Masters Theses

Author

Haythem Gaja

Abstract

"This study addresses depth-of-cut detection and tool-workpiece engagement using an acoustic emission monitoring system during milling machining for a deposited material. Online detection of depth-of-cut presents many technical difficulties. Researchers have used various types of sensors and methods to assess the depth-of-cut and surface errors. Due to the strong correlation between acoustic emission and cutting depth during the depth end milling process, it is useful to forecast the depth-of-cut from the acoustic emission signal. This work used regression analysis to model and detect the depth-of-cut. The experiments were carried out on a Fadal vertical 5-Axis computer numerical control machine using a carbide end-mill tool, and a piezoelectric sensor (Kistler 8152B211) was used to acquire the acoustic emission signal. A National Instruments real-time system, combined with a National Instruments LabVIEW graphical development environment, was used as a data acquisition system. A series of experiments were conducted to create a depth-of-cut model. The inputs were used to predict depth-of cut are the identified root mean square of the acoustic emission, spindle speed, feed rate, and tool status. The effects of these inputs were evaluated using a fractional factorial design-of-experiment approach"--Abstract, page iii.

Advisor(s)

Liou, Frank W.

Committee Member(s)

Landers, Robert G.
Newkirk, Joseph William

Department(s)

Mechanical and Aerospace Engineering

Degree Name

M.S. in Manufacturing Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2011

Pagination

xi, 55 pages

Note about bibliography

Includes bibliographical references (pages 81-83).

Rights

© 2011 Haythem Gaja, All rights reserved.

Document Type

Thesis - Open Access

File Type

text

Language

English

Library of Congress Subject Headings

Acoustic emission
Cutting machines
Pulsed laser deposition

Thesis Number

T 9818

Print OCLC #

784152474

Electronic OCLC #

748287682

Included in

Manufacturing Commons

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