A reference architecture for flexibly integrating machine vision within manufacturing

A reference architecture provides an overall framework that may embrace models, methodologies and mechanisms which can support the lifecycle of their target domain. The work described in this thesis makes a contribution to establishing such a generally applicable reference architecture for supporting the lifecycIe of a new generation of integrated machine vision systems. Contemporary machine vision systems consist of a complex combination of mechanical engineering, the hardware and software of an electronic processor, plus optical, sensory and lighting components. "This thesis is concerned with the structure of the software which characterises the system application. The machine vision systems which are currently used within manufacturing industry are difficult to integrate within the information systems required within modem manufacturing enterprises. They are inflexible in all but the execution of a range of similar operations, and their design and implementation is often such that they are difficult to update in the face of the required change inherent within modem manufacturing. The proposed reference architecture provides an overall framework within which a number of supporting models, design methodologies, and implementation mechanisms can combine to provide support for the rapid creation and maintenance of highly structured machine vision applications. These applications comprise modules which can be considered as building blocks of CIM systems. Their integrated interoperation can be enabled by the emerging infrastructural tools which will be required to underpin the next generation of flexibly integrated manufacturing systems. The work described in this thesis concludes that the issues of machine vision applications and the issues of integration of these applications within manufacturing systems are entirely separate. This separation is reflected in the structure of the thesis. PART B details vision application issues while PAIIT C deals with integration. The criteria for next generation integrated machine vision systems, derived in PART A of the thesis, are extensive. In order to address these criteria and propose a complete architecture, a "thin slice" is taken through the areas of vision application, and integration at the lifecycle stages of design, implementation, runtime and maintenance. The thesis describes the reference architecture, demonstrates its use though a proof of concept implementation and evaluates the support offered by the architecture for easing the problems of software change

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, micro-electronics, and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.Framework of the IQONIC Project; European Union’s Horizon 2020 Research and Innovation Program

Range images

This article gives an overview of range‐imaging techniques with an aim to let the reader better understand how the difficult issue, such as the registration of overlapping range images, can be approached and solved. It firstly introduces the characteristics of range images and highlights examples of 3D image visualizations, associated technical issues, applications, and the differences of range imaging with respect to the traditional digital broadband imaging. Subsequently, one of the most popular feature extraction and matching methods, the signature of histograms of orientations (SHOT) method, is then outlined. However, the “matched” points generated by SHOT usually generate high proportion of false positives due to various factors such as imaging noise, lack of features, and cluttered backgrounds. Thus, the article discusses more about image‐matching issues, particularly to emphasize how the widely employed range image alignment technique, the random sample consensus (RANSAC) method, is compared with a simple, yet effective, technique based on normalized error penalization (NEP). This simple NEP method utilizes a strategy to penalize point matches whose errors are far away from the majority. The capability of the method for the evaluation of point matches between overlapping range images is illustrated by experiments using real range image data sets. Interestingly enough, these range images appear to be easier to register than expected. Finally, some conclusions have been drawn and further readings for other fundamental techniques and concepts have been suggested

Extraction of Vehicle Groups in Airborne Lidar Point Clouds with Two-Level Point Processes

In this paper we present a new object based hierarchical model for joint probabilistic extraction of vehicles and groups of corresponding vehicles - called traffic segments - in airborne Lidar point clouds collected from dense urban areas. Firstly, the 3-D point set is classified into terrain, vehicle, roof, vegetation and clutter classes. Then the points with the corresponding class labels and echo strength (i.e. intensity) values are projected to the ground. In the obtained 2-D class and intensity maps we approximate the top view projections of vehicles by rectangles. Since our tasks are simultaneously the extraction of the rectangle population which describes the position, size and orientation of the vehicles and grouping the vehicles into the traffic segments, we propose a hierarchical, Two-Level Marked Point Process (L2MPP) model for the problem. The output vehicle and traffic segment configurations are extracted by an iterative stochastic optimization algorithm. We have tested the proposed method with real data of a discrete return Lidar sensor providing up to four range measurements for each laser pulse. Using manually annotated Ground Truth information on a data set containing 1009 vehicles, we provide quantitative evaluation results showing that the L2MPP model surpasses two earlier grid-based approaches, a 3-D point-cloud-based process and a single layer MPP solution. The accuracy of the proposed method measured in F-rate is 97% at object level, 83% at pixel level and 95% at group level

A Vision System for Automating Municipal Waste Collection

This thesis describes an industry need to make municipal waste collection more efficient. In an attempt to solve this need Waterloo Controls Inc. and a research team at UWO are exploring the idea of combining a vision system and a robotic arm to complete the waste collection process. The system as a whole is described during the introduction section of this report, but the specific goal of this thesis was the development of the vision system component. This component is the main contribution of this thesis and consists of a candidate selection step followed by a verification step

Numerical modelling of additive manufacturing process for stainless steel tension testing samples

Nowadays additive manufacturing (AM) technologies including 3D printing grow rapidly and they are expected to replace conventional subtractive manufacturing technologies to some extents. During a selective laser melting (SLM) process as one of popular AM technologies for metals, large amount of heats is required to melt metal powders, and this leads to distortions and/or shrinkages of additively manufactured parts. It is useful to predict the 3D printed parts to control unwanted distortions and shrinkages before their 3D printing. This study develops a two-phase numerical modelling and simulation process of AM process for 17-4PH stainless steel and it considers the importance of post-processing and the need for calibration to achieve a high-quality printing at the end. By using this proposed AM modelling and simulation process, optimal process parameters, material properties, and topology can be obtained to ensure a part 3D printed successfully

Technology 2003: The Fourth National Technology Transfer Conference and Exposition, volume 2

Proceedings from symposia of the Technology 2003 Conference and Exposition, Dec. 7-9, 1993, Anaheim, CA, are presented. Volume 2 features papers on artificial intelligence, CAD&E, computer hardware, computer software, information management, photonics, robotics, test and measurement, video and imaging, and virtual reality/simulation

Novel Approaches for Nondestructive Testing and Evaluation

Nondestructive testing and evaluation (NDT&E) is one of the most important techniques for determining the quality and safety of materials, components, devices, and structures. NDT&E technologies include ultrasonic testing (UT), magnetic particle testing (MT), magnetic flux leakage testing (MFLT), eddy current testing (ECT), radiation testing (RT), penetrant testing (PT), and visual testing (VT), and these are widely used throughout the modern industry. However, some NDT processes, such as those for cleaning specimens and removing paint, cause environmental pollution and must only be considered in limited environments (time, space, and sensor selection). Thus, NDT&E is classified as a typical 3D (dirty, dangerous, and difficult) job. In addition, NDT operators judge the presence of damage based on experience and subjective judgment, so in some cases, a flaw may not be detected during the test. Therefore, to obtain clearer test results, a means for the operator to determine flaws more easily should be provided. In addition, the test results should be organized systemically in order to identify the cause of the abnormality in the test specimen and to identify the progress of the damage quantitatively