Development of Experiment-Based Mathematical Models of Acoustic Signals for Machine Condition Monitoring

Derek Shaffer, Paige Lorson, Zach Plunkett, Ihab Ragai, Amir Danesh-Yazdi, Omar Ashour

Research output: Contribution to journalConference article

2 Scopus citations

Abstract

In this work, acoustic signals were analyzed in order to develop mathematical models to evaluate machining operations. The preliminary experiments consisted of milling operations with a single carbide cutting edge. The process parameters included feed rate, spindle speed, depth of cut, cutting direction, and work material. The goal of these models is to determine the feasibility of using acoustic emission in real-time monitoring of machining operations and machine health. Multiple linear regression analyses were used in developing the mathematical models. It was found that the models accurately reflect the machining operation and thus can be used in machine monitoring.

Original languageEnglish (US)
Pages (from-to)1316-1320
Number of pages5
JournalProcedia CIRP
Volume72
DOIs
StatePublished - Jan 1 2018
Event51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018 - Stockholm, Sweden
Duration: May 16 2018May 18 2018

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

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