Quantitative thermal imaging analysis of aircraft materials: through skin sensing (Invited Paper)

Thermal non-destructive testing (NDT) is commonly used for assessing aircraft structures. This research work evaluates the potential of pulsed -- transient thermography for locating fixtures beneath aircraft skins in order to facilitate accurate automated assembly operations. Representative aluminium and carbon fibre aircraft skin-fixture assemblies were modelled using thermal modelling software. The assemblies were also experimentally investigated with an integrated pulsed thermographic evaluation system, as well as using a custom built system incorporating a miniature un-cooled camera. Modelling showed that the presence of an air gap between skin and fixture significantly reduced the thermal contrast developed, especially in aluminium. Experimental results show that fixtures can be located to accuracies of 0.5 mm.

Use of thermography and fibre optics microscopy for the evaluation of railway structures in the Athens Metro in Greece (Invited Paper)

Rolling Contact Fatigue (RCF) is one of the main issues that concern, at least initially, the head of the railway; progressively they can be of very high importance as they can propagate inside the material with the risk of damaging the railway. In this work, two different non-destructive techniques, infrared thermography (IRT) and fibre optics microscopy (FOM), were used in the inspection of railways for the tracing of defects and deterioration signs. In the first instance, two different approaches (dynamic and pulsed thermography) were used, whilst in the case of FOM, microscopic characterisation of the railway heads and classification of the deterioration -- damage on the railways according to the UIC (International Union of Railways) code, took place. Results from both techniques are presented and discussed.

Knowledge-elicitation and data-mining: fusing human and industrial plant information

Knowledge-elicitation is a common technique used to produce rules about the operation of a plant from the knowledge that is available from human expertise. Similarly, data-mining is becoming a popular technique to extract rules from the data available from the operation of a plant. In the work reported here knowledge was required to enable the supervisory control of an aluminium hot strip mill by the determination of mill set-points. A method was developed to fuse knowledge-elicitation and data-mining to incorporate the best aspects of each technique, whilst avoiding known problems. Utilisation of the knowledge was through an expert system, which determined schedules of set-points and provided information to human operators. The results show that the method proposed in this paper was effective in producing rules for the on-line control of a complex industrial process. (C) 2005 Elsevier Ltd. All rights reserved.