In an automated machining process, monitoring the conditions of the tool is essential for deciding to replace or repair the tool without any manual intervention. Intelligent models built with sensor information and machine learning techniques are predicting the condition of the tool with good accuracy. In this study, statistical models are developed to identify the conditions of the abrasive grinding wheel using the Acoustic Emission (AE) signature acquired during the surface grinding operation. Abrasive grinding wheel conditions are identified using the abrasive wheel wear plot established by conducting experiments. The piezoelectric sensor is used to capture the AE from the grinding process, and statistical features of the abrasive wheel conditions are extracted in time and wavelet domains of the signature. Machine learning algorithms, namely, Classification and Regression Trees (CART) and Support Vector Classifiers (SVC), are used to build statistical models. AE features extracted from the wavelet domain using Discrete Wavelet Transforms (DWT) are predicting the conditions of the abrasive wheel with an accuracy of more than 90%.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Machine Learning Models for Predicting Grinding Wheel Conditions Using Acoustic Emission Features


    Additional title:

    Sae Int. J. Mater. Manf
    Sae International Journal of Materials and Manufacturing


    Contributors:


    Publication date :

    2021-05-28


    Size :

    20 pages




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    Novel mounting method for grinding wheel of grinding machine

    ZHU WEICHAO / ZHU BAOJIANG / GUO YUAN | European Patent Office | 2023

    Free access

    A grinding wheel

    LEE GYEONG GEUN | European Patent Office | 2019

    Free access

    Grinding wheel accidents

    Engineering Index Backfile | 1943


    Monitoring of rail-wheel interaction using acoustic emission (AE)

    Thakkar, N.A. / Steel, J.A. / Reuben, R.L. et al. | Tema Archive | 2006


    Grinding wheel accidents

    Waldo, R.J. | Engineering Index Backfile | 1934