Advanced Characterization Techniques for Turbine Blade Wear and Damage

Schlobohm, Jochen; Bruchwald, Oliver; Frackowiak, Wojciech; Li, Yinan; Kästner, Markus; Posch, Andreas E.; Reimche, W.; Maier, Hans Jürgen; Reithmeier, Eduard

doi:10.1016/j.procir.2016.09.005

Cited by 14 publications

(7 citation statements)

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“…Sahraoui et al [50] revealed that friction and wear are the most severe degradations undergone by several main components of gas turbines. A similar study was carried out by Schlobohm et al [51], who found that 70% of the total of hot-gas-path components experience wear problems. Detailed wear characterization is important to investigate the wear mechanisms and to monitor damages during inspection intervals.…”

Section: Relation Of Wear To the Hot-gas-path Components Of Gas Turbinessupporting

confidence: 54%

Wear Characteristics of Superalloy and Hardface Coatings in Gas Turbine Applications–A Review

Pauzi

Ghazali

Zamri

et al. 2020

Metals

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In the gas-turbine research field, superalloys are some of the most widely used materials as they offer excellent strength, particularly at extreme temperatures. Vital components such as combustion liners, transition pieces, blades, and vanes, which are often severely affected by wear, have been identified. These critical components are exposed to very high temperatures (ranging from 570 to 1300 °C) in hot-gas-path systems and are generally subjected to heavy repair processes for maintenance works. Major degradation such as abrasive wear and fretting fatigue wear are predominant mechanisms in combustion liners and transition pieces during start–stop or peaking operation, resulting in high cost if inadequately protected. Another type of wear-like erosion is also prominent in turbine blades and vanes. Nimonic 263, Hastelloy X, and GTD 111 are examples of superalloys used in the gas-turbine industry. This review covers the development of hardface coatings used to protect the surfaces of components from wear and erosion. The application of hardface coatings helps reduce friction and wear, which can increase the lifespan of materials. Moreover, chromium carbide and Stellite 6 hardface coatings are widely used for hot-section components in gas turbines because they offer excellent resistance against wear and erosion. The effectiveness of these coatings to mitigate wear and increase the performance is further investigated. We also discuss in detail the current developments in combining these coating with other hard particles to improve wear resistance. The principles of this coating development can be extended to other high-temperature applications in the power-generation industry.

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Section: Relation Of Wear To the Hot-gas-path Components Of Gas Turbinessupporting

confidence: 54%

Wear Characteristics of Superalloy and Hardface Coatings in Gas Turbine Applications–A Review

Pauzi

Ghazali

Zamri

et al. 2020

Metals

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show abstract

“…It is the conventional sections of compression rate and the part that is tangential is unheeded. [8,9] Warm Shower James Browing in 1983, who procured that the speed oxygen system. In HVOF that the feedstock covering powder is heated up to close over stage destroys by consuming gas flow generated from continuous beginning of energies including methane, gas, propylene or hydrogen.…”

Section: Materials and Methods Erosion Mechanismmentioning

confidence: 99%

An Analyzing Erosion Wear on Blade of Turbine

Senapati¹,

Tjprc²

2019

IJMPERD

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This paper discusses the strategy for degradation of surface of hydro turbine edge section brought about by the impact of unpleasant standard particles passed on spilling water is a troublesome issue of disintegration wear. To neutralize the same, surface alteration of turbine edge material by use of defensive covering is getting commonness now days. WC based hard metals are generally utilized in the industry for the most part are being created for a wide variety of employments to contradict wear. Slurry crumbling burdens are especially fundamental in the midst of blustery periods in the hydro control plant. The erosive wear impediment of 86WC-10CO-4cr cermet's is upgraded by theextension of 2% wt.% of littler scale estimated yttrium oxide, which fills in hard stage. These cermet's covering were put away on 13cr-4Ni martensitic treated steel substrates through fast oxygen fuel method to get an ordinary thickness.

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Section: Introductionmentioning

confidence: 99%

“…Non-destructive testing (NDT) techniques are of high importance for aircraft engines manufacturers, to ensure an aircraft engine's safety and optimal operational performance [1,2]. Of particular interest are the turbine blades, as those complex and very thin components are exposed to high loads, extreme conditions [3] and are chemically highly impure [2]. Elevated requirements imposed on these high performance components make it increasingly necessary to introduce new and improved NDT techniques into the process of turbine blade production [3].…”

Section: Introductionmentioning

confidence: 99%

Infrared Thermal Imaging-Based Turbine Blade Crack Classification Using Deep Learning

et al. 2022

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Non-destructive testing is widely applied for the detection and identification of defects in turbine blades of modern aircraft engines. Cracks in turbine blades can affect the turbine performance and pose a risk to safety and service life. For Original Equipment Manufacturers it is, therefore, essential to be able to identify all defects. Heat flow thermography offers, compared to the often used penetrant testing, the potential to improve the detection of defects in turbine blades and is contact-free, reproducible, quick to apply, and can be automated. With induction (heat flow) thermography, it is even possible to detect cracks that lie below the surface and therefore are not externally visible. However, manual inspection of thermography images is very time-consuming. By automating the image classification procedure with a deep learning technique, the speed and accuracy of the classification can be improved over a manually performed classification. The development objective of this AI application is expected to support and assist the highly skilled and experienced inspection specialists in the medium term. Our solution is based on convolutional neural networks. Several challenges of the AI training process, including data imbalance, a small dataset, and extremely small cracks in large images are addressed.

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Advanced Characterization Techniques for Turbine Blade Wear and Damage

Cited by 14 publications

References 10 publications

Wear Characteristics of Superalloy and Hardface Coatings in Gas Turbine Applications–A Review

Wear Characteristics of Superalloy and Hardface Coatings in Gas Turbine Applications–A Review

An Analyzing Erosion Wear on Blade of Turbine

Infrared Thermal Imaging-Based Turbine Blade Crack Classification Using Deep Learning

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