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Identification of machine tools linear axes performance using on-machine embedded inertia measurement units
KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Machine and Process Technology. KTH, School of Industrial Engineering and Management (ITM), Centres, Design and Management of Manufacturing Systems, DMMS.ORCID iD: 0000-0001-9185-4607
2017 (English)In: Laser Metrology and Machine Performance XII - 12th International Conference and Exhibition on Laser Metrology, Machine Tool, CMM and Robotic Performance, LAMDAMAP 2017, euspen , 2017, p. 65-74Conference paper, Published paper (Refereed)
Abstract [en]

The current trend in manufacturing industry is from mass production towards flexible and adaptive manufacturing systems and cloud manufacturing. Self-learning machines and robot systems can play an essential role in the development of intelligent manufacturing systems and can be deployed to deal with a variety of tasks that can require flexibility and accuracy. However, in order for the machine tool (physical and control system) to deal with the desired task in a cognitive and efficient manner, the system must be "aware" of its capability and,most importantly,its limitations in order to avoid them and adjust itself to the desired task. Thus, characterization of machine tool accuracy and capability is necessary to realize that. In this study,data from a machine-embedded inertial measurement unit (IMU), consisting of accelerometers and rate gyroscopes,was used for identification of changes in linearand angular errormotions due to changes in operational conditionsor component degradation.The IMU-based results were validated against laser-based measurement results,demonstratingthat the IMU-based method is capable of detecting micrometer-level and microradian-level degradation of machine tool linearaxes.Thus, manufacturers could use themethod to efficientlyand robustly diagnose the condition of their machine tool linear axeswith minimal disruptions to production.

Place, publisher, year, edition, pages
euspen , 2017. p. 65-74
Keywords [en]
Coordinate measuring machines, Engineering education, Gyroscopes, Intelligent robots, Learning systems, Manufacture, Units of measurement, Adaptive manufacturing, Cloud Manufacturing, Inertia measurement units, Inertial Measurement Unit (IMU), Intelligent manufacturing system, Laser-based measurement, Machine tool accuracies, Manufacturing industries, Machine tools
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:kth:diva-227833Scopus ID: 2-s2.0-85043392318ISBN: 9780956679093 OAI: oai:DiVA.org:kth-227833DiVA, id: diva2:1206514
Conference
12th International Conference and Exhibition on Laser Metrology, Coordinate Measuring Machine and Machine Tool Performance, LAMDAMAP 2017, 15 March 2017 through 16 March 2017
Note

QC 20180517

Available from: 2018-05-17 Created: 2018-05-17 Last updated: 2018-05-17Bibliographically approved

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Archenti, Andreas

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  • apa
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