Tag Archives: innovation

Alleviating industrial uncertainty

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Want to know how to assess the quality of predictions of structural deformation from a computational model and how to diagnose the causes of differences between measurements and predictions?  The MOTIVATE project has the answers; that might seem like an over-assertive claim but read on and make your own judgment.  Eighteen months ago, I reported on a new method for quantifying the uncertainty present in measurements of deformation made in an industrial environment [see ‘Industrial uncertainty’ on December 12th, 2018] that we were trialling on a 1 m square panel of an aircraft fuselage.  Recently, we have used the measurement uncertainty we found to make judgments about the quality of predictions from computer models of the panel under compressive loading.  The top graphic shows the outside surface of the panel (left) with a speckle pattern to allow measurements of its deformation using digital image correlation (DIC) [see ‘256 shades of grey‘ on January 22, 2014 for a brief explanation of DIC]; and the inside surface (right) with stringers and ribs.  The bottom graphic shows our results for two load cases: a 50 kN compression (top row) and a 50 kN compression and 1 degree of torsion (bottom row).  The left column shows the out-of-plane deformation measured using a stereoscopic DIC system and the middle row shows the corresponding predictions from a computational model using finite element analysis [see ‘Did cubism inspire engineering analysis?’ on January 25th, 2017].  We have described these deformation fields in a reduced form using feature vectors by applying image decomposition [see ‘Recognizing strain’ on October 28th, 2015 for a brief explanation of image decomposition].  The elements of the feature vectors are known as shape descriptors and corresponding pairs of them, from the measurements and predictions, are plotted in the graphs on the right in the bottom graphic for each load case.  If the predictions were in perfect agreement with measurements then all of the points on these graphs would lie on the line equality [y=x] which is the solid line on each graph.  However, perfect agreement is unobtainable because there will always be uncertainty present; so, the question arises, how much deviation from the solid line is acceptable?  One answer is that the deviation should be less than the uncertainty present in the measurements that we evaluated with our new method and is shown by the dashed lines.  Hence, when all of the points fall inside the dashed lines then the predictions are at least as good as the measurements.  If some points lie outside of the dashed lines, then we can look at the form of the corresponding shape descriptors to start diagnosing why we have significant differences between our model and experiment.  The forms of these outlying shape descriptors are shown as insets on the plots.  However, busy, or non-technical decision-makers are often not interested in this level of detailed analysis and instead just want to know how good the predictions are.  To answer this question, we have implemented a validation metric (VM) that we developed [see ‘Million to one’ on November 21st, 2018] which allows us to state the probability that the predictions and measurements are from the same population given the known uncertainty in the measurements – these probabilities are shown in the black boxes superimposed on the graphs.

These novel methods create a toolbox for alleviating uncertainty about predictions of structural behaviour in industrial contexts.  Please get in touch if you want more information in order to test these tools yourself.

The MOTIVATE project has received funding from the Clean Sky 2 Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 754660 and the Swiss State Secretariat for Education, Research and Innovation under contract number 17.00064.

The opinions expressed in this blog post reflect only the author’s view and the Clean Sky 2 Joint Undertaking is not responsible for any use that may be made of the information it contains.

Spatio-temporal damage maps for composite materials

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Earlier this year, my group published a new technique for illustrating the development of damage as a function of both space and time in materials during testing in a laboratory.  The information is presented in a damage-time map and shows where and when damage appears in the material.  The maps are based on the concept that damage represents a change in the structure of the material and, hence, produces changes in the load paths or stress distribution in the material.  We can use any of a number of optical techniques to measure strain, which is directly related to stress, across the surface of the material; and then look for changes in the strain distribution in real-time.  Wherever a permanent change is seen to occur there must also be permanent deformation or damage. We use image decomposition techniques that we developed some time ago [see ‘Recognizing strain‘ on October 28th, 2018], to identify the changes. Our damage-time maps remove the need for skilled operators to spend large amounts of time reviewing data and making subjective decisions.  They also allow a large amount of information to be presented in a single image which makes detailed comparisons with computer predictions easier and more readily quantifiable that, in turn, supports the validation of computational models [see ‘Model validation‘ on September 18th, 2012].

The structural integrity of composite materials is an on-going area of research because we only have a limited understanding of these materials.  It is easy to design structures using materials that have a uniform or homogeneous structure and mechanical properties which do not vary with orientation, i.e. isotropic properties.  For simple components, an engineer can predict the stresses and likely failure modes using the laws of physics, a pencil and paper plus perhaps a calculator.  However, when materials contain fibres embedded in a matrix, such as carbon-fibres in an epoxy resin, then the analysis of structural behaviour becomes much more difficult due to the interaction between the fibres and with the matrix.  Of course, these interactions are also what make these composite materials interesting because they allow less material to be used to achieve the same performance as homogeneous isotropic materials.  There are very many ways of arranging fibres in a matrix as well as many different types of fibres and matrix; and, engineers do not understand most of their interactions nor the mechanisms that lead to failure.

The image shows, on the left, the maximum principal strain in a composite specimen loaded longitudinally in tension to just before failure; and, on the right, the corresponding damage-time map indicating when and where damage developing during the tension loading.

Source:

Christian WJR, Dvurecenska K, Amjad K, Pierce J, Przybyla C & Patterson EA, Real-time quantification of damage in structural materials during mechanical testing, Royal Society Open Science, 7:191407, 2020.

Gustatory technology stimulates on-line get-togethers

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It has been known for some time that over or under responsivity to sensory stimulation encountered in everyday life, such as noise, light and smell, can be a cause of anxiety and stress [e.g. Lipowski, 1975].  Most virtual reality systems provide visual and audio stimuli through headsets and tactile stimuli can be provided through haptic devices; however, that leaves two senses under stimulated: smell and taste.  So, researchers have been exploring how to extend virtual reality to include smell and taste in order to give a complete sensory experience and thus reduce the level of stress and anxiety that many people feel when using immersive reality systems.  This had led to digital scent technology that allows smells to be transmitted electronically [e.g. Isokoski et al, 2020].  So, it’s time to update your preferred communication tool to one that allows you to smell that fresh cup of coffee your colleague has just brewed before joining the meeting from their home-office.  Of course, if they have not taken a shower recently then you might want to ‘mute’ the smell function!  These advances in technology have led a spin-out company, Day91, to start work on gustatory technology that modifies the water in your glass to simulate the after-work drink that your team-mate is enjoying during your virtual get-together online.

References:

Lipowski, Z. J. (1975). Sensory and information inputs overload: Behavioural effects. Comprehensive Psychiatry, 16(3), 199–221.

Isokoski, P., Salminen, K., Müller, P., Rantala, J., Nieminen, V., Karjalainen, M., Väliaho, J., Kontunen, A., Savia, M., Leivo, J. and Telembeci, A., (2020). Transferring scents over a communication network. In Proceedings of the 23rd International Conference on Academic Mindtrek (pp. 126-133).

Devaluing novelty: not all that glitters is gold

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My regular readers will have recognised the novel nature of a blog that seeks, in a unique way, to present promising engineering ideas in a favourable and robust manner.  Actually, I hope my regular readers will recognise this opening sentence as completely uncharacteristic.  It was a blatant effort on my part to include the five words, underlined, with positive meanings that are most used in the titles and abstracts of articles published in clinical research and the life sciences.  A recent survey of more than 100,000 articles showed the prevalence of these words, with them being used significantly more in articles in which the first or last authors were male compared to those in which the first and last authors were female.  In other words, female authors are significantly less likely to describe their research findings in these positive terms and this influences the subsequent citations of their work and probably their prospects for research funding and advancement.  Sunday was International Women’s Day and, hence this is an appropriate week for everyone responsible for decisions about research to be conscious of this trend.  They should also be aware that the use of these positive words has increased in clinical and life sciences research by around 150% in the fifteen years to 2017.  In other words, the modesty of researchers has declined and they are more likely to describe their results as ‘novel’; however, I think it is unlikely that the results are any more novel than typical results published 20 years.  Of course, like most researchers, I always think my last breakthrough is the most exciting yet but many of us have been letting that enthusiasm lead us to exaggerate its novelty and value.

Source: Lerchenmueller MJ, Sorensen O & Jena AB, Gender differences in how scientists present the importance of their research: observational study, BMJ, 367:16573, 2019.