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Utilization of machine tool repeatability in kinematic modelling
KTH, School of Industrial Engineering and Management (ITM), Production Engineering. (Manufacturing and Metrology Systems)ORCID iD: 0000-0003-0045-2085
KTH, School of Industrial Engineering and Management (ITM), Production Engineering. (Manufacturing and Metrology Systems)
2017 (English)In: Proceedings of the 17th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 20172017, euspen , 2017, p. 49-50Conference paper, Published paper (Refereed)
Abstract [en]

Modelling of non-systematic variations in the positioning performance of machine tools can support the understanding of capability variation in manufacturing processes. Kinematic characterisation is implemented through repeated measurements, which include variations connected to the performance of the machine tool. This paper addresses the integration of the positional repeatability to kinematic modelling through the utilization of direct measurement results. The statistical population of random errors along the single-axis travel first requires the proper management of experimental data. In this paper a methodology is presented for the determination of repeatability under static and unloaded conditions as an inhomogeneous parameter in the workspace. In a case study the component errors of a linear axis were investigated with repeated laser interferometer measurements to quantify the estimated repeatability and express it in the composed repeatability budget. The conclusions of the proposed methodology outline the sensitivity of kinematic models relying on measurement data, as the repeatability of the system can be in the same magnitude as systematic errors.

Place, publisher, year, edition, pages
euspen , 2017. p. 49-50
Keywords [en]
Machine tool repeatability, Uncertainty estimation, Kinematic modelling
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
SRA - Production
Identifiers
URN: urn:nbn:se:kth:diva-210652Scopus ID: 2-s2.0-85041292135ISBN: 9780995775107 (print)OAI: oai:DiVA.org:kth-210652DiVA, id: diva2:1119132
Conference
17th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2017, Hannover Congress CentreHannover, Germany, 29 May 2017 through 2 June 2017
Funder
XPRES - Initiative for excellence in production research
Note

QC 20170703

Available from: 2017-07-03 Created: 2017-07-03 Last updated: 2018-10-15Bibliographically approved
In thesis
1. Modelling and Management of Uncertainty in Production Systems: from Measurement to Decision
Open this publication in new window or tab >>Modelling and Management of Uncertainty in Production Systems: from Measurement to Decision
2018 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The advanced handling of uncertainties arising from a wide range of sources is fundamental in quality control and dependability to reach advantageous decisions in different organizational levels of industry. Es-pecially in the competitive edge of production, uncertainty shall not be solely object of estimation but the result of a systematic management process. In this process, the composition and utilization of proper in-formation acquisition systems, capability models and propagation tools play an inevitable role. This thesis presents solutions from production system to operational level, following principles of the introduced con-cept of uncertainty-based thinking in production. The overall aim is to support transparency, predictability and reliability of production sys-tems, by taking advantage of expressed technical uncertainties. On a higher system level, the management of uncertainty in the quality con-trol of industrial processes is discussed. The target is the selection of the optimal level of uncertainty in production processes integrated with measuring systems. On an operational level, a model-based solution is introduced using homogeneous transformation matrices in combination with Monte Carlo method to represent uncertainty related to machin-ing system capability. Measurement information on machining systems can significantly support decision-making to draw conclusions on man-ufactured parts accuracy, by developing understanding of root-causes of quality loss and providing optimization aspects for process planning and maintenance.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2018. p. 60
Series
TRITA-ITM-AVL ; 2018:37
Keywords
Precision engineering, Uncertainty modelling, Machin-ing system capability
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production Engineering
Identifiers
urn:nbn:se:kth:diva-235825 (URN)978-91-7729-846-5 (ISBN)
Presentation
2018-11-09, M311, Kungliga Tekniska högskolan, Brinellvagen 68, Stockholm, 10:00 (English)
Opponent
Supervisors
Funder
XPRES - Initiative for excellence in production research
Note

QC 20181015

Available from: 2018-10-15 Created: 2018-10-06 Last updated: 2018-10-16Bibliographically approved

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Citation style
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