"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.
Liou, Frank W.
Landers, Robert G.
Newkirk, Joseph William
Mechanical and Aerospace Engineering
M.S. in Manufacturing Engineering
Missouri University of Science and Technology
xi, 55 pages
© 2011 Haythem Gaja, All rights reserved.
Thesis - Open Access
Pulsed laser deposition
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Electronic OCLC #
Link to Catalog Record
Gaja, Haythem, "Analysis and modeling of depth-of-cut during end milling of deposited material" (2011). Masters Theses. 4988.