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  <title><![CDATA[MS Defense by Edward Nguyen]]></title>
  <body><![CDATA[<p>SUBJECT</p>

<p>M.S. Thesis Presentation</p>

<p>PRESENTER</p>

<p>Edward Nguyen</p>

<p>DATE</p>

<p>Monday, April 20, 2020 @12:00 PM</p>

<p>LOCATION</p>

<p><a href="https://bluejeans.com/564086576">https://bluejeans.com/564086576</a></p>

<p>TITLE</p>

<p>Machine Learning for Anomalous Toolpath Identification in Subtractive Manufacturing</p>

<p>COMMITTEE</p>

<p>Dr. Christopher Saldana, Chair (ME)</p>

<p>Dr. Thomas Kurfess, Co-Chair (ME)</p>

<p>Dr. Katherine Fu (ME)</p>

<p>&nbsp;</p>

<p><strong>SUMMARY</strong></p>

<p>The 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.</p>

<p>&nbsp;</p>

<p><strong>Presentation Participation Information </strong></p>

<p>Phone Dial-in</p>

<p>+1.888.748.9073 (United States(Primary))</p>

<p>+1.844.540.8065 (United States(Primary))</p>

<p>+1.408.419.1715 (United States(San Jose))</p>

<p>+1.408.915.6290 (United States(San Jose))</p>

<p>Global Numbers: <a href="https://www.bluejeans.com/premium-numbers">https://www.bluejeans.com/premium-numbers</a></p>

<p>&nbsp;</p>

<p>Meeting ID: 564 086 576</p>

<p>&nbsp;</p>

<p>&nbsp;</p>
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