Surface Modification of AH36 Steel Using ENi-P-nano TiO2 Composite Coatings Through ANN-Based Modelling and Prediction

Selvan, R. Anthoni Sagaya; Thakur, D. G.; Seeman, M.; Naik, Mahesh

doi:10.1007/s11804-022-00288-5

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…Numerous recent studies have adopted a data-driven methodology in the material and surface modification science [11,12]. The previous research effectively established the application of machine learning (ML) in material science, particularly in the electroless deposition process for predicting microhardness in ENi-P-TiO 2 coatings [13]. The current research promises a novel application of an Artificial Neural Network (ANN) based model for designing and fabricating the ENi-P composite coatings.…”

Section: Introductionmentioning

confidence: 99%

ANN for design and fabrication of ENi-P-nano TiO₂ coatings on AH36 steel

Selvan

Thakur

Salunkhe

2023

Materials Science and Technology

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Designing optimal ENi-P-nano TiO2 with higher surface hardness is time-consuming and expensive. Thus, this study is aims to develop the optimal optimiser-based ANN model to forcast Vickers hardness for newer process parametric combinations and find the ideal combination before manufacture. Consequently, three ANN model were devised using ADAM, AdaMax, and RMSprop optimisers with identical data set for training and testing. mean Squared Error (MSE) and R2 values were used to compare and assess their performance. Rectified Linear Unit (ReLU) is used to provide nonlinearity to the neural network's output since the activation function affects ANN model performance. The study also highlights hyperparameter setting's impact on optimiser performance. The ADAM-based ANN model was determined as optimal design through comparing the predicted and experimental Vickers hardness for the unknown combinations using Taguchi DOE. In addition, FESEM and XRD micrographs confirmed the optimal coating's morphology and phases. This study results shown the compatibility of utilising ANN-based prediction models for ENi-P-nanoTiO2 coating design and prediction, however future research much incoporates more process parameters and their level ranges.

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Section: Introductionmentioning

confidence: 99%

ANN for design and fabrication of ENi-P-nano TiO₂ coatings on AH36 steel

Selvan

Thakur

Salunkhe

2023

Materials Science and Technology

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Parametric optimization and minimization of corrosion rate of electroless Ni–P coating using Box-Behnken design and Artificial Neural Network

Mallick,

Mandal,

Mondal

et al. 2024

Results in Surfaces and Interfaces

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The Determination of the Stress Field Surrounding a Quasi-Constrained Inclusion in a Chromium-3.5nickel Alloy

Ashok Kumar,

Vimal Raja,

Vel Prakhash

et al. 2024

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">A method is proposed for estimating the stress field near a semi constrained inclusion. The volume of the stress-free misfit inclusion was separated into totally restricted and only loosely constrained parts. To achieve this, a fictional notch mirroring the inclusion matrices was introduced, and misfit displacement was applied. An elastic finite element analysis under a planar stress condition was conducted to identify the stress field surrounding a hydride blister in the Cr-3.5Ni pressure tube alloy. By assuming the orientation of hydride platelets in the matrix, the precision of computations was demonstrated. Our findings unveil distinct characteristics in the hydride platelet's axial dimension and orientation, highlighting their resistance to self-reorientation, even in the presence of radial hydrides. The consistent size and spacing of hydride platelets around the blister, despite nearby radial hydrides, emphasize the alloy's unique behavior. This study contributes essential insights into the stress dynamics and microstructural intricacies of hydride blister formation, crucial for ensuring the structural integrity of pressure tubes in nuclear reactors.</div></div>

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Surface Modification of AH36 Steel Using ENi-P-nano TiO2 Composite Coatings Through ANN-Based Modelling and Prediction

Cited by 3 publications

References 30 publications

ANN for design and fabrication of ENi-P-nano TiO₂ coatings on AH36 steel

ANN for design and fabrication of ENi-P-nano TiO₂ coatings on AH36 steel

Parametric optimization and minimization of corrosion rate of electroless Ni–P coating using Box-Behnken design and Artificial Neural Network

The Determination of the Stress Field Surrounding a Quasi-Constrained Inclusion in a Chromium-3.5nickel Alloy

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Surface Modification of AH36 Steel Using ENi-P-nano TiO2 Composite Coatings Through ANN-Based Modelling and Prediction

Cited by 3 publications

References 30 publications

ANN for design and fabrication of ENi-P-nano TiO2 coatings on AH36 steel

ANN for design and fabrication of ENi-P-nano TiO2 coatings on AH36 steel

Parametric optimization and minimization of corrosion rate of electroless Ni–P coating using Box-Behnken design and Artificial Neural Network

The Determination of the Stress Field Surrounding a Quasi-Constrained Inclusion in a Chromium-3.5nickel Alloy

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Product

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ANN for design and fabrication of ENi-P-nano TiO₂ coatings on AH36 steel

ANN for design and fabrication of ENi-P-nano TiO₂ coatings on AH36 steel