Degree Name

Master of Engineering (Hons.)


Department of Business Systems


In recent years, there has been significant growth in the use of unmanned cutting systems, which have been used as a means of improving productivity and quality, reducing product costs and removing the operator from tedious and potentially hazardous environments. Acoustic Emissions (AE) have shown a significant potential for adaptive control processes in many manufacturing areas, especially for the condition monitoring of the tool and workpiece in cutting operations. Attempts have been made to develop some relationship between acoustic emissions and basic cutting parameters in turning operations. These models, however, lack accuracy in the prediction of AE and hence are unsuitable for determination of suitable cutting process parameters and for use in the adaptive control of machining operations. Development of these models is very difficult because the cutting process parameters can affect the geometry as well as the condition of the tool and workpiece. The objective of this research is to develop mathematical models for the prediction of acoustic emissions with the variation of cutting process parameters, and to study the effects of the cutting process parameters on acoustic emissions and cutting forces generated in Computer Numerical Controlled (CNC) turning operations.