First Advisor

Grag Spolek

Term of Graduation

Fall 1996

Date of Publication


Document Type


Degree Name

Master of Science (M.S.) in Mechanical Engineering


Mechanical Engineering




Metal-cutting -- Mathematical models, Machine-tools



Physical Description

1 online resource (85 pages)


Tool Condition Monitoring offers a new approach to reducing the costs associated with catastrophic tool failure. The manufacturing process is becoming more computationally oriented all the time. Computational methods of determining the condition of cutting tools will lend themselves to easy integration with current and forthcoming manufacturing techniques. Coupling the results of this computational approach with a cost analysis will provide a means of maximizing the investment in cutting tools, while minimizing the damage caused by tool failure. Tool Condition Monitoring, as defined here, is a method of computationally determining the condition of a cutting tool. The problem of determining a tool's condition is approached by geometrically modeling the cutting process using a solid modeling software package. From a model of a process, an approximation of the forces that act on a cutting tool during that process are made. The calculated cutting forces are used to generate a stress history of the cutting tool, which can be maintained in a database. Statistical methods can then be applied to the stress history data to determine the probability that a cutting tool will fail at any point in a cutting process. The main purpose of this work is to develop an approach to predicting the condition of a cutting tool based on solid geometric models of cutting processes. The result of this work is an algorithm that describes how models are set up and executed to produce a set of intersections called chips. The algorithm continues by processing the chips to compute the cutting forces acting on the nodes of a finite element mesh of a cutting tool. The forces generated from these routines are used by a finite element analysis software package to produce a history of the stresses that occur in a tool. From this information, the condition of the cutting tool can be approximated using statistical methods.


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