AUTOMATED IMAGE PROCESSING USING MAGNETIC DEFECTOSCOPY
To identify defects, for example, cracks in the surface layers of steel parts, methods are applied that base on the analysis of magnetic field dissipation nearby the defects after magnetization of these parts. Magnetic flux will vary in the areas with discontinuity. Magnetic particle inspection is one of the best known magnetic defectoscopy methods. In this case magnetic particle powder (dry method) or suspension (wet method) is applied on the magnetized part surface. Usage of fluorescent powders or suspensions makes defects more easily seen in the images of the inspected parts. Therefore, it becomes possible to carry out automated processing of such images. The paper presents automated procedure for selection of the image processing methods. It exemplifies image processing for a steel part with a view to defect detection with the help of luminous lines appearing after the wet method application.
Abstract Machine learning techniques have been widely applied to production processes with the aim of improving product quality, supporting decision-making, or implementing process diagnostics. These techniques proved particularly useful in the investment casting manufacturing industry, where huge variety of heterogeneous data, related to different production processes, can be gathered and recorded but where traditional models fail due to the complexity of the production process. In this study, we apply Support Vector Representation Machine to production data from a manufacturing plant producing turbine blades through investment casting. We obtain an instance ranking that may be used to infer proper values of process parameter set-points.