Optimization of fatigue life of pearlitic Grade 900A steel based on the combination of genetic algorithm and artificial neural network

Nejad, Reza Masoudi; Sina, Nima; Ma, Wenchen; Liu, Zhiliang; Berto, Filippo; Gholami, Aboozar

doi:10.1016/j.ijfatigue.2022.106975

Cited by 27 publications

(12 citation statements)

References 41 publications

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“…These parameters vary depending on the number of input and output data. The process of generating outputs involves summing the weighted input signals, adding a bias, passing through an activation function, and producing the outputs, as expressed by Equation (8) [ 44 ]:

where x i is the input value, w i is the weight, m is the number of data samples, b is the bias, and φ is the activation function.…”

Section: Methodsmentioning

confidence: 99%

Modeling Xanthan Gum Foam’s Material Properties Using Machine Learning Methods

Ergün,

Ergün

2024

Polymers

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Xanthan gum is commonly used in the pharmaceutical, cosmetic, and food industries. However, there have been no studies on utilizing this natural biopolymer as a foam material in the insulation and packaging sectors, which are large markets, or modeling it using an artificial neural network. In this study, foam material production was carried out in an oven using different ratios of cellulose fiber and xanthan gum in a 5% citric acid medium. As a result of the physical and mechanical experiments conducted, it was determined that xanthan gum had a greater impact on the properties of the foam material than cellulose. The densities of the produced foam materials ranged from 49.42 kg/m3 to 172.2 kg/m3. In addition, the compressive and flexural moduli were found to vary between 235.25 KPa and 1257.52 KPa and between 1939.76 KPa and 12,736.39 KPa, respectively. Five machine-learning-based methods (multiple linear regression, support vector machines, artificial neural networks, least squares methods, and generalized regression neural networks) were utilized to analyze the effects of the components used in the foam formulation. These models yielded accurate results without time, material, or cost losses, making the process more efficient. The models predicted the best results for density, compression modulus, and flexural modulus achieved in the experimental tests. The generalized regression neural network model yielded impressive results, with R2 values above 0.97, enabling the acquisition of more quantitative data with fewer experimental results.

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where x i is the input value, w i is the weight, m is the number of data samples, b is the bias, and φ is the activation function.…”

Section: Methodsmentioning

confidence: 99%

Modeling Xanthan Gum Foam’s Material Properties Using Machine Learning Methods

Ergün,

Ergün

2024

Polymers

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“…Therefore, artificial neural networks could be employed to predict the load bearing and efficiency of FSW joints. The works [14,15] discuss the use of machine learning methods to analyze the results of experimental test in the regression issue. Based on the analysis of backpropagation neural networks, support vector regression, Gaussian process regression, and random forest (RF), the developed backpropagation neural network model showed greater accuracy and excellent capabilities compared to other methods.…”

Section: Parameter Optimization Using a Machine Learning Approachmentioning

confidence: 99%

Development of FSW Process Parameters for Lap Joints Made of Thin 7075 Aluminum Alloy Sheets

Lacki,

Derlatka,

Więckowski

et al. 2024

Materials

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The article describes machine learning using artificial neural networks (ANNs) to develop the parameters of the friction stir welding (FSW) process for three types of aluminum joints (EN AW 7075). The ANNs were built using a total of 608 experimental data. Two types of networks were built. The first one was used to classify good/bad joints with MLP 7-19-2 topology (one input layer with 7 neurons, one hidden layer with 19 neurons, and one output layer with 2 neurons), and the second one was used to regress the tensile load-bearing capacity with MLP 7-19-1 topology (one input layer with 7 neurons, one hidden layer with 19 neurons, and one output layer with 1 neuron). FSW parameters, such as rotational speed, welding speed, and joint and tool geometry, were used as input data for ANN training. The quality of the FSW joint was assessed in terms of microstructure and mechanical properties based on a case study. The usefulness of both trained neural networks has been demonstrated. The quality of the validation set for the regression network was approximately 93.6%, while the errors for the confusion matrix of the test set never exceeded 6%. Only 184 epochs were needed to train the regression network. The quality of the validation set was approximately 87.1%. Predictive maps were developed and presented in the work, allowing for the selection of optimal parameters of the FSW process for three types of joints.

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“…Many researchers have utilized ANNs to predict liquefaction [ 27 ], compaction properties of fine-grained soils [ 28 ], mixing soil [ 29 , 30 ], displacement at selected points of the clayey cover of the landfill model [ 4 ], soil thermal conductivity [ 31 ], and soil compaction [ 32 ]. In addition to geotechnical engineering, artificial neural networks have been applied in many fields of engineering [ 33 ], and to improve the accuracy of ANN predictions, genetic algorithms combined with ANNs [ 34 ] and deep learning combined with ANNs [ 35 ] have been applied. In this study, a catastrophe theory and a B-spline model were used to improve prediction quality.…”

Section: Introductionmentioning

confidence: 99%

The use of polyurethane foam-sand mixtures in sandy embankment design- predicting seismic response using FEM, catastrophe theory, B-spline method, and artificial neural networks

Mughieda,

Namdar,

Nie

2024

Heliyon

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Optimization of fatigue life of pearlitic Grade 900A steel based on the combination of genetic algorithm and artificial neural network

Cited by 27 publications

References 41 publications

Modeling Xanthan Gum Foam’s Material Properties Using Machine Learning Methods

Modeling Xanthan Gum Foam’s Material Properties Using Machine Learning Methods

Development of FSW Process Parameters for Lap Joints Made of Thin 7075 Aluminum Alloy Sheets

The use of polyurethane foam-sand mixtures in sandy embankment design- predicting seismic response using FEM, catastrophe theory, B-spline method, and artificial neural networks

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