the selection and assessment of input variables for the characteristic diagram based correction of thermo-elastic deformations in machine tools

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
It is a well-known problem of milling machines, that waste heat from motors, friction effects on guides and most importantly the milling process itself greatly affect positioning accuracy and thus production quality. An economic and energy-efficient method of correcting this thermo-elastic positioning error is to gather sensor data from the machine tool and the process and to use that information to predict and correct the resulting tool center point displacement using high dimensional characteristic diagrams. On the one hand, the selection of which and how many input variables to use in the characteristic diagrams is critical to their performance. On the other hand, however, there are often a great number of possible variable combinations available and testing them all is practically impossible. This paper will discuss the suitability of many different input variable types and present a new method of input variable selection which will be compared to existing methods and demonstrated on measurements performed on a machine tool.
Słowa kluczowe
Rocznik
Strony
25--38
Opis fizyczny
Bibliogr. 27 poz., rys.
Twórcy
  • Fraunhofer Institute for Machine Tools and Forming Technology IWU, Chemnitz, Germany
  • Dresden University of Technology, Institute of Machine Tools and Control Engineering, Dresden, Germany
autor
  • Fraunhofer Institute for Machine Tools and Forming Technology IWU, Chemnitz, Germany
autor
  • Fraunhofer Institute for Machine Tools and Forming Technology IWU, Chemnitz, Germany
Bibliografia
  • [1] BONSE R., McKEOWN P., WECK M., 1995, Reduction and compensation of thermal errors in machine tools, Annals of the CIRP, 44/2, 589-598.
  • [2] BRIAN J., 1990, International status on thermal error research, Annals of the CIRP, 39/2, 645-656.
  • [3] GROSSMANN K., et al., 2015. Thermo-energetic design of machine tools, Springer, 1-11.
  • [4] GROSSMANN K., MÜHL A., THIEM X., 2014, Modular control integrated correction of thermoelastic errors of machine tools based on the thermoelastic functional chain, Advanced Materials Research, 1018, 411-418.
  • [5] THIEM X., RIEDEL M., KAUSCHINGER B., MÜLLER J., 2016, Principle and verification of a structure model based correction approach, Procedia CIRP, 46, 111-114.
  • [6] BRECHER C., FEY M., WENNEMER M., 2014, Correction model of load-dependent structural deformations based on transfer functions, Thermo-Energetic Design of Machine Tools, Springer, 175-184.
  • [7] NAUMANN C., PRIBER U., 2012, Modellierung des thermo-elastischen verhaltens von werkzeugmaschinen mittels hochdimensionaler kennfelder, Proceedings Workshop Computational Intelligence, Dortmund, Germany.
  • [8] IHLENFELDT S., NAUMANN C., PRIBER U., RIEDEL I., 2015, Characteristic diagram based correction algorithms for the thermo-elastic deformation of machine tools, Proceedings 48th CIRP CMS, Naples.
  • [9] BRECHER C., KLATTE M., WENZEL C., 2015, Application of machine integrated deformation sensors, Proceedings of the 11th International LAMDAMAP Conference, Huddersfield, UK, 817.
  • [10] BRECHER C., KLATTE M., TZANETOS F., 2017, Analysis of spatial and temporal dependencies of the TCP-dislocation measurement for the assessment of the thermo-elastic behavior of 3-axis machine tools, Proceedings of the 12th International LAMDAMAP Conference, Bristol, UK, 122-132.
  • [11] LOHSE H., WEBER J., WEBER J., 2016, Thermo-energetic analysis of the fluid systems in cutting machine tools, Proceedings 10th International Fluid Power Conference, 2, 195-206.
  • [12] DROSSEL W.-G., VOIGT I., 2016, Wärmespeicher in werkzeugmaschinen, VDI-Z, 158/12, 4244.
  • [13] DROSSEL W.-G., LAUER M., SCHNEIDER D., VOIGT I., 2016, Development and examination of switchable heat pipes, Applied thermal engineering, 99, 857-865.
  • [14] GIM T., HA J.-Y., LEE J.-Y., LEE C., KO T., 2001, Ball screw as thermal error compensator, Proceedings of the 16th ASPE Annual Meeting.
  • [15] MARCKS P., UHLMANN E., 2008, Compensation of thermal deformations at machine tools using Adaptronic CRP-Structures, Proceedings 41st CIRP Conference on Manufacturing Systems, Springer, 1831-86.
  • [16] HERZOG R., NAUMANN C., PRIBER U., RIEDEL I., 2015, Correction algorithms and high-dimensional characteristic diagrams, Thermo-energetic Design of Machine Tools, Lecture Notes in Production Engineering, Springer, 159-174.
  • [17] NI J., YANG H., 2005, Dynamic neural network modeling for nonlinear, nonstationary machine tool thermally induced error, International Journal of Machine Tools and Manufacture, 45/4-5, 455-465.
  • [18] LEE J.-H., LEE J.-H., YANG S.-H., 2001, Thermal error modeling of a horizontal machining center using fuzzy logic strategy, Journal of Manufacturing Processes, 3/2, 120-127.
  • [19] ESS M., 2012, Simulation and compensation of thermal errors of machine tools, Dissertation, ETH Zurich.
  • [20] PRIBER U., 2003, Smoothed grid regression, Proceedings Workshop Fuzzy Systems, 13, Dortmund, Germany, 159-172.
  • [21] GLAENZEL J., IHLENFELDT S., NAUMANN C., PUTZ M., 2017, Optimized grid structures for the characteristic diagram based estimation of thermo-elastic tool center point displacements in machine tools, Journal of Machine Engineering, 17/3.
  • [22] GLÄNZEL J., IHLENFELDT S., NAUMANN C., PUTZ M., 2016, Decoupling of fluid and thermo-elastic simulations of machine tools using characteristic diagrams, Proceedings CIRP ICME, Ischia, Italy.
  • [23] BAUM C., BRECHER C., IHLENFELDT S., NAUMANN C., PUTZ M., TZANETOS F., 2018, Hybrid correction of thermal errors using temperature and deformation sensors, Proceedings 1st Conference on Thermal Issues in Machine Tools, Dresden, Germany.
  • [24] HERZOG R., RIEDEL I., 2015, Sequentially optimal sensor placement in thermo-elastic models for real time applications, Optimization and Engineering, 16/4, 737-766.
  • [25] CHATFIELD C., COLLINS A.J., 1980. Introduction to multivariate analysis, Springer, US, 57-81.
  • [26] FLETCHER S., LONGSTAFF A.P., MIAN N.S., POTDAR A.A., 2015, Application of multi sensor data fusion based on principal component analysis and artificial neural network for machine tool thermal monitoring, Proceedings of the 11th International LAMDAMAP Conference, Huddersfield, UK.
  • [27] LIU L., REN X., SUN Y., ZENG H., 2011, Error compensation on a NC-machine tool based on integrated intelligent computation, Applied Mechanics and Materials, 121–126, 1436-1442.
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-3c7c2b28-af9c-4845-929b-d18cb2bad936
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