Optimizing tool service life using tool vibration monitoring

Johannes Pan, Clemens Gutschi, Nikolaus Furian

Publikation: Konferenzbeitrag › Abstract › Forschung › Begutachtung

Abstract

Numerous operations in manufacturing industry include machining activities, e.g. drilling, milling, turning, honing and more. This not only removes material from the work piece, but also wear the tool over time. Service life determines when the tool is replaced or comes to the revision. The service life of the tool is difficult to calculate precisely due to the different influences that the tool is exposed during work and thus results in a certain fluctuation range. Too early or too late intervention increases the setup time or is reflected in lack of part quality in case of too late intervention and is therefore costly. One of the keys to achieving maximum productivity in the use of tools is the individual determination of the condition of the tool over its service life for each individual. This study presents a method how to use the row data from vibration sensors placed on the tool holder. Statistical correlation analysis is used to extract features which show the correlation between the increasing wear of the tool and the produced product quality. Machine learning algorithms are used for automated diagnosis of tool service life to find the optimal time for use of tools.
Originalspracheenglisch
PublikationsstatusVeröffentlicht - Jun 2019
Veranstaltung30th European Conference on Operational Research - University College Dublin, Dublin, Irland
Dauer: 23 Jun 2019 → 26 Jun 2019
https://www.euro2019dublin.com/

Konferenz

Konferenz30th European Conference on Operational Research
KurztitelEURO 2019
LandIrland
OrtDublin
Zeitraum23/06/19 → 26/06/19
Internetadresse

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Service life
Monitoring
Wear of materials
Honing
Milling (machining)
Learning algorithms
Learning systems
Drilling
Machining
Productivity
Sensors

Schlagwörter

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    Pan, J., Gutschi, C., & Furian, N. (2019). Optimizing tool service life using tool vibration monitoring. Abstract von 30th European Conference on Operational Research, Dublin, Irland.

    Optimizing tool service life using tool vibration monitoring. / Pan, Johannes; Gutschi, Clemens; Furian, Nikolaus.

    2019. Abstract von 30th European Conference on Operational Research, Dublin, Irland.

    Publikation: Konferenzbeitrag › Abstract › Forschung › Begutachtung

    Pan, J, Gutschi, C & Furian, N 2019, 'Optimizing tool service life using tool vibration monitoring', Dublin, Irland, 23/06/19 - 26/06/19, .
    Pan J, Gutschi C, Furian N. Optimizing tool service life using tool vibration monitoring. 2019. Abstract von 30th European Conference on Operational Research, Dublin, Irland.
    Pan, Johannes ; Gutschi, Clemens ; Furian, Nikolaus. / Optimizing tool service life using tool vibration monitoring. Abstract von 30th European Conference on Operational Research, Dublin, Irland.
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    title = "Optimizing tool service life using tool vibration monitoring",
    abstract = "Numerous operations in manufacturing industry include machining activities, e.g. drilling, milling, turning, honing and more. This not only removes material from the work piece, but also wear the tool over time. Service life determines when the tool is replaced or comes to the revision. The service life of the tool is difficult to calculate precisely due to the different influences that the tool is exposed during work and thus results in a certain fluctuation range. Too early or too late intervention increases the setup time or is reflected in lack of part quality in case of too late intervention and is therefore costly. One of the keys to achieving maximum productivity in the use of tools is the individual determination of the condition of the tool over its service life for each individual. This study presents a method how to use the row data from vibration sensors placed on the tool holder. Statistical correlation analysis is used to extract features which show the correlation between the increasing wear of the tool and the produced product quality. Machine learning algorithms are used for automated diagnosis of tool service life to find the optimal time for use of tools.",
    keywords = "Predictive maintenance, predictive analytics, random forest, ensemble prediction, machine learning, Probability of failure estimation, maintenance, Maintenance Management, Data analysis, data mining, Fast Fourier transforms, feature selection, feature engineering, Remaining useful life, life cycle assessment, condition monitoring",
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    AU - Pan, Johannes

    AU - Gutschi, Clemens

    AU - Furian, Nikolaus

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    N2 - Numerous operations in manufacturing industry include machining activities, e.g. drilling, milling, turning, honing and more. This not only removes material from the work piece, but also wear the tool over time. Service life determines when the tool is replaced or comes to the revision. The service life of the tool is difficult to calculate precisely due to the different influences that the tool is exposed during work and thus results in a certain fluctuation range. Too early or too late intervention increases the setup time or is reflected in lack of part quality in case of too late intervention and is therefore costly. One of the keys to achieving maximum productivity in the use of tools is the individual determination of the condition of the tool over its service life for each individual. This study presents a method how to use the row data from vibration sensors placed on the tool holder. Statistical correlation analysis is used to extract features which show the correlation between the increasing wear of the tool and the produced product quality. Machine learning algorithms are used for automated diagnosis of tool service life to find the optimal time for use of tools.

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    KW - predictive analytics

    KW - random forest

    KW - ensemble prediction

    KW - machine learning

    KW - Probability of failure estimation

    KW - maintenance

    KW - Maintenance Management

    KW - Data analysis

    KW - data mining

    KW - Fast Fourier transforms

    KW - feature selection

    KW - feature engineering

    KW - Remaining useful life

    KW - life cycle assessment

    KW - condition monitoring

    M3 - Abstract

    ER -