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C.Cooper MS Thesis.pdf (4.44 MB)

ETD Abstract Container

Abstract Header

Milling Tool Condition Monitoring Using Acoustic Signals and Machine Learning

Cooper, Clayton Alan
http://rave.ohiolink.edu/etdc/view?acc_num=case1575539872711423

Abstract Details

2019, Master of Sciences (Engineering), Case Western Reserve University, EMC - Mechanical Engineering.
The objective of this research is to further document and bring feasibility to milling tool condition monitoring using acoustic signals. In order to accomplish this objective, a sound signal model is developed which characterizes the acoustic signals of the milling process. Using this model, two machine learning methods are developed to detect tool wear. One method utilizes data from all tool wear classes available for learner training and the other utilizes only a single class for training. The latter technique solves a data availability issue regarding running milling machines under suboptimal conditions, which is discussed herein. Each machine learning model is shown to be effective at tool wear detection tasks. This research demonstrates the power of machine learning in acoustic tool condition monitoring and makes significant novel contributions to the field. This research demonstrates the feasibility of the monitoring technique and lays a groundwork for future work in the field.
Robert Gao (Committee Chair)
Michael Lewicki (Committee Member)
Chris Yuan (Committee Member)
45 p.
Acoustics; Engineering; Mechanical Engineering
Tool condition monitoring; manufacturing; machining; milling; convolutional networks, generative networks; tool wear

Recommended Citations

Citations

  • Cooper, C. A. (2019). Milling Tool Condition Monitoring Using Acoustic Signals and Machine Learning [Master's thesis, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1575539872711423

    APA Style (7th edition)

  • Cooper, Clayton. Milling Tool Condition Monitoring Using Acoustic Signals and Machine Learning. 2019. Case Western Reserve University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1575539872711423.

    MLA Style (8th edition)

  • Cooper, Clayton. "Milling Tool Condition Monitoring Using Acoustic Signals and Machine Learning." Master's thesis, Case Western Reserve University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1575539872711423

    Chicago Manual of Style (17th edition)

case1575539872711423
297
© 2019, all rights reserved.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.