Degree Name

Doctor of Philosophy


Department of Mechanical Engineering


Chip control and tool wear estimation are two major concerns in automated machining systems. Chip control is essential for the safety of the machining operation, the maintenance of good surface finish on the machined part, the convenience of chip disposal, and possible power reduction; while tool wear estimation is vital to an effective tool change policy and quality control strategy, especially in finish-machining.

This thesis first presents a new method for quantifying chip breaking and chip shapes with a fuzzy rating system, and further for predicting the chip breakability for arbitrary combinations of machining conditions through a fuzzy-set mathematical model. A predictive expert system for off-line assessment of machining performance, with chip control as a major criterion and due consideration to surface finish and power consumption, is then developed.

A knowledge-based system for designing optimum chip breakers is set up with a criterion of efficient chip breaking at reduced power consumption. The method is based on the analysis of three-dimensional chip flow in oblique machining for a wide range of work materials, cutting conditions, tool geometries, chip breaker styles/sizes and restricted contact lengths.

Experimental results of tool wear patterns in finish-machining show that estimation of more than one type of tool wear is required to assure the quality of a finished product. In order to achieve this, a dispersion analysis algorithm, derived from the established multivariate time series models, is used for the overall estimation of tool wear, including major flank wear, crater wear, minor flank wear and groove wear at the minor cutting edge.

Finally, neural network techniques are used for modelling the dynamic interrelationship between the chip forming behaviour and different tool wear states. By integrating the developed methods for predicting chip breakability/shapes and for estimating comprehensive tool wear, the initially-predicted chip forming/breaking patterns can be updated with tool wear progression during the machining process through the use of neural network techniques.

The results show that the methods developed in this thesis, for predicting chip breaking and shapes, and for evaluating machining performance, including chip control, surface finish and power consumption, m a y be used to form a basis for the off-line assessment of "total machinability" for automated machining systems. The strategy of comprehensive tool wear estimation provides a feasible means for on-line tool wear monitoring to assure product quality, especially under finish-machining conditions. The results also show that by using neural networks, chip forming behaviour with tool wear progression can be evaluated in-process, thus providing a feasible approach for achieving the on-line assessment of machining performance including chip forming patterns, surface finish and overall tool wear progression.