Automatic coverage measurement of shot peened panels

Abstract

Shot peening is the process of treating metallic surfaces with a regulated blast of shots to increase material strength and durability. Determining the coverage level of the shots is an important parameter in the assessment of the quality of treatment. Traditionally, coverage measurement is performed manually using a magnifying glass, which leads to inefficiency. Despite the proposal for the use of image segmentation techniques for determining the coverage measurement, literature on this topic is not extensively developed. In this thesis, various relevant image segmentation techniques are investigated including thresholding, edge detection, watershed segmentation, active contour, graph cut and neural network. The aim is to develop a generic coverage measurement algorithm, which is accurate and robust to variations in illumination, shot type, coverage level and has real-time capabilities using a simple experimental setup. The results obtained from each method are discussed and compared against a set of relevant performance criteria.