Correction to: Machine Learning, Optimization, and Data Science

Nicosia, Giuseppe; Ojha, Varun Kumar; Malfa, Emanuele La; Jansen, Giorgio; Sciacca, Vincenzo; Pardalos, Pãnos M.; Giuffrida, Giovanni; Umeton, Renato

doi:10.1007/978-3-030-64583-0_64

Cited by 2 publications

(1 citation statement)

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“…Al 2 O 3 . Since nickel is the ductile phase in the coating, the deformation is the most evident fundamental mechanism that occurred [25]. Research has shown that the high toughness and microhardness of alumina help to reduce the amount of erosion and wear that occurs [10].…”

Section: Erosion Mechanismsmentioning

confidence: 99%

Study on wear analysis of Ni-20Al2O3 HVOF micron layers using artificial neural network technique

Singh

Vasudev

Kumar

et al. 2023

Int J Interact Des Manuf

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The paper presents the study of the erosion performance of stainless steel SS316L grade. The micron layers of Ni-20Al 2 O 3 powder were sprayed on the surface SS316L by utilizing a high-velocity oxygen-fuel (HVOF) thermal spraying process. Wear experiments were performed in a slurry pot tester (Ducom TR-41) at different speeds varying from 600 to 1500 rpm. During the experiments, the sand was used to erode the coated surface of SS316L. Multi-sized slurry having a solid concentration lies in the range of 30-60% (by weight) was prepared to perform the wear experiments. A neural network technique was implemented to predict the erosion data by training 75% of the total data. Results show that the microhardness of bare and Ni-20Al 2 O 3 HVOF coated SS316L was tested as 196±21 and 393.32±17 VHN respectively. The current ANN model yielded an R-value that was 0.98871 for training, 0.97209 for validation, and 0.96264 for testing accordingly. It was determined that the total R-value of the model was 0.98153. The ANN's performance evaluated based on the mean squared error (MSE) was determined as 0.0176, 0.00265, and 0.00334 respectively for the training, validation, and testing.

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Section: Erosion Mechanismsmentioning

confidence: 99%

Study on wear analysis of Ni-20Al2O3 HVOF micron layers using artificial neural network technique

Singh

Vasudev

Kumar

et al. 2023

Int J Interact Des Manuf

View full text Add to dashboard Cite

show abstract

Study on Wear Analysis of Ni-20Al2O3 HVOF Micron Layers Using Artificial Neural Network Technique

Singh

Vasudev

et al. 2023

Preprint

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The paper presents the study of the erosion performance of stainless steel SS316L grade. The micron layers of Ni-20Al2O3 powder were sprayed on the surface SS316L by utilizing a high-velocity oxygen–fuel (HVOF) thermal spraying process. Wear experiments were performed in a slurry pot tester (Ducom TR-41) at different speeds varying from 600 to 1500 rpm. During the experiments, the sand was used to erode the coated surface of SS316L. Multi-sized slurry having a solid concentration lies in the range of 30-60% (by weight) was prepared to perform the wear experiments. A neural network technique was implemented to predict the erosion data by training 75% of the total data. Results show that the microhardness of bare and Ni-20Al2O3 HVOF coated SS316L was tested as 196±21 and 393.32±17 VHN respectively. The current ANN model yielded an R-value that was 0.98871 for training, 0.97209 for validation, and 0.96264 for testing accordingly. It was determined that the total R-value of the model was 0.98153. The ANN’s performance evaluated based on the mean squared error (MSE) was determined as 0.0176, 0.00265, and 0.00334 respectively for the training, validation, and testing.

show abstract

Correction to: Machine Learning, Optimization, and Data Science

Cited by 2 publications

References 0 publications

Study on wear analysis of Ni-20Al2O3 HVOF micron layers using artificial neural network technique

Study on wear analysis of Ni-20Al2O3 HVOF micron layers using artificial neural network technique

Study on Wear Analysis of Ni-20Al2O3 HVOF Micron Layers Using Artificial Neural Network Technique

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