Investigation of SLZhS32 BR economically doped heat-resistant alloy with single-crystal structure

Logunov, A. V.; Shmotin, Yu. N.; Zavodov, S. A.; Leshchenko, I. A.; Данилов, Д. В.; Khryashchev, I. I.; Mikhaylov, A. M.; Semin, A. E.; Mikhaylov, Maxim A.

doi:10.1134/s2075113316040195

Cited by 3 publications

(3 citation statements)

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Section: Implementation Methods Based On Machine Vision-based Metallo...mentioning

confidence: 99%

“…Machine vision uses computers to simulate the visual function of the human eye, 8,9 extracts information from images or image sequences, and performs shape and motion recognition on three-dimensional (3D) scenes and objects in the objective world. 10 A typical industrial machine vision application system includes the object under test, light source, optical system, sensing unit, control unit, and signal processing unit, etc., to complete the measurement, detection, identification, and guidance of the object under test, as shown in Fig. 1.…”

Section: Machine Vision Technologymentioning

confidence: 99%

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(Retracted) Image restoration and quantitative metallographic tissue based on machine vision

Shi²,

Ke³

et al. 2022

J. Electron. Imag.

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. In recent years, with the development of computer image processing technology and quantitative metallography, quantitative metallographic analysis and image restoration based on machine vision image processing have attracted extensive attention. To deeply study the special ability of machine vision technology in metallographic image restoration and quantitative analysis, we adopt a model construction method, parameter design method and comparative analysis method to collect samples and analyze the machine vision system. Through quantitative metallographic organization, based on the progress in artificial neural networks and classification algorithms, a set of analysis system equipment and image restoration algorithm was finally established. The experimental results show that the recognition rate of this method is 5.69% higher than that of method 3, and the overall recognition rate is 5.1% higher than that of method 3, reaching 98.65%. It basically proves the superiority of the image classification algorithm in this paper and also indirectly proves that the system can play a role in the restoration and quantitative analysis of metallographic tissue images.

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Section: Implementation Methods Based On Machine Vision-based Metallo...mentioning

confidence: 99%

Section: Machine Vision Technologymentioning

confidence: 99%

(Retracted) Image restoration and quantitative metallographic tissue based on machine vision

Shi²,

Ke³

et al. 2022

J. Electron. Imag.

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“…Таким образом, предложенная формула (11), наряду с результатами регрессионной модели [5], обеспечила наилучшую расчетную точность -средняя ошибка 0.1 %. Однако в отличие от регрессионных методов физически обоснованная аналитическая модель (11) имеет всего один подбираемый параметр -делитель в показателе степени, что делает ее удобной для формализации, например, в рамках пакетов автоматизированных расчетов.…”

Section: результаты и обсуждениеunclassified

An accurate empirical formula for determining the density of heat-resistant nickel alloys

2021

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The density of a substance is one of its main physical characteristics. This is especially true for materials used in aviation, where the mass of each structural element should be minimized as much as possible. When developing new structural materials, for example, heat-resistant nickel alloys, which are widely used in the manufacture of gas turbine engine parts, it is extremely important to have a reliable and accurate method for assessing the density of the material being developed. Until now, no unified method has been proposed for calculating the density of heat-resistant nickel alloys. The paper reviews the available approaches to assessing the density of alloys and proposes a new formula that allows one to calculate the density of an alloy with a high accuracy based on the information on its composition. The proposed approach takes into account the spatial fcc structure of heat-resistant nickel alloys as well as the molar mass and molar volume of the elements that form the alloy.To check the accuracy of the calculations, a database of 69 heat-resistant nickel alloys was collected, containing information on the composition of the alloys and their known density. According to the proposed formula, as well as using some other known approaches, the density for the alloys from the database was calculated. The calculation results showed that the proposed method provided the best accuracy among all considered ones: the standard deviation of the calculated values from the real ones for the entire sample was 0.1 %, the mean values and medians practically coincide. In addition, the calculation errors are normally distributed and have an average value of −0.0001. The existing methods give a minimum error of 1.2 %, thus, the proposed approach improved the accuracy of calculating the density of heat-resistant nickel alloys by about an order of magnitude, which is a significant result both from the point of view of the general scientific approach and from the point of view of engineering practice. Taking into account the results obtained, the proposed formula can be widely used in the development of new and modification of existing heat-resistant nickel alloys.

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Computer Simulation and Optimization of the Single-Crystal Nickel Superalloy Compositions: IV. Computer Method for Optimizing the Superalloy Composition

Logunov,

Shmotin,

Khramin

et al. 2023

Russ. Metall.

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Investigation of SLZhS32 BR economically doped heat-resistant alloy with single-crystal structure

Cited by 3 publications

References 0 publications

(Retracted) Image restoration and quantitative metallographic tissue based on machine vision

(Retracted) Image restoration and quantitative metallographic tissue based on machine vision

An accurate empirical formula for determining the density of heat-resistant nickel alloys

Computer Simulation and Optimization of the Single-Crystal Nickel Superalloy Compositions: IV. Computer Method for Optimizing the Superalloy Composition

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