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Integration of machining system capability information into a CAx software environment for complex tool trajectory prediction
KTH, School of Industrial Engineering and Management (ITM), Production Engineering. (Manufacturing and Metrology Systems)ORCID iD: 0000-0003-0045-2085
(Fraunhofer IPT, Aachen)
KTH, School of Industrial Engineering and Management (ITM), Production Engineering. (Manufacturing and Metrology Systems)ORCID iD: 0000-0001-9185-4607
(Fraunhofer IPT, Aachen)
Show others and affiliations
2018 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271Article in journal (Refereed) Published
Abstract [en]

Integration of machine tool specific capability information related to a manufactured part’s accuracy can significantly support the decision-making in production, help to understand root-cases of quality loss and optimize cutting processes. In this paper, a systematic methodology is proposed to bridge the gap between machine tool specific capability and finished part’s accuracy. For this purpose, a measurement-based model is implemented in a CAx software environment for the prediction of geometrical deviations in complex milling processes. Results are presented in a case study to demonstrate errors on the workpiece level due to the quasi-static capabilities of a given machine tool.

Place, publisher, year, edition, pages
2018.
Keywords [en]
Machine tool; Quasi-static capability; CAx software environment; Tool trajectory prediction
National Category
Engineering and Technology
Research subject
SRA - Production
Identifiers
URN: urn:nbn:se:kth:diva-230612Scopus ID: 2-s2.0-85049590812OAI: oai:DiVA.org:kth-230612DiVA, id: diva2:1217667
Conference
51st CIRP Conference on Manufacturing Systems (CIRP CMS 2018)
Projects
CHARMS - Characterisation of Machining Systems at PMH Application Lab
Funder
XPRES - Initiative for excellence in production research
Note

QC 20180625

Available from: 2018-06-13 Created: 2018-06-13 Last updated: 2018-10-16Bibliographically 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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