47,522 research outputs found
Vision guided robotic inspection for parts in manufacturing and remanufacturing industry
No full textEnvironmental and commercial drivers are leading to a circular economy, where systems and components are routinely recycled or remanufactured. Unlike traditional manufacturing, where components typically have a high degree of tolerance, components in the remanufacturing process may have seen decades of wear, resulting in a wider variation of geometries. This makes it difficult to translate existing automation techniques to perform Non-Destructive Testing (NDT) for such components autonomously. The challenge of performing automated inspections, with off-line tool-paths developed from Computer Aided Design (CAD) models, typically arises from the fact that those paths do not have the required level of accuracy. Beside the fact that CAD models are less available for old parts, these parts often differ from their respective virtual models. This paper considers flexible automation by combining part geometry reconstruction with ultrasonic tool-path generation, to perform Ultrasonic NDT. This paper presents an approach to perform custom vision-guided ultrasonic inspection of components, which is achieved through integrating an automated vision system and a purposely developed graphic user interface with a robotic work-cell. The vision system, based on structure from motion, allows creating 3D models of the parts. Also, this work compares four different tool-paths for optimum image capture. The resulting optimum 3D models are used in a virtual twin environment of the robotic inspection cell, to enable the user to select any points of interest for ultrasonic inspection. This removes the need of offline robot path-planning and part orientation for assessing specific locations on a part, which is typically a very time-consuming phase
Index-based triangulation method for efficient generation of large three-dimensional ultrasonic C-scans
No full textThe demand for high-speed ultrasonic scanning of large and complex components is driven by a desire to reduce production bottlenecks during the non-destructive evaluation (NDE) of critical parts. Emerging systems (including robotic inspection) allow for the collection of large volumes of data in short time spans, compared to existing inspection systems. To maximise throughput, it is crucial that the reconstructed inspection datasets are generated and evaluated rapidly without loss of detail. This requires new data visualisation and analysis tools capable of mapping complex geometries while guaranteeing full coverage. This paper presents an entirely new approach for the visualisation of threedimensional (3D) ultrasonic C-scans, suitable for application to high data throughput ultrasonic phased array inspection of large and complex parts. Existing reconstruction approaches are discussed and compared with the new index-based triangulation (IBT) method presented. The IBT method produces 3D C-scan representation, presented as coloured tessellated surfaces, and the approach is shown to work efficiently, even on challenging geometries. An additional differentiating characteristic of the IBT method is that it allows for easy detection of lack of coverage (an essential feature for ensuring that inspection coverage can be guaranteed on critical components). The results demonstrate that the IBT C-scan generation approach runs over 60 times faster than a C-scan display based on Delaunay triangulation and over 500 times faster than surface reconstruction C-scans. In summary, the main benefits of the new IBT technique include: high-speed generation of C-scans on large ultrasonic datasets (orders of magnitude improvement compared to surface reconstruction C-scans); the ability to operate efficiently on 3D mapped datasets (allowing 3D interpretation of C-scans on complex geometry components); and intrinsic indication of lack of inspection coverage
Evolution of the G+C content frontier in the rat cytomegalovirus genome
No full textWithin the 230138 bp of the rat cytomegalovirus (RCMV) genome, the G+C content changes abruptly at position 142644, constituting a G+C content frontier. To the left of this point, overall G+C content is 69.2%, and to the right it is only 47.6%. A region of extremely low G+C content (33.8%) is found in the 5 kb immediately to the right of the frontier, in which there are no predicted coding sequences. To the right of position 147501, the G+C content rises and predicted coding sequences reappear. However, these genes are much shorter (average 848bp, 50% G+C) than those in the left two-thirds of the genome (average 1462bp, 70% G+C). Whole genome alignment of several viruses indicates that the initial ultra-low G+C region appeared in the common ancestor of the genera Cytomegalovirus and Muromegalovirus, and that the lowering of G+C in the right third has been a subsequent process in the lineage leading to RCMV. The left two-thirds of RCMV has stop codon occurrences at 67.5% of their expected level, based on a modified Markov chain model of stop codon distribution, and the corresponding figure for the right third is 78%. Therefore, despite heavy mutation pressure, selective constraint has operated in the right third of the RCMV genome to maintain a degree of gene length unusual for such low G+C sequences
Introducing a new method for efficient visualization of complex shape 3D ultrasonic phased-array C-scans
No full textAutomated robotic inspection systems allow the collection of large data volumes, compared to existing inspection systems. To maximize the throughput associated with the nondestructive evaluation phase, it is crucial that the reconstructed inspection data sets are generated and examined rapidly without a loss of detail. Data analysis often becomes the bottleneck of automated inspections. Therefore, new data visualization tools, suitable to screen the NDT information obtained through robotic systems, are urgently required. This paper presents a new approach, for the generation of three-dimensional ultrasonic C-scans of large and complex parts, suitable for application to high data throughput ultrasonic phased array inspection. This approach produces 3D C-scan presented as colored tessellated surfaces and the approach works efficiently on challenging geometry, with concave and convex regions. Qualitative and quantitative results show that the approach runs up to 500 times faster than other C-scan visualization techniques
Hybridization masks speciation in the evolutionary history of the Galápagos marine iguana
No full textMacLeod A, Rodríguez A, Vences M, et al. Hybridization masks speciation in the evolutionary history of the Galápagos marine iguana. Proceedings of the Royal Society B: Biological Sciences. 2015;282(1809): 20150425
Inferring the lithology of borehole rocks by applying neural network classifiers to downhole logs: an example from the Ocean Drilling Program
No full textIn boreholes with partial or no core recovery, interpretations of lithology in the remainder of the hole are routinely attempted using data from downhole geophysical sensors. We present a practical neural net-based technique that greatly enhances lithological interpretation in holes with partial core recovery by using downhole data to train classifiers to give a global classification scheme for those parts of the borehole for which no core was retrieved. We describe the system and its underlying methods of data exploration, selection and classification, and present a typical example of the system in use. Although the technique is equally applicable to oil industry boreholes, we apply it here to an Ocean Drilling Program (ODP) borehole (Hole 792E, Izu-Bonin forearc, a mixture of volcaniclastic sandstones, conglomerates and claystones). The quantitative benefits of quality-control measures and different subsampling strategies are shown. Direct comparisons between a number of discriminant analysis methods and the use of neural networks with back-propagation of error are presented. The neural networks perform better than the discriminant analysis techniques both in terms of performance rates with test data sets (2–3 per cent better) and in qualitative correlation with non-depth-matched core. We illustrate with the Hole 792E data how vital it is to have a system that permits the number and membership of training classes to be changed as analysis proceeds. The initial classification for Hole 792E evolved from a five-class to a three-class and then to a four-class scheme with resultant classification performance rates for the back-propagation neural network method of 83, 84 and 93 per cent respectively
A new probe concept for internal pipework inspection
No full textThe interior visual inspection of nuclear pipework is a critical inspection activity required to ensure the continued safe, reliable operation of plant and thus avoid costly outages. Typically, the video output from a manually deployed probe is viewed by an operator online with the task of identifying and estimating the location of surface defects such as cracks, corrosion and pitting. However, it is very difficult to estimate the nature and spatial extent of defects from the often disorientating small field of view video of a relatively large structure. This work describes a new visual inspection system incorporating a wide field of view machine vision camera and additional sensors designed for inspecting 3 - 6 inch diameter pipes. The output of the system is a photorealistic model of the internal surface of the pipework. The generation of this model relies upon a core component of the system in the form of image feature extraction which estimates camera location. This paper considers the accuracy of this estimation as a function of the number and configuration of the extracted image features
Lithological classification within ODP holes using neural networks trained from integrated core-log data
No full textNeural networks offer an attractive way of using downhole logging data to infer the lithologies of those sections of ODP holes from which there is no core recovery. This is best done within a computer program that enables the user to explore the dimensionality of the log data, design the structure for the neural network appropriate to the particular problem and select and prepare the log- and core-derived data for training, testing and using the neural network as a lithological classifier. Data quality control and the ability to modify lithological classification schemes to particular circumstances are particularly important. We illustrate these issues with reference to a 250 m section of ODP Hole 792E drilled through a sequence of island arc turbidites of early Oligocene age. Applying a threshold of > 90% recovery per 9.7 m core section, we have available about 50% of the cored interval that is sufficiently well depth-matched for use as training data for the neural network classifier. The most useful logs available are from resistivity, natural gamma, sonic and geochemistry tools, a total of 15. In general, the more logs available to the neural network the better its performance, but the optimum number of nodes on a single ‘hidden’ layer in the network has to be determined by experimentation. A classification scheme, with 3 classes (claystone, sandstone and conglomerate) derived from shipboard observation of core, gives a success rate of about 76% when tested with independent data. This improves to about 90% when the conglomerate class is split into two, based on the relative abundance of claystone versus volcanic clasts
Error Analysis and Calibration for a Novel Pipe Profiling Tool
No full textIntegrity of industrial pipework is ensured through routine inspection. Internal visual inspection tools are capable of characterising degradation in the form of corrosion, pitting, erosion and cracking. The accuracy of such inspection systems has a direct impact on decisions regarding the remaining lifetime of the asset. By minimising error margins, the asset may be operated with confidence for longer, with less uncertainty. This paper considers a probe system consisting of a laser profiler and camera that produces a textured 3D model of the internals of 2 - 6 inch pipework. The accuracy of the system is defined by the ability to extract laser projections from an image as it travels down the pipe, to accurately reconstruct these projections into 3D and to estimate the probe trajectory as it travels through the pipe. This paper presents an error model of the laser profiler. It then presents a novel calibration routine to reduce the error caused by misalignment and tolerances during fabrication of the system. A key advantage of the proposed calibration technique over alternatives is that we can calibrate for errors without manually adjusting the probe, which enables fabrication of a smaller more robust measurement system. In lab-based trials our calibration technique reduced peak sizing errors from 2.7 mm to 0.14 mm in 120 mm diameter pipes
- …
