"Unmanned machining centers are starting to emerge as the ultimate and most versatile form of computer integrated manufacturing. To utilize the full potential of unmanned machining centers, the crucial problem that must be solved is the development of a real-time machining monitoring and diagnostic system that integrates quality assurance into manufacturing processes. This is necessary for successful realization of unmanned factory of the future.
This research investigates multiple sensor data signatures which are correlated to quality characteristics of the machined parts. The work involves machining of three different workpiece materials of different hardness level and different cutting conditions. The materials investigated are 6061-T6 aluminum, 7075-T6 aluminum and 4140 steel. The investigated sensor signals are acoustic emission (AE), cutting force components in two axis direction (Fx, Fy) and spindle quill vibrations (acceleration) in two axis direction (Ax, Ay) of the machine tool.
These signals were processed in both time and frequency domain. The processed signals in the form of root mean square (RMS) values and power spectral amplitudes at cutter tooth frequencies were correlated with quality characteristics of the machined parts such as surface roughness (Ra) and bore tolerance (Tol).
In the first part of this study, the method and results for 6061-T6 aluminum workpiece material are presented. One of the results for this material shows that the RMS values of the cutting force signal increases with respect to tool wear. Another result shows that cutting force (Fx) and spindle vibrations (Ax) are highly correlated with surface roughness of the part with a correlation coefficient of R=.71.
In the second part of this work, the method and results for 7075-T6 aluminum workpiece material are presented. In this case, the RMS values of the resultant cutting force in the horizontal plane gives an increasing trend with progressive tool wear. Also spindle vibrations in x direction shows a decreasing trend with progressive wear of the cutting tool.
In the third part of this work, the analysis and results for 4140 steel workpiece material are presented. Here, DC component of Acoustic Emission signal shows a very sharp increase with cutting time. RMS values of the spindle vibrations gives a decreasing trend with progressive tool wear"--Abstract, page iv.
Okafor, A. Chukwujekwu (Anthony Chukwujekwu)
Lehnhoff, T. F., 1939-
Askeland, Donald R.
Mechanical and Aerospace Engineering
M.S. in Mechanical Engineering
University of Missouri--Rolla
Journal article titles appearing in thesis/dissertation
- Multi-sensor data characterization and prediction of surface roughness and bore tolerance in circular end milling: 6061-T6 aluminum
- Multi-sensor data characterization and prediction of surface roughness and bore tolerance in circular end milling: 7075-T6 aluminum
xi, 155 pages
© 1993 Yalcin M. Ertekin, All rights reserved.
Thesis - Restricted Access
Library of Congress Subject Headings
Manufacturing processes -- Automation
Neural networks (Computer science)
Print OCLC #
Electronic OCLC #
Link to Catalog RecordElectronic access to the full-text of this document is restricted to Missouri S&T users. Otherwise, request this publication directly from Missouri S&T Library or contact your local library. http://laurel.lso.missouri.edu/record=b2640676~S5
Ertekin, Yalcin M., "Extended multi-sensor data characterization and prediction of surface roughness and bore tolerance in circular end milling" (1993). Masters Theses. 1259.