Scan Planning Strategy for a General Digitized Surface

In reverse engineering, the point cloud data processing is the key, it will directly affect the quality and efficiency of the subsequent reconstruction of the surface. However, the amount of data obtained by three-dimensional laser scanning is often larger and redundant, which will increase the difficulty of the surface reconstruction, therefore, the point cloud data is needed to streamline. Depth analysis of the efficiency and accuracy of curvature grid method, a secondary streamlined algorithm is proposed, which take use of non-uniform grid streamline law based on octree to streamline to the point cloud, and re-use curvature streamline method based on B-spline surface fitting to streamline it. This method, whose streamline ratio is relatively high, can effectively retain the feature point cloud data, providing a great convenience for the follow-up of reverse engineering.

The processes applied on the pixels by the image processing methods provide us information about images. Image processing is a computer programming area which can be used in computer integrated industrial applications and it is one of the research areas of reverse engineering. In this study, a new system in which color digital images are interpreted by using image processing method was developed. The purpose of this study is to improve a system to interpret the images, which are taken with a hand camcorder or digital camera, and to obtain point clouds by evaluating with image processing and to convert these point clouds surface or solid model in a computer aided design program. In accordance with these purposes, the three dimensional point clouds are obtained from a rectangular prism shaped object in the specified area. These point clouds can easily be converted into three dimensional solid or surface models in a Computer Aided Design (CAD) program. In this reverse engineering study, the developed system is explained and sample parts are given.

Reverse engineering is an approach for constructing geometric models from a physical part through dimensional measurement and surface modeling. In most applications, the method to reconstructed model from point clouds data which is called NURBS. The accuracy of surface reconstruction depends on number of point clouds data, more point clouds data that make calculations more accurate, but it makes to slowly compute. A tangent function method is a new algorithm for reduction point cloud data had presented in this paper. A tangent function finds turning point and selection point to decrease matrix inversion size in NURBS curve and surface fitting equation while maintaining the accuracy.

Reverse engineering modelling of free-form surfaces from point clouds subject to boundary conditions

In view of the complex development process of mechanical equipment and high research and development costs, the virtual assembly technology can quickly and effectively discover the problems and risks that may occur in the assembly process of the structure. A set of general schemes for the reconstruction and updating of assembly geometric models, which are comprehensively used by three modeling methods: parametric modeling, synchronous modeling and reverse engineering modeling, are given. Taking a surface block structure with free-form surface as the measurement object, the surface of the surface block generated by 3D printing is scanned by laser, and the k-d tree topological relationship is established for the obtained point cloud data. Combining the search strategy based on k-d tree and ICP algorithm, the point cloud data scanned from different angles in the same area are spliced. Through the analysis of the surface point cloud data model of the surface block, the spatial point cloud data is mapped to the two-dimensional plane by using the projection plane method to realize triangulation, and finally complete the surface reconstruction of the surface block.

Conventional reverse engineering uses 3D scanning or image processing technologies to extract the shape information of a given object in terms of point clouds before creating its virtual and real models. It requires complex and user-intensive geometric modeling processes such as noise removal and surface reconstruction. In order to avoid all these complex processes, this paper presents a scanning-free reverse engineering approach defined as a human-cognition-based reverse engineering. The approach consists of four spaces. The first space is defined as a human cognition space wherein the information of a given object is represented using some predefined linguistic expressions. The second space is defined as a point cloud creation space wherein the predefined linguistic expressions are translated to a point cloud model. The third space is defined as a virtual model creation space wherein the point cloud model is converted into a virtual model using a commercially available CAD system. The last space is defined as a real model creation space wherein the virtual model is converted into a real model using 3D printing technology. The efficacy of the proposed approach is demonstrated by performing a case study. The findings will help those who want to simplify the current practices regarding reverse engineering.

To address perception and navigation challenges in precision agriculture caused by GPS signal loss and weakly structured environments in greenhouses, this study proposes an integrated framework for real-time semantic reconstruction and path planning. This framework comprises three core components: First, it introduces a semantic segmentation method tailored for greenhouse environments, enhancing recognition accuracy of key navigable areas such as furrows. Second, it designs a visual-semantic fusion SLAM point cloud reconstruction algorithm and proposes a semantic point cloud rasterization method. Finally, it develops a semantic-constrained A* path planning algorithm adapted for semantic maps. We collected a segmentation dataset (1083 images, 4 classes) and a reconstruction dataset from greenhouses in Shanghai. Experiments demonstrate that the segmentation algorithm achieves 95.44% accuracy and 87.93% mIoU, with a 3.9% improvement in furrow category recognition accuracy. The reconstructed point cloud exhibits an average relative error of 7.37% on furrows. In practical greenhouse validation, single-frame point cloud fusion took approximately 0.35 s, while path planning was completed in under 1 s. Feasible paths avoiding crops were successfully generated across three structurally distinct greenhouses. Results demonstrate that this framework can stably and in real-time accomplish semantic mapping and path planning, providing effective technical support for digital agriculture.

In a manufacturing area, design changes for an original surface model are frequently required: for example, when the physical parts are modified or when the parts are partially manufactured from analogous shapes. In this case, an efficient surface updating method by locally adding scan data for the modified area is highly desirable. For this purpose, this paper presents a new procedure to update an initial model that is composed of combinatorial triangular facets based on a set of locally added point data. Nowadays, many people are using surface models that are represented by triangular facets in reverse engineering, since it is fast and easy to create the triangular meshes directly from large amounts of point data. The initial surface model is first created from the initial point set by tight cocone, which is a water-tight surface reconstructor; and then the point cloud data for the updates is locally added onto the initial model maintaining the same coordinate system. In order to update the initial model, the special region on the initial surface that needs to be updated is recognized through the detection of the overlapping area between the initial model and the boundary of the newly added point cloud. After that, the initial surface model is eventually updated to the final output by replacing the recognized region with the newly added point cloud. The proposed method has been implemented and tested with several examples. This algorithm will be practically useful to modify the surface model with physical part changes and free-form surface design.

Three-dimensional (3-D) point cloud data are increasingly being used to describe a wide range of physical objects in detail, corresponding to customized and flexible shape designs. The advent of a new generation of optical sensors has simplified and reduced the costs of acquiring 3-D data in near-real-time. However, the variation of the acquired point clouds, and methods to describe them, create bottlenecks in manufacturing practices such as Reverse Engineering (RE) and metrology in additive manufacturing. We address this issue by developing an automated variance modeling algorithm that utilizes a physical object’s local geometric descriptors and Bayesian Extreme Learning Machines (BELMs). Our proposed ensemble and residual BELM-variants are trained by a scanning history that is composed of multiple scans of other, distinct objects. The specific scanning history is selected by a new empirical Kullback–Leibler divergence we developed to identify objects that are geometrically similar to an object of interest. A case study of our algorithm on additively manufactured products demonstrates its capability to model the variance of point cloud data for arbitrary freeform shapes based on a scanning history involving simpler, and distinct, shapes. Our algorithm has utility for measuring the process capability of 3-D scanning for RE processes.

The issue of surface reconstruction and slicing from point clouds has been receiving extensive attention recently. When using the B-spline surface fitting technique, the difficulty of parameterization exists. At the same time, for interfacing between reverse engineering and rapid prototyping, the point clouds are usually converted to an stereolithography (STL) model. This leads to a huge file size and requires expert modeling skills. The objective of this work is to establish a base surface parameterization and direct slicing strategy for scattered data based on a cross-sectional design technique. We first present a new method of directly extracting sectional contours from point clouds. Then, we create a base surface by skinning the primary boundary curves and interior sectional curves. Based on a good parameterization, the final surface is achieved with tight tolerance. Several practical examples have demonstrated the feasibility of the proposed method. It can be widely used in Number Control (NC) machining and rapid prototyping.

A Sustainable Reverse Engineering Process

Measuring method for abrasive volume of biomimetic embossed surfaces using reverse engineering

With the help of advances in technology, bone imaging has become easier when compared to its initial stages. The purpose of this study is to analyse and interpret the morphometry of the human sacrum, through Reverse Engineering. Eight different sets of human sacra were obtained for which reverse engineered 3D CAD models were generated by employing a portable Coordinate Measuring Machine with a laser scanner. An appropriate software is used for the generation of the CAD model from the point cloud data is obtained through the scanner. The study highlights the upper hand of reverse engineering to the conventional methods employed for bone imaging, which can be further used for surgical implications, design of sacral implants and other applications

The non-contact 3D surface measurement theory and CAD technology integration, the reverse engineering means can be three-dimensional entity model accurately obtain irregular products. Application of reverse engineering method to establish CAD model and suitable to finite element analysis, the shape and size of surface reconstruction of entity can be guaranteed, through the component structure design and optimization, the realization of rapid prototyping and quality testing of new products. The point cloud data acquisition products surface using 3D laser scanner, the scanner itself, the measurement process vulnerable to the external environment and the surrounding objects of interference noise, therefore, the research take the suitable method of point cloud data denoising. The key research based on the mathematical model of NURBS curve and surface, revealing the control principle of curve and surface, the shape is easier to achieve the best effect, the completion of surface reconstruction and materialization process in CAD software. The final will be imported into the finite element software reverse molding analysis and optimization of the characteristics of the local, through the analysis of cloud drop test, stress generation, to provide a theoretical basis for the optimization design of structure. Keywords-reverse engineering; CAD; finite element analysis; quality detection; 3D entity model

Ultrasonic scanning is the most commonly used method for detecting breast cancer. Traditional breast ultrasound mainly relies on manual examination, which requires doctors with rich clinical experience. The scanning path planning of the robot for breast ultrasound examination based on binocular vision and non-uniform rational B-Splines (NURBS) is proposed in this paper to solve the limitations of traditional examination methods. First, breast images are acquired by the binocular camera. The depth information of the images is obtained through stereo matching, and the 3D point cloud reconstruction of the breast model is completed. Second, the spatial index algorithm based on the K-dimension tree (Kd-tree) and uniform sampling method are selected to denoise and down-sample the point cloud model to complete the preliminary optimization of the model. The point cloud information for the scanning path is extracted according to the method of point cloud extraction and the principle of normal vector calculation. Then, the interpolation optimization of the scanning path based on the NURBS curve is completed. The optimized scanning path is transformed into the robot motion path. Finally, the robot motion is simulated and analyzed, and the experimental results show that the rationality of robot operation and the smoothness of robot motion meet the requirements of path planning defined in this paper. The error analysis experiment shows that the path planned by this method can ensure the efficiency and accuracy of ultrasound examination.