Application of classification neural networks for identification of damage stages of degraded low alloy steel based on acoustic emission data analysis

Krajewska-Śpiewak, Joanna; Lasota, Igor; Kozub, B.

doi:10.1007/s43452-020-00112-3

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…Multilayer perceptron type ANNs have been used as well to enhance the spatial resolution in detector modules constituting the PET (positron emission tomography) scanner [47]. Artificial neural networks are also used in agriculture, agrophysics or agricultural engineering [16,40,41,[43][44][45][48][49][50][51][52][53][54][55][56].…”

Section: Introductionmentioning

confidence: 99%

The Use of Artificial Neural Networks for Determining Values of Selected Strength Parameters of Miscanthus × Giganteus

et al. 2022

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The aim of this paper is to develop neural models enabling the determination of biomechanical parameters for giant miscanthus stems. The static three-point bending test is used to determine the bending strength parameters of the miscanthus stem. In this study, we assume the modulus of elasticity bending and maximum stress in bending as the dependent variables. As independent variables (inputs of the neural network) we assume water content, internode number, maximum bending force value and dimensions characterizing the cross-section of miscanthus stem: maximum and minimum stem diameter and stem wall thickness. The four developed neural models, enabling the determination of the value of the modulus of elasticity in bending and the maximum stress in bending, demonstrate sufficient and even very high accuracy. The neural networks have an average relative error of 2.18%, 2.21%, 3.24% and 0.18% for all data subsets, respectively. The results of the sensitivity analysis confirmed that all input variables are important for the accuracy of the developed neural models—correct semantic models.

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Section: Introductionmentioning

confidence: 99%

The Use of Artificial Neural Networks for Determining Values of Selected Strength Parameters of Miscanthus × Giganteus

et al. 2022

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Application of Instrumented Indentation Test and Neural Networks to determine the constitutive model of in-situ austenitic stainless steel components

Ma,

Basterrech,

Halama

et al. 2024

Archiv.Civ.Mech.Eng

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Over the last few decades, Instrumented Indentation Test (IIT) has evolved into a versatile and convenient method for assessing the mechanical properties of metals. Unlike conventional hardness tests, IIT allows for incremental control of the indenter based on depth or force, enabling the measurement of not only hardness but also tensile properties, fracture toughness, and welding residual stress. Two crucial measures in IIT are the reaction force (F) exerted by the tested material on the indenter and the depth of the indenter (D). Evaluation of the mentioned properties from F–D curves typically involves complex analytical formulas that restricts the application of IIT to a limited group of materials. Moreover, for soft materials, such as austenitic stainless steel SS304L, with excessive pile-up/sink-in behaviors, conducting IIT becomes challenging due to improper evaluation of the imprint depth. In this work, we propose a systematic procedure for replacing complex analytical evaluations of IIT and expensive physical measurements. The proposed approach is based on the well-known potential of Neural Networks (NN) for data-driven modeling. We carried out physical IIT and tensile tests on samples prepared from SS304L. In addition, we generated multiple configurations of material properties and simulated the corresponding number of IITs using Finite Element Method (FEM). The information provided by the physical tests and simulated data from FEM are integrated into an NN, to produce a parametric mapping that can predict the parameters of a constitutive model based on any given F–D curve. Our physical and numerical experiments successfully demonstrate the potential of the proposed approach.

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Deformation stage identification in steel material using acoustic emission with a hybrid denoising method and artificial neural network

Cheng,

Sun,

Yan

et al. 2025

Mechanical Systems and Signal Processing

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Application of classification neural networks for identification of damage stages of degraded low alloy steel based on acoustic emission data analysis

Cited by 4 publications

References 16 publications

The Use of Artificial Neural Networks for Determining Values of Selected Strength Parameters of Miscanthus × Giganteus

The Use of Artificial Neural Networks for Determining Values of Selected Strength Parameters of Miscanthus × Giganteus

Application of Instrumented Indentation Test and Neural Networks to determine the constitutive model of in-situ austenitic stainless steel components

Deformation stage identification in steel material using acoustic emission with a hybrid denoising method and artificial neural network

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