Error Propagation in Digital Additive Remanufacturing Process Planning

Author(s):  
Mojahed Alkhateeb ◽  
Jeremy L. Rickli ◽  
Nicholas J. Christoforou

Abstract A point cloud is a digital representation of a part that consists of a set of data points in space. Typically point clouds are produced by 3D scanners that hover above a part and records points in a large number that represent the external surface of a part. Additive remanufacturing offers a sustainable solution to end-of-use (EoU) core disposal and recovery and requires quantification of part damage or wear that requires reprocessing. This paper proposes an error propagation approach that models the interaction of each step of the additive remanufacturing process. This proposed model is formulated, and the results of the errors generated from the parameters of the scanner and point cloud smoothing are presented. Smoothing is an important step to reduce the noises generated from scanning, knowing the right smoothing factor is important since over smoothing results in dimensional inaccuracies and errors, especially in cores with smaller degrees of damage. It is important to know the error generated from scanning and point cloud smoothing to compensate in the following steps and generate appropriate material deposition paths. Inaccuracies in the 3D model renders can impact the remainder of the additive remanufacturing accuracy, especially because there are multiple steps in the process. Sources of error from smoothing, meshing, slicing, and material deposition are proposed in the error propagation model for additive remanufacturing. Results of efforts to quantify the scanning and smoothing steps within this model are presented.

Author(s):  
T. O. Chan ◽  
D. D. Lichti

Lamp poles are one of the most abundant highway and community components in modern cities. Their supporting parts are primarily tapered octagonal cones specifically designed for wind resistance. The geometry and the positions of the lamp poles are important information for various applications. For example, they are important to monitoring deformation of aged lamp poles, maintaining an efficient highway GIS system, and also facilitating possible feature-based calibration of mobile LiDAR systems. In this paper, we present a novel geometric model for octagonal lamp poles. The model consists of seven parameters in which a rotation about the z-axis is included, and points are constrained by the trigonometric property of 2D octagons after applying the rotations. For the geometric fitting of the lamp pole point cloud captured by a terrestrial LiDAR, accurate initial parameter values are essential. They can be estimated by first fitting the points to a circular cone model and this is followed by some basic point cloud processing techniques. The model was verified by fitting both simulated and real data. The real data includes several lamp pole point clouds captured by: (1) Faro Focus 3D and (2) Velodyne HDL-32E. The fitting results using the proposed model are promising, and up to 2.9 mm improvement in fitting accuracy was realized for the real lamp pole point clouds compared to using the conventional circular cone model. The overall result suggests that the proposed model is appropriate and rigorous.


2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Ruizhen Gao ◽  
Xiaohui Li ◽  
Jingjun Zhang

With the emergence of new intelligent sensing technologies such as 3D scanners and stereo vision, high-quality point clouds have become very convenient and lower cost. The research of 3D object recognition based on point clouds has also received widespread attention. Point clouds are an important type of geometric data structure. Because of its irregular format, many researchers convert this data into regular three-dimensional voxel grids or image collections. However, this can lead to unnecessary bulk of data and cause problems. In this paper, we consider the problem of recognizing objects in realistic senses. We first use Euclidean distance clustering method to segment objects in realistic scenes. Then we use a deep learning network structure to directly extract features of the point cloud data to recognize the objects. Theoretically, this network structure shows strong performance. In experiment, there is an accuracy rate of 98.8% on the training set, and the accuracy rate in the experimental test set can reach 89.7%. The experimental results show that the network structure in this paper can accurately identify and classify point cloud objects in realistic scenes and maintain a certain accuracy when the number of point clouds is small, which is very robust.


2019 ◽  
Vol 12 (1) ◽  
pp. 61
Author(s):  
Miloš Prokop ◽  
Salman Ahmed Shaikh ◽  
Kyoung-Sook Kim

Modern robotic exploratory strategies assume multi-agent cooperation that raises a need for an effective exchange of acquired scans of the environment with the absence of a reliable global positioning system. In such situations, agents compare the scans of the outside world to determine if they overlap in some region, and if they do so, they determine the right matching between them. The process of matching multiple point-cloud scans is called point-cloud registration. Using the existing point-cloud registration approaches, a good match between any two-point-clouds is achieved if and only if there exists a large overlap between them, however, this limits the advantage of using multiple robots, for instance, for time-effective 3D mapping. Hence, a point-cloud registration approach is highly desirable if it can work with low overlapping scans. This work proposes a novel solution for the point-cloud registration problem with a very low overlapping area between the two scans. In doing so, no initial relative positions of the point-clouds are assumed. Most of the state-of-the-art point-cloud registration approaches iteratively match keypoints in the scans, which is computationally expensive. In contrast to the traditional approaches, a more efficient line-features-based point-cloud registration approach is proposed in this work. This approach, besides reducing the computational cost, avoids the problem of high false-positive rate of existing keypoint detection algorithms, which becomes especially significant in low overlapping point-cloud registration. The effectiveness of the proposed approach is demonstrated with the help of experiments.


2021 ◽  
Vol 16 (4) ◽  
pp. 579-587
Author(s):  
Pitisit Dillon ◽  
Pakinee Aimmanee ◽  
Akihiko Wakai ◽  
Go Sato ◽  
Hoang Viet Hung ◽  
...  

The density-based spatial clustering of applications with noise (DBSCAN) algorithm is a well-known algorithm for spatial-clustering data point clouds. It can be applied to many applications, such as crack detection, rockfall detection, and glacier movement detection. Traditional DBSCAN requires two predefined parameters. Suitable values of these parameters depend upon the distribution of the input point cloud. Therefore, estimating these parameters is challenging. This paper proposed a new version of DBSCAN that can automatically customize the parameters. The proposed method consists of two processes: initial parameter estimation based on grid analysis and DBSCAN based on the divide-and-conquer (DC-DBSCAN) approach, which repeatedly performs DBSCAN on each cluster separately and recursively. To verify the proposed method, we applied it to a 3D point cloud dataset that was used to analyze rockfall events at the Puiggcercos cliff, Spain. The total number of data points used in this study was 15,567. The experimental results show that the proposed method is better than the traditional DBSCAN in terms of purity and NMI scores. The purity scores of the proposed method and the traditional DBSCAN method were 96.22% and 91.09%, respectively. The NMI scores of the proposed method and the traditional DBSCAN method are 0.78 and 0.49, respectively. Also, it can detect events that traditional DBSCAN cannot detect.


Author(s):  
Huan Zhou ◽  
Wei Zheng ◽  
Guojian Tang

A ballistic error propagation algorithm for glide trajectories of a hypersonic glide vehicle is originally proposed based on the perturbation theory. Perturbation equations that treat perturbations as external inputs and flight state deviations as state variables are established. Based on the reasonable simplification assumptions, the analytic expression of the state transition matrix is deduced and thus the ballistic error propagation model is established. A transposed coordinate frame is introduced to simplify the development of the perturbation equations and the error propagation model. By employing the gravity anomaly as the single perturbation factor, the proposed algorithm is validated and verified by numerical experiments. When compared with the traditional method, the proposed method takes only just a quarter computational costs and restrains the method errors beneath 10% of the total terminal deviations. It is an effort that initially focuses on the error propagation mechanism of the glide trajectory and the proposed model has sufficient precision for the analysis of modeling deviations, thus laying the foundation of correcting the modeling deviations and enhancing the accuracy of vehicle’s flight states.


Author(s):  
Lee J. Wells ◽  
Mohammed S. Shafae ◽  
Jaime A. Camelio

Ever advancing sensor and measurement technologies continually provide new opportunities for knowledge discovery and quality control (QC) strategies for complex manufacturing systems. One such state-of-the-art measurement technology currently being implemented in industry is the 3D laser scanner, which can rapidly provide millions of data points to represent an entire manufactured part’s surface. This gives 3D laser scanners a significant advantage over competing technologies that typically provide tens or hundreds of data points. Consequently, data collected from 3D laser scanners have a great potential to be used for inspecting parts for surface and feature abnormalities. The current use of 3D point clouds for part inspection falls into two main categories; 1) Extracting feature parameters, which does not complement the nature of 3D point clouds as it wastes valuable data and 2) An ad-hoc manual process where a visual representation of a point cloud (usually as deviations from nominal) is analyzed, which tends to suffer from slow, inefficient, and inconsistent inspection results. Therefore our paper proposes an approach to automate the latter approach to 3D point cloud inspection. The proposed approach uses a newly developed adaptive generalized likelihood ratio (AGLR) technique to identify the most likely size, shape, and magnitude of a potential fault within the point cloud, which transforms the ad-hoc visual inspection approach to a statistically viable automated inspection solution. In order to aid practitioners in designing and implementing an AGLR-based inspection process, our paper also reports the performance of the AGLR with respect to the probability of detecting specific size and magnitude faults in addition to the probability of a false alarms.


2021 ◽  
Vol 30 ◽  
pp. 126-130
Author(s):  
Jan Voříšek ◽  
Bořek Patzák ◽  
Edita Dvořáková ◽  
Daniel Rypl

Laser scanning is used widely in architecture and construction to document existing buildings by providing accurate data for creating a 3D model. The output is a set of data points in space, so-called point cloud. While point clouds can be directly rendered and inspected, they do not hold any semantics. Typically, engineers manually obtain floor plans, structural models, or the whole BIM model, which is a very time-consuming task for large building projects. In this contribution, we present the design and concept of a PointCloud2BIM library [1]. It provides a set of algorithms for automated or user assisted detection of fundamental entities from scanned point cloud data sets, such as floors, rooms, walls, and openings, and identification of the mutual relationships between them. The entity detection is based on a reasonable degree of human interaction (i.e., expected wall thickness). The results reside in a platform-agnostic JSON database allowing future integration into any existing BIM software.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Linh Truong-Hong ◽  
Roderik Lindenbergh ◽  
Thu Anh Nguyen

PurposeTerrestrial laser scanning (TLS) point clouds have been widely used in deformation measurement for structures. However, reliability and accuracy of resulting deformation estimation strongly depends on quality of each step of a workflow, which are not fully addressed. This study aims to give insight error of these steps, and results of the study would be guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds. Thus, the main contributions of the paper are investigating point cloud registration error affecting resulting deformation estimation, identifying an appropriate segmentation method used to extract data points of a deformed surface, investigating a methodology to determine an un-deformed or a reference surface for estimating deformation, and proposing a methodology to minimize the impact of outlier, noisy data and/or mixed pixels on deformation estimation.Design/methodology/approachIn practice, the quality of data point clouds and of surface extraction strongly impacts on resulting deformation estimation based on laser scanning point clouds, which can cause an incorrect decision on the state of the structure if uncertainty is available. In an effort to have more comprehensive insight into those impacts, this study addresses four issues: data errors due to data registration from multiple scanning stations (Issue 1), methods used to extract point clouds of structure surfaces (Issue 2), selection of the reference surface Sref to measure deformation (Issue 3), and available outlier and/or mixed pixels (Issue 4). This investigation demonstrates through estimating deformation of the bridge abutment, building and an oil storage tank.FindingsThe study shows that both random sample consensus (RANSAC) and region growing–based methods [a cell-based/voxel-based region growing (CRG/VRG)] can be extracted data points of surfaces, but RANSAC is only applicable for a primary primitive surface (e.g. a plane in this study) subjected to a small deformation (case study 2 and 3) and cannot eliminate mixed pixels. On another hand, CRG and VRG impose a suitable method applied for deformed, free-form surfaces. In addition, in practice, a reference surface of a structure is mostly not available. The use of a fitting plane based on a point cloud of a current surface would cause unrealistic and inaccurate deformation because outlier data points and data points of damaged areas affect an accuracy of the fitting plane. This study would recommend the use of a reference surface determined based on a design concept/specification. A smoothing method with a spatial interval can be effectively minimize, negative impact of outlier, noisy data and/or mixed pixels on deformation estimation.Research limitations/implicationsDue to difficulty in logistics, an independent measurement cannot be established to assess the deformation accuracy based on TLS data point cloud in the case studies of this research. However, common laser scanners using the time-of-flight or phase-shift principle provide point clouds with accuracy in the order of 1–6 mm, while the point clouds of triangulation scanners have sub-millimetre accuracy.Practical implicationsThis study aims to give insight error of these steps, and the results of the study would be guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds.Social implicationsThe results of this study would provide guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds. A low-cost method can be applied for deformation analysis of the structure.Originality/valueAlthough a large amount of the studies used laser scanning to measure structure deformation in the last two decades, the methods mainly applied were to measure change between two states (or epochs) of the structure surface and focused on quantifying deformation-based TLS point clouds. Those studies proved that a laser scanner could be an alternative unit to acquire spatial information for deformation monitoring. However, there are still challenges in establishing an appropriate procedure to collect a high quality of point clouds and develop methods to interpret the point clouds to obtain reliable and accurate deformation, when uncertainty, including data quality and reference information, is available. Therefore, this study demonstrates the impact of data quality in a term of point cloud registration error, selected methods for extracting point clouds of surfaces, identifying reference information, and available outlier, noisy data and/or mixed pixels on deformation estimation.


2020 ◽  
Vol 10 (10) ◽  
pp. 3340 ◽  
Author(s):  
Pavel Chmelar ◽  
Lubos Rejfek ◽  
Tan N. Nguyen ◽  
Duy-Hung Ha

Nowadays, mobile robot exploration needs a rangefinder to obtain a large number of measurement points to achieve a detailed and precise description of a surrounding area and objects, which is called the point cloud. However, a single point cloud scan does not cover the whole area, so multiple point cloud scans must be acquired and compared together to find the right matching between them in a process called registration method. This method requires further processing and places high demands on memory consumption, especially for small embedded devices in mobile robots. This paper describes a novel method to reduce the burden of processing for multiple point cloud scans. We introduce our approach to preprocess an input point cloud in order to detect planar surfaces, simplify space description, fill gaps in point clouds, and get important space features. All of these processes are achieved by applying advanced image processing methods in combination with the quantization of physical space points. The results show the reliability of our approach to detect close parallel walls with suitable parameter settings. More importantly, planar surface detection shows a 99% decrease in necessary descriptive points almost in all cases. This proposed approach is verified on the real indoor point clouds.


2015 ◽  
Vol 3 (2) ◽  
pp. 102-111 ◽  
Author(s):  
Kai Wah Lee ◽  
Pengbo Bo

Abstract In this paper, we study the problem of computing smooth feature curves from CAD type point clouds models. The proposed method reconstructs feature curves from the intersections of developable strip pairs which approximate the regions along both sides of the features. The generation of developable surfaces is based on a linear approximation of the given point cloud through a variational shape approximation approach. A line segment sequencing algorithm is proposed for collecting feature line segments into different feature sequences as well as sequential groups of data points. A developable surface approximation procedure is employed to refine incident approximation planes of data points into developable strips. Some experimental results are included to demonstrate the performance of the proposed method.


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