• Login
    View Item 
    •   SMARTech Home
    • Georgia Tech Theses and Dissertations
    • Georgia Tech Theses and Dissertations
    • View Item
    •   SMARTech Home
    • Georgia Tech Theses and Dissertations
    • Georgia Tech Theses and Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Using machine learning for anomalous toolpath identification in subtractive manufacturing

    Thumbnail
    View/Open
    NGUYEN-THESIS-2020.pdf (2.016Mb)
    Date
    2020-04-28
    Author
    Nguyen, Edward Pham
    Metadata
    Show full item record
    Abstract
    The emphasis and application of machine learning with respect to manufacturing and machining has focused primarily on tool wear or bearing health. Few studies have focused on the parts produced by these processes and how changing parameters during machining operations can affect the final outcome. Quality control is a costly but necessary step in the manufacturing process to ensure that a finished part meets specification. For a machined part, this is usually accomplished using inspection and measurement techniques. However, inspection of a machined part has typically occurred after certain predetermined milestones. This study aims to identify and classify machining phenomenon compared to a reference signal to determine if the toolpath mimics reflects the intended behavior. To accomplish this, a Computer Numerical Control (CNC) milling machine is instrumented with accelerometers to track and record vibrations. This data is collected from the spindle and processed using a machine learning algorithm that segregates signatures based on selected features and classifies them as expected behaviors or anomalous. The results of the study indicate that certain phenomena can be accurately identified and labeled as normal or abnormal with respect to feed rate or spindle speed overrides. It is a promising insight into more complex toolpath identification and integration with Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) software to anticipate and mitigate machining errors.
    URI
    http://hdl.handle.net/1853/62850
    Collections
    • Georgia Tech Theses and Dissertations [23877]
    • School of Mechanical Engineering Theses and Dissertations [4086]

    Browse

    All of SMARTechCommunities & CollectionsDatesAuthorsTitlesSubjectsTypesThis CollectionDatesAuthorsTitlesSubjectsTypes

    My SMARTech

    Login

    Statistics

    View Usage StatisticsView Google Analytics Statistics
    facebook instagram twitter youtube
    • My Account
    • Contact us
    • Directory
    • Campus Map
    • Support/Give
    • Library Accessibility
      • About SMARTech
      • SMARTech Terms of Use
    Georgia Tech Library266 4th Street NW, Atlanta, GA 30332
    404.894.4500
    • Emergency Information
    • Legal and Privacy Information
    • Human Trafficking Notice
    • Accessibility
    • Accountability
    • Accreditation
    • Employment
    © 2020 Georgia Institute of Technology