| Summary: | The rising demand for exacting performances from manufacturing systems has led to new challenges for the development of complex tool condition monitoring techniques. Although a wide range of monitoring methods have been investigated and developed, there has been very little migration of these innovations into industrial practice. The principal factor behind this phenomenon is the stochastic nature of the environment in which the system must function. A truly universal application has yet to be developed. The work presented here centres around the application of an unsupervised neural network model to the said problem. These networks learn without the aid of a human teacher or supervisor and learn to organise and re-organise themselves in accordance to the input data. This leads to the network structure reflecting the given input distribution more precisely than a predefined model, which generally follows a decay schedule. The dynamic nature of the process provides an evaluation of the underlying connectivity and topology in the original data space. This makes the network far more capable of capturing details in the target space. These networks have been successfully used in speech recognition applications and various pattern recognition tasks involving very noisy signals. Work is in progress on their application to robotics, process control and telecommunications. The procedure followed here has been to conduct experimental drilling trials using solid carbide drills on a Duplex Stainless Steel workpiece. Duplex Stainless Steel was chosen as a preferred metal for drilling experiments because of this high strength, good resistance to corrosion, low thermal expansion and good fatigue resistance. During the drilling trials, forces on the workpiece along the x, y and z axes were captured in real time and moments of the forces were calculated using these values. These three axial forces, along with their power spectral densities and moments were used as input parameters to the Artificial Neural Network model which followed the Self-Organising Map algorithm to classify this data. After the network was able to adapt itself to classify this real world data, the generated model was tested against a different set of data values captured during the drilling trials. The network was able to correctly identify a worn out drill from a new drill from this previously unseen set of data. This autonomous classification of the drill wear state by the neural network is a step towards creating a "universal" application that will eventually be able to predict tool wear in any machining operation without prior training.
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