Artificial Neural Networks Prediction of Rubber Mechanical Properties in Aged and Nonaged State

Ružiak, Ivan; Koštial, Pavel; Jančíková, Zora; Gajtanska, Milada; Krišťák, Ľuboš; Kopal, Ivan; Polakovič, Peter

doi:10.1007/978-3-319-59590-0_3

Cited by 8 publications

(6 citation statements)

References 8 publications

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“…Figure 2 illustrates the flowchart of the adopted methodology in this study to estimate the mechanical properties of weathered wood through machine learning (ML) modeling. Different ML models and artificial neural networks (ANNs) have been employed for damage/defect detection [ 69 , 70 ], prediction of the material’s properties [ 71 , 72 ], and process condition monitoring [ 73 , 74 , 75 , 76 ]. Decision tree regression modeling was used in this study for the prediction of the mechanical properties of wood samples.…”

Section: Methodsmentioning

confidence: 99%

Prediction of Mechanical Properties of Artificially Weathered Wood by Color Change and Machine Learning

et al. 2021

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Color parameters were used in this study to develop a machine learning model for predicting the mechanical properties of artificially weathered fir, alder, oak, and poplar wood. A CIELAB color measuring system was employed to study the color changes in wood samples. The color parameters were fed into a decision tree model for predicting the MOE and MOR values of the wood samples. The results indicated a reduction in the mechanical properties of the samples, where fir and alder were the most and least degraded wood under weathering conditions, respectively. The mechanical degradation was correlated with the color change, where the most resistant wood to color change exhibited less reduction in the mechanical properties. The predictive machine learning model estimated the MOE and MOR values with a maximum R2 of 0.87 and 0.88, respectively. Thus, variations in the color parameters of wood can be considered informative features linked to the mechanical properties of small-sized and clear wood. Further research could study the effectiveness of the model when analyzing large-sized timber.

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Section: Methodsmentioning

confidence: 99%

Prediction of Mechanical Properties of Artificially Weathered Wood by Color Change and Machine Learning

et al. 2021

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“…In the research, we deal with the effect of plasticizer and the amount of surfactant on certain mechanical properties such as strength R m and moduli M100, M200, M300. The analysis method we applied was based on our previous experimental work (Ružiak et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis: A tool for tailoring environmentally friendly materials

Seidl

Ružiak

Jančíková

et al. 2022

Expert Systems with Applications

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“…The issue of ANNs is discussed in detail in a number of publications, such as the monograph [27], or in our works [28,29,30], where there is a significant space devoted to various learning algorithms of various ANNs types. Therefore, only the type of ANN will be mentioned here, namely the radial basis function ANN, which is directly related to the solution of the presented work issue, i.e.…”

Section: Introductionmentioning

confidence: 99%

Radial Basis Function Neural Network-Based Modeling of the Dynamic Thermo-Mechanical Response and Damping Behavior of Thermoplastic Elastomer Systems

et al. 2019

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The presented work deals with the creation of a new radial basis function artificial neural network-based model of dynamic thermo-mechanical response and damping behavior of thermoplastic elastomers in the whole temperature interval of their entire lifetime and a wide frequency range of dynamic mechanical loading. The created model is based on experimental results of dynamic mechanical analysis of the widely used thermoplastic polyurethane, which is one of the typical representatives of thermoplastic elastomers. Verification and testing of the well-trained radial basis function neural network for temperature and frequency dependence of dynamic storage modulus, loss modulus, as well as loss tangent prediction showed excellent correspondence between experimental and modeled data, including all relaxation events observed in the polymeric material under study throughout the monitored temperature and frequency interval. The radial basis function artificial neural network has been confirmed to be an exceptionally high-performance artificial intelligence tool of soft computing for the effective predicting of short-term viscoelastic behavior of thermoplastic elastomer systems based on experimental results of dynamic mechanical analysis.

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Artificial Neural Networks Prediction of Rubber Mechanical Properties in Aged and Nonaged State

Cited by 8 publications

References 8 publications

Prediction of Mechanical Properties of Artificially Weathered Wood by Color Change and Machine Learning

Prediction of Mechanical Properties of Artificially Weathered Wood by Color Change and Machine Learning

Sensitivity analysis: A tool for tailoring environmentally friendly materials

Radial Basis Function Neural Network-Based Modeling of the Dynamic Thermo-Mechanical Response and Damping Behavior of Thermoplastic Elastomer Systems

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