Flexible manufacturing systems

This thesis presents a systematic method for the design and modeling of flexible manufacturing systems, using object-oriented concepts and Petri nets. In the method proposed, we first define the system components in terms of an object model consisting of hierarchical sets of classes and operations. Secondly, we model the dynamic aspects of the system using statecharts, including exceptions. As a third step, we derive Petri nets from those statecharts to realize the concurrency present in the system. Finally we develop a hierarchy of controllers, corresponding to the layers of the object model, for the independent components of the system based on the Petri nets obtained in the previous step.

An on-line scheme for monitoring tool wear in unmanned machining operations using artificial neural networks (ANNs) is proposed. Various configurations of ANNs are studied to increase the accuracy of tool wear estimation. With this aim three configurations of the ANNs namely, an ANN without memory, an ANN with one phase memory, and an ANN with two phase memory are considered. Each ANN is trained to associate an input vector which consists of values of cutting conditions, with an output vector containing flank wear as a single output. The training data and evaluation data is generated using the popular analytical tool wear model. The performance of all the ANNs are compared by considering four different cases of evaluation data. The proposed scheme of tool wear modeling using ANNs is easily extendible to include other cutting parameters and can be implemented in real-time.