Applying a Neural Network-Based Machine Learning to Laser-Welded Spark Plasma Sintered Steel: Predicting Vickers Micro-Hardness

Olanipekun, Ayorinde Tayo; Mashinini, Peter Madindwa; Owojaiye, Oluwakemi Adejoke; Maledi, Nthabiseng

doi:10.3390/jmmp6050091

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…The low RMSE value is a parameter that indicates whether the performance of the chosen model is suitable or not [39]. It is observed in Table 3 that it is better to evaluate the model based on the testing set due to more data points in the training set that can lead to more likely high non-normal values [40]. In sum, it was determined that the whole performance of the model decreased as the number of neurons increased beyond a set limit.…”

Section: Resultsmentioning

confidence: 99%

Application of the Artificial Neural Network to Predict the Bending Strength of the Engineered Laminated Wood Produced Using the Hydrolyzed Soy Protein-Melamine Urea Formaldehyde Copolymer Adhesive

2023

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The artificial neural network (ANN) was used to predict the modulus of rupture (MOR) of the laminated wood products adhered by melamine/urea formaldehyde (MUF) resin with different formaldehyde to melamine/urea molar ratios combined with different weight ratios of the protein adhesive resulting from the alkaline treatment (NaOH) of the soybean oil meal to MUF resin pressed at different temperatures according to the central composite design (CCD). After making the boards and performing the mechanical test to measure the MOR, based on experimental data, different statistics such as determination coefficient (R2), root mean square error (RMSE), mean absolute error (MAE) and sum of squares error (SSE) were determined, and then the suitable algorithm was selected to determine the estimated values. After comparing estimated values with the experimental values, the direct and interactive effects of the independent variables on MOR were determined. The results indicated that using suitable algorithms to train the ANN well, a very good estimate of the bending strength of the laminated wood products can be offered with the least error. In addition, based on the estimated and measured strengths and FTIR and TGA diagnostic analyses, it was found that the replacement of the MUF resin by the protein bio-based adhesive when using low F to M/U molar ratios, the MOR is maximized if a high range of temperature is used during the press.

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Section: Resultsmentioning

confidence: 99%

Application of the Artificial Neural Network to Predict the Bending Strength of the Engineered Laminated Wood Produced Using the Hydrolyzed Soy Protein-Melamine Urea Formaldehyde Copolymer Adhesive

2023

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“…NNs were employed in building predictive models in many disciplines [27,28]. In much research, neural networks are coupled with DOE techniques for developing predictive models [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Drilling GFRP Composites Using ANN Enhanced by Particle Swarm Algorithm

Abd-Elwahed

2023

Processes

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This paper aims to optimize the quality characteristics of the drilling process in glass fiber-reinforced polymer (GFRP) composites. It focuses on optimizing the drilling parameters with drill point angles concerning delamination damage and energy consumption, simultaneously. The effects of drilling process parameters on machinability were analyzed by evaluating the machinability characteristics. The cutting power was modeled through drilling parameters (speed and feed), drill point angle, and laminate thickness. The response surface analysis and artificial neural networks enhanced by the particle swarm optimization algorithm were applied for modeling and evaluating the effect of process parameters on the machinability of the drilling process. The most influential parameters on machinability properties and delamination were determined by analysis of variance (ANOVA). A multi-response optimization was performed to optimize drilling process parameters for sustainable drilling quality characteristics. The obtained models were applied to predict drilling process characteristics, and exhibited an excellent harmony with the experiment results. The optimal drilling process factors were the highest spindle speed and the lowest feed, with a drill point angle of 118° for the laminate of 4.75 mm thickness.

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Comparison Analysis of Activation and Optimizer on Long Short-Term Memory Algorithm for Artificial Intelligence in Transportation Stock Price Prediction

Pramudya,

Kristiyanti

2023

Preprint

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Applying a Neural Network-Based Machine Learning to Laser-Welded Spark Plasma Sintered Steel: Predicting Vickers Micro-Hardness

Cited by 3 publications

References 35 publications

Application of the Artificial Neural Network to Predict the Bending Strength of the Engineered Laminated Wood Produced Using the Hydrolyzed Soy Protein-Melamine Urea Formaldehyde Copolymer Adhesive

Application of the Artificial Neural Network to Predict the Bending Strength of the Engineered Laminated Wood Produced Using the Hydrolyzed Soy Protein-Melamine Urea Formaldehyde Copolymer Adhesive

Multi-Objective Optimization of Drilling GFRP Composites Using ANN Enhanced by Particle Swarm Algorithm

Comparison Analysis of Activation and Optimizer on Long Short-Term Memory Algorithm for Artificial Intelligence in Transportation Stock Price Prediction

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