Neural computing for erosion assessment in Al-20TiO2 HVOF thermal spray coating

Singh, Jashanpreet; Vasudev, Hitesh; Szala, Mirosław; Gill, Harjot Singh

doi:10.1007/s12008-023-01372-y

Cited by 10 publications

(4 citation statements)

References 64 publications

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“…Artificial intelligence (AI), with its subsets machine learning (ML) [1] and deep learning artificial neural networks (ANNs) [2][3][4], has recently garnered substantial attention in scientific literature [5,6]. The evolution of ML models has been particularly noteworthy in various engineering disciplines, driven by an abundance of digital data, increasing computational capabilities, and advancements in algorithmic methodologies.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Buckling Behaviour of Composite Plate Element Using Artificial Neural Networks

Falkowicz,

Kulisz

2024

Adv. Sci. Technol. Res. J.

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This article presents the use of artificial neural networks (ANNs) to analysis of the composite plate elements with cut-outs which can work as a spring element. The analysis were based on results from numerical approach. ANNs models have been developed utilizing the obtained numerical data to predict the composite plate's flexuraltorsional form of buckling as natural form for different cut-outs and angels configurations. The ANNs models were trained and tested using a large dataset, and their accuracy is evaluated using various statistical measures. The developed ANNs models demonstrated high accuracy in predicting the critical force and buckling form of thin-walled plates with different cut-out and fiber angels configurations under compression. The combination of numerical analyses with ANNs models provides a practical and efficient solution for evaluating the stability behaviour of composite plates with cut-outs, which can be useful for design optimization and structural monitoring in engineering applications.

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Section: Introductionmentioning

confidence: 99%

Prediction of Buckling Behaviour of Composite Plate Element Using Artificial Neural Networks

Falkowicz,

Kulisz

2024

Adv. Sci. Technol. Res. J.

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“…First, to manage that task, material wear mechanisms should be understood [12][13][14]. Computer simulation, numerical calculations or artificial neural networks can be employed to facilitate the selection and design of wear-resistant materials [15][16][17]. Therefore, papers containing experimental and numerical results combined with the effect of material properties on wear, erosion and corrosion resistance are presented in this Special Issue.…”

Section: Introduction and Scopementioning

confidence: 99%

Metallic and Ceramic Materials Integrity—Surface Engineering for Wear, Corrosion and Erosion Prevention

Szala,

Walczak

2024

Materials

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“…On the other hand, there are some limitations according to the methods and feedstock materials, e.g., in case of the cermet coatings deposition. Among many thermal spray methods, the high velocity oxy-fuel (HVOF) is characterized by homogenous structure with low porosity level, as well as low crack density [10][11][12]. It is caused by relatively low flame temperature and high particles velocity [13][14][15] in comparison to atmospheric plasma (APS) and suspension plasma spraying (SPS) [16,17].…”

Section: Introductionmentioning

confidence: 99%

“…In the available literature on the discussed issue, the aspect related to the difficulty in selecting the parameters of the process of spraying cermet coatings on a relatively soft substrate material, especially aluminum alloys is repeatedly described [18][19][20][21]. Usually structural stainless steel or nickel alloys are commonly used as substrate materials [12,22,23]. These alloys show relatively high melting points, which facilitates the selection of HVOF process parameters to obtain high-quality coating and prevent melting down the substrate.…”

Section: Introductionmentioning

confidence: 99%

The Effect of HVOF Spray Distance on Solid Particle Erosion Resistance of WC-based Cermets Bonded by Co, Co-Cr and Ni Deposited on Mg-alloy Substrate

Jonda,

Łatka,

Lont

et al. 2024

Adv. Sci. Technol. Res. J.

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Magnesium alloys are very interesting engineering materials because of their high strength-to-density ratio. On the other hand, they are characterized by low hardness as well as low erosion resistance. Because of these reasons, their applications in the industry are very limited. The article presents the results of the high velocity oxy-fuel (HVOF) spraying of the hard cermet coatings onto AZ31 magnesium alloy substrate. Three feedstock powders were used in the process with composition (wt.%): WC-12Co, WC-10Co-4Cr and WC-20Cr 3 C 2 Ni. The spray distance (SD) was selected as a variable parameter with values equal to 320 and 400 mm. Observations carried out under a scanning electron microscope (SEM) revealed a typical HVOF-sprayed microstructure with a compact structure and low porosity (below 3 vol.%). The hardness of the manufactured coatings, ranging from 912 HV0.2 to 1328 HV0.2, what was significantly higher than the substrate. The solid particle erosion tests were carried out according to the ASTM G76-04 standard. Erosive experiments were done for 30°, 60° and 90° inclination angles of the nozzle using Al 2 O 3 abrasive. Erosion tests confirm that cermets exhibit substantial erosion resistance better than the substrate. The highest erosion resistance was noted for WC-10Co-4Cr coatings. The erosion rate for cermet coatings was mostly below 0.9 mg/min, whereas for the AZ31 it was more than 1.5 mg/min. In the case of the average erosion value, it was between 12 and 22 times lower than for the substrate. Results analysis reveal that shorter spray distance decreases porosity, increases hardness, and finally supports erosion resistance of the cermets.

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Neural computing for erosion assessment in Al-20TiO2 HVOF thermal spray coating

Cited by 10 publications

References 64 publications

Prediction of Buckling Behaviour of Composite Plate Element Using Artificial Neural Networks

Prediction of Buckling Behaviour of Composite Plate Element Using Artificial Neural Networks

Metallic and Ceramic Materials Integrity—Surface Engineering for Wear, Corrosion and Erosion Prevention

The Effect of HVOF Spray Distance on Solid Particle Erosion Resistance of WC-based Cermets Bonded by Co, Co-Cr and Ni Deposited on Mg-alloy Substrate

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