Investigation of material transfer in sliding friction-topography or surface chemistry?

To differentiate between the roles of surface topography and chemical composition on influencing friction and transfer in sliding contact, a series of tests were performed in situ in an SEM. The initial sliding during metal forming was investigated, using an aluminum tip representing the work material, put into sliding contact with a polished flat tool material. Both DLC-coated and uncoated tool steel was used. By varying the final polishing step of the tool material, different surface topographies were obtained. The study demonstrates the strong influence from nano topography of an unpolished DLC coated surface on both coefficient of friction and material transfer. The influence of tool surface chemistry is also discussed.

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.

Varmsmidning av aluminiumlegering 6026 T9 : Warm forging of aluminum alloy 6026 T9

In this project forging of aluminum alloy Al 6026 T9 has been performed in the temperature range of 400 °C – 470 °C. The alloy which was in the shape of a cylindrical billet was formed in a press with the aim of analyzing the effect of different forging temperatures and required press load for optimal die filling. The component’s dimensions were later measured and compared to a reference piece. To ease the flow of material a lubricant was used between the billet and the die. This was demonstrated by compressing the billet with and without any lubricant.The performed experiments show that the lubricant reduces friction and makes it easier for the material to flow into the die. Higher billet temperature than 450 °C is deemed unnecessary as it does not give any significant improvement in filling the die. The experiments also conclude that a press load of at least 280 tons is required for these conditions.