Semi-Supervised Anomaly Detection for the Determination of Vehicle Hijacking Tweets

In South Africa, there is an ever-growing issue of vehicle hijackings. This leads to travellers constantly being in fear of becoming a victim to such an incident. This work presents a new semi-supervised approach to using tweets to identify hijacking incidents by using unsupervised anomaly detection algorithms. Tweets consisting of the keyword "hijacking" are obtained, stored, and processed using the term frequency-inverse document frequency (TF-IDF) and further analyzed by using two anomaly detection algorithms: 1) K-Nearest Neighbour (KNN); 2) Cluster Based Outlier Factor (CBLOF). The comparative evaluation showed that the KNN method produced an accuracy of 89%, whereas the CBLOF produced an accuracy of 90%. The CBLOF method was also able to obtain a F1-Score of 0.8, whereas the KNN produced a 0.78. Therefore, there is a slight difference between the two approaches, in favour of CBLOF, which has been selected as a preferred unsupervised method for the determination of relevant hijacking tweets. In future, a comparison will be done between supervised learning methods and the unsupervised methods presented in this work on larger dataset. Optimisation mechanisms will also be employed in order to increase the overall performance

Characterization of Ti-6Al-4V Bar for Aerospace Fastener Pin Axial Forging

Ti-6Al-4V warm forged fasteners are a critical part of the aerospace industry, as they are used in vast quantities for mechanical joining of components for the fuselage, wing-skin and aero-engine. These components are produced in vast quantities at rapid production rates through multi-blow axial forging However the rate that they are manufactured means that manufacturers rely upon periodic part conformance testing to understand if the part is within tolerance or if any undesirable manufacturing defects such as cracks or underfilling are present. Thus, a right-first-time manufacturing approach is essential to minimize non-conformant scrap. An analysis of the Ti-6Al-4V supplied raw material for axial forging, in a variety of different bar diameter sizes and from different industrial suppliers, was conducted. This was to attempt to understand whether material property variation or operator variation was the root cause for some material behaving differently during the manufacture route. Experimental testing was performed through microstructure characterization and mechanical testing methods. The volume fraction of the β-phase was noted to be marginally higher in material with good forgeability. The hardness of the inner core of the bar appears to be a critical material property for the Ti-6Al4V bar, with an overly hard bar-core hindering forgeability of the bar. This is believed to be due to the hotter central region malleability being key for forgeability. Micro-void porosity was also noted which could lead to stress concentration locations, or crack initiation, and as such is a deleterious property for forgeability. The experienced forgeability of the Ti-6Al-4V bars have been demonstrated to be sensitive to rather small variation in measured microstructure and mechanical property. It is believed that cumulative impacts of small differences, 1% variation in α-phase volume fraction, small variations in elongation to failure, 1% variation in elastic modulus and microhardness profile variation at the center of the bar of less than 10 HV0.3, can combine to significantly impact the forgeability of Ti-6Al-4V bar