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Uncertainty Management for Automated Diagnostics of Production Machinery
KTH, School of Industrial Engineering and Management (ITM), Production Engineering. (KTH IIP)ORCID iD: 0000-0003-0045-2085
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 [en]
Uncertainty Management, Automated Diagnostics, Inertial Measurement Unit
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
SRA - Production
Identifiers
URN: urn:nbn:se:kth:diva-273580ISBN: 978-91-7873-558-7 (print)OAI: oai:DiVA.org:kth-273580DiVA, id: diva2:1431198
Public defence
2020-06-12, https://kth-se.zoom.us/webinar/register/WN_arJFK6C3Qd2pbTR5zjTHkQ, http://Vid fysisk närvaro eller Du som saknar dator/ datorvana kan kontakta service@itm.kth.se (English), 14:00 (English)
Opponent
Supervisors
Funder
XPRES - Initiative for excellence in production researchAvailable from: 2020-05-19 Created: 2020-05-19 Last updated: 2020-06-03Bibliographically approved
List of papers
1. Measurement and analysis of machine tool errors under quasi-static and loaded conditions
Open this publication in new window or tab >>Measurement and analysis of machine tool errors under quasi-static and loaded conditions
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
Keywords
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:nbn:se:kth:diva-213990 (URN)10.1016/j.precisioneng.2017.07.011 (DOI)000418978200006 ()2-s2.0-85027406281 (Scopus ID)
Funder
XPRES - Initiative for excellence in production research
Note

QC 20171206

Available from: 2017-09-08 Created: 2017-09-08 Last updated: 2020-05-19Bibliographically approved
2. Mechanistic approach for the evaluation of machine tools quasi-static capability
Open this publication in new window or tab >>Mechanistic approach for the evaluation of machine tools quasi-static capability
2017 (English)In: NEWTECH 2017: Proceedings of 5th International Conference on Advanced Manufacturing Engineering and Technologies, Springer Berlin/Heidelberg, 2017, p. 229-243Chapter in book (Refereed)
Abstract [en]

One of the greatest challenges in the manufacturing industry is to increase the understanding of the error sources and their effect on machine tool capability. This challenge is raised by the complexity of machining systems and the high requirements on accuracy. In this paper, a mechanistic evaluation approach is developed to handle the complexity and to describe the underlying mechanisms of the machine tools capability under quasi-static condition. The capability in this case is affected by the geometric errors of the multi-axis system and the quasi-static deflections due to process loads. In the assessment of these sources a mechanistic model is introduced. The model is composed of two parts, combining direct and indirect measurements. The direct measurement modelling method was applied to predict the effects of individual axis geometric errors on the functional point of machine tools. First, the direct measurement is employed to allow measuring each single machine tool axis motion error individually. The computational in the direct measurement model calculates the deviations from a given toolpath in the work space. Then, indirect measurements are used to determine the static stiffness and its variation in the workspace of machine tools. A case study demonstrates the applicability of the proposed approach, where laser interferometry was implemented as direct and loaded double ball bar as indirect measurement. The methodology was investigated on a three and a five axis machine tool and the results demonstrate the potential of the approach.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2017
Keywords
Accuracy, Machine tool, Static stiffness
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:kth:diva-216365 (URN)10.1007/978-3-319-56430-2_16 (DOI)2-s2.0-85019572220 (Scopus ID)
Note

QC 20171023

Available from: 2017-10-23 Created: 2017-10-23 Last updated: 2020-05-19Bibliographically approved
3. Integration of machining system capability information into a CAx software environment for complex tool trajectory prediction
Open this publication in new window or tab >>Integration of machining system capability information into a CAx software environment for complex tool trajectory prediction
Show others...
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.

Keywords
Machine tool; Quasi-static capability; CAx software environment; Tool trajectory prediction
National Category
Engineering and Technology
Research subject
SRA - Production
Identifiers
urn:nbn:se:kth:diva-230612 (URN)10.1016/j.procir.2018.03.015 (DOI)2-s2.0-85049590812 (Scopus ID)
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: 2020-05-19Bibliographically approved
4. Utilization of Multi-Axis Positioning Repeatability Performance in Kinematic Modelling
Open this publication in new window or tab >>Utilization of Multi-Axis Positioning Repeatability Performance in Kinematic Modelling
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.

Keywords
Machine tool repeatability, Uncertainty estimation, Kinematic modelling
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
SRA - Production
Identifiers
urn:nbn:se:kth:diva-236060 (URN)
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
5. Measurement uncertainty associated with the performance of machine tool under quasi-static loaded test condition
Open this publication in new window or tab >>Measurement uncertainty associated with the performance of machine tool under quasi-static loaded test condition
2017 (English)In: Laser Metrology and Machine Performance XII / [ed] L. Blunt & W. Knapp, Renishaw Innovation Centre, UK, 2017Conference paper, Published paper (Refereed)
Abstract [en]

For proper characterisation of different physical quantities in machine tools, it is necessary to report the uncertainties associated to the measurements. The uncertainty evaluation, according to international standards, expresses information of the quality and the reliability of the measurement result. Applications like calibration and compensation are sensitive for the quality of the input data, thus the reliability of the characterisation results need to be interpreted accurately to avoid significant residual errors or overcompensation. General approaches take several factors into consideration during the estimation of measurement uncertainty such as the environmental variations or the uncertainties of the measurement device or the setup. At the same time, various reproducible and non-reproducible error sources associated to the performance testing of the machine tool are ignored. The reason behind it can be the lack of the applicable standardized measurement instruments.

This paper highlights the significance of the uncertainty sources connected to the performance of the machine tool under quasi-static loaded condition. The variation of the static stiffness of machine tools, the hysteresis and play in the system can be even more significant uncertainty sources than the above mentioned ones. Under the framework of elastically linked systems (ELS), a circular test device, the loaded double ball bar (LDBB), is used in a case study to identify this effect. The LDBB can be used as a double ball bar, with the additional capability of applying a load, thus it enables the measurement of machine tool deviations under quasi-static loaded conditions. A measurement methodology is proposed to properly describe and demonstrate the variation of the contributing uncertainties associated with repeatability performance of the machine tool. With this approach important interdependencies can be expressed as uncertainty sources.

Place, publisher, year, edition, pages
Renishaw Innovation Centre, UK: , 2017
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
SRA - Production
Identifiers
urn:nbn:se:kth:diva-203967 (URN)
Conference
Lamdamap 12th International Conference & Exhibition,15th-16th March 2017, Renishaw Innovation Centre, UK
Funder
XPRES - Initiative for excellence in production research
Note

QC 20170322

Available from: 2017-03-21 Created: 2017-03-21 Last updated: 2020-05-19Bibliographically approved
6. Identification of machine tool squareness errors via inertial measurements
Open this publication in new window or tab >>Identification of machine tool squareness errors via inertial measurements
2019 (English)In: CIRP annals, ISSN 0007-8506, E-ISSN 1726-0604, Vol. 68, no 1, p. 547-550Article in journal (Refereed) Published
Abstract [en]

The accuracy of multi-axis machine tools is affected to a large extent by the behavior of the system's axes and their error sources. In this paper, a novel methodology using circular inertial measurements quantifies changes in squareness between two axes of linear motion. Conclusions are reached through direct utilization of measured accelerations without the need for double integration of sensor signals. Results revealed that the new methodology is able to identify squareness values verified with traditional measurement methods. The work supports the integration of sensors into machine tools in order to reach higher levels of measurement automation. behalf of CIRP.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV, 2019
Keywords
Measurement, Machine tool, Squareness error
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:kth:diva-255510 (URN)10.1016/j.cirp.2019.04.070 (DOI)000474213500137 ()2-s2.0-85065523357 (Scopus ID)
Note

QC 20191016

Available from: 2019-10-16 Created: 2019-10-16 Last updated: 2020-05-19Bibliographically approved
7. Root-cause analysis of wear-induced error motion changes of machine tool linear axes
Open this publication in new window or tab >>Root-cause analysis of wear-induced error motion changes of machine tool linear axes
Show others...
2019 (English)In: International journal of machine tools & manufacture, ISSN 0890-6955, E-ISSN 1879-2170, Vol. 143, p. 38-48Article in journal (Refereed) Published
Abstract [en]

Manufacturers need online methods that give up-to-date information of system capabilities to know and predict the performance of their machine tools. Use of an inertial measurement unit (IMU) is attractive for on-machine condition monitoring, so methods based on spatial filters were developed to determine rail wear conditions of linear guideways of a carriage from its IMU-based error motion. Rail wear-induced changes in translational and angular error motions as small as 1.5 mu m and 3.0 microradians, respectively, could be resolved. A corresponding two-part root-cause analysis procedure was developed to determine the rail locations of error motion degradation as well as the most probable physical location of damage that causes the detected error motion changes. Another analysis method determined the root cause of non-localized damage along each rail. These approaches support the development of smart machine tools that provide actionable intelligence to manufacturers for early warnings of system degradation.

Place, publisher, year, edition, pages
ELSEVIER SCI LTD, 2019
Keywords
Machine tool, Error, Diagnostics, Wear, Condition monitoring
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-272452 (URN)10.1016/j.ijmachtools.2019.05.004 (DOI)000471196800004 ()32116408 (PubMedID)2-s2.0-85066451304 (Scopus ID)
Note

QC 20200421

Available from: 2020-04-21 Created: 2020-04-21 Last updated: 2020-05-19Bibliographically approved

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