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Neural computing for erosion assessment in Al-20TiO2 HVOF thermal spray coating
https://orcid.org/http://orcid.org/0000-0002-4562-199X
https://orcid.org/http://orcid.org/0000-0002-1668-8765
Stainless steel (SS) 316L is widely used for hydraulic machinery of ash disposal slurry pumps. In this work, the Al-20TiO2 coating powders were sprayed on SS316L materials using the HVOF technique. The various properties such as erosion resistance, microhardness, microstructure, roughness, etc. were tested during the experiments. A pot tester was used to examine the rate of erosion. At an impact angle of 60 degrees, Al-20TiO2 coatings were found to erode the most. The neural computing was performed by using the artificial neural network model (ANN). The present ANN model produced the Pearson coefficient (R) of 0.99903, 0.99301, and 0.99194 respectively for training, validation, and testing. The overall R-value of the model was found as 0.99686. Microscopically, Al-20TiO2 demonstrated semi-brittle erosion behavior. Craters and ductile fractures were the most common erosion wear mechanisms detected on the Al-20TiO2 coating, indicating that this material had semi-ductile properties.
Neural computing for erosion assessment in Al-20TiO2 HVOF thermal spray coating
https://orcid.org/http://orcid.org/0000-0002-4562-199X
https://orcid.org/http://orcid.org/0000-0002-1668-8765
Stainless steel (SS) 316L is widely used for hydraulic machinery of ash disposal slurry pumps. In this work, the Al-20TiO2 coating powders were sprayed on SS316L materials using the HVOF technique. The various properties such as erosion resistance, microhardness, microstructure, roughness, etc. were tested during the experiments. A pot tester was used to examine the rate of erosion. At an impact angle of 60 degrees, Al-20TiO2 coatings were found to erode the most. The neural computing was performed by using the artificial neural network model (ANN). The present ANN model produced the Pearson coefficient (R) of 0.99903, 0.99301, and 0.99194 respectively for training, validation, and testing. The overall R-value of the model was found as 0.99686. Microscopically, Al-20TiO2 demonstrated semi-brittle erosion behavior. Craters and ductile fractures were the most common erosion wear mechanisms detected on the Al-20TiO2 coating, indicating that this material had semi-ductile properties.
Neural computing for erosion assessment in Al-20TiO2 HVOF thermal spray coating
https://orcid.org/http://orcid.org/0000-0002-4562-199X
https://orcid.org/http://orcid.org/0000-0002-1668-8765
Stainless steel (SS) 316L is widely used for hydraulic machinery of ash disposal slurry pumps. In this work, the Al-20TiO2 coating powders were sprayed on SS316L materials using the HVOF technique. The various properties such as erosion resistance, microhardness, microstructure, roughness, etc. were tested during the experiments. A pot tester was used to examine the rate of erosion. At an impact angle of 60 degrees, Al-20TiO2 coatings were found to erode the most. The neural computing was performed by using the artificial neural network model (ANN). The present ANN model produced the Pearson coefficient (R) of 0.99903, 0.99301, and 0.99194 respectively for training, validation, and testing. The overall R-value of the model was found as 0.99686. Microscopically, Al-20TiO2 demonstrated semi-brittle erosion behavior. Craters and ductile fractures were the most common erosion wear mechanisms detected on the Al-20TiO2 coating, indicating that this material had semi-ductile properties.
Neural computing for erosion assessment in Al-20TiO2 HVOF thermal spray coating
Int J Interact Des Manuf
Singh, Jashanpreet (author) / Vasudev, Hitesh (author) / Szala, Mirosław (author) / Gill, Harjot Singh (author)
2024-05-01
12 pages
Article (Journal)
Electronic Resource
English
Neural computing for erosion assessment in Al-20TiO2 HVOF thermal spray coating
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