Computational methods for on-line shape inspection

Abstract: This licentiate thesis describes computational methods that solve problems occurring in industrial on-line shape quality inspection of produced items. These items are measured and compared with their corresponding CAD object. The meaning of on-line is that the inspection is done on-line in the production line, i.e. the items are not removed from the line. In practice this means that the inspection must be done very fast, both the measurement and the data analysis. The measurement is done using an optical non-contact method based on projection of fringes. The presented methods are mainly based on finding a transformation, a rotation and a translation, of the measurement values which consists of a point cloud representing the measured surface. This transformation is calculated using the iterative closest point (ICP) method such that the point cloud fits the corresponding surface of the CAD object properly. The method for finding this transformation is adapted for reiterated use, i.e. it makes use of the fact that the same CAD object is used several times for different measurements. A search tree making it possible to do this fast is proposed. When dealing with real measurements obtained from optical methods undesired measurement errors will occur, caused by reflections, dirt on lenses or other likely matters in the industrial environment. The iteratively re-weighted least squares (IRLS) method for different robust functions are used in combination with ICP for handling these errors, in order to do a correct surface matching. This result in much higher matching accuracy and almost no additional computations are needed.