A Review on Enabling Technologies for Resilient and Traceable on-Machine Measurements
More details
Hide details
Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University, Chair of Production Metrology and Quality Management, Aachen, Germany
Submission date: 2020-03-01
Acceptance date: 2020-05-02
Online publication date: 2020-06-24
Publication date: 2020-06-24
Journal of Machine Engineering 2020;20(2):5-17
On Machine Measurements are a key factor for shorter closed quality control loops in industrial manufacturing. Especially for the production of large components, they promote the first-time-right approach, which is highly desirable, due to small quantities and steep value chains. In contrast to measurement rooms for CMMs, the production environment conditions are unregulated and impact multiple factors along the on-machine measurement metrological chain. As presented as a keynote speech at the XXXI CIRP Sponsored Conference on Supervising and Diagnostics of Machining Systems "MANUFACTURING ACTIVE IMPROVEMENT" by Professor Dr. Robert H. Schmitt, this article reviews current research and ideas regarding on-machine measurements. The authors collect necessary process data with the help of new technologies in the course of digitalization and thus propose a holistic model for systematic error compensation and measurement uncertainty prediction. For assessing the machine’s volumetric accuracy under thermal loads, the authors develop a novel modelling approach, which determines transient geometric errors by abstracting structural parts as spline curve with typical deformation modes. To address the workpiece’s influence on the measurement process, a data-driven framework, fusing real-time sensor-data with the virtual component, is used to model and predict transient thermo-mechanical workpiece states. For dissemination, the authors continue working on ISO standardization and, as subjects of future research, explore new paths in terms of data-driven modelling approaches, using physical abstractions coupled with machine learning and live process data.
SCHMITT R.H., PETEREK M., MORSE E., KNAPP W., GALETTO M., HÄRTIG F., GOCH G., HUGHES B., FORBES A., ESTLER W.T., 2016, Advances in Large-Scale Metrology – Review and Future Trends, CIRP Annals, 65/2, 643–665.
PETEREK M., 2017, Messunsicherheitsbestimmung für Geometriemessungen mit Werkzeug-maschinen, Diss., RWTH Aachen, IIF – Institut für Industriekommunikation und Fachmedien GmbH.
DAHLEM P., LEHMANN N., EMONTS D., PETEREK M., SCHMITT R.H., 2019, Qualifizierung und Optimierung von Bauteilprüfprozessen auf Werkzeugmaschinen: Eignungsnachweis und Reduktion der Messabweichung mittels Modellwissen und integrierter Sensorik, VDI Wissensforum GmbH.
SCHMITT R.H., PETEREK M., 2015, Traceable Measurements on Machine Tools – Thermal Influences on Machine Tool Structure and Measurement Uncertainty, Procedia CIRP, 33, 576–580.
OHLENFORST M., 2019, Model-Based Thermoelastic State Evaluation of Large Workpieces for Geometric Inspection, ISBN: 978-3-86359-752-8.
OHLENFORST M., JANTZEN M., SCHMITT R.H., 2018, Verfahren und System zur in-process-Berechnung einer dreidimensionalen Temperaturverteilung, Rheinisch-Westfälische Technische Hochschule (RWTH), Aachen.
UHLMANN E., LAGHMOUCHI H., GEISERT C., HOHWIELER E., 2017, Smart Wireless Sensor Network and Configuration of Algorithms for Condition Monitoring Applications, Journal of Machine Engineering, 17, 45–55.
FUJISHIMA M., MORI M., NARIMATSU K., IRINO N., 2019, Utilisation of IoT and Sensing for Machine Tools, Journal of Machine Engineering, 19/1, 38–47.
ISO., 2012, Test code for machine tools – Part 1: Geometric accuracy of machines operating under no-load or quasi-static conditions (230-1:2012), 3rd ed.
DONMEZ M.A., BLOMQUIST D.S., HOCKEN R.J., LIU C.R., BARASH M.M., 1986, A General Methodology for Machine Tool Accuracy Enhancement by Error Compensation, Precision Engineering, 8/4, 187–196.
KRENG V.B., LIU C.R., CHU C.N., 1994, A Kinematic Model for Machine Tool Accuracy Characterisation, Int. J. Adv. Manuf. Technol., 9/2, 79–86.
LIN Y., SHEN Y., 2003, Modelling of Five-Axis Machine Tool Metrology Models Using the Matrix Summation Approach, Int. J. Adv. Manuf. Technol., 21/4, 243–248.
SCHWENKE H., KNAPP W., HAITJEMA H., WECKENMANN A., SCHMITT R.H., DELBRESSINE F., 2008, Geometric Error Measurement and Compensation of Machines – An Update, CIRP Annals, 57/2, 660–675.
JATZKOWSKI P., 2011, Ressourceneffiziente Kalibrierung von 5-Achs-Werkzeugmaschinen mit Tracking-Interferometern, Zugl., Aachen, Techn. Hochsch., Diss., 1st ed. Apprimus-Verl., Aachen.
IBARAKI S., KNAPP W., 2012, Indirect Measurement of Volumetric Accuracy ffor Three-Axis and Five-Axis Machine Tools: A Review, International Journal of Automation Technology, 6/2, 110–124.
SHAGLUF A., LONGSTAFF A., FLETCHER S., 2015, Derivation of a Cost Model to Aid Management of CNC Machine Tool Accuracy Maintenance, Journal of Machine Engineering, 15/2, 17–43.
KWASNY W., TUREK P., JEDRZEJEWSKI J., 2011, Survey of Machine Tool Error Measuring Methods, Journal of Machine Engineering, 11/4, 7–38.
BRYAN J., 1990, International Status of Thermal Error Research, CIRP Annals, 39/2, 645–656.
MAYR J., JEDRZEJEWSKI J., UHLMANN E., DONMEZ M., KNAPP W., HÄRTIG F., WENDT K., MORIWAKI T., SHORE P., SCHMITT R.H., BRECHER C., WÜRZ T., WEGENER K., 2012, Thermal Issues in Machine Tools, CIRP Annals – Manufacturing Technology, 61/2, 771–791.
OKAFOR A.C., ERTEKIN Y.M., 2000, Derivation of Machine Tool Error Models and Error Compensation Procedure for Three Axes Vertical Machining Center Using Rigid Body Kinematics, International Journal of Machine Tools and Manufacture, 40/8, 1199–1213.
TUREK P., JĘDRZEJEWSKI J., MODRZYCKI W., 2010, Methods of Machine Tool Error Compensation, Journal of Machine Engineering, 10/4, 5–25.
HOREJS O., MARES M., KOHOUT P., BARTA P., HORNYCH J., 2010, Compensation of Machine Tool Thermal Errors Based on Transfer Functions, MM SJ 2010, 01, 163–166.
ZIEGERT J.C., KALLE P,. 1994, Error Compensation in Machine Tools: A Neural Network Approach. J. Intell. Manuf., 5/3, 143–151.
WECK M., MCKEOWN P., BONSE R., HERBST U., 1995, Reduction and Compensation of Thermal Errors in Machine Tools, CIRP Annals, 44/2, 589–598.
CHEN J.S., 1996, Neural Network-Based Modelling and Error Compensation of Thermally-Induced Spindle Errors, Int. J. Adv. Manuf. Technol., 12/4, 303–308.
ZHANG X., YANG L., LOU P., JIANG X., LI Z., 2019, Thermal Error Modeling for Heavy Duty CNC Machine Tool Based on Convolution Neural Network, 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), IEEE, 665–669.
CHEN T.C., CHANG C.J., HUNG J.P., LEE R.M., WANG C.C., 2016, Real-Time Compensation for Thermal Errors of the Milling Machine, Applied Sciences, 6, 101, 1–13.
NAUMANN C., PUTZ M., 2019, A New Multigrid Based Method for Characteristic Diagram Based Correction of Thermo-Elastic Deformations in Machine Tools, Journal of Machine Engineering, 19/4, 42–57.
JEDRZEJEWSKI J., KWASNY W., KOWAL Z., WINIARSKI Z., 2014, Development of the Modelling and Numerical Simulation of the Thermal Properties of Machine Tools, Journal of Machine Engineering 14/3, 5–20.
WENNEMER M., 2018, Methode zur messtechnischen Analyse und Charakterisierung volumetrischer thermo-elastischer Verlagerungen von Werkzeugmaschinen, 1st ed., Apprimus Wissenschaftsverlag, Aachen.
GLAENZEL J., SURESH KUMAR T., NAUMANN C., PUTZ M., 2019, Parameterization of Environmental Influences by Automated Characteristic Diagrams for the Decoupled Fluid and Structural-Mechanical Simulations, Journal of Machine Engineering, 19/1, 98–113.
GLAENZEL J., IHLENFELDT S., NAUMANN C., 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.
Da SILVA P., PENA-GONZALEZ L.E., TANABE I., TAKAHASHI S., 2018, Machine Tool Distortion Estimation Due to Environmental Thermal Fluctuations – A Focus on Heat Transfer Coefficient, Journal of Machine Engineering, 18/2, 17–30.
LIU Y., SUN L., LIU Y., CEN Z., 2011, Multi-Scale B-Spline Method for 2-D Elastic Problems, Applied Mathematical Modelling, 35/8, 3685–3697.
HÖLLIG K., 2003, Finite Element Methods with B-Splines, Society for Industrial and Applied Mathematics.
TÖNSHOFF H.K., REHLING S., TRACHT K., 2002, An Alternative Approach to Elasto-Kinematic Modelling of Machine Tool Structures, Kulianic E. (Ed.), AMST’02 Advanced Manufacturing Systems and Technology, Proceedings of the Sixth International Conference, Springer Vienna, Vienna, l, 215–223.
OHLENFORST M., DAHLEM P., PETEREK M., SCHMITT R.H., 2016, Geometriemessungen auf Werkzeug-maschinen, wt-online – Ausgabe, 11/12, 782–786.
PAVLIČEK F., MAYR J., WEIKERT S., WEGENER K., 2015, Acclimatisation Time of Precise Workpieces for Quality Inspection, Euspen’s 15th International Conference & Exhibition, Leuven, Belgium, 114–116.
DIN EN ISO 15530-3., 2018, Geometrische Produktspezifikation – Anwendung von kalibrierten Werkstücken oder Normalen.
DAWSON W., 2017, EMRP-Publishable_JRP_Report.
JONES M.T., 2008, Artificial Intelligence: A Systems Approach, Computer Science Series, Jones and Bartlett Publishers.
KLEIJNEN J.P.C., 2017, Regression and Kriging Metamodels with Their Experimental Designs in Simulation: A Review, European Journal of Operational Research, 256/1, 1–16.
MUELLER T., HUBER M., SCHMITT R.H., 2020, Modelling Complex Measurement Processes for Measurement Uncertainty Determination, IJQRM ahead-of-print, 251.
MÜLLER T., VOIGTMANN C., SCHMITT R.H., 2019, Messunsicherheitsbestimmung komplexer Prüfprozesse. ZWF, 114/3, 124–127.
Journals System - logo
Scroll to top