Quantitative Evaluation of Background Noise in Acoustic Maps of Abrasive Grinding Wheels

The condition of abrasive grinding wheels after the dressing process can be visualized through acoustic maps, which enable the identification of patterns associated with the wheel topography and surface characteristics. Initially, acoustic maps were generated exclusively from Acoustic Emission (AE) signals. Due to the low sharpness of these maps, resulting from the inherent nature of acoustic emission signals, multimodal fusion techniques combining Acoustic Emission and vibration signals (AE+Vib) were developed, as well as approaches based on Digital Image Processing (AE+Vib+DIP). Traditionally, the quantitative evaluation of these acoustic maps has been based on cross-correlation, in which regions of interest in the topological map are compared with predefined patterns. However, this approach does not consider analyses related to background noise in the images, which can affect the sharpness and contrast of the acoustic maps, making their visual interpretation more difficult. Therefore, this study effectively evaluates the background noise in acoustic maps obtained using different methods. The study investigates background homogeneity and the spatial definition of regions of interest through statistical metrics and digital image processing techniques. The results demonstrate that the integration of multiple information sources significantly reduces background noise, leading to substantial improvements in the overall quality of the acoustic maps.