Use of neural networks and artificial intelligence tools for modeling, characterization, and predicting in material engineering

Temtam, Ahmed; Abusoua, Abdraouf; Benyounis, Khaled; Tamtam, Abdalmonem

doi:10.1016/b978-0-323-96020-5.00088-1

Comprehensive Materials Processing 2024

DOI: 10.1016/b978-0-323-96020-5.00088-1

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Use of neural networks and artificial intelligence tools for modeling, characterization, and predicting in material engineering

Ahmed Temtam,

Abdraouf Abusoua,

Khaled Benyounis

et al.

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2024

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Cited by 2 publications

References 24 publications

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Review on applications of artificial neural networks to develop high entropy alloys: A state-of-the-art technique

Dewangan,

Nagarjuna,

Jain

et al. 2023

Materials Today Communications

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Review on applications of artificial neural networks to develop high entropy alloys: A state-of-the-art technique

Dewangan,

Nagarjuna,

Jain

et al. 2023

Materials Today Communications

View full text Add to dashboard Cite

A multivariate method of forecasting the nonlinear dynamics of production network based on multilayer neural models

Martsenyuk,

Kit

2024

Visnyk TNTU

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Design of production network based on multilayer neural models is considered in this paper. Design of production network is crucial because it determines the optimal location of production and logistics facilities, affects cost efficiency, customer service level and overall competitiveness in the global market. Multi-layer neural networks play an important role in this process, using advanced algorithms, machine learning models and optimization techniques to analyze huge amounts of data. Special attention is focused on qualitative analysis of dynamic behavior, dynamic lattice model. The model includes rate constants and initial conditions affecting the model trajectories, which can be classified as a stable site, limit cycle, or chaotic attractor. We aim to solve the problem of qualitative behavior of the model as a problem of multilayer neural models. A multivariate method of predicting nonlinear dynamics was used to construct the training data set. Neural networks defined by regenerative architectures with linear and non-linear outputs were analyzed and compared. As a result of the analysis, it was found that architectures with linear outputs show better correspondence between expected and predicted values. Architectures with non-linear outputs, despite their complexity, exhibit less accuracy and more deviation compared to linear ones. The single-layer architecture with linear outputs shows the best accuracy, although the two-layer architecture with linear outputs has the lowest rms error. Architectures with non-linear outputs have faster training times but poor accuracy, while architectures with linear outputs require more training time but have lower errors. The results obtained in the work indicate the importance of choosing the right architecture of the neural network depending on the tasks and requirements for accuracy and training time of the model.

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Use of neural networks and artificial intelligence tools for modeling, characterization, and predicting in material engineering

Cited by 2 publications

References 24 publications

Review on applications of artificial neural networks to develop high entropy alloys: A state-of-the-art technique

Review on applications of artificial neural networks to develop high entropy alloys: A state-of-the-art technique

A multivariate method of forecasting the nonlinear dynamics of production network based on multilayer neural models

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