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Parameterization of environmental influences by automated characteristic diagrams for the decoupled fluid and structural-mechanical simulations

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Języki publikacji
EN
Abstrakty
EN
Thermo-elastic effects contribute the most to positioning errors in machine tools especially in operations where high precision machining is involved. When a machine tool is subjected to changes in environmental influences such as ambient air temperature, velocity or direction, then flow (CFD) simulations are necessary to effectively quantify the thermal behaviour between the machine tool surface and the surrounding air (fluid). Heat transfer coefficient (HTC) values effectively represent this solid-fluid heat transfer and it serves as the boundary data for thermo-elastic simulations. Thereby, deformation results can be obtained. This two-step simulation procedure involving fluid and thermo-structural simulations is highly complex and time-consuming. A suitable alternative for the above process can be obtained by introducing a clustering algorithm (CA) and characteristic diagrams (CDs) in the workflow. CDs are continuous maps of a set of input variables onto a single output variable, which are trained using data from a limited number of CFD simulations which is optimized using the clustering technique involving genetic algorithm (GA) and radial basis function (RBF) interpolation. The parameterized environmental influences are mapped directly onto corresponding HTC values in each CD. Thus, CDs serve as look-up tables which provide boundary data (HTC values along with nodal information) under several load cases (combinations of environmental influences) for thermo-elastic simulations. Ultimately, a decoupled fluid-structural simulation system is obtained where boundary (convection) data for thermo-mechanical simulations can be directly obtained from CDs and would no longer require fluid simulations to be carried out again. Thus, a novel approach for the correction of thermo-elastic deformations on a machine tool is obtained.
Rocznik
Strony
98--113
Opis fizyczny
Bibliogr. 22 poz., rys., tab.
Twórcy
  • Fraunhofer Institute for Machine Tools and Forming Technology IWU, Chemnitz, Germany
  • Fraunhofer Institute for Machine Tools and Forming Technology IWU, Chemnitz, Germany
  • Fraunhofer Institute for Machine Tools and Forming Technology IWU, Chemnitz, Germany
  • Fraunhofer Institute for Machine Tools and Forming Technology IWU, Chemnitz, Germany
Bibliografia
  • [1] BRYAN J., 1990, International Status of Thermal Error Research, CIRP Annals Manufacturing Technology, 39/2, 645–456.
  • [2] MIAN N.S., FLETCHER S.., LONGSTAFF A.P., MYERS A., 2013, Efficient estimation by FEA of machine tool distortion due to environmental temperature perturbations, Centre for Precision Technologies, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK.
  • [3] GLÄNZEL J., IHLENFELDT S., NAUMANN C., PUTZ M., 2016, Decoupling of fluid and thermo-elastic simulations on machine tools using characteristic diagrams, 10th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME '16, Ischia, Italy.
  • [4] GLÄNZEL J., IHLENFELDT S., NAUMANN C., 2017, Effiziente Quantifizierung der Konvektion durch Entkoppelte Strömungs- und Strukturmechanische Simulation, Beispielhaft am Maschinenständer, 5 Kolloquium SFB/TR96, Chemnitz.
  • [5] BUHMANN M D., 2003, Radial Basis Functions: Theory and Implementations, Cambridge University Press; 271.
  • [6] WARD J.Jr., 1963, Hierarchical grouping to optimize an objective function, Journal of the American Statistical Association, 58/301, 236–244.
  • [7] MACQUEEN., 1967, Some methods for classification and analysis of multivariate observations, Proceedings of the fifth Berkeley symposium, 233/233, 281–297.
  • [8] KOHONEN T., 1982, Self-organized formation of topologically correct feature maps, Biological Cybernetics, 43/1, 59–69.
  • [9] BEZDEK C., EHRLICH R., 1984, The fuzzy c-means clustering algorithm. Computers and Geosciences, 10/2-3, 191–203.
  • [10] KOZA J.R., 1995, Survey of Genetic Algorithms and Genetic Programming, Computer Science Department, Margaret Jacks Hall, Stanford University, California.
  • [11] ANDREAS C., KOENIG., 2002, A study of mutation methods for Evolutionary Algorithms, CS 447-Advanced topics in Artificial Intelligence.
  • [12] GLÄNZEL J., UNGER R., IHLENFELDT I., 2018, Clustering by optimal subsets to describe environment interdependencies, 1st Conference on Thermal Issues in Machine Tools, Dresden.
  • [13] UMBARKAR A.J., SHETH P.D., 2015, Crossover operators in Genetic Algorithm: a review, ICTACT JOURNAL ON SOFT COMPUTING, 06/01, 1083–1092.
  • [14] NITASHA S., KUMAR T., 2014, Study of Various Mutation Operators in Genetic Algorithms, International Journal of Computer Science and Information Technologies, 5/3, 4519-4521.
  • [15] GLÄNZEL J., NAUMANN C., IHLENFELDT S., PUTZ M., 2018, Efficient Quantification of Free and Forced Convection via the Decoupling of Thermo-Mechanical and Thermo-Fluidic Simulations of Machine Tools, Journal of Machine Engineering, 18/2, 41–53.
  • [16] PRIBER U., 2003, Smoothed Grid Regression, Proceedings Workshop Fuzzy Systems, Dortmund, Germany, 13, 159–172.
  • [17] PUTZ M., IHLENFELDT S., KAUSCHINGER B., NAUMANN CH., THEIM X., RIEDEL M., 2016, Implementation And Demonstration of Characteristic Diagram as Well as Structure Model Based Correction of Thermo-Elastic Tool Center Point Displacements, Journal of Machine Engineering, 16/3, .88–101.
  • [18] 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.
  • [19] NAUMANN C., PRIBER U., 2012, Modellierung des Thermo-Elastischen Verhaltens von Werkzeugmaschinen mittels Hochdimensionaler Kennfelder, Proceedings Workshop Computational Intelligence, Dortmund, Germany.
  • [20] 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.
  • [21] PUTZ M., IHLENFELDT S., NAUMANN C., GLAENZEL J., 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, 36–50.
  • [22] GOTSHALL S., RYLANDER B., 1992, Optimal Population Size and the Genetic Algorithm, School of Engineering, University of Portland, USA.
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-95bb3e6a-dd03-4416-9972-f70bbc0bb5fe
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