Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Smart manufacturing and predictive maintenance are current trends in the manufacturing industry. However, the holistic understanding of the machine tool health condition in terms of accuracy, functions, process and availability is still unclear. This uncertainty renders the development of models and the data acquisition related to machine tool health condition ineffective. This paper proposes the term machine tool ability as an interconnection between the accuracy, functions, the process and the availability to overcome the lack of the holistic understanding of the machine tool. This will facilitate the further development of qualitative or quantitative methods as well as models. The research highlights the challenges and gaps to understand the machine tool ability.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
5--16
Opis fizyczny
Bibliogr. 50 poz., rys., tab.
Twórcy
autor
- Manufacturing and Metrology Systems Division, Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
- Department of Sustainable Production Development, KTH Royal Institute of Technology, Södertälje, Sweden
autor
- Manufacturing and Metrology Systems Division, Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
- Department of Sustainable Production Development, KTH Royal Institute of Technology, Södertälje, Sweden
autor
- Manufacturing and Metrology Systems Division, Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
Bibliografia
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- [8] JEDRZEJEWSKI J., KWASNY W., 2015, Discussion of Machine Tool Intelligence, Based on Selected Concepts and Research, Journal of Machine Engineering, 15/4, 5-26.
- [9] PEREIRA A.C., ROMERO F., 2017, A Review of the Meaning and the Implications of the Industry 4.0 Concept, Procedia Manufacturing, 13, 1206-1214.
- [10] LEE J., BAGHERI B., KAO H.A., 2015, A Cyber-Physical Systems Architecture of Industry 4.0- Based Manufacturing Systems, Manufacturing Letters, 3, 18-23.
- [11] LEE J., KAO A., 2013, Industry 4.0 – Factory in Big Data Environment, German Harting Magazine, 26, 8-9.
- [12] PERKINS C., LONGSTAFF A.P., FLETCHER S., WILLOUGHBY P., 2012, Practical Implementation of Machine Tool Metrology and Maintenance Management Strategy, Journal of Physics: Conference Series, 364/012105.
- [13] ARCHANTI A, 2011, A Computational Framework for Control of Machining System Capability: From Formulation to Implementation, Doctoral Thesis, KTH Royal Institute of Technology.
- [14] WILLOUGHBY P., VERMA M., LONGSTAFF A.P., FLETCHER S., 2010, A Holistic Approach to Quantifying and Controlling the Accuracy, Performance and Availability of Machine Tools, Proceedings of the 36th International MATADOR Conference, 313-316.
- [15] MEO F., FOURSA M., KOPÀCSI S., SCHLEGEL T., 2008, Predictive Maintenance and Diagnostics of Machine Tools, Proceedings of the 4th Intelligent Production Machines and Systems (IPROMS), Virtual Conference.
- [16] SKF Life Cycle Management for Machine Tools, http://www.skf.com/group/industry-solutions/machine-tool/life-cycle-management/index.html
- [17] ALTINTAS Y., VERL A., BRECHER C., URIARTE L., PRITSCHOW G., 2011, Machine tool feed drives, CIRP Annals – Manufacturing Technology, 60, 779-796.
- [18] AFSHARIZAND B., ZHANG X., NEWMAN S.T., NASSEHI A., 2014, Determination of Machinability Consideration Degradation of Accuracy over Machine Tool Life Cycle, Procedia CIRP, 17, 760-765.
- [19] VICHARE P., NASSEHI A., THOMPSON J., NEWMAN S.T., WOOD F., KUMAR S., 2015, Machine Tool Capability Profiles for Representing Machine Tool Health, Robotics and Computer-Integrated Manufacturing, 34, 70-78.
- [20] IRIARTE I., HOVESKOG M., JUSTEL D., VAL E., HALILA F., 2018, Service Design Visualization Tools for Supporting Servitization in a Machine Tool Manufacturer, Industrial marketing Management.
- [21] KAMP B., OCHOA A., DIAZ J., 2017, Smart Servitization within the Context of Industrial User-Supplier Relationships: Contingencies According to a Machine Tool Manufacturer, International Journal on Interactive Design and Manufacturing, 11, 651-663.
- [22] LEE J., KAO H A., YANG S., 2014, Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment, Procedia CIRP, 16, 3-8.
- [23] BAINES T.S., LIGHTFOOT H.W., BENEDETTINI O., KAY J.M., 2009, The Servitization of Manufacturing: A review of literature and reflection of future challenges, Journal of Manufacturing Technology Management, 5, 547-567.
- [24] NEELY A., 2011, The Servitization of Manufacturing: An Analysis of Global Trends, 14th European Operations Management Association Conference.
- [25] BRECHER C., et al., 2016, Qualifying Multi-Technology Machine Tools for Complex Machining Processes, CIRP Journal of Manufacturing Science and Technology, 13, 1-14.
- [26] RUSHEL E., SANTOS E.A.P., LOURES E.D.F.R., 2017, Industrial Maintenance Decision-Making: A Systematic Literature Review, Journal of Manufacturing Systems, 45, 180-194.
- [27] AHMED N., DAY A J., VICTORY J. L., ZEALL L., YOUNG B., 2012, Condition Monitoring in the Management of Maintenance in a Large Scale Precision CNC Machining Manufacturing Facility, Proceedings of 2012 IEEE, International Conference on Condition Monitoring and Diagnosis, 842-845.
- [28] MEIER H., ROY R., SELIGER G., 2010, Industrial Product-Service Systems – IPS2, CIRP Annals, 59/2, 607-627.
- [29] EUROPEAN COMMISSION, 2015, Closing the Loop – An EU Action Plan for the Circular Economy, COM (2015) 614 final.
- [30] ROY R., STARK R., TRACHT K., TAKATA S., MORI M., 2016, Continuous Maintenance and the Future – Foundations and Technological Challenges, CIRP Annals – Manufacturing Technology, 65, 667-668.
- [31] LUNG B., VÉRON M., SUHNER M.C., MULLER A., 2005, Integration of Maintenance Strategies into Prognosis Process to Decision- Making Aid on System Operation, CIRP Annals, 54/1, 5-8.
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- [33] LUNG B., MONNIN M., VOISIN A., COCHETEUX P., LEVRAT E., 2008, Degradation State Model-Based Prognosis for Proactively Maintaining Product Performance, CIRP Annals – Manufacturing Technology, 57, 49-52.
- [34] OKOH C., ROY R., MEHNEN J., 2017, Predictive Maintenance Modelling for Through-Life Engineering Services, Procedia CIRP, 59, 196-201.
- [35] LIANG S.Y., SHIH A.J., 2016, Analysis of Machining and Machine Tool, Springer, Chapter 1, 7.
- [36] PEKLENIK J., JERELE A., 1992, Some Basic Relationships for Identification of the Machining Processes, CIRP Annals, 41/1, 155-159.
- [37] DEKYS V., 2017, Condition Monitoring and Fault Diagnosis, Procedia Engineering 117, 502-509.
- [38] DENG C., MIAO J., WEI B., FENG Y., ZHAO Y., 2018, Evaluation of Machine Tool with Position-Dependent Milling Stability Based, International Journal of Machine Tools and Manufacture, 124, 33-42.
- [39] KJELLBERG T., et al., 2009, The Machine Tool Model – A Core Part of the Digital Factory, CIRP Annals, 58, 425-428.
- [40] LEE Y T., SOONS J.A., DONMEZ M.A., 2001, Information Model for Machine-Tool-Performance Tests, Journal of Research of NIST, 106/2, 413-439.
- [41] MEKID S., OGEDENGBE T., 2010, A Review of Machine Tool Accuracy Enhancement Through Error Compensation in Serial and Parallel Kinematic Machines, International Journal of Precision Technology, 1, 251-286.
- [42] NEWMAN S.T., NASSEHI A., 2009, Machine Tool Capability Profile for Intelligent Process Planning, CIRP Annals – Manufacturing Technology, 58/1, 421-424.
- [43] HONG W.P., 2013, Machine Capability Index Evaluation of Machining Centre, Journal of Mechanical Science and Technology, 10, 2905-2910.
- [44] ARCHENTI A., NICOLESCU M., 2013, Accuracy analysis of machine tools using Elastically Linked Systems, CIRP Annals - Manufacturing Technology, 62/1, 503-506.
- [45] MAIER W., KIMMELMANN M., 2017, Spindle Crash – Bearing Damages Detected by Vibration Tests, Journal of Machine Engineering, 17/1, 46-56.
- [46] MAYR J., et al., 2012, Thermal Issues in Machine Tools, CIRP Annals – Manufacturing Technology, 61, 771- 791.
- [47] WEGENER K., WEIKERT S., MAYR J., 2016, Age of Compensation – Challenege and Chance for Machine Tool Industry, International Journal of Automation Technology, 10/4, 609-623.
- [48] LI Y., et al. 2015, A Review on Spindle Thermal Error Compensation in Machine Tools, International Journal of Machine Tools and Manufacturing, 95, 20-38.
- [49] LIU T., et al., 2017, Analytical Modeling for Thermal Errors of Motorized Spindle Unit, International Journal of Machine Tools and Manufacture, 112, 53-70.
- [50] SCHMITT R., PETEREK M., 2015, Traceable Measurements on Machine Tools – Thermal Influences on Machine Tool Structure and Measurement Uncertainty, Procedia CIRP, 33, 576-580.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2e27a4f5-985d-43bf-a016-e49447f52347