Diagnosis of tool wear based on regression analysis and fuzzy logic

Erkki Jantunen (Corresponding Author)

    Research output: Contribution to journalArticleScientificpeer-review

    5 Citations (Scopus)

    Abstract

    Tool wear monitoring is important for a number of reasons. Automatic diagnosis of tool wear enables the unmanned use of flexible manufacturing systems and machine tools. Besides, a worn tool if unnoticed could cause a lot of damage, i.e. the machined products could be damaged and unfit for their planned use. As such the machining process is very challenging to monitor due to various reasons. Tool type and cutting parameters may vary resulting in variation of the monitored parameters. Also, there can be a lot of noise in the measured signals. The paper deals with the use of regression analysis techniques together with fuzzy logic in order to overcome the challenges in tool wear monitoring. Regression analysis, based on a higher order polynomial function that emphasizes the most recent measured data and has a limited number of terms, can very well follow and give prognosis of the development of the monitored parameters from such signals as vibration, sound and acoustic emission. The use of fuzzy logic makes it possible to automatically define limits for the monitored parameters and to combine the information from a number of signals. The proposed approach is tested with data from drilling tests.
    Original languageEnglish
    Pages (from-to)47-60
    Number of pages14
    JournalIMA Journal of Management Mathematics
    Volume17
    Issue number1
    DOIs
    Publication statusPublished - 2006
    MoE publication typeA1 Journal article-refereed

    Fingerprint

    Tool Wear
    Regression Analysis
    Regression analysis
    Fuzzy Logic
    Fuzzy logic
    Wear of materials
    Monitoring
    Acoustic Emission
    Flexible Manufacturing Systems
    Machine Tool
    Prognosis
    Drilling
    Polynomial function
    Machining
    Flexible manufacturing systems
    Acoustic emissions
    Monitor
    Machine tools
    Damage
    Vibration

    Keywords

    • tool wear
    • drilling
    • tool condition monitoring
    • regression analysis
    • fuzzy clarification
    • fuzzy logic
    • diagnosis

    Cite this

    @article{b1f27ae38dd04b048d8579ef206820ba,
    title = "Diagnosis of tool wear based on regression analysis and fuzzy logic",
    abstract = "Tool wear monitoring is important for a number of reasons. Automatic diagnosis of tool wear enables the unmanned use of flexible manufacturing systems and machine tools. Besides, a worn tool if unnoticed could cause a lot of damage, i.e. the machined products could be damaged and unfit for their planned use. As such the machining process is very challenging to monitor due to various reasons. Tool type and cutting parameters may vary resulting in variation of the monitored parameters. Also, there can be a lot of noise in the measured signals. The paper deals with the use of regression analysis techniques together with fuzzy logic in order to overcome the challenges in tool wear monitoring. Regression analysis, based on a higher order polynomial function that emphasizes the most recent measured data and has a limited number of terms, can very well follow and give prognosis of the development of the monitored parameters from such signals as vibration, sound and acoustic emission. The use of fuzzy logic makes it possible to automatically define limits for the monitored parameters and to combine the information from a number of signals. The proposed approach is tested with data from drilling tests.",
    keywords = "tool wear, drilling, tool condition monitoring, regression analysis, fuzzy clarification, fuzzy logic, diagnosis",
    author = "Erkki Jantunen",
    year = "2006",
    doi = "10.1093/imaman/dpi027",
    language = "English",
    volume = "17",
    pages = "47--60",
    journal = "IMA Journal of Management Mathematics",
    issn = "1471-678X",
    publisher = "Oxford University Press",
    number = "1",

    }

    Diagnosis of tool wear based on regression analysis and fuzzy logic. / Jantunen, Erkki (Corresponding Author).

    In: IMA Journal of Management Mathematics, Vol. 17, No. 1, 2006, p. 47-60.

    Research output: Contribution to journalArticleScientificpeer-review

    TY - JOUR

    T1 - Diagnosis of tool wear based on regression analysis and fuzzy logic

    AU - Jantunen, Erkki

    PY - 2006

    Y1 - 2006

    N2 - Tool wear monitoring is important for a number of reasons. Automatic diagnosis of tool wear enables the unmanned use of flexible manufacturing systems and machine tools. Besides, a worn tool if unnoticed could cause a lot of damage, i.e. the machined products could be damaged and unfit for their planned use. As such the machining process is very challenging to monitor due to various reasons. Tool type and cutting parameters may vary resulting in variation of the monitored parameters. Also, there can be a lot of noise in the measured signals. The paper deals with the use of regression analysis techniques together with fuzzy logic in order to overcome the challenges in tool wear monitoring. Regression analysis, based on a higher order polynomial function that emphasizes the most recent measured data and has a limited number of terms, can very well follow and give prognosis of the development of the monitored parameters from such signals as vibration, sound and acoustic emission. The use of fuzzy logic makes it possible to automatically define limits for the monitored parameters and to combine the information from a number of signals. The proposed approach is tested with data from drilling tests.

    AB - Tool wear monitoring is important for a number of reasons. Automatic diagnosis of tool wear enables the unmanned use of flexible manufacturing systems and machine tools. Besides, a worn tool if unnoticed could cause a lot of damage, i.e. the machined products could be damaged and unfit for their planned use. As such the machining process is very challenging to monitor due to various reasons. Tool type and cutting parameters may vary resulting in variation of the monitored parameters. Also, there can be a lot of noise in the measured signals. The paper deals with the use of regression analysis techniques together with fuzzy logic in order to overcome the challenges in tool wear monitoring. Regression analysis, based on a higher order polynomial function that emphasizes the most recent measured data and has a limited number of terms, can very well follow and give prognosis of the development of the monitored parameters from such signals as vibration, sound and acoustic emission. The use of fuzzy logic makes it possible to automatically define limits for the monitored parameters and to combine the information from a number of signals. The proposed approach is tested with data from drilling tests.

    KW - tool wear

    KW - drilling

    KW - tool condition monitoring

    KW - regression analysis

    KW - fuzzy clarification

    KW - fuzzy logic

    KW - diagnosis

    U2 - 10.1093/imaman/dpi027

    DO - 10.1093/imaman/dpi027

    M3 - Article

    VL - 17

    SP - 47

    EP - 60

    JO - IMA Journal of Management Mathematics

    JF - IMA Journal of Management Mathematics

    SN - 1471-678X

    IS - 1

    ER -