Manufacturing is the process of making the products that have been designed and prototyped. Recently, efficient manufacturing of curved objects is an important issue in modern industry as more variety of industrial products is being designed with compound surfaces. The objective of this thesis is to develop a computationally efficient reverse engineering technique for objects composed of compound surfaces and a tool path generation method for HSM (high-speed machining). While conventional engineering transforms engineering concepts and models into real parts, in reverse engineering real parts are transformed into engineering models and concepts. In reverse engineering, a shape containing multi-patched surfaces is digitized, the boundary curves of these surfaces should be detected. For extracting boundary curves, and partitioning the 3D measured point data based on the location of the boundaries, the procedure begins with the identification of the edge points. An automatic edge-based approach is developed on the basis of local geometry. A parametric quadric surface approximation method is used to estimate the local surface curvature properties. The least-square approximation scheme minimizes the sum of the squares of the actual Euclidean distance between the neighborhood data points and the parametric quadric surface. The surface curvatures and the principal directions are computed from the locally approximated surfaces. Edge points are identified as the curvature extremes, and zero crossings, which are found from the estimated surface curvatures. After edge points are identified, edge-neighborhood chain-coding algorithm is used for forming boundary curves. The original point set is then broken down into subsets, which meet along the boundaries, by scan line algorithm. All point data are applied to each boundary loops to partition the points to different regions.
A reconstruction method is presented for the compound surfaces from the segmented data points. The surface reconstruction scheme based on a neural network is applied. The reconstruction of the surfaces is realized by training the network. A series of measured points from existing sculptured surfaces is used as a training set. Once the neural network has been trained, it serves as geometric models to generate all points that are needed. However, the learning rate for the neural network is relatively slow, and the learning accuracy is often unacceptably low. In this paper, a pre-processor is proposed before the input layer for improving the performance of the neural network. The pre-processor is to map the input into the larger space by generating a set of linearly independent values. The effect of the pre-processor is to increase modeling accuracy, and reduce learning time. The reconstructed surfaces are faithful to the original data points and the proposed scheme is useful for the regular or irregular digitized data.
To be competitive in today's worldwide marketplace, businesses must be able to take their products from development to manufacturing as quickly as possible. This can be achieved by bringing the manufacturing process through the application of HSM. HSM reduces real machining time through the increase of machining speed. The rapid change of cutting force is the cause of tool chipping/breakage. Therefore, it is required a tool-path generation technique for safe HSM. In this thesis, the continuous spiral tool-path generation method is developed in order to implement practically safe HSM for machining objects composed of compound surfaces. The procedure of the developed tool-path generation method begins with mapping meshes on a target surface. Continuous spiral tool-paths are then inserted based on the placed meshes for satisfying a machining allowance.
The developed methods have improved the design and manufacturing processes more efficiently for reverse engineering of compound surfaces considering boundary curves.
