Acoustic Maps Processing with Image Enhancement Techniques in Grinding Wheel Dressing for Industry 4.0

Abstract

In certain applications of acoustic emission sensors, acoustic maps can be generated from captured signals. The work “In-Dressing Acoustic Map by Low-Cost Piezoelectric Transducer” introduces an innovative technique using these sensors to map grinding wheel surfaces, essential for finishing machined parts. However, producing sharp acoustic maps is challenging due to industrial interference. This study explores digital image processing techniques to enhance these maps, using cloud-based tools. Techniques such as smoothing, equalization, and edge detection (Sobel, Canny, Roberts, and Prewitt) were applied. The processed acoustic maps revealed sharper details, enabling more accurate assessments of dressing conditions. The results demonstrate the effectiveness of digital image processing when applied to acoustic maps, significantly improving the evaluation of the dressing process and contributing to the development of Industry 4.0.