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.

Reverse Engineering of Mechanical Parts: A Brief Overview of Existing Approaches and Possible New Strategies

2016 ◽  
Author(s):  
Francesco Buonamici ◽  
Monica Carfagni
Keyword(s):  
Reverse Engineering ◽  
A Priori ◽  
Reconstruction Process ◽  
Mechanical Parts ◽  
Cad System ◽  
Software Packages ◽  
Geometrical Constraints ◽  
Cad Models ◽  
Data Points ◽  
New Strategies

Reverse Engineering (RE), also known as “CAD reconstruction”, aims at the reconstruction of 3D geometric models of objects/mechanical parts, starting from 3D measured data (points/mesh). In recent years, considerable developments in RE were achieved thanks to both academic and industrial research (e.g. RE software packages). The aim of this work is to provide an overview of state of the art techniques and approaches presented in recent years (considering at the same time tools and methods provided by commercial CAD software and RE systems). In particular, this article focuses on the “constrained fitting” approach, which considers geometrical constraints between the generated surfaces, improving the reconstruction result. On the basis of the overview, possible theoretical principles are drafted with the aim of suggest new strategies to make the CAD reconstruction process more effective in order to obtain more ready/usable CAD models. Finally, a new RE framework is briefly outlined: the proposed approach hypothesizes a tool built within the environment of an existing CAD system and considers the fitting of a custom-built archetypal model, defined with all the a-priori known dimensions and constraints, to the scanned data.

Application of the 'Iterative Closest Point' Algorithm for Determination of Rotation Axis of a Turntable

2013 ◽  
Vol 199 ◽  
pp. 273-278
Author(s):  
Ireneusz Wróbel
Keyword(s):  
Reverse Engineering ◽  
Point Cloud ◽  
Rotation Axis ◽  
Rapid Development ◽  
Point Clouds ◽  
Iterative Closest Point ◽  
Mechanical Parts ◽  
Scanning Process ◽  
Iterative Closest Point Algorithm

Reverse engineering [ is a field of technology which has been under rapid development for several recent years. Optic scanners are basic devices used as reverse engineering tools. Point cloud describes the shape of a scanned object. Automatic turntable is a device which enables a scanning process from different viewing angles. In the paper, the algorithm is described which has been used for determination of rotation axis of a turntable. The obtained axis constitutes the base for an aggregation of particular point clouds into single resultant common cloud describing the shape of the scanned object. Usability of this algorithm for precise scanning of mechanical parts was validated, precision of shape replication was also evaluated.

scholarly journals MESH MODELLING OF 3D POINT CLOUD FROM UAV IMAGES BY POINT CLASSIFICATION AND GEOMETRIC CONSTRAINTS

2018 ◽  
Vol XLII-2 ◽  
pp. 507-511 ◽  
Author(s):  
S. Kim ◽  
H. G. Kim ◽  
T. Kim
Keyword(s):  
Surface Reconstruction ◽  
Point Cloud ◽  
Point Clouds ◽  
Geometric Constraints ◽  
Reconstruction Technique ◽  
Reconstruction Process ◽  
Mesh Model ◽  
Surface Normal ◽  
Normal Vectors

The point cloud generated by multiple image matching is classified as an unstructured point cloud because it is not regularly point spaced and has multiple viewpoints. The surface reconstruction technique is used to generate mesh model using unstructured point clouds. In the surface reconstruction process, it is important to calculate correct surface normals. The point cloud extracted from multi images contains position and color information of point as well as geometric information of images used in the step of point cloud generation. Thus, the surface normal estimation based on the geometric constraints is possible. However, there is a possibility that a direction of the surface normal is incorrectly estimated by noisy vertical area of the point cloud. In this paper, we propose an improved method to estimate surface normals of the vertical points within an unstructured point cloud. The proposed method detects the vertical points, adjust their normal vectors by analyzing surface normals of nearest neighbors. As a result, we have found almost all vertical points through point type classification, detected the points with wrong normal vectors and corrected the direction of the normal vectors. We compared the quality of mesh models generated with corrected surface normals and uncorrected surface normals. Result of comparison showed that our method could correct wrong surface normal successfully of vertical points and improve the quality of the mesh model.

scholarly journals Automated Measurement of Heart Girth for Pigs Using Two Kinect Depth Sensors

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3848
Author(s):  
Xinyue Zhang ◽  
Gang Liu ◽  
Ling Jing ◽  
Siyao Chen
Keyword(s):  
Mirror Symmetry ◽  
Point Cloud ◽  
Point Clouds ◽  
Depth Image ◽  
View Point ◽  
Important Indicator ◽  
Depth Sensors ◽  
Starting Point ◽  
Feature Based ◽  
Girth Measurement

The heart girth parameter is an important indicator reflecting the growth and development of pigs that provides critical guidance for the optimization of healthy pig breeding. To overcome the heavy workloads and poor adaptability of traditional measurement methods currently used in pig breeding, this paper proposes an automated pig heart girth measurement method using two Kinect depth sensors. First, a two-view pig depth image acquisition platform is established for data collection; the two-view point clouds after preprocessing are registered and fused by feature-based improved 4-Point Congruent Set (4PCS) method. Second, the fused point cloud is pose-normalized, and the axillary contour is used to automatically extract the heart girth measurement point. Finally, this point is taken as the starting point to intercept the circumferential perpendicular to the ground from the pig point cloud, and the complete heart girth point cloud is obtained by mirror symmetry. The heart girth is measured along this point cloud using the shortest path method. Using the proposed method, experiments were conducted on two-view data from 26 live pigs. The results showed that the heart girth measurement absolute errors were all less than 4.19 cm, and the average relative error was 2.14%, which indicating a high accuracy and efficiency of this method.

scholarly journals A HYBRID MODEL FOR THE REVERSE ENGINEERING OF THE MILAN CATHEDRAL. CHALLENGES AND LESSON LEARNT

2021 ◽  
Author(s):  
Franco Spettu ◽  
Simone Teruggi ◽  
Francesco Canali ◽  
Cristiana Achille ◽  
Francesco Fassi
Keyword(s):  
Reverse Engineering ◽  
Point Cloud ◽  
Learning Algorithm ◽  
A Priori ◽  
Point Clouds ◽  
Logical Structure ◽  
Cloud Data ◽  
Survey Techniques ◽  
Periodic Maintenance ◽  
Maintenance And Inspection

Cultural Heritage (CH) 3D digitisation is getting increasing attention and importance. Advanced survey techniques provide as output a 3D point cloud, wholly and accurately describing even the most complex architectural geometry with a priori established accuracy. These 3D point models are generally used as the base for the realisation of 2D technical drawings and 3D advanced representations. During the last 12 years, the 3DSurveyGroup (3DSG, Politecnico di Milano) conduced an omni-comprehensive, multi-technique survey, obtaining the full point cloud of Milan Cathedral, from which were produced the 2D technical drawings and the 3D model of the Main Spire used by the Veneranda Fabbrica del Duomo di Milano (VF) to plan its periodic maintenance and inspection activities on the Cathedral. Using the survey product directly to plan VF activities would help to skip a long-lasting, uneconomical and manual process of 2D and 3D technical elaboration extraction. In order to do so, the unstructured point cloud data must be enriched with semantics, providing a hierarchical structure that can communicate with a powerful, flexible information system able to effectively manage both point clouds and 3D geometries as hybrid models. For this purpose, the point cloud was segmented using a machine-learning algorithm with multi-level multi-resolution (MLMR) approach in order to obtain a manageable, reliable and repeatable dataset. This reverse engineering process allowed to identify directly on the point cloud the main architectonic elements that are then re-organised in a logical structure inserted inside the informative system built inside the 3DExperience environment, developed by Dassault Systémes.

Converting Point Cloud to Revolved Solids

2013 ◽  
Vol 371 ◽  
pp. 468-472
Author(s):  
Mircea Viorel Drăgoi ◽  
Slobodan Navalušić
Keyword(s):  
Reverse Engineering ◽  
Cross Section ◽  
Software Package ◽  
Point Cloud ◽  
3D Model ◽  
Point Clouds ◽  
3D Scanning ◽  
Main Drawback ◽  
Mass Properties

3D scanning is one of the basic methods to gather data for reverse engineering. The main drawback of 3D scanning is that its output - the point cloud - can never be used directly to define surfaces or solids useful to reconstruct the electronic 3D model of the scanned part.The paper presents a piece of software designed in VisualLISP for AutoCAD, software that acts as a point cloud to 3D primitives converter. The novelty consists of the method used to find the parameters of the primitive that best fits to the point cloud: the mass properties of regions are used to find the center of a cones cross section. Parts have been scanned and the point clouds processed. The results obtained prove the correctness of the algorithm and of the method applied. A piece of software that processes the point cloud in order to find the 3D primitive that it fits the best has been developed. The output is the 3D primitive that successfully and accurate replaces the point cloud. Some adjacent tools were designed, so the entire software package becomes a useful tool for the reverse engineering user. The ways the researches can be continued and developed are foreseen, as well

scholarly journals A PROJECTION-BASED RECONSTRUCTION ALGORITHM FOR 3D MODELING OF BRIDGE STRUCTURES FROM DRONE-BASED POINT CLOUD

2021 ◽  
Vol XLVI-4/W1-2021 ◽  
pp. 77-83
Author(s):  
M. Mehranfar ◽  
H. Arefi ◽  
F. Alidoost
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
3D Model ◽  
Reconstruction Algorithm ◽  
Point Clouds ◽  
3D Models ◽  
Reconstruction Technique ◽  
Reconstruction Process ◽  
Median Error ◽  
Bridge Structures

Abstract. This paper presents a projection-based method for 3D bridge modeling using dense point clouds generated from drone-based images. The proposed workflow consists of hierarchical steps including point cloud segmentation, modeling of individual elements, and merging of individual models to generate the final 3D model. First, a fuzzy clustering algorithm including the height values and geometrical-spectral features is employed to segment the input point cloud into the main bridge elements. In the next step, a 2D projection-based reconstruction technique is developed to generate a 2D model for each element. Next, the 3D models are reconstructed by extruding the 2D models orthogonally to the projection plane. Finally, the reconstruction process is completed by merging individual 3D models and forming an integrated 3D model of the bridge structure in a CAD format. The results demonstrate the effectiveness of the proposed method to generate 3D models automatically with a median error of about 0.025 m between the elements’ dimensions in the reference and reconstructed models for two different bridge datasets.

scholarly journals Enriching Assembly CAD Models With Functional and Mechanical Informations to Ease CAE

2010 ◽  
Author(s):  
Gilles Foucault ◽  
Jean-Claude Le´on
Keyword(s):  
Product Development ◽  
Geometric Constraints ◽  
Geometric Models ◽  
Bill Of Materials ◽  
Starting Point ◽  
Cad Models ◽  
Development Processes ◽  
Mechanical Information ◽  
Standard Components ◽  
Assembly Models

Assembly models can be regarded as a kernel for product development processes where they can efficiently contribute to many product simulation behaviors. Assembly models are often containing 3D B-Rep CAD models, possibly with geometric constraints between the components and bill of materials. However, these models are often difficult to process for simulations because algorithms often face a very large diversity of configurations. One origin of such difficulties can be found in companies’ practice where components may be represented differently from one company to another and their interfaces as well. In any case, interfaces between components are not explicit, which leads to tedious model processing tasks. This paper illustrates preparation of assembly models to ease CAE through an analysis of company practices, showing that a concept of conventional representations is an important starting point to efficient treatments of assemblies. In addition, it is described how interfaces and conventional representations can be combined to derive functional and mechanical information from geometric models of components. Illustrations of the proposed approach is given throughout the paper using various standard components.

scholarly journals Automated Feature-Based Down-Sampling Approaches for Fine Registration of Irregular Point Clouds

2020 ◽  
Vol 12 (7) ◽  
pp. 1224 ◽  
Author(s):  
Abdulla Al-Rawabdeh ◽  
Fangning He ◽  
Ayman Habib
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Geometric Constraints ◽  
Superior Performance ◽  
Accurate Estimation ◽  
Transformation Parameters ◽  
Matching Techniques ◽  
Mean Square Errors ◽  
Fine Registration ◽  
Sampling Approach

The integration of three-dimensional (3D) data defined in different coordinate systems requires the use of well-known registration procedures, which aim to align multiple models relative to a common reference frame. Depending on the achieved accuracy of the estimated transformation parameters, the existing registration procedures are classified as either coarse or fine registration. Coarse registration is typically used to establish a rough alignment between the involved point clouds. Fine registration starts from coarsely aligned point clouds to achieve more precise alignment of the involved datasets. In practice, the acquired/derived point clouds from laser scanning and image-based dense matching techniques usually include an excessive number of points. Fine registration of huge datasets is time-consuming and sometimes difficult to accomplish in a reasonable timeframe. To address this challenge, this paper introduces two down-sampling approaches, which aim to improve the efficiency and accuracy of the iterative closest patch (ICPatch)-based fine registration. The first approach is based on a planar-based adaptive down-sampling strategy to remove redundant points in areas with high point density while keeping the points in lower density regions. The second approach starts with the derivation of the surface normals for the constituents of a given point cloud using their local neighborhoods, which are then represented on a Gaussian sphere. Down-sampling is ultimately achieved by removing the points from the detected peaks in the Gaussian sphere. Experiments were conducted using both simulated and real datasets to verify the feasibility of the proposed down-sampling approaches for providing reliable transformation parameters. Derived experimental results have demonstrated that for most of the registration cases, in which the points are obtained from various mapping platforms (e.g., mobile/static laser scanner or aerial photogrammetry), the first proposed down-sampling approach (i.e., adaptive down-sampling approach) was capable of exceeding the performance of the traditional approaches, which utilize either the original or randomly down-sampled points, in terms of providing smaller Root Mean Square Errors (RMSE) values and a faster convergence rate. However, for some challenging cases, in which the acquired point cloud only has limited geometric constraints, the Gaussian sphere-based approach was capable of providing superior performance as it preserves some critical points for the accurate estimation of the transformation parameters relating the involved point clouds.

Automatic Detection of Directions of Dimensional Control in Mechanical Parts

2014 ◽  
Author(s):  
Prashant Mohan ◽  
Payam Haghighi ◽  
Jami J. Shah ◽  
Joseph K. Davidson
Keyword(s):  
Linear Chain ◽  
Tolerance Analysis ◽  
Relative Orientation ◽  
Graphical Form ◽  
Computer Assisted ◽  
Dimensional Control ◽  
Mechanical Parts ◽  
Geometric Tolerancing ◽  
Feature List ◽  
Cad Models

This research is part of a larger project which aims at developing a tool to help designers create effective GD&T schemas. The first step towards this goal is to determine the particular directions in which dimensions and tolerances need to be controlled. These directions we label here as “Directions of (Dimensional) Control” or DoC for short. Regardless of whether one uses chain dimensioning, reference dimensioning or geometric tolerancing, all size and basic dimensions of position line up in a finite number of directions or Directions of Control. This paper presents an approach for automatically identifying the directions of control from CAD models of mechanical parts. The only input to the system is the geometry of parts or assemblies in STEP file format. The analysis is done part by part for an assembly. First, planar and cylindrical features are recognized and their normal/axes extracted. The extracted features are then organized into groups of parallel normal or axes directions. Cylindrical features can belong to two or more Directions of Control, while planar features belong can only belong to one. Features in each DoC are then ordered based on perpendicular relative distances. Each ordered feature list forms a linear chain in which nodes represent features and links are attributed with relative distance to the nearest neighbors on each side. DoC chains are related to each other by relative orientation. Therefore, the chains are combined into a unified graph, using the junction nodes to contain the relative orientation between the chains. The extracted Directions of Control can be output in both textual and graphical form. Although the primary motivation for automatic DoC graph generation is computer assisted tolerancing and automatic tolerance analysis, the paper also discusses other applications in manufacturing.

Sign in / Sign up

Export Citation Format

Share Document