Optimizing tool service life using tool vibration monitoring

Research output: Contribution to conferenceAbstractResearchpeer-review

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.
LanguageEnglish
StatusPublished - Jun 2019
Event30th European Conference on Operational Research - University College Dublin, Dublin, Ireland
Duration: 23 Jun 201926 Jun 2019
https://www.euro2019dublin.com/

Conference

Conference30th European Conference on Operational Research
Abbreviated titleEURO 2019
CountryIreland
CityDublin
Period23/06/1926/06/19
Internet address

Fingerprint

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

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

Cite this

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

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

2019. Abstract from 30th European Conference on Operational Research, Dublin, Ireland.

Research output: Contribution to conferenceAbstractResearchpeer-review

Pan, J, Gutschi, C & Furian, N 2019, 'Optimizing tool service life using tool vibration monitoring' 30th European Conference on Operational Research, Dublin, Ireland, 23/06/19 - 26/06/19, .
Pan J, Gutschi C, Furian N. Optimizing tool service life using tool vibration monitoring. 2019. Abstract from 30th European Conference on Operational Research, Dublin, Ireland.
Pan, Johannes ; Gutschi, Clemens ; Furian, Nikolaus. / Optimizing tool service life using tool vibration monitoring. Abstract from 30th European Conference on Operational Research, Dublin, Ireland.
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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.

AB - 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.

KW - Predictive maintenance

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

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