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Detection of damage in a single tow ceramic matrix composite specimen has been achieved using orthogonal decomposition of volumetric tomographic datasets collected at four tensile loads. This decomposition approach has been applied at two different length scales: (i) individual fibres and (ii) bulk volumes containing fibres and matrix material. Volumes were first decomposed to feature vectors, orders of magnitude smaller than the original volume they describe, and then comparisons between datasets at different load levels were made in feature vector space. The results show quantitative measurements of damage location, damage morphology and the relative growth of this damage with increased load when compared with a dataset with less or no damage. No prior knowledge of the dataset or training of algorithms is required for damage to be detected, it is only necessary that at least two datasets are available for comparison, e.g. from in situ or repeated scanning measurements. Results are generated on significantly shorter timescales when compared with previous automated approaches to tomography data processing. This approach has the potential to be applied to damage detection in a range of materials through comparisons of volumetric datasets from a range of measurement or computational techniques. [ABSTRACT FROM AUTHOR]

A small, novel, integrated SHM system has been deployed during full-scale testing of a wing fatigue test for several weeks and a fuselage pressurisation test for several months. Complementary NDE measurement techniques were combined, with inputs from visible and infrared optical sensors, as well as resistance strain gauges. Sensor units were deployed at regions of interest and integrated board computers permitted near real-time data processing. The outputs were full-field measurement datasets from digital image correlation and thermoelastic stress analysis systems. Changes in these datasets in the regions of interest were successfully quantified using orthogonal decomposition and were indicative of changes in the condition of the structure. The results from these case studies demonstrate that this system can be successfully deployed in spatially restricted areas within airframe structures to monitor crack growth. The low cost and small footprint of the system presents the opportunity for installation of arrays of similar sensors for both test and in-service data collection. Near real-time data processing would allow timely reporting to service engineers, informing maintenance or operational decisions. [ABSTRACT FROM AUTHOR]

The management and exploitation of knowledge can contribute to the competitive advantage of an organisation and hence can be a driver of its value. This paper examines knowledge management practices within two engineering organisations with an emphasis on barriers to its effectiveness and the influence of current and anticipated deployment of digital technologies. Two methods were used to gather research data across both organisations by combining a series of semi-structured interviews with a quantitative social network analysis. Examination of the acquired data provided insights into the relationship between the knowledge management culture in both organisations and their adoption of digital tools. Hudson's evolutionary model of safety culture has been modified to describe the culture of knowledge management in an organisation and the acquired data used to locate the two organisations on their knowledge management journey. It is proposed that social networks could be used as an indicator of the stage of evolution of knowledge management in engineering organisations more generally with low network densities and dispersed networks representing higher stages of evolution. [ABSTRACT FROM AUTHOR]