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Utilization of Multi-Axis Positioning Repeatability Performance 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.ORCID iD: 0000-0001-9185-4607
2018 (English)In: International Journal of Automation Technology, ISSN 1881-7629, E-ISSN 1883-8022Article in journal (Refereed) Accepted
Abstract [en]

Detailed description of multi-axis repeatability performance and modelling of non-systematic variations in the positioning performance of machine tools can support the understanding of root-causes of capability variations in manufacturing processes. Kinematic characterization 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 employment of direct measurement results. The findings of this research can be used to further develop standardized approaches. The statistical population of random errors along the multi-axis travel first requires the proper management of experimental data. In this paper a methodology and its application is presented for the determination of repeatability under static and unloaded conditions as an inhomogeneous parameter in the work space. The work is exemplified in a case study, where 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
2018.
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-236060OAI: oai:DiVA.org:kth-236060DiVA, id: diva2:1255762
Funder
XPRES - Initiative for excellence in production research
Note

QC 20181015

Available from: 2018-10-15 Created: 2018-10-15 Last updated: 2020-05-19Bibliographically 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
2. Uncertainty Management for Automated Diagnostics of Production Machinery
Open this publication in new window or tab >>Uncertainty Management for Automated Diagnostics of Production Machinery
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Neither production machinery, nor production systems will ever become completely describable or predictable. This results in the continuous need for monitoring and diagnostics of such systems in order to manage related uncertainties. In advanced production systems uncertainty has to be the subject to a systematic management process to maintain machine health and improve performance. Automation of diagnostics can fundamentally improve this management process by providing an affordable and scalable information source. In this thesis, the important aspects of uncertainty management in production systems are established and serve as a basis for the composition of an uncertainty-based machine diagnostics framework. The proposed framework requires flexible, fast, integrated and automated diagnostics methods. An inertial measurement-based test method is presented in order to satisfy these requirements and enable automated measurements for diagnostics of production machinery. The gained insights and knowledge about production machine health and capability improve transparency, predictability and dependability of production machinery and production systems. These improvements lead to increased overall equipment effectiveness and higher level of sustainability in operation.

Abstract [sv]

Varken produktionsmaskiner eller produktionssystem kommer någonsin att bli fullständigt beskrivbara eller förutsägbara. Detta resulterar i ett kontinuerligt behov av övervakning och diagnostik av anordningar och system för att kunna hantera relaterade osäkerheter. I avancerade produktionssystem måste osäkerhet vara föremål för en systematisk hanteringsprocess för att upprätthålla maskinshälsa och förbättra prestanda. Automatisering av diagnostik kan fundamentalt förbättra denna hanteringsprocess genom att tillhandahålla en prisvärd och skalbar informationsk

älla.

I den här avhandlingen fastställs de viktiga aspekterna av osäkerhetshantering i produktionssystem och detta utgör grunden för konstruktionen av ett osäkerhetsbaserat ramverk för maskindiagnostik. Det föreslagna ramverket kräver flexibla, snabba, integrerade och automatiserade diagnostiska metoder. En tröghetsmätningsbaserad testmetod presenteras för att uppfylla dessa krav och möjliggöra automatiserade mätningar för diagnostik av produktionsmaskiner. De erhållna insikterna och kunskaperna relaterade till produktionsmaskinens hälsa och kapacitet förbättrar transparens, förutsägbarhet och pålitlighet för produktionsmaskiner och produktionssystem. Dessa förbättringar leder till ökad övergripande utrustningseektivitet och högre resurseektivitet.

 

Nyckelord: Osäkerhetshantering, Automatiserad Diagnostik,

Tröghetsmätningsenhet

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2020. p. 132
Series
TRITA-ITM-AVL ; 2020:29
Keywords
Uncertainty Management, Automated Diagnostics, Inertial Measurement Unit
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
SRA - Production
Identifiers
urn:nbn:se:kth:diva-273580 (URN)978-91-7873-558-7 (ISBN)
Public defence
2020-06-12, Publikt via ZOOM, 14:00 (English)
Opponent
Supervisors
Funder
XPRES - Initiative for excellence in production research
Available from: 2020-05-19 Created: 2020-05-19 Last updated: 2020-05-28Bibliographically approved

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Archenti, Andreas

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