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Measurement and analysis of machine tool errors under quasi-static and loaded conditions
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)ORCID iD: 0000-0002-6989-2989
KTH, School of Industrial Engineering and Management (ITM), Production Engineering. (Manufacturing and Metrology Systems)ORCID iD: 0000-0001-9185-4607
2018 (English)In: Precision engineering, ISSN 0141-6359, E-ISSN 1873-2372, Vol. 51, p. 59-67Article in journal (Refereed) Published
Abstract [en]

Machine tool testing and accuracy analysis has become increasingly important over the years as it offers machine tool manufacturers and end-users updated information on a machine’s capability. A machine tooĺs capability may be determined by mapping the distribution of deformations and their variation range, in the machine tool workspace, under the cumulative effect of thermal and mechanical loads. This paper proposes a novel procedure for the prediction of machine tool errors under quasi-static and loaded conditions. Geometric errors and spatial variation of static stiffness in the work volume of machines are captured and described through the synthesis of bottom-up and top-down model building approaches. The bottom-up approach, determining individual axis errors using direct measurements, is applied to estimate the geometric errors in unloaded condition utilizing homogeneous transformation matrix theory. The top-down approach, capturing aggregated quasi-static deviations using indirect measurements, estimates through an analytical procedure the resultant deviations under loaded conditions. The study introduces a characterization of the position and direction dependent static stiffness and presents the identification how the quasi-static behavior of the machine tool affects the part accuracy. The methodology was implemented in a case study, identifying a variation of up to 27% in the stiffness response of the machine tool. The prediction results were experimentally validated through cutting tests and the uncertainty of the measurements and the applied methodology was investigated to determine the reliability of the predicted errors.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 51, p. 59-67
Keywords [en]
Machine tool, Accuracy, Quasi-static stiffness, Geometric errorMachine tool, Accuracy, Quasi-static stiffness, Geometric error
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
SRA - Production
Identifiers
URN: urn:nbn:se:kth:diva-213990DOI: 10.1016/j.precisioneng.2017.07.011ISI: 000418978200006Scopus ID: 2-s2.0-85027406281OAI: oai:DiVA.org:kth-213990DiVA, id: diva2:1139577
Funder
XPRES - Initiative for excellence in production research
Note

QC 20171206

Available from: 2017-09-08 Created: 2017-09-08 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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