Tytuł artykułu
Treść / Zawartość
Pełne teksty:
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Increasing autonomy and sustainability are major goals in manufacturing. Main technological trends provide enablers for achieving these goals and need to be implemented and combined in manufacturing machinery in a suitable manner. The paper exposes a vision of modern manufacturing machines, where the complexity of manufacturing processes is handled within the manufacturing machine and a simplistic front end is presented to the operator, which means that major elements of operators’ tasks are fulfilled by the intelligence of the machine. Research vectors paving the ground for this concept from different points of view are then discussed. Research is presented on intelligent grinding, intelligent recognition and suppression of chatter, adaptive thermal and motion error compensation exploting also self-learning abilities. It is necessary to point out, that not only intelligent mastering of process and machine becomes more and more important but communications among machine tools enabling process chain overarching intelligent approaches and creating intelligent factories.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
5--28
Opis fizyczny
Bibliogr. 68 poz., rys., tab.
Twórcy
autor
- Institute of Machine Tools and Manufacturing (IWF), ETH Zurich, Switzerland
autor
- Institute of Machine Tools and Manufacturing (IWF), ETH Zurich, Switzerland
autor
- Institute of Machine Tools and Manufacturing (IWF), ETH Zurich, Switzerland
autor
- Institute of Machine Tools and Manufacturing (IWF), ETH Zurich, Switzerland
autor
- Institute of Machine Tools and Manufacturing (IWF), ETH Zurich, Switzerland
autor
- Institute of Machine Tools and Manufacturing (IWF), ETH Zurich, Switzerland
Bibliografia
- [1] UEDA K., 1992, An Approach to Bionic Manufacturing Systems Based on DNA-Type Information, Proc. of the ICOOMS’92, 303–308.
- [2] MALSHE A., RAJURKAR K., SAMANT A., NOERGAARD-HANSEN H., BAPAT S., JIANG W., 2013, Bioinspired Surfaces for Advanced Applications, CIRP Annals, 2/2, 607–628.
- [3] BYRNE G., DIMITROV D., MONOSTORI L., TETI R., VAN HOUTEN F., WERTHEIM R., 2018, Biologicalisation: Biological Transformation in Manufacturing, CIRP Journal of Manufacturing Science and Technology, 21, 1–32.
- [4] EL MARAGHY H., MONOSTORI L., SCHUH G., EL MARAGHY W., 2021, Evolution and Future of Manufacturing Systems, CIRP Annals 70/2, 35–658.
- [5] WEGENER K., SPIERINGS AB., STAUB A., 2020, Bioinspired Intelligent SLM Cell, 12th CIRP Conference on Intelligent Computation in Manufacturing Engineering (ICME), Procedia CIRP, 88, 624–630.
- [6] MÖHRING H-C., WIEDERKEHR P., ERKORKMAZ K., KAKINUMA Y., 2020, Self-Optimizing Machining Systems, CIRP Annals 69/2, 740–763. https://doi.org/10.1016/j.cirp.2020.05.007.
- [7] DALAEE M., 2020, Enhancement in Deposition rate of LASER DMD, PhD-thesis ETH Zürich, No. 26987.
- [8] WEGENER K., SPIERINGS A.B., TETI R., CAGGIANO A., KNÜTTEL D., STAUB A., 2021, A Conceptual Vision for a Bio-Intelligent Manufacturing Cell for Selective Laser Melting, CIRP Journal of Manufacturing Science and Technology, 34, 61–83.
- [9] MC CANN R., OBEIDI M.A., HUGHES C., MC CARTHY E., 2021, In-Situ Sensing, Process Monitoring and Machine Control in Laser Powder Bed Fusion: A Review, Additive Manufacturing, https://doi.org/10.1016/j.addma.2021.102058
- [10] RENKEN V., von FREYBERG A., SCHÜNEMANN K., PASTORS F., FISCHER A., 2019, In-Process ClosedLoop Control for Stabilising the Melt Pool Temperature in Selectivelaser Melting, Progress in Additive Manufacturing, 4/4, 411–421, https://doi.org/10.1007/s40964-019-00083-9.
- [11] FLEMING T.G., NESTOR S.G.L., ALLEN T.R., BOUKHALED M.A., SMITH N.J., FRASER J.M., 2020, Tracking and Controlling the Morphology Evolution of 3D Powder-Bedfusion in Situ Using Inline Coherent Imaging, Additive Manufacturing, 32, doi:10.1016/j.addma.2019.100978.
- [12] BARSCHDORFF D., MONOSTORI L., 1991, Neural Networks – Their Applications and Perspectives in Intelligent Machining, Computers in Industry, 17, 101–119.
- [13] MONOSTORI L., MARKUS A., VAN BRUSSEL H., WESTKÄMPER E., 1996, Machine Learning Approaches to Manufacturing, CIRP Annals, 45/2, 675–712.
- [14] PHAM D.T., PHAM P.T.N., 1999, Artificial Intelligence in Engineering, International Journal of Machine Tools and Manufacture, 39, 937–949.
- [15] BYRNE G., DAMM O., MONOSTORI L., TETI R., VAN HOUTEN F., WEGENER K., WERTHEIM R., SAMMLER F., 2021, Towards High Performance Living Manufacturing Systems A New Convergence Between Biology and Engineering, CIRP Journal of Manufacturing Science and Technology, 34, 6–21.
- [16] ROWE W.B., YAN L., INASAKI I., MALKIN S., 1994, Applications of Artificial Intelligence in Grinding, CIRP Annals, 43/2 521–531, https://doi.org/10.1016/S0007-8506(07)60498-3.
- [17] SAKAKURA M., INASAKI I., 1992, A Neural Network Approach to the Decision Making Process for Grinding Operations, CIRP Annals, 41, 353–356. https://doi.org/10.1016/S0007-8506(07)61221-9.
- [18] MORGAN M.N., CAI, A.R.. GUIDOTTI ALLANSON D.R., MORUZZI J., ROWE W., 2007, Design and Implementation of an Intelligent Grinding Assistant System, International Journal of Abrasive Technology, 1/1, 106–135.
- [19] BARRENETXEA D., MARQUINEZ J. I., ÁLVAREZ J., FERNÁNDEZ R., GALLEGO I., MADARIAGA J., GARITAONAINDIA I. , 2012, Model-Based Assistant Tool for the Setting-up and Optimization of Centerless Grinding Process, Machining Science and Technology, 16/4, 501-523.
- [20] GOTTLOB G., FRÜHWIRTH T., HORN W., 1990, Expertensysteme, Springer Vienna.
- [21] WATERMAN D.A., 1986, A Guide to Expert Systems. The Teknowledge Series in Knowledge Engineering, Reading-Mass, a.o. Addison-Wesley.
- [22] GAEGAUF F., 2011, Technologie Schafft Wettbewerbsvorteile, Schweizer Präzisionstechnik, 26–28.
- [23] GHOLAMI M.H., AZIZI M.R., 2014, , Constrained Grinding Optimization for Time, Cost, and Surface Roughness Using NSGA-II, The International Journal of Advanced Manufacturing Technology, 73/5, 981-988.
- [24] KWAK J-S., 2005, Application of Taguchi and Response Surface Methodologies for Geometric Error in Surface Grinding Process, International Journal of Machine Tools and Manufacture, 45/3, 327–334.
- [25] SATHYANARAYANAN G., JOSEPH LIN I., CHEN M-K., 1992, Neural Network Modelling and Multiobjective Optimization of Creep Feed Grinding of Superalloys, International Journal of Production Research, 30/10, 2421–2438.
- [26] ZADEH L., 1963, Optimality and Non-Scalar-Valued Performance Criteria, IEEE Transactions on Automatic Control, 8/1, 59–60.
- [27] LIAO T.W., CHEN L., 1994, A Neural Network Approach for Grinding Processes: Modelling and Optimization, International Journal of Machine Tools and Manufacture, 34/7, 919–937.
- [28] WEN X.M., TAY A.A.O., NEE A.Y.C., 1992, Micro-Computer-Based Optimization of the Surface Grinding Process, Journal of Materials Processing Technology, 29/1, 75–90.
- [29] PAWAR P.J., RAO R.V., DAVIM J.P., 2010, Multiobjective Optimization of Grinding Process Parameters Using Particle Swarm Optimization Algorithm, Materials and Manufacturing Processes, 25/6, 424–431.
- [30] RAO R., PAWAR P., 2010, Grinding Process Parameter Optimization Using Non-Traditional Optimization Algorithms, Proceedings of the Institution of Mechanical Engineers, Part B, Journal of Engineering Manufacture, 224/6, 887–898.
- [31] MANIMARAN G., KUMAR M.P., 2013, Multiresponse Optimization of Grinding AISI 316 Stainless Steel Using Grey Relational Analysis, Materials and Manufacturing Processes, 28/4, 418–423.
- [32] SIDDIQUEE A.N., KHAN Z.A., MALLICK Z., 2010, Grey Relational Analysis Coupled with Principal Component Analysis for Optimisation Design of the Process Parameters in In-Feed Centreless Cylindrical Grinding, The International Journal of Advanced Manufacturing Technology, 46/9, 983–992.
- [33] RUDRAPATI R., PAL P.K., BANDYOPADHYAY A., 2016, Modeling and Optimization of Machining Parameters in Cylindrical Grinding Process, The Inter. J. of Advanced Manufacturing Technology, 82/9, 2167–2182.
- [34] KRAJNIK P., KOPAC J., SLUGA A., 2005, Design of Grinding Factors Based on Response Surface Methodology, Journal of Materials Processing Technology, 162, 629–636.
- [35] MAIER M., RUPENYAN A., BOBST C., WEGENER K., 2020, Self-Optimizing Grinding Machines Using Gaussian Process Models and Constrained Bayesian optimization, The International Journal of Advanced Manufacturing Technology, 108/1, 539–552.
- [36] ALTINTAS Y., BER A.A., 2001, Manufacturing Automation: Metal Cutting Mechanics, Machine Tool Vibrations, and CNC Design, Appl. Mech. Rev., 54/5, B84.
- [37] NAMAZI M., ALTINTAS Y., ABE T., RAJAPAKSE N., 2007, Modeling and Identification of Tool Holder–Spindle Interface Dynamics, International Journal of Machine Tools and Manufacture, 47, 1333–1341.
- [38] LÖSER M., GROSSMANN K., 2016, Influence of Parameter Uncertainties on the Computation of Stability Lobe diagrams, Procedia CIRP, 46, 460–463.
- [39] POSTEL M., BUGDAYCI B., MONNIN J., KUSTER F., WEGENER K., 2018, Improved Stability Predictions in Milling Through More Realistic Load Conditions, Procedia CIRP, 77, 102–105.
- [40] GROSSI N., SALLESE L., SCIPPA A., CAMPATELLI G., 2014, Chatter Stability Prediction in Milling Using Speed-Varying Cutting Force Coefficients, Procedia CIRP, 14, 170–175.
- [41] FRIEDRICH J., HINZE C., RENNER A., VERL A., LECHLER A., 2017, Estimation of Stability Lobe Diagrams in Milling with Continuous Learning Algorithms, Robotics and Computer-Integrated Manufacturing, 43, 124–134.
- [42] CHERUKURI H., PEREZ-BERNABEU E., SELLES M., SCHMITZ T., 2019, Machining Chatter Prediction Using a Data Learning Model, Journal of Manufacturing and Materials Processing, 3, 45.
- [43] POSTEL M., BUGDAYCI B., KUSTER F., WEGENER K., 2020, Neural Network Supported Inverse Parameter Identification for Stability Predictions in Milling, CIRP Journal of Manufacturing Science and Technol., 29, 71–87.
- [44] POSTEL M., 2020, Model-Supported Improvement of Stability Limit Predictions in Milling Through Artifical Neural Networks, PhD thesis. (IWF), ETH Zurich.
- [45] POSTEL M., BUGDAYCI B., WEGENER K., 2020, Ensemble Transfer Learning for Refining Stability Predictions in Milling Using Experimental Stability States, The International Journal of Advanced Manufacturing Technology, 107, 4123–4139.
- [46] FRIEDRICH J., HINZE C., LECHLER A., VERL A., 2016, On-line Learning Artificial Neural Networks for Stability Classification of Milling Processes, International Conference on Advanced Intelligent Mechatronics (AIM), 357–364.
- [47] MAYR J., JEDRZEJEWSKI J., UHLMANN E., ALKAN DONMEZ M., KNAPP W., HÄRTIG F., WENDT K., MORIWAKI T., SHORE P., SCHMITT R., BRECHER C., WÜRZ T., WEGENER K., 2012, Thermal Issues in Machine Tools, CIRP Annals – Manufacturing Technology, 61/2, 771–791.
- [48] GEBHARDT M., MAYR J., FURRER N., WIDMER T., WEIKERT S., KNAPP W., 2014, High Precision Greybox Model for Compensation of Thermal Errors on Five-Axis Machines, CIRP Annals – Manufacturing Technology, 63/1, 509–512.
- [49] BRECHER C., HIRSCH P., WECK M., 2004, Compensation of Thermo-Elastic Machine Tool Deformation Based on Control Internal Data, CIRP Annals – Manufacturing Technology, 53/1, 299–304.
- [50] MAREŠ M., HOREJŠ O., HAVLÍK L., 2020, Thermal Error Compensation of a 5-Axis Machine Tool Using Indigenous Temperature Sensors and CNC Integrated Python Code Validated with a Machined Test Piece, Precision Engineering, 66, 21–30.
- [51] BLASER P., PAVLIČEK F., MORI K., MAYR J., WEIKERT S., WEGENER K., 2017, Adaptive Learning Control for Thermal Error Compensation of 5-Axis Machine Tools, Journal of Manufacturing Systems, 44, 302–309.
- [52] MAYR J., BLASER P., RYSER A., HERNANDEZ-BECERRO P., 2018, An Adaptive Self-Learning Compensation Approach for Thermal Errors on 5-Axis Machine Tools Handling an Arbitrary Set of Sample Rates, CIRP Annals, 67/1, 551–554.
- [53] ZIMMERMANN N., LANG S., BLASER P., MAYR J., 2020, Adaptive Input Selection for Thermal Error Compensation Models, CIRP Annals, 69/1, 485–488.
- [54] BLASER P., MAYRJ., WEGENER, K., 2020, Simulation Based Comparison of Thermal Error Modelling Methods for Machine Tools, In euspen Special Interest Group Meeting, Thermal Issues, 2–5.
- [55] BLASER P., MAYR J., WEGENER K., 2019, Long-Term Thermal Compensation of 5-Axis Machine Tools Due to Thermal Adaptive Learning Control, MM Science Journal, 4, 3164–3171.
- [56] WIESSNER M., BLASER P., BÖHL S., MAYR J., KNAPP W., WEGENER K., 2018, Thermal Test Piece for 5Axis Machine Tools, Precision Engineering, 52, 407–417.
- [57] TANIGUCHI N., 1983, Current Status in, and Future Trends of, Ultraprecision Machining and Ultrafine Materials Processing, Annals of the CIRP, 32/2, 573–582.
- [58] SARTORI S., 1995, Geometric Error Measurement and Compensation of Machines, Annals of the CIRP, 44/2, 599–609.
- [59] WEIKERT S., 2000, Beitrag zur Analyse des dynamischen Verhaltens von Werkzeugmaschinen, Diss. ETHZ No 13596, doi:10.3929/ethz-a-003896403.
- [60] SCHWENKE H., 2008, Geometric Error Measurement and Compensation of Machines – An update, Annals of the CIRP, 57/2, 660–675.
- [61] ISO 230-1:2012, Test Code for Machine Tools – Part 1: Geometric Accuracy of Machines Operating Under Noload or Quasi-Static Conditions.
- [62] ISO 230-2:2014, Test Code for Machine Tools – Part 2: Determination of Accuracy and Repeatability of Positioning of Numerically Controlled Axes.
- [63] ISO 230-4, Test code for machine tools – Part 4: Circular Tests for Numerically Controlled Machine Tools.
- [64] ISO/TR 16907:2015(E), Machine tools – Numerical Compensation of Geometric Errors.
- [65] http://www.heidenhain.com/site.html.
- [66] THOMA S., HAAS T., NGUYEN M.H., WEIKERT S., WEGENER K., 2015, Inand Cross-Talk Evaluation of Different Machine Concepts, Landamap Conference.
- [67] https://www.klartext-portal.de/de_DE/tipps/funktionen/dynamic-precision/.
- [68] HAAS T., LANZ N., KELLER R., WEIKERT S., WEGENER K., 2016, Iterative Learning Approach for Machine Tools Using a Convex Optimisation Approach, Procedia CIRP, 46, 391–395.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8359d37f-1eae-4705-9ef2-0fce388fa817