Tag Archives: fatigue

Noisy progressive failure of a composite panel

Photograph showing close-up of progressive failure in a composite materialComposite materials have revolutionized many fields of engineering by providing lightweight strong components whose internal structure can be tailored to optimise their load-bearing capabilities. Engineering composites consist of high-strength fibres embedded in a lightweight matrix that keeps the fibres in position and provides the shape of the component.  While many composite materials have an impressive structural performance, some also exhibit spectacular failure modes with noises like guitar strings snapping when fibres start to fail and with jagged eruptions of material appearing on the surface, as shown in the image.  A year ago, I reported on our work in the DIMES project, to test the capabilities of our integrated measurement system to detect and track damage in real-time in a metallic section from an aircraft wing [see ‘Condition monitoring using infrared imaging‘ on June 17th, 2020].  Last month, we completed a further round of tests at Empa to demonstrate the system’s capabilities on composite structures which have been tested almost to destruction.  One of the advantages of composite structures is their capability to function and bear load despite quite high levels of damage, which meant we were able to record the progressive rupture of one of our test panels during cyclic fatigue loading.  Watch and listen to this short video to see and hear the material being torn apart – ignore the loud creaking and groaning from the test rig, it’s the quieter sound like dead leaves being swept up.

The University of Liverpool is the coordinator of the DIMES project and the other partners are Empa, Dantec Dynamics GmbH and Strain Solutions LtdAirbus is the topic manager on behalf of the Clean Sky 2 Joint Undertaking.

The DIMES 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. 820951.

 

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.

It’s tiring look at yourself

Picture of the authorFor many people Zoom fatigue is very real. It has been studied by Jeremy Bailenson at Stanford University [see ‘‘Zoom fatigue’ brought into focus by Stanford study‘ in the FT on February 26th, 2021]. He found that one source of tiredness was the high level of self-evaluation that arises from continually having to look at a video of yourself. Of course, it is easy to fix by choosing not to display your own video on your screen. We all have two egos: one is our subjectivity or the physical sensations registered by our body via our senses; and, the other is our reputation or the reflection of ourselves which forms our social identity [see ‘A reflection on existentialism‘ on December 20th, 2017]. Gloria Orrigi describes this second self as not a simple reflection but one that is ‘warped, amplified, redacted and multiplied in the eyes of others’. Perhaps it is hardly surprising that being constantly exposed to the view of ourselves being seen by others over a video conference raises our level of fatigue as we subconsciously and constantly review the impression being made on others. Orrigi describes our reputation as being like the trail left by snails has they slither over surfaces. Our social interactions with others leave deposits in their minds that become an information trail that we cannot erase and can only partially control. The pandemic has forced many of our social interactions to be via the internet which means they also leave electronic trails over which we have little control and cannot erase; however, we probably worry less about these traces than we do those left in the minds of others. Perhaps that is why our conversations lack spontaneity when conducted via a Zoom call [see ‘Distancing ourselves from each other‘ on January 13th, 2021] or maybe it’s just because it’s very difficult to gossip on a video conference even using the ‘chat’ function. Robin Dunbar has suggested that the real reason language evolved in humans is to allow us to gossip and thus maintain the social cohesion. If we are suffering from a loss of social cohesion caused by a lack of gossip then it is likely our stress levels would be raised causing us further fatigue. So, maybe we should be picking up our phones and calling people for a chat instead of scheduling meetings on Zoom.

The picture is the photograph of me that others see when I switch off my camera in an internet call.  It’s a selfie with which I am happy, for the moment anyway.

Sources:

Robin Dunbar, Grooming, gossip and the evolution of language, London: Faber and Faber, 1996.

Gloria Origgi, Reputation: what it is and why it matters, Princeton: Princeton University Press, 2018.

 

Credible predictions for regulatory decision-making

detail from abstract by Zahrah ReshRegulators are charged with ensuring that manufactured products, from aircraft and nuclear power stations to cosmetics and vaccines, are safe.  The general public seeks certainty that these devices and the materials and chemicals they are made from will not harm them or the environment.  Technologists that design and manufacture these products know that absolute certainty is unattainable and near-certainty in unaffordable.  Hence, they attempt to deliver the service or product that society desires while ensuring that the risks are As Low As Reasonably Practical (ALARP).  The role of regulators is to independently assess the risks, make a judgment on their acceptability and thus decide whether the operation of a power station or distribution of a vaccine can go ahead.  These are difficult decisions with huge potential consequences – just think of the more than three hundred people killed in the two crashes of Boeing 737 Max airplanes or the 10,000 or so people affected by birth defects caused by the drug thalidomide.  Evidence presented to support applications for regulatory approval is largely based on physical tests, for example fatigue tests on an aircraft structure or toxicological tests using animals.  In some cases the physical tests might not be entirely representative of the real-life situation which can make it difficult to make decisions using the data, for instance a ground test on an airplane is not the same as a flight test and in many respects the animals used in toxicity testing are physiologically different to humans.  In addition, physical tests are expensive and time-consuming which both drives up the costs of seeking regulatory approval and slows down the translation of new innovative products to the market.  The almost ubiquitous use of computer-based simulations to support the research, development and design of manufactured products inevitably leads to their use in supporting regulatory applications.  This creates challenges for regulators who must judge the trustworthiness of predictions from these simulations.  [see ‘Fake facts & untrustworthy predictions‘ on December 4th, 2019]. It is standard practice for modellers to demonstrate the validity of their models; however, validation does not automatically lead to acceptance of predictions by decision-makers.  Acceptance is more closely related to scientific credibility.  I have been working across a number of disciplines on the scientific credibility of models including in engineering where multi-physics phenomena are important, such as hypersonic flight and fusion energy [see ‘Thought leadership in fusion energy‘ on October 9th, 2019], and in computational biology and toxicology [see ‘Hierarchical modelling in engineering and biology‘ on March 14th, 2018]. Working together with my collaborators in these disciplines, we have developed a common set of factors which underpin scientific credibility that are based on principles drawn from the literature on the philosophy of science and are designed to be both discipline-independent and method-agnostic [Patterson & Whelan, 2019; Patterson et al, 2021]. We hope that our cross-disciplinary approach will break down the subject-silos that have become established as different scientific communities have developed their own frameworks for validating models.  As mentioned above, the process of validation tends to be undertaken by model developers and, in some sense, belongs to them; whereas, credibility is not exclusive to the developer but is a trust that needs to be shared with a decision-maker who seeks to use the predictions to inform their decision [see ‘Credibility is in the eye of the beholder‘ on April 20th, 2016].  Trust requires a common knowledge base and understanding that is usually built through interactions.  We hope the credibility factors will provide a framework for these interactions as well as a structure for building a portfolio of evidence that demonstrates the reliability of a model. 

References:

Patterson EA & Whelan MP, On the validation of variable fidelity multi-physics simulations, J. Sound & Vibration, 448:247-258, 2019.

Patterson EA, Whelan MP & Worth A, The role of validation in establishing the scientific credibility of predictive toxicology approaches intended for regulatory application, Computational Toxicology, 17: 100144, 2021.

Image: Extract from abstract by Zahrah Resh.

Scattering electrons reveal dislocations in material structure

Figure 9 from Yang et al, 2012. Map of plastic strain around the crack tip (0, 0) based on the full width of half the maximum of the discrete Fourier transforms of BSE images, together with thermoelastic stress analysis data (white line) and estimates of the plastic zone size based on approaches of Dugdale's (green line) and Irwin's (blue line; dimensions in millimetres).

Figure 9 from Yang et al, 2012. Map of plastic strain around the crack tip (0, 0) based on the full width of half the maximum of the discrete Fourier transforms of BSE images, together with thermoelastic stress analysis data (white line) and estimates of the plastic zone size based on approaches of Dugdale’s (green line) and Irwin’s (blue line; dimensions in millimetres).

It is almost impossible to manufacture metal components that are flawless.  Every flaw or imperfection in a metallic component is a potential site for the initiation of a crack that could lead to the failure of the component [see ‘Alan Arnold Griffith’ on April 26th, 2017].  Hence, engineers are very interested in understanding the mechanisms of crack initiation and propagation so that these processes can be prevented or, at least, inhibited.  It is relatively easy to achieve these outcomes by not applying loads that would supply the energy to drive failure processes; however, the very purpose of a metal component is often to carry load and hence a compromise must be reached.  The deep understanding of crack initiation and propagation, required for an effective and safe compromise, needs detailed measurements of evolution of the crack and of its advancing front or tip [depending whether you are thinking in three- or two-dimensions].  When a metal is subjected to repeated cycles of loading, then a crack can grow incrementally with each load cycle; and in these conditions a small volume of material, just ahead of the crack and into which the crack is about to grow, has an important role in determining the rate of crack growth.  The sharp geometry of the crack tip causes localisation of the applied load in the material ahead of the crack thus raising the stress sufficiently high to cause permanent deformation in the material on the macroscale.  The region of permanent deformation is known as the crack tip plastic zone.  The permanent deformation induces disruptions in the regular packing of the metal atoms or crystal lattice, which are known as dislocations and continued cyclic loading causes the dislocations to move and congregate around the crack tip.  Ultimately, dislocations combine to form voids in the material and then voids coalesce to form the next extension of the crack.  In reality, it is an oversimplification to refer to a crack tip because there is a continuous transition from a definite crack to definitely no crack via a network of loosely connected voids, unconnected voids, aggregated dislocations almost forming a void, to a progressively more dispersed crowd of dislocations and finally virgin or undamaged material.  If you know where to look on a polished metal surface then you could probably see a crack about 1 mm in length and, with aid of an optical microscope, you could probably see the larger voids forming in the material ahead of the crack especially when a load is applied to open the crack.  However, dislocations are very small, of the order tens of nanometres in steel, and hence not visible in an optical microscope because they are smaller than the wavelength of light.  When dislocations congregate in the plastic zone ahead of the crack, they disturb the surface of the metal and causing a change its texture which can be detected in the pattern produced by electrons bouncing off the surface.  At Michigan State University about ten years ago, using backscattered electron (BSE) images produced in a scanning electron microscope (SEM), we demonstrated that the change in texture could be measured and quantified by evaluating the frequency content of the images using a discrete Fourier transform (DFT).  We collected 225 square images arranged in a chessboard pattern covering a 2.8 mm by 2.8 mm square around a 5 mm long crack in a titanium specimen which allowed us to map the plastic zone associated with the crack tip (figure 9 from Yang et al, 2012).  The length of the side of each image was 115 microns and 345 pixels so that we had 3 pixels per micron which was sufficient to resolve the texture changes in the metal surface due to dislocation density.  The images are from our paper published in the Proceedings of the Royal Society and the one below (figure 4 from Yang et al, 2012) shows four BSE images along the top at increasing distances from the crack tip moving from left to right.  The middle row shows the corresponding results from the discrete Fourier transform that illustrate the decreasing frequency content of the images moving from left to right, i.e. with distance from the crack.  The graphs in the bottom row show the profile through the centre of the DFTs.  The grain structure in the metal can be seen in the BSE images and looks like crazy paving on a garden path or patio.  Each grain has a particular and continuous crystal lattice orientation which causes the electrons to scatter differently from it compared to its neighbour.  We have used the technique to verify measurements of the extent of the crack tip plastic zone made using thermoelastic stress analysis (TSA) and then used TSA to study ‘Crack tip plasticity in reactor steels’ [see post on March 13th, 2019].

Figure 4 from Yang et al, 2012. (a) Backscattered electron images at increasing distance from crack from left to right; (b) their corresponding discrete Fourier transforms (DFTs) and (c) a horizontal line profile across the centre of each DFT.

Figure 4 from Yang et al, 2012. (a) Backscattered electron images at increasing distance from crack from left to right; (b) their corresponding discrete Fourier transforms (DFTs) and (c) a horizontal line profile across the centre of each DFT.

Reference: Yang, Y., Crimp, M., Tomlinson, R.A., Patterson, E.A., 2012, Quantitative measurement of plastic strain field at a fatigue crack tip, Proc. R. Soc. A., 468(2144):2399-2415.