scholarly journals Application of Reverse Engineering Technology in Part Design for Shipbuilding Industry

2019 ◽  
Vol 26 (2) ◽  
pp. 126-133
Author(s):  
Mariusz Deja ◽  
Michał Dobrzyński ◽  
Marcin Rymkiewicz
Keyword(s):  
Reverse Engineering ◽  
Point Cloud ◽  
3D Model ◽  
Cad Model ◽  
Engineering Technology ◽  
Freeform Surfaces ◽  
Shipbuilding Industry ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Cad Models

Abstract In the shipbuilding industry, it is difficult to create CAD models of existing or prototype parts, especially with many freeform surfaces. The paper presents the creation of the CAD 3D model of a shipbuilding component with the application of the reverse engineering technology. Based on the data obtained from the digitization process, the component is reconstructed in point cloud processing programs and the CAD model is created. Finally, the accuracy of the digital model is estimated.

Point Cloud Data Acquisition Based on Reverse Engineering Technology

2012 ◽  
Vol 159 ◽  
pp. 186-190 ◽  
Author(s):  
Xian Sheng Ran ◽  
Li Lin ◽  
Han Bing Wei
Keyword(s):  
Reverse Engineering ◽  
Data Acquisition ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Cloud Data ◽  
Engineering Technology ◽  
Cloud Data ◽  
Cloud Processing ◽  
Optical Scanner ◽  
Point Cloud Processing

A reverse engineering based point cloud data acquisition method is addressed. The most critical part of reverse engineering (RE) is data acquisition of the digital model, the quality of design is determined by point cloud data acquisition, which is related to the accuracy of design. The measurement of relative position of different parts has been a difficulty of data acquisition. In this paper, the ATOS optical scanner was used as an example to illustrate the principle of three-dimensional scanner, the positioning methods and procedure of point cloud processing. A case study of point cloud data acquisition of car body was used to illustrate three-point positioning principle, which improves the accuracy of measurement compare with traditional method.

Surface Reconstruction Based on the 3D Laser Scanner

2012 ◽  
Vol 215-216 ◽  
pp. 656-659
Author(s):  
Le Yang Chen
Keyword(s):  
Reverse Engineering ◽  
Surface Reconstruction ◽  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
3D Laser Scanning ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Key Technologies ◽  
Rapid Scanning

3D laser scanning is one of the key technologies of reverse engineering. Digital point cloud is produced by the rapid scanning technology. Some technology about reverse engineering is introduced in this thesis. The curved surface can be generated by the point cloud processing, when the point cloud can be processed by the software called Geomagic Studio.

scholarly journals An Efficient and General Framework for Aerial Point Cloud Classification in Urban Scenarios

2021 ◽  
Vol 13 (10) ◽  
pp. 1985
Author(s):  
Emre Özdemir ◽  
Fabio Remondino ◽  
Alessandro Golkar
Keyword(s):  
Point Cloud ◽  
State Of The Art ◽  
Computational Power ◽  
Specific Data ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Current State ◽  
Data Source ◽  
Point Cloud Classification ◽  
And Training

With recent advances in technologies, deep learning is being applied more and more to different tasks. In particular, point cloud processing and classification have been studied for a while now, with various methods developed. Some of the available classification approaches are based on specific data source, like LiDAR, while others are focused on specific scenarios, like indoor. A general major issue is the computational efficiency (in terms of power consumption, memory requirement, and training/inference time). In this study, we propose an efficient framework (named TONIC) that can work with any kind of aerial data source (LiDAR or photogrammetry) and does not require high computational power while achieving accuracy on par with the current state of the art methods. We also test our framework for its generalization ability, showing capabilities to learn from one dataset and predict on unseen aerial scenarios.

User-Driven Computer-Assisted Reverse Engineering of Editable CAD Assembly Models

2021 ◽  
pp. 1-16
Author(s):  
Ghazanfar Ali Shah ◽  
Jean-Philippe Pernot ◽  
Arnaud Polette ◽  
Franca Giannini ◽  
Marina Monti
Keyword(s):  
Reverse Engineering ◽  
Point Cloud ◽  
Point Clouds ◽  
Geometric Constraints ◽  
Computer Assisted ◽  
Reconstruction Process ◽  
Mechanical Parts ◽  
Starting Point ◽  
Cad Models ◽  
Incomplete Datasets

Abstract This paper introduces a novel reverse engineering technique for the reconstruction of editable CAD models of mechanical parts' assemblies. The input is a point cloud of a mechanical parts' assembly that has been acquired as a whole, i.e. without disassembling it prior to its digitization. The proposed framework allows for the reconstruction of the parametric CAD assembly model through a multi-step reconstruction and fitting approach. It is modular and it supports various exploitation scenarios depending on the available data and starting point. It also handles incomplete datasets. The reconstruction process starts from roughly sketched and parameterized geometries (i.e 2D sketches, 3D parts or assemblies) that are then used as input of a simulated annealing-based fitting algorithm, which minimizes the deviation between the point cloud and the reconstructed geometries. The coherence of the CAD models is maintained by a CAD modeler that performs the updates and satisfies the geometric constraints as the fitting process goes on. The optimization process leverages a two-level filtering technique able to capture and manage the boundaries of the geometries inside the overall point cloud in order to allow for local fitting and interfaces detection. It is a user-driven approach where the user decides what are the most suitable steps and sequence to operate. It has been tested and validated on both real scanned point clouds and as-scanned virtually generated point clouds incorporating several artifacts that would appear with real acquisition devices.

scholarly journals Automatic Processing of Aerial LiDAR Data to Detect Vegetation Continuity in the Surroundings of Roads

2020 ◽  
Vol 12 (10) ◽  
pp. 1677 ◽  
Author(s):  
Ana Novo ◽  
Noelia Fariñas-Álvarez ◽  
Joaquin Martínez-Sánchez ◽  
Higinio González-Jorge ◽  
Henrique Lorenzo
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Light Detection And Ranging ◽  
Applicable Law ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Preventive Actions ◽  
Total Study Area ◽  
Processing Techniques ◽  
Flammable Material

The optimization of forest management in the surroundings of roads is a necessary task in term of wildfire prevention and the mitigation of their effects. One of the reasons why a forest fire spreads is the presence of contiguous flammable material, both horizontally and vertically and, thus, vegetation management becomes essential in preventive actions. This work presents a methodology to detect the continuity of vegetation based on aerial Light Detection and Ranging (LiDAR) point clouds, in combination with point cloud processing techniques. Horizontal continuity is determined by calculating Cover Canopy Fraction (CCF). The results obtained show 50% of shrubs presence and 33% of trees presence in the selected case of study, with an error of 5.71%. Regarding vertical continuity, a forest structure composed of a single stratum represents 81% of the zone. In addition, the vegetation located in areas around the roads were mapped, taking into consideration the distances established in the applicable law. Analyses show that risky areas range from a total of 0.12 ha in a 2 m buffer and 0.48 ha in a 10 m buffer, representing a 2.4% and 9.5% of the total study area, respectively.

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