Investigation of wear mechanisms in AlSi-polyester abradable - Ti(6Al4V) blade contacts using stroboscopic imaging

Rahimov, Eldar; Watson, Michael; Hadjisoteriou, Andreas; Marshall, Matthew

doi:10.1016/j.wear.2021.204207

Cited by 7 publications

(12 citation statements)

References 10 publications

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“…A camera has been installed into the rig which can record the front of the blade during the test. This setup is based off of the stroboscopic techniques developed by Rahimov et al 13 Due to the spatial constraints of the spindle test rig – and lessons learnt by Rahimov – some changes have been implemented.…”

Section: Methodsmentioning

confidence: 99%

“…14 These developments have shown how gathering data throughout the rub makes it possible to better understand how adhesions form, and how wear mechanics are dependent on events which have occurred previously. This was seen in the work completed by Rahimov et al 13 which showed how an adhesion was more likely to form at a location from which an adhesion had previously broken off. This new insight, alongside force measurement techniques, has provided a strong foundation to develop theories on why adhesions form.…”

Section: Introductionmentioning

confidence: 92%

“…The stroboscopic set up by Rahimov et al 13 made use of two separate LEDs to illuminate the blade. This resulted in a distinct lighting gradient with bright spots at the centre of each diode (Figure 8), this had to be corrected in post processing.…”

Section: Front Cameramentioning

confidence: 99%

“…Previously there has been a large volume of research looking into how In-situ measurement techniques can be applied to abradable research, such as the addition of cameras recording the front of the blade 13 and dynamometers recording rub forces. 14 These developments have shown how gathering data throughout the rub makes it possible to better understand how adhesions form, and how wear mechanics are dependent on events which have occurred previously.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

In situ measurements of thermal-mechanical wear in blade-abradable liner contacts

Baillieu,

Parody,

Rahimov

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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The abradable coating on the casing of a jet engine minimises any air gaps by allowing blades to cut a path into the abradable, reducing efficiency losses. Unfortunately, abradable cutting performance varies significantly with rub conditions and abradable type, leading to poor cutting performance and potentially damaging the blades. This study further develops the abradable testing capabilities of a 200 m/s spindle test rig by applying previously researched stroboscopic imagining techniques to record the front of the blade alongside thermal imaging of the blade and the abradable. The front camera makes it possible to see how adhesions are forming along the width of the blade, and how the adhesion growth differs between materials. The thermal camera can then be used to identify hot spots on both the blade and the abradable, providing an insight into how hot spots relate to adhesions both spatially and temporally. These tools have been proven with testing of Metco 601 at a range of incursion rates. At the incursion rate of 0.2 μm/pass the tools were able to provide conclusive results, with video footage of the front, side, blade temperature and abradable temperatures being aligned temporally and spatially. Numerical data could then be extracted to produce rub maps of adhesion height, blade temperature and abradable temperature on separate plots which is a powerful tool when looking for relationships between the data sets, and for identifying time-based patterns. Further benefits of this tool set were shown when focussing on individual adhesions events within a rub, allowing for the delays between abradable heating, adhesions forming and blade heating to be observed and quantified. When applied to multiple materials and at different test conditions this tool will provide further insight into how adhesion formation differs, and potentially into why adhesions form.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 92%

Section: Front Cameramentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

In situ measurements of thermal-mechanical wear in blade-abradable liner contacts

Baillieu,

Parody,

Rahimov

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

Self Cite

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“…Particularly, metallic coatings have a wide variety of applications due to their excellent properties. For instance, in the aerospace industry, titanium-based coatings are frequently used to protect aerospace engines, because of their excellent corrosion resistance and stable strength [ 1 , 2 , 3 , 4 ]. To achieve corrosion resistance of steel components in automotive industry, aluminum, zinc, and zinc-boron compounds are the most popular metals utilized as coatings [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Indentation Reverse Algorithm of Mechanical Response for Elastoplastic Coatings Based on LSTM Deep Learning

Long

Ding

et al. 2023

Materials

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The load-penetration depth (P–h) curves of different metallic coating materials can be determined by nanoindentation experiments, and it is a challenge to obtain stress–strain response and elastoplastic properties directly using P–h curves. These problems can be solved by means of finite element (FE) simulation along with reverse analyses and methods, which, however, typically occupy a lengthy time, in addition to the low generality of FE methodologies for different metallic materials. To eliminate the challenges that exist in conventional FE simulations, a long short-term memory (LSTM) neural network is proposed in this study and implemented to deep learn the time series of P–h curves, which is capable of mapping P–h curves to the corresponding stress–strain responses for elastoplastic materials. Prior to the operation of the neural network, 1000 sets of indentation data of metallic coating materials were generated using the FE method as the training and validating sets. Each dataset contains a set of P–h curves as well as the corresponding stress–strain curves, which are used as input data for the network and as training targets. The proposed LSTM neural networks, with various numbers of hidden layers and hidden units, are evaluated to determine the optimal hyperparameters by comparing their loss curves. Based on the analysis of the prediction results of the network, it is concluded that the relationship between the P–h curves of metallic coating materials and their stress–strain responses is well predicted, and this relationship basically coincides with the power-law equation. Furthermore, the deep learning method based on LSTM is advantageous to interpret the elastoplastic behaviors of coating materials from indentation measurement, making the predictions of stress–strain responses much more efficient than FE analysis. The established LSTM neural network exhibits the prediction accuracy up to 97%, which is proved to reliably satisfy the engineering requirements in practice.

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Protective coatings for aeroengine blade tips: a review

Yang,

Wu,

Gao

et al. 2024

J Mater Sci

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Investigation of wear mechanisms in AlSi-polyester abradable - Ti(6Al4V) blade contacts using stroboscopic imaging

Cited by 7 publications

References 10 publications

In situ measurements of thermal-mechanical wear in blade-abradable liner contacts

In situ measurements of thermal-mechanical wear in blade-abradable liner contacts

Indentation Reverse Algorithm of Mechanical Response for Elastoplastic Coatings Based on LSTM Deep Learning

Protective coatings for aeroengine blade tips: a review

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