Indirect multisignal monitoring and diagnosis of drill wear

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en Jantunen, Erkki 2012-02-17T07:27:13Z 2012-02-17T07:27:13Z 2006-01-20
dc.identifier.isbn 951-38-6693-9
dc.identifier.issn 1455-0849
dc.description.abstract A machine tool utilisation rate can be improved by an advanced condition monitoring system using modern sensor and signal processing techniques. A drilling test and analysis program for indirect tool wear measurement forms the basis of this thesis. For monitoring the drill wear a number of monitoring methods such as vibration, acoustic emission, sound, spindle power and axial force were tested. The signals were analysed in the time domain using statistical methods such as root mean square (rms) value and maximum. The signals were further analysed using Fast Fourier Transform (FFT) to determine their frequency contents. The effectiveness of the best sensors and analysis methods for predicting the remaining lifetime of a tool in use has been defined. The results show that vibration, sound and acoustic emission measurements are more reliable for tool wear monitoring than the most commonly used measurements of power consumption, current and force. The relationships between analysed signals and tool wear form a basis for the diagnosis system. Higher order polynomial regression functions with a limited number of terms have been developed and used to mimic drill wear development and monitoring parameters that follow this trend. Regression analysis solves the problem of how to save measuring data for a number of tools so as to follow the trend of the measuring signal; it also makes it possible to give a prognosis of the remaining lifetime of the drill. A simplified dynamic model has been developed to gain a better understanding of why certain monitoring methods work better than others. The simulation model also serves the testing of the developed automatic diagnostic method, which is based on the use of simplified fuzzy logic. The simplified fuzzy approach makes it possible to combine a number of measuring parameters and thus improves the reliability of diagnosis. In order to facilitate the handling of varying drilling conditions and work piece materials, the use of neural networks has been introduced in the developed approach. The scientific contribution of the thesis can be summarised as the development of an automatically adaptive diagnostic tool for drill wear detection. The new approach is based on the use of simplified fuzzy logic and higher order polynomial regression analysis, and it relies on monitoring methods that have been tested in this thesis. The diagnosis program does not require a lot of memory or processing power and consequently is capable of handling a great number of tools in a machining centre. en
dc.format.extent 80, [110]
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher VTT Technical Research Centre of Finland en
dc.publisher VTT fi
dc.relation.ispartofseries VTT publications en
dc.relation.ispartofseries 590 en
dc.relation.haspart Jantunen, E. 2002. A Summary of Methods Applied to Tool Condition Monitoring in Drilling. International Journal of Machine Tools and Manufacture, Vol. 42, pp. 997-1010. ISSN 0890-6955.
dc.relation.haspart Jantunen, E. & Jokinen, H. 1996. Automated On-Line Diagnosis of Cutting Tool Condition. International Journal of Flexible Automation and Integrated Manufacturing, 4 (3 & 4), pp. 273-287. ISSN 1064-6345.
dc.relation.haspart Jantunen, E. 2001. The Applicability of Various Indirect Monitoring Methods to Tool Condition Monitoring in Drilling. International Journal of Comadem, Vol. 7, No. 3, July 2004, pp. 24-31. ISSN 1363-7681 (also published in COMADEM 01. September 4-6, Manchester, UK, ISBN 0 08 0440363).
dc.relation.haspart Jantunen, E. 2004. Dynamic Effects Influencing Drill Wear Monitoring. Proceedings of the MFPT 58th Meeting, Ed. H.C. Pusey, S.C. Pusey & W.R. Hobbs, April 25-30, Virginia Beach, USA. Pp. 51-60. [article4.pdf] © 2004 MFPT. By permission.
dc.relation.haspart Jantunen, E., Jokinen, H. & Milne, R. 1996. Flexible Expert System for Automated On-Line Diagnosis of Tool Condition. Proceedings of a Joint Conference, Technology Showcase, Integrated Monitoring, Diagnostics & Failure Prevention, MFPT 50th Meeting, Joint Oil Analysis Program Technical Support Center, University of Wales, Ed. H.C. Pusey & S.C. Pusey, Mobile, Alabama, USA, April 22-26. Pp. 259-268. [article5.pdf] © 1996 MFPT. By permission.
dc.relation.haspart Jantunen, E. 2003. Prognosis of Wear Progress Based on Regression Analysis of Condition Monitoring Parameters. Finnish Journal of Tribology, Vol. 22/2003, 4, pp. 3-15. ISSN 0780-2285 (also published in COMADEM 03 August 27-29, Växjö, Sweden. ISBN 91-7636-376-7). [article6.pdf] © 2003 TRIBOLOGIA and COMADEM. By permission.
dc.relation.haspart Jantunen, E. 2006. Diagnosis of Tool Wear Based on Regression Analysis and Fuzzy Logic. IMA Journal of Management Mathematics, Vol. 17, No 1, January, pp. 47-60. ISSN 1471-6798.
dc.relation.haspart Jantunen, E. 2000. Flexible Hierarchical Neuro-Fuzzy System for Prognosis. Proceedings of COMADEM 2000, 13th International Congress on Condition Monitoring and Diagnostic Engineering Management. Ed. H.C. Pusey & Raj B.K.N. Rao, December 3-8, Houston, USA. Pp. 699-708. ISBN 0-9635450-2-7. [article8.pdf] © 2000 COMADEM. By permission.
dc.subject.other Materials science en
dc.subject.other Mechanical engineering en
dc.subject.other Automation en
dc.title Indirect multisignal monitoring and diagnosis of drill wear en
dc.type G5 Artikkeliväitöskirja fi
dc.description.version reviewed en
dc.contributor.department Department of Mechanical Engineering en
dc.contributor.department Konetekniikan osasto fi
dc.subject.keyword drill wear en
dc.subject.keyword condition monitoring en
dc.subject.keyword signal analysis en
dc.subject.keyword polynomial regression analysis en
dc.subject.keyword fuzzy logic en
dc.subject.keyword diagnosis en
dc.identifier.urn urn:nbn:fi:tkk-006366
dc.type.dcmitype text en
dc.type.ontasot Väitöskirja (artikkeli) fi
dc.type.ontasot Doctoral dissertation (article-based) en

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