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.

Behaviour of Surface Defects in Wire Rod Rolling

Defects are often present in rolled products, such as wire rod. The markets demand for wire rod without any defects has increased. In the final wire rod products, defects originating from the steel making, casting, pre-rolling of billets and during the wire rod rolling can appear. In this work, artificial V-shaped longitudinal surface cracks has been analysed experimentally and by means of FEM. The results indicate that the experiments and FEM calculations show the same tendency except in two cases, where instability due to a fairly “round” false round bars disturbed the experiment. FE studies in combination with practical experiments are necessary in order to understand the behaviour of the material flows in the groove and to explain whether the crack will open up as a V-shape or if it will be closed as an I-shape.

Evolution of artificial defects during shape rolling

Very often defects are present in rolled products. For wire rods, defects are very deleterious since the wire rods are generally used directly in various applications. For this reason, the market nowadays requires wire rods to be completely defect-free. Any wire with defects must be rejected as scrap which is very costly for the production mill. Thus, it is very important to study the formation and evolution of defects during wire rod rolling in order to better understand and minimize the problem, at the same time improving quality of the wire rods and reducing production costs. The present work is focused on the evolution of artificial defects during rolling. Longitudinal surface defects are studied during shape rolling of an AISI M2 high speed steel and a longitudinal central inner defect is studied in an AISI 304L austenitic stainless steel during ultra-high-speed wire rod rolling. Experimental studies are carried out by rolling short rods prepared with arteficial defects. The evolution of the defects is characterised and compared to numerical analyses. The comparison shows that surface defects generally reduce quicker in the experiments than predicted by the simulations whereas a good agreement is generally obtained for the central defect.

Influence of grinding operations on surface integrity and chloride induced stress corrosion cracking of stainless steels

Stainless steels were developed in the early 20th century and are used where both the mechanical properties of steels and corrosion resistance are required. There is continuous research to allow stainless steel components to be produced in a more economical way and be used in more harsh environments. A necessary component in this effort is to correlate the service performance with the production processes. The central theme of this thesis is the mechanical grinding process.  This is commonly used for producing stainless steel components, and results in varied surface properties that will strongly affect their service life. The influence of grinding parameters including abrasive grit size, machine power and grinding lubricant were studied for 304L austenitic stainless steel (Paper II) and 2304 duplex stainless steel (Paper I). Surface integrity was proved to vary significantly with different grinding parameters. Abrasive grit size was found to have the largest influence. Surface defects (deep grooves, smearing, adhesive/cold welding chips and indentations), a highly deformed surface layer up to a few microns in thickness and the generation of high level tensile residual stresses in the surface layer along the grinding direction were observed as the main types of damage when grinding stainless steels. A large degree of residual stress anisotropy is interpreted as being due to mechanical effects dominating over thermal effects. The effect of grinding on stress corrosion cracking behaviour of 304L austenitic stainless steel in a chloride environment was also investigated (Paper III). Depending on the surface conditions, the actual loading by four-point bend was found to deviate from the calculated value using the formula according to ASTM G39 by different amounts. Grinding-induced surface tensile residual stress was suggested as the main factor to cause micro-cracks initiation on the ground surfaces. Grinding along the loading direction was proved to increase the susceptibility to chloride-induced SCC, while grinding perpendicular to the loading direction improved SCC resistance. The knowledge obtained from this work can provide a reference for choosing appropriate grinding parameters when fabricating stainless steel components; and can also be used to help understanding the failure mechanism of ground stainless steel components during service.

Experimental study of strain fields during shearing of medium and high-strength steel sheet

There is a shortage of experimentally determined strains during sheet metal shearing. These kinds of data are a requisite to validate shearing models and to simulate the shearing process. In this work, strain fields were continuously measured during shearing of a medium and a high strength steel sheet, using digital image correlation. Preliminary studies based on finite element simulations, suggested that the effective surface strains are a good approximation of the bulk strains below the surface. The experiments were performed in a symmetric set-up with large stiffness and stable tool clearances, using various combinations of tool clearance and clamping configuration. Due to large deformations, strains were measured from images captured in a series of steps from shearing start to final fracture. Both the Cauchy and Hencky strain measures were considered, but the difference between these were found negligible with the number of increments used (about 20 to 50). Force-displacement curves were also determined for the various experimental conditions. The measured strain fields displayed a thin band of large strain between the tool edges. Shearing with two clamps resulted in a symmetric strain band whereas there was an extended area with large strains around the tool at the unclamped side when shearing with one clamp. Furthermore, one or two cracks were visible on most of the samples close to the tool edges well before final fracture. The fracture strain was larger for the medium strength material compared with the high-strength material and increased with increasing clearance.