Abstract
In this study, acoustic emission monitoring with a wavelet-based signal processing technique is developed to detect different surface damage mechanisms during experimental simulation of sheet metal forming processes. Firstly, the obtained acoustic emission signals are decomposed into various wavelet levels. Each level includes detail and approximation that are called components and related to a specific frequency range. Secondly, the energy distribution criterion is applied to find more significant components each one of which is in relation to a distinct type of damage. Results showed that the energy of acoustic emission signals was concentrated in four significant components which can be attributed to three specific dominant phenomena occurred during different stages of a well-known surface damage, i.e. galling. Indeed to compare the results with previous investigations, surface observations were used to determine how the different surface damage mechanisms are related to the dominant corresponding wavelet components.
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