Towards Automated Visual Inspection and Classification of Micro-Parts
The mechanical manufacturing of micro-parts is a challenging field of research. Because of high quality requirements and small tolerances, manufacturing processes have to be very stable and precise. In order to achieve high precision a suitable quality assurance concept is required. Only measured features can be used for statistical process control (SPC). Therefore, this paper presents a concept of an automated visual inspection and classification methods for micro-parts. Using confocal laser scanning microscopy, high-resolution 3D-height maps with depth selectivity are obtained. The identification of surface imperfections is done with a set of conventional image processing techniques. Afterwards, these kinds of surface defects are classified by the use of classification methods like decision trees.
RECONSTRUCTION OF 3D DRAWINGS FOR ENGINEERING OBJECTS FROM 2D CAMERA PERSPECTIVES
Engineering industry requires line drawings for manufacturing, machining and production of engineering equipments/objects.The generation of these paper-based drawings or computerized drawings is a complex and time consuming task. Conventionally, these drawings contain three two dimensional (2D) orthographic views, namely top, front and side of an object. Modern trends in engineering industry require three dimensional (3D) engineering drawings. Therefore, to fulfill this requirement the conversion of these 2D drawings to 3D drawings is essential.This conversion is referred to as the reconstruction. Various approaches have been proposed for the conversion/reconstruction using existing drawings. 
In this thesis, we developed 3D reconstruction methodology that uses camera perspectives in the reconstruction process. Note that in the existing approaches this feature (camera perspective) is not used. Another salient feature of our approach is in its underlying mechanism of tangential lines and hypothetical cuboid. Using our proposed approach, manufacturing cost and time can be saved, and it can also be helpful in technology transfer.
Inspection of defects in optical fibers based on back-propagation neural networks
As the application of optical fiber is boasting, higher quality optical fibers are required in some applied fields. Some pinhole defects such as air bubbles, caused during the manufacture of optical fiber, may have severe (or fatal) impacts on precise instruments because of affecting optical fiber's toughness and signal propagating along the fiber. Therefore, it is very important to detecting the defects in optical fibers using an effective, non-touched, on-line and fast method. In this paper, a novel defect-detection method based on back-propagation (BP) neural network is proposed. Methods on preprocessing and extracting feature from the high-dimension source data obtained by the optical device are investigated. A setting threshold operation and a cosine transformation method are proposed being used to filter the background noise from and reduce dimension of the source data respectively. The experimental results exhibit the processing methods are very effective on doing them. Resilient BP method is considered as network training algorithm because of its fast convergence. The experimental results given for training sets and testing sets denote that the method with BP neural network is competent for optical fiber detection.