An analysis of the reflection of the fundamental Lamb mode a0 from surface-breaking rectangular notches in isotropic plates is presented. The results are obtained from finite element time domain simulations together with experimental measurements. Good agreement is found between the simulations and the measurements. Results are shown for a range of notch widths and depths, including the special case of a crack, defined as a zero-width notch. The reflection coefficient, when plotted as a function of the notch width, exhibits a cosinusoidal periodic shape, and this is explained by interference between the separate reflections from the start and the end of the notch. The reflection coefficient, when plotted as a function of notch depth, shows that in general the reflection increases with both frequency and notch depth, but the shapes of the functions are complex and there are some surprising features. An analysis of the reflection from cracks using the S-parameter scattering approach and some simplified descriptions of the crack-opening behavior yields physical explanations of the nature of these reflection functions. It is found that opening of the crack can be described adequately by a quasistatic assumption only when the crack is small, and in other cases a ray theory approach is more representative. The reflection function is shown to be a result of contributions from both the axial stress and the shear stress in the wave, and the relative importance of these varies with the crack depth and the frequency.

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In this paper, the optimisation of the EDM process parameters from the rough cutting stage to the finish cutting stage has been reported. A trained neural network was used to establish the relationship between the process parameters and machining performance. Genetic algorithms with properly defined objective functions were then adapted to the neural network to determine the optimal process parameters. Examples with specifications intentionally assigned the same values as those recorded in the database or selected arbitrarily have been fed into the developed GA-based neural network in order to verify the optimisation ability throughout the machining process. Accordingly, the optimised results indicate that the GA-based neural network can be successfully used to generate optimal process parameters from the rough cutting stage to the finish cutting stage.