Study On CAD Modeling Techniques Based On Variation Design In Reverse Engineering

With development of measurement and geometric modeling and introduction of new techniques, reverse engineering has become an important means of digesting and absorbing advanced technology for new product design and innovation design instead of original product copy. Researches and applications in reverse engineering field mainly focus on reconstructing CAD model of geometric shape from physical product. Three stages of CAD modeling methodology in reverse engineering are investigated systematically, such as CAD model reconstruction for product geometric shape, feature based CAD model reconstruction and CAD model reconstruction supporting product innovation design. Based on the physical product, recovering original design intent and supporting product innovation design are highly regarded as the research emphasis of CAD modeling methodology in reverse engineering.In order to recover product design intent and support product innovation design, some techniques were proposed, such as feature modeling incorporating constraint and CAD modeling method based on feature and constraint. In the research of feature modeling incorporating constraint process, automatic feature identification of composite surface and complex surface and static solution of large scale non-linear equation set are still research hot topic. Based on developed feature modeling technology in reverse engineering, it is necessary to enrich CAD modeling theory in reverse engineering by integrating CAD modeling methodology. It is helpful and valuable to improve technologies of CAD model reconstruction in reverse engineering. CAD model, which supports product innovation design and design intent restoration, is reconstructedThe research situation of methods, techniques of CAD model reconstruction in reverse engineering is investigated systematically. Variation design based CAD modeling methodology in reverse engineering is proposed. Key techniques related to the proposed methodology are studied by geometric feature understanding of data point and geometric feature modeling. The correctness and validity of Variation design based CAD modeling methodology in reverse engineering and corresponding technology is proved by examples.The main contents are as follows:From the viewpoint of theory and methodology, characteristics of variation design modeling are analyzed. Product geometric shape is represented with feature-based parameters. Optimization and modification of feature model is driven by geometric Constraint. Characteristics of variation design modeling satisfy requirement of CAD model reconstruction on recovering design intent and supporting product innovation design. Techniques related to variation design modeling are investigated, such as constraint decomposition based on theory of graph and constraint numerical solving method. Stable numerical solution of large scale non-linear equations is achieved for feature model reconstruction by incorporating constraints in reverse engineering. Variation design modeling is introduced into reverse engineering. Variation design based CAD modeling methodology in reverse engineering is proposed, which lay the foundation in theory for reconstructing CAD feature model supporting product innovation design.Classification and presentation of features and constraints are analyzed and summarized thoroughly in variation design based CAD modeling methodology in reverse engineering. The architecture of variation design based CAD modeling methodologies is studied systematically. Surface feature and geometric constraints are extracted from laser scanning data point. CAD model is reconstructed with optimized feature parameters driven by constraint. The proposed methodology provides a clear modeling way for CAD modeling in reverse engineering, which support product design intent extraction and innovation design.Curvature based feature identification method are synthesized. Feature identification is different according to curvature calculated with laser scanning data point in different size window. A technical idea of automatic feature segmentation is proposed based on multi-scale analysis. A new technical approach is provided for capturing high level product structure.Based on feature representation in CAD model reconstruction, feature fitting method with distinct geometric significance parameters is discussed. A new technical way is developed for recovering product design intent.The process of feature model optimization driven by constraints is implemented. Constraint is decomposed with constraint directional graph and DSM. Stable numerical solving method of geometric constraint systems is discussed. Technological basis is provided for reconstructing CAD model supporting product innovative design.In technical research, preprocessing techniques of laser scanning data point are developed for preserving shape. Sectional feature extraction is achieved based on multi scale analysis of laser scanning data point. Surface feature extraction is accomplished with similarity measure of sectional curve feature. Feature model optimization driven by constraint is studied, including constraint decomposition and numerical solution.Global statistical characteristics of laser scanning data point are investigated. Adaptive detection method of impulse noise is proposed based on the chord deviation. The impulse noise filtering is achieved with median filter by adaptively choosing the data that is not impulse noise in filter window. The proposed method can effectively solve the problem of previous impulse noise filtering methods on processing laser-scanning data of sharp area and data of broken-line area with unwanted detection and skipped detection. A random noise filtering method is presented within 3D neighbors of laser scanning data, which outperforms other filters in noise smoothing along the scan-line way and achieves good result in noise smoothing along the scan way. Local statistical characteristics analysis of laser scanning data point is surveyed. According to signal change factor and signal mean change rate, area property of laser scanning data point is determined with sharp area or smooth area. A two-steps data reduction method including modified least distance method and angle deviation is presented to improve the precision and efficiency of model reconstruction. The presented method can solve the problem of previous data reduction method that can not effectively preserve feature information of data point.Theory of multi scale is introduced into section feature curve segmentation. Based on curvature scale space, automatic feature segmentation of sectional composite curve is presented to obtain segmentation of primary curve primitives and secondary curve primitives. A seed growing segmentation method is developed based on multi scale analysis. Automatic selection of seed region is achieved with feature detection at large scale. According to information correlations among multi scale feature detection, a seed growing algorithm is studied with the homogeneity criteria relative angle to obtain feature segmentation. The statistic characteristic of projection height function is analyzed. Improved criterions of sectional curve feature classification are presented to identify line and arc. Automatic identification method of conics is studied based on the curve geometric invariants. Representation and fitting method of curve feature are systematically studied. A general conic curve can be modeled by translating and rotating the standard conic. The transformation matrix parameters can be seen as parameters of conic curve feature fitting. A general conic curve fitting is achieved with the standard conic curve parameters and translation matrix parameters and rotation matrix parameters.Surface feature segmentation method is studied based on similarity measure of sectional curve feature. The shape description of arc length and rotation angle is discussed. Complex surface is segmented into individual surfaces according to similarity measure rules. Automatic surface identification method is investigated. Feature extraction of simple free form modeling surface is achieved by slicing the laser scanning data point, such as sweeping surface, revolution surface and lofting surface. Surface representation and surface fitting method with explicit geometric parameter are systematically studied. Cylinder is fitted by least square method with the general surface representation. Feature parameters are achieved by comparing the coefficient of standard representation and general representation. As for cone, parameter of general representation has no explicit geometric meaning and can not reflect design intent. A general cone can be modeled by translating and rotating the standard conic surface. The transformation matrix parameters can be seen as parameters of cone feature fitting. A general cone feature fitting is achieved with the standard conic cone parameters and translation matrix parameters and rotation matrix parameters.Feature model optimization driven by constraint is studied, including constraint decomposition and numerical solution. Representation of directed graph and Design Structure matrix (DSM) of geometric constraint system are discussed. Coupled constraints of complex surface are eliminated by DSM partitioning algorithm. A new clustering method based on multi scale feature is proposed to reduce and decompose the geometric constraint system for identifying the constraint subset. Constraint representation between feature curves of composite curve is investigated. And constraint representation between feature surfaces of complex surface is discussed. Mathematical models of CAD model optimization are built with exponential penalty to translate constraint optimization into unconstraint optimization. The principle and steps of BFGS in Quasi-Newton method are studied for feature model optimization problems of variation design based CAD model reconstruction in reverse engineering. Steps of variation design based CAD modeling methodology are summarized. According to the optimized feature parameters, CAD model is reconstructed with the general CAD software UG.As stated above, variation design based CAD modeling methodology is proposed to improve the theory of CAD model reconstruction in reverse engineering. Key techniques related to this methodology are studied, such as feature extraction and constraint driving feature optimization for CAD model reconstruction. The theory of multi scale is introduced into reverse engineering. Based on curvature scale space, a section feature automatic segmentation method is presented. Feature fitting method with explicit geometric meaning parameters is systematically studied. Some innovation techniques are presented to recover the original design intent feature from laser scanning data point. Based on multi scale feature, a new clustering method is proposed to reduce the geometric constraint system. The constraint driving feature model optimization is achieved by exponential penalty and BFGS in Quasi-Newton method. All these researches provide the basis for product innovation design.