A Novel Approach for 3D Part Inspection Using Laser-plane Sensors

AbstractThe paper deals with the relevance of using laser-plane sensors in 3D part inspection. First, based on the evaluation of the measuring system capacities, a digitizing strategy permits to obtain a set of points with a sufficient quality as regards geometrical specifications. Despite the optimized strategy, the digitizing noise associated to the sensor alters data quality, and may affect the estimation of the surface defects (form deviation for instance is strongly affected by digitizing noise). An original filtering method is proposed to remove digitizing noise before the evaluation of the specifications

Optical measurement of deformations of flange joints

Tato diplomová práce se zabývá optickým měřením přírubových spojů při utahování a teplotním zatěžování. Jsou porovnány komerčně dostupné optické systémy se sestupnou invazivitou v průmyslovém prostředí. Různé stavy zatěžovaní jsou vyhodnoceny jak ve 3D (skenování, DIC), tak ve 2D (DIC). Měřením při zatěžování jsou získána data pro vyhodnocení citlivosti i přesnosti systému s přidruženým vyhodnocením o šíři poskytovaných dat. Vyhodnocení dat prokázalo stejnou přesnost pro využití 2D i 3D systému a možnost kontrolovat kvalitu spoje vyhodnocováním kontrolních rozměrů v provozu.The diploma thesis addresses the topic of optical measurements of flange joints during tightening and thermal loading. Commercially available optical systems with decreasing invasiveness were compared in opportunity which offer to an industrial environment. Various conditions are evaluated in 3D (scanning, DIC) as well as in 2D (DIC). The data for evaluation of sensitivity and system accuracy together with the evaluation of the data range were obtained by the measurement. Data evaluation showed the same accuracy for 2D and 3D system and the possibility of quality inspection of the joint by assessing the inspection dimensions in operation.

Investigation and development of an advanced virtual coordinate measuring machine

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityDimensional measurement plays a critical role in product development and quality control. With the continuously increasing demand for tighter tolerances and more complex workpiece shapes in the industry, dimensional metrology often becomes the bottleneck of taking the quality and performance of manufacturing to the next level. As one kind of the most useful and powerful measuring instruments, coordinate measuring machines (CMMs) are widely employed in manufacturing industries. Since the accuracy and efficiency of a CMM have a vital impact on the product quality, productivity and manufacturing cost, the evaluation and improvement of CMM performance have always been important research topics since the invention of CMM. A novel Advanced Virtual Coordinate Measuring Machine (AVCMM) is proposed against such a background. The proposed AVCMM is a software package that provides an integrated virtual environment, in which user can plan inspection strategy for a given task, carry out virtual measurement, and evaluate the uncertainty associated with the measurement result, all without the need of using a physical machine. The obtained estimate of uncertainty can serve as a rapid feedback for user to optimize the inspection plan in the AVCMM before actual measurement, or as an evaluation of the result of a performed measurement. Without involving a physical CMM in the inspection planning or evaluation of uncertainty, the AVCMM can greatly reduce the time and cost needed for such processes. Furthermore, as the package offers vivid 3D visual representation of the virtual environment and supports operations similar to a physical CMM, it does not only allow the user to easily plan and optimise the inspection strategy, but also provide a cost-effective, risk-free solution for training CMM operators. A modular, multitier architecture has been adopted to develop the AVCMM system, which incorporates a number of functional components covering CMM and workpiece modelling, error simulation, inspection simulation, feature calculation, uncertainty evaluation and 3D representation. A new engine for detecting collision/contact has been developed and utilized, which is suitable for the virtual environment of simulated CMM inspections. A novel approach has been established to calculate errors required for the error simulation, where the data are obtained from FEA simulations in addition to conventional experimental method. Monte Carlo method has been adopted for uncertainty evaluation and has been implemented with multiple options available to meet different requirements. A prototype of the proposed AVCMM system has been developed in this research. Its validity, usability and performance have been verified and evaluated through a set of experiments. The principles for utilising the AVCMM in practical use have also been established and demonstrated. The results have indicated that the proposed AVCMM system has great potentials to improve the functionalities and overall performance of CMMs.ORSAS and the School of Engineering and Design of Brunel University

Investigation of 3D Non-Contact Laser-Based Inspection Techniques for Application in Gear Metrology

Gear shape accuracy, surface quality and, as a consequence, a proper gear inspection needed to guarantee these features, are critical in order to improve drivetrain efficiency as well as to reduce noise in automotive power transmission systems. Contact stylus type measuring methods using contact probes are today’s dominant indus- trial solution for gear metrology. Due to the difficulties of further improving those methods, new non-contact measuring systems have been developed in the past few years. The most promising option that meets the requirements of accuracy, repeatability and high cycle time is the 3D non-contact measurement method based on triangulation laser sensors. These laser scanners have been improved over the last few years both in terms of resolu- tion, optical quality, image processing and data analysis to make them comparable, if not superior, to the traditional contact probe. This thesis provides an evaluation of the surface profilometer Urano HC-N400, using the contact technology currently employed by Omega gear metrology labs as a benchmark. The measurements obtained with the alternative inspection system indicate that the analyzed non-contact solution is not ready yet for in-line and high volume inspection applications, but is well-suited to research and development purposes. Omega is also looking for the possible causes of a particular noise problem which is difficult to detect using current technology. One gear that exhibited this phantom phenomenon, also know as the ghost noise , has been analyzed and compared with another gear identified as the best of best . During the analysis, undulations have been found in both gears. The combination of those waves through the use of the Ripple Analysis software represents the best solution to discover other gears with the same problem in the early stages of inspection

Pothole 3D Reconstruction With a Novel Imaging System and Structure From Motion Techniques

Machine vision based evaluation systems are receiving increased attention, day by day, for automated quality inspection of roads. Industrial pavement scanners consist of laser scanners and are very expensive, hence inaccessible for everyone. The proposed work presents a simple and novel approach for 3D reconstruction of potholes for an automated inspection and road surface evaluation. The technique utilizes a Structure from Motion based 3D reconstruction algorithm, along with laser triangulation, to generate 3D point clouds of potholes. Alongside, a novel low-cost system, consisting of a single camera and a laser pointer, is also proposed. Keypoint matching techniques are employed, with the 5-point algorithm, on successive image frames to generate a point cloud. However, this point cloud is not metric yet, without scale information. The scale ambiguity is solved by making use of the laser pointer, and using the principle of triangulation. The laser spot is also detected in the same image sequence that is used for point-cloud building, cutting down the image capturing and processing overhead. The system has been benchmarked on artificial indentations with known dimensions, proving the robustness of the measurement scheme and hardware. Static and dynamic tests have been performed. The mean depth errors for measurement made by the imager statically and at dynamic speeds of 10 km/hr, 15 km/hr, and 20 km/hr are 5.3%, 7.9%, 14.4%, and 26.6%, whereas for perimeter the errors are 5.2%, 6.83 %, 11.8%, and 27.8%. The proposed, low-cost technique shows promising results in generating 3D point clouds for potholes

The LONI QC System: A Semi-Automated, Web-Based and Freely-Available Environment for the Comprehensive Quality Control of Neuroimaging Data.

Quantifying, controlling, and monitoring image quality is an essential prerequisite for ensuring the validity and reproducibility of many types of neuroimaging data analyses. Implementation of quality control (QC) procedures is the key to ensuring that neuroimaging data are of high-quality and their validity in the subsequent analyses. We introduce the QC system of the Laboratory of Neuro Imaging (LONI): a web-based system featuring a workflow for the assessment of various modality and contrast brain imaging data. The design allows users to anonymously upload imaging data to the LONI-QC system. It then computes an exhaustive set of QC metrics which aids users to perform a standardized QC by generating a range of scalar and vector statistics. These procedures are performed in parallel using a large compute cluster. Finally, the system offers an automated QC procedure for structural MRI, which can flag each QC metric as being 'good' or 'bad.' Validation using various sets of data acquired from a single scanner and from multiple sites demonstrated the reproducibility of our QC metrics, and the sensitivity and specificity of the proposed Auto QC to 'bad' quality images in comparison to visual inspection. To the best of our knowledge, LONI-QC is the first online QC system that uniquely supports the variety of functionality where we compute numerous QC metrics and perform visual/automated image QC of multi-contrast and multi-modal brain imaging data. The LONI-QC system has been used to assess the quality of large neuroimaging datasets acquired as part of various multi-site studies such as the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Study and the Alzheimer's Disease Neuroimaging Initiative (ADNI). LONI-QC's functionality is freely available to users worldwide and its adoption by imaging researchers is likely to contribute substantially to upholding high standards of brain image data quality and to implementing these standards across the neuroimaging community

Feature and viewpoint selection for industrial car assembly

Abstract. Quality assurance programs of today’s car manufacturers show increasing demand for automated visual inspection tasks. A typical example is just-in-time checking of assemblies along production lines. Since high throughput must be achieved, object recognition and pose estimation heavily rely on offline preprocessing stages of available CAD data. In this paper, we propose a complete, universal framework for CAD model feature extraction and entropy index based viewpoint selection that is developed in cooperation with a major german car manufacturer

Computer aided inspection procedures to support smart manufacturing of injection moulded components

This work presents Reverse Engineering and Computer Aided technologies to improve the inspection of injection moulded electro-mechanical parts. Through a strong integration and automation of these methods, tolerance analysis, acquisition tool-path optimization and data management are performed. The core of the procedure concerns the automation of the data measure originally developed through voxel-based segmentation. This paper discusses the overall framework and its integration made according to Smart Manufacturing requirements. The experimental set-up, now in operative conditions at ABB SACE, is composed of a laser scanner installed on a CMM machine able to measure components with lengths in the range of 5÷250 mm, (b) a tool path optimization procedure and (c) a data management both developed as CAD-based applications

Investigation on the sampling size optimisation in gear tooth surface measurement using a Co-ordinate Measuring Machine

Co-ordinate Measuring Machines (CMMs) are widely used in gear manufacturing industry. One of the main issues for contact inspection using a CMM is the sampling technique. In this paper the gear tooth surfaces are expressed by series of parameters and inspection error compensation and initial value optimisation method are presented. The minimum number of measurement points for 3D tooth surfaces are derived. If high precision is required, more points need to be inspected. The sampling size optimisation is obtained from the criterion equation. The surface form deviation and initial values are optimised using the minimum zone method and Genetic Algorithms. A feature based inspection system for spur/helical gears is developed and trials and simulations demonstrated the developed method is very effective and suitable