Degree Type


Date of Award


Degree Name

Doctor of Philosophy


Industrial and Manufacturing Systems Engineering

First Advisor

Ranga Narayanaswami


Monitoring cutting forces in end milling is a necessary step toward the full automation of milling. To monitor the end milling process successfully, the selection of an appropriate signal and signal processing algorithm is very important. In this research, cutting force trends and tool wear effects in ramp cut machining are experimentally observed as machining progresses.;Ramp cuts are unique in the sense that the depth of cut is continuously changing. Traditionally, a series of straight slot cuts are used to machine a deep slot. Ramp cuts in which the depth of cut is continuously changing offers an alternative. Cutting force signals, table motor currents, spindle motor currents, and tool wear in ramp cuts are experimentally observed and compared to the results of straight cuts. Trends in X, Y, and Z cutting forces for straight and ramp cuts are compared.;Tool wear and its identification and estimation are a fundamental problem in machining. With tool wear there is an increase in cutting forces, and leads to deterioration in process stability, part accuracy, and surface finish. Cutting forces using new tools are compared with cutting forces obtained from a progressively wearing tool. The wavelet transform is used for signal processing and is found to be useful for observing cutting force trends.;The Root Mean Square (RMS) value of the wavelet transformed signal and linear regression are used for tool wear estimation. Tool wear is also estimated by measuring a machined slot thickness on a coordinate measuring machine. Picture analysis of the cutting tools using a SEM and microscope are used for tool wear estimation between cutting force and tool worn area.



Digital Repository @ Iowa State University,

Copyright Owner

Yong-hoon Choi



Proquest ID


File Format


File Size

115 pages