MACHINE VISION FOR AUTOMATICVISUAL INSPECTION OF WOODENRAILWAY SLEEPERS USING UNSUPERVISED NEURAL NETWORKS

The motivation for this thesis work is the need for improving reliability of equipment and quality of service to railway passengers as well as a requirement for cost-effective and efficient condition maintenance management for rail transportation. This thesis work develops a fusion of various machine vision analysis methods to achieve high performance in automation of wooden rail track inspection.The condition monitoring in rail transport is done manually by a human operator where people rely on inference systems and assumptions to develop conclusions. The use of conditional monitoring allows maintenance to be scheduled, or other actions to be taken to avoid the consequences of failure, before the failure occurs. Manual or automated condition monitoring of materials in fields of public transportation like railway, aerial navigation, traffic safety, etc, where safety is of prior importance needs non-destructive testing (NDT).In general, wooden railway sleeper inspection is done manually by a human operator, by moving along the rail sleeper and gathering information by visual and sound analysis for examining the presence of cracks. Human inspectors working on lines visually inspect wooden rails to judge the quality of rail sleeper. In this project work the machine vision system is developed based on the manual visual analysis system, which uses digital cameras and image processing software to perform similar manual inspections. As the manual inspection requires much effort and is expected to be error prone sometimes and also appears difficult to discriminate even for a human operator by the frequent changes in inspected material. The machine vision system developed classifies the condition of material by examining individual pixels of images, processing them and attempting to develop conclusions with the assistance of knowledge bases and features.A pattern recognition approach is developed based on the methodological knowledge from manual procedure. The pattern recognition approach for this thesis work was developed and achieved by a non destructive testing method to identify the flaws in manually done condition monitoring of sleepers.In this method, a test vehicle is designed to capture sleeper images similar to visual inspection by human operator and the raw data for pattern recognition approach is provided from the captured images of the wooden sleepers. The data from the NDT method were further processed and appropriate features were extracted.The collection of data by the NDT method is to achieve high accuracy in reliable classification results. A key idea is to use the non supervised classifier based on the features extracted from the method to discriminate the condition of wooden sleepers in to either good or bad. Self organising map is used as classifier for the wooden sleeper classification.In order to achieve greater integration, the data collected by the machine vision system was made to interface with one another by a strategy called fusion. Data fusion was looked in at two different levels namely sensor-level fusion, feature- level fusion. As the goal was to reduce the accuracy of the human error on the rail sleeper classification as good or bad the results obtained by the feature-level fusion compared to that of the results of actual classification were satisfactory.

Design/Evaluation of A Methodology For Performance Optimization Of Indexable Carbide Inserts

In this project, two broad facets in the design of a methodology for performance optimization of indexable carbide inserts were examined. They were physical destructive testing and software simulation.For the physical testing, statistical research techniques were used for the design of the methodology. A five step method which began with Problem definition, through System identification, Statistical model formation, Data collection and Statistical analyses and results was indepthly elaborated upon. Set-up and execution of an experiment with a compression machine together with roadblocks and possible solution to curb road blocks to quality data collection were examined. 2k factorial design was illustrated and recommended for process improvement. Instances of first-order and second-order response surface analyses were encountered. In the case of curvature, test for curvature significance with center point analysis was recommended. Process optimization with method of steepest ascent and central composite design or process robustness studies of response surface analyses were also recommended.For the simulation test, AdvantEdge program was identified as the most used software for tool development. Challenges to the efficient application of this software were identified and possible solutions proposed. In conclusion, software simulation and physical testing were recommended to meet the objective of the project.

ANALYSIS & STUDY OF AI TECHNIQUES FORAUTOMATIC CONDITION MONITORING OFRAILWAY TRACK INFRASTRUCTURE : Artificial Intelligence Techniques

Since the last decade the problem of surface inspection has been receiving great attention from the scientific community, the quality control and the maintenance of products are key points in several industrial applications.The railway associations spent much money to check the railway infrastructure. The railway infrastructure is a particular field in which the periodical surface inspection can help the operator to prevent critical situations. The maintenance and monitoring of this infrastructure is an important aspect for railway association.That is why the surface inspection of railway also makes importance to the railroad authority to investigate track components, identify problems and finding out the way that how to solve these problems. In railway industry, usually the problems find in railway sleepers, overhead, fastener, rail head, switching and crossing and in ballast section as well. In this thesis work, I have reviewed some research papers based on AI techniques together with NDT techniques which are able to collect data from the test object without making any damage. The research works which I have reviewed and demonstrated that by adopting the AI based system, it is almost possible to solve all the problems and this system is very much reliable and efficient for diagnose problems of this transportation domain. I have reviewed solutions provided by different companies based on AI techniques, their products and reviewed some white papers provided by some of those companies. AI based techniques likemachine vision, stereo vision, laser based techniques and neural network are used in most cases to solve the problems which are performed by the railway engineers.The problems in railway handled by the AI based techniques performed by NDT approach which is a very broad, interdisciplinary field that plays a critical role in assuring that structural components and systems perform their function in a reliable and cost effective fashion. The NDT approach ensures the uniformity, quality and serviceability of materials without causing any damage of that materials is being tested. This testing methods use some way to test product like, Visual and Optical testing, Radiography, Magnetic particle testing, Ultrasonic testing, Penetrate testing, electro mechanic testing and acoustic emission testing etc. The inspection procedure has done periodically because of better maintenance. This inspection procedure done by the railway engineers manually with the aid of AI based techniques.The main idea of thesis work is to demonstrate how the problems can be reduced of thistransportation area based on the works done by different researchers and companies. And I have also provided some ideas and comments according to those works and trying to provide some proposal to use better inspection method where it is needed.The scope of this thesis work is automatic interpretation of data from NDT, with the goal of detecting flaws accurately and efficiently. AI techniques such as neural networks, machine vision, knowledge-based systems and fuzzy logic were applied to a wide spectrum of problems in this area. Another scope is to provide an insight into possible research methods concerning railway sleeper, fastener, ballast and overhead inspection by automatic interpretation of data.In this thesis work, I have discussed about problems which are arise in railway sleepers,fastener, and overhead and ballasted track. For this reason I have reviewed some research papers related with these areas and demonstrated how their systems works and the results of those systems. After all the demonstrations were taking place of the advantages of using AI techniques in contrast with those manual systems exist previously.This work aims to summarize the findings of a large number of research papers deploying artificial intelligence (AI) techniques for the automatic interpretation of data from nondestructive testing (NDT). Problems in rail transport domain are mainly discussed in this work. The overall work of this paper goes to the inspection of railway sleepers, fastener, ballast and overhead.

Induktiv provning av ferritiskt rostfritt stå

The report examines the factors which may be a contributing cause to the problems that are present when ferritic stainless steel are eddy current tested in a warm condition. The work is carried out at Fagersta Stainless AB in Fagersta which manufactures stainless steel wire. In the rolling mill there is an eddy current equipment for detection of surface defects on the wire. The ferritic stainless steels cause a noise when testing and this noise complicates the detection of defects.Because of this, a study was made of how the noise related to factors such as steel grade, temperature, size and velocity. By observing the signal and with the possibilities to change the equipment settings the capability to let a signal filter reduce the noise level were evaluated. Theories about the material's physical properties have also been included, mainly the magnetic properties, electrical conductivity and the material's tendency to oxidize.Results from the tests show that a number of factors do not affect the inductive test significantly and to use a filter to reduce the noise level does not seem to be a viable option. The level of noise does not relate to the presence of superficial particles in form of oxides.The ferritic stainless steels showed some difference in noise level. Which noise level there was did match well with the steels probability for a precipitation of a second phase, and precipitation of austenite may in this case contribute to noise when using an eddy current instrument.The noise is probably due to some physical material property that varies within the thread.

Mathematical modelling of the refractive index reconstruction through global optimization

We examine a mathematical model of non-destructive testing of planar waveguides, based on numerical solution of a nonlinear integral equation. Such problem is ill-posed, and the method of Tikhonov regularization is applied. To minimize Tikhonov functional, and find the parameters of the waveguide, we use two new optimization methods: the cutting angle method of global optimization, and the discrete gradient method of nonsmooth local optimization. We examine how the noise in the experimental data influences the solution, and how the regularization parameter has to be chosen. We show that even with significant noise in the data, the numerical solution is of high accuracy, and the method can be used to process real experimental da.ta..

Depth-profile analysis of elements by glow discharge optical emmission specrometry

Glow-discharge optical emission spectrometry (GD-OES) is a powerful tool for the rapid analysis of elements in the surface of solids. One may employ GD-OES to determine quantitatively the bulk concentration of elements in a sample. With further calibration, one may also obtain elemental concentrations as a function of depth into the sample. This allows depth profiling on a host of advanced materials: treated metals, coated metals and other materials, multi-layers, painted surfaces, hard samples coated with polymers, thin films, and many others.

A consortium of institutions in Victoria, led by Deakin University, has purchased a new glow-discharge optical emission spectrometer. This instrument has the ability to perform elemental depth profiling on a wide range of materials. This technique, the first of its kind in Australia, is of particular interest to those working on metals, ceramics, glasses, coatings, semi-conductors, and multi-layers. We present here an overview of depth profiling by GD-OES and some examples of its use.

Desenvolvimento de um núcleo versátil e integrado de ferramentas CAD/CAE para a modelagem e simulação de peças mecânicas

Currently there is still a high demand for quality control in manufacturing processes of mechanical parts. This keeps alive the need for the inspection activity of final products ranging from dimensional analysis to chemical composition of products. Usually this task may be done through various nondestructive and destructive methods that ensure the integrity of the parts. The result generated by these modern inspection tools ends up not being able to geometrically define the real damage and, therefore, cannot be properly displayed on a computing environment screen. Virtual 3D visualization may help identify damage that would hardly be detected by any other methods. One may find some commercial softwares that seek to address the stages of a design and simulation of mechanical parts in order to predict possible damages trying to diminish potential undesirable events. However, the challenge of developing softwares capable of integrating the various design activities, product inspection, results of non-destructive testing as well as the simulation of damage still needs the attention of researchers. This was the motivation to conduct a methodological study for implementation of a versatile CAD/CAE computer kernel capable of helping programmers in developing softwares applied to the activities of design and simulation of mechanics parts under stress. In this research it is presented interesting results obtained from the use of the developed kernel showing that it was successfully applied to case studies of design including parts presenting specific geometries, namely: mechanical prostheses, heat exchangers and piping of oil and gas. Finally, the conclusions regarding the experience of merging CAD and CAE theories to develop the kernel, so as to result in a tool adaptable to various applications of the metalworking industry are presented

Damping behavior of continuous fiber/metal composite materials by the free vibration method

Fiber metal laminates (FML) offer significant improvements over current available materials for aircraft structures due to their excellent mechanical characteristics and relatively low density. Non-destructive testing techniques are being used in the characterization of composite materials. Among these, vibration testing is one of the most used tools because it allows the determination of the mechanical properties. In this work, the viscoelastic properties such as elastic (E') and viscous (E) responses were obtained for aluminum 2024 alloy; carbon fiber/epoxy; glass fiber/epoxy and their hybrids aluminum 2024 alloy/carbon fiber/epoxy and aluminum 2024 alloy/glass fiber/epoxy composites. The experimental results were compared to calculated E modulus values by using the composite micromechanics approach. For all specimens studied, the experimental values showed good agreement with the theoretical values. The damping behavior, i.e. The storage modulus and the loss factor, from the aluminum 2024 alloy and fiber epoxy composites can be used to estimate the viscoelastic response of the hybrid FML. (c) 2005 Elsevier Ltd. All rights reserved.

Quantitative microscopy characterization of hydrous niobium phosphate into bleached cellulose

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Advances in phase-stepping real-time holography using photorefractive sillenite crystals

This work presents a review of recent developments in phase-stepping real-time holographic interferometry with photorefractive sillenite crystals. Quantitative results are shown in micro-rotation, micro-displacement, and micro-deformation measurements, and in wave-optics and surface analysis as well. The phase stepping was carried out in a four-frame process and the resulting phase map was unwrapped by applying a sin/cos filter. The experimental results are in good agreement with the ones obtained through other means, showing the promising potentialities of phase-stepping real-time holographic interferometry for in situ visualisation, monitoring and analysis in non-destructive testing.

On the development of the electrochemical potentiokinetic method

The development of electrochemical potentiokinetic methods as applied to the testing of metals and alloys is followed from its early phases up to its latest advances relating to intergranular corrosion, SCC and pitting corrosion tests of stainless steels and special alloys and to the examination of their structure and properties. In assessing the susceptibility to intergranular and pitting corrosion by potentiokinetic polarization tests, the polarization curves which apply to the bulk of the alloy grains (the matrix) must be distinguished from those pertaining to grain boundaries. Cyclic polarization measurements such as the electrochemical potentiokinetic reactivation (EPR) test make it possible to derive the alloy's susceptibility to intergranular, pitting and crevice corrosion from characteristic potentials and other quantities determined in the 'double loop' test. EPR is rapid and responds to the combined effects of a number of factors that influence the properties of materials. The electrochemical p otentiokinetic tests are sensitive enough to detect structural changes in heat treated materials ranging far beyond the stainless steels alone, and can be used for non-destructive testing aimed at elucidating the properties and behavior of materials. © 2001 Elsevier Science Ltd. All rights reserved.

Steel bars identification in reinforced concrete structures by using ANN and magnetic fields

This work proposes a methodology for non destructive testing (NDT) of reinforced concrete structures, using superficial magnetic fields and artificial neural networks, in order to identify the size and position of steel bars, embedded into the concrete. For the purposes of this paper, magnetic induction curves were obtained by using a finite element program. Perceptron Multilayered (PML) ANNs, with Levemberg-Marquardt training algorithm were used. The results presented very good agreement with the expect ones, encouraging the development of real systems based upon the proposed methodology.