Artificial neural network for random fatigue loading analysis including the effect of mean stress

Durodola, J.F.; Ramachandra, Shashidhar; Gerguri, Shpend; Fellows, N.A.

doi:10.1016/j.ijfatigue.2018.02.007

Cited by 68 publications

(49 citation statements)

References 24 publications

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“…The training, validation and testing process followed in this work is similar to those in previous works by the author and colleagues [30,31,43]. Generally, the feed forward backpropagation multilayer perceptron (MLP) model was followed.…”

Section: Training Validation and Testing Of The Ann Modelsmentioning

confidence: 97%

“…The input parameters considered are presented in section 4.2. The number of hidden layer used was 25 as in previous studies [30,31]. This number was found to be adequate by checking the residual error and goodness of fit of the ANN prediction with target results.…”

Section: Ann Architecturementioning

confidence: 99%

“…Stationary zero mean Gaussian signals were covered in the first instance; this was followed by ANN models which cover globally non zero mean stress signals [31]. As highlighted in the foregoing, non Gaussian loading cause more damage because of burst effects than zero mean stationary Gaussian data.…”

Section: Introductionmentioning

confidence: 99%

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Artificial neural network for Gaussian and non-Gaussian random fatigue loading analysis

Durodola

2019

Proceedings of the Institution of Mechanical Engineers, Part C:

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There has been a lot of work done on the analysis of Gaussian loading analysis perhaps because its occurrence is more common than non-Gaussian loading problems. It is nevertheless known that non-Gaussian load occurs in many instances especially in various forms of transport, land, sea and space. Part of the challenge with non-Gaussian loading analysis is the increased number of variables that are needed to model the loading adequately. Artificial neural network approach provides a versatile means to develop models that may require many input variables in order to achieve applicable predictive generalisation capabilities. Artificial neural network has been shown to perform much better than existing frequency domain methods for random fatigue loading under stationary Gaussian load forms especially when mean stress effects are included. This paper presents an artificial neural network model with greater predictive capability than existing frequency domain methods for both Gaussian and non-Gaussian loading analysis. Both platykurtic and leptokurtic non-Gaussian loading cases were considered to demonstrate the scope of application. The model was also validated with available SAE experimental data, even though the skewness and kurtosis of the signal in this case were mild.

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Section: Training Validation and Testing Of The Ann Modelsmentioning

confidence: 97%

Section: Ann Architecturementioning

confidence: 99%

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Artificial neural network for Gaussian and non-Gaussian random fatigue loading analysis

Durodola

2019

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…The maintained level of deviations in the plot proves it to be normalized from higher deviations of data 29 . The higher mean square error value is due to the lower samples of data 30 . The decrease in the mean square error value also proves the working of feed-back gradient descent optimization mechanism of algorithm, through which the parameters are made to fit into the model correctly by improving the weight of the input data 31 .…”

Section: Mean Square Errormentioning

confidence: 99%

Construction of Feed Forward MultiLayer Perceptron Model For Genetic Dataset in Leishmaniasis Using Cognitive Computing

Mahalingam

Kabra

Singh

2018

Preprint

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Leishmaniasis is an endemic parasitic disease, predominantly found in the poor locality of Africa, Asia and Latin America. It is associated with malnutrition, weak immune system of people and their housing locality. At present, it is diagnosed by microscopic identification, molecular and biochemical characterisation or serum analysis for parasitic compounds. In this study, we present a new approach for diagnosing Leishmaniasis using cognitive computing. The Genetic datasets of leishmaniasis are collected from Gene Expression Omnibus database and it's then processed. The algorithm for training and developing a model, based on the data is prepared and coded using python. The algorithm and their corresponding datasets are integrated using TensorFlow dataframe. A feed forward Artificial Neural Network trained model with multi-layer perceptron is developed as a diagnosing model for Leishmaniasis, using genetic dataset. It is developed using recurrent neural network. The cognitive model of the trained network is interpreted using the maps and mathematical formula of the influencing parameters. The credit of the system is measured using the accuracy, loss and error of the system. This integrated system of the leishmaniasis genetic dataset and neural network proved to be the good choice for diagnosis with higher accuracy and lower error. Through this approach, all records of the data are effectively incorporated into the system. The experimental results of feed forward multilayer perceptron model after normalization; mean square error (219.84), loss function (1.94) and accuracy (85.71%) of the model, shows good fit of model with the process and it could possibly serve as a better solution for diagnosing Leishmaniasis in future, using genetic datasets.The code is available in Github repository: https://github.com/shailzasingh/Machine-Learning-code-for-analyzing-genetic-dataset-in-Leishmaniasis

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“…KhudariBek et al indicated that the overall elastic modulus of Nanocomposite material increases with the volume fraction of nano-particles and inter-phase thickness [11]. According to J F Durodola et al greater resolution was observed with the artificial neural network method than with other available methods that include the effect of mean stress on the frequency domain approach and predict the fatigue damage [12]. Aparna Gangele et al mentioned in the report that the FEM model shows that the single layer graphene helps to enhance the mechanical properties of silicon nanosheets, which is helpful for the silicon-based semiconductor industry [13].…”

Section: Introductionmentioning

confidence: 99%

Fatigue Characterization Of Sisal Fiber Reinforced Composites Using Experimental And Finite Element Method

Babu¹,

Kumar²,

Prasanthi³

et al. 2019

IJITEE

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The objective of this study is to investigate the fatigue behavior of sisal fiber reinforced with carbon nanotubes. The hand lay-up technique is used to prepare the composite material samples. The fatigue response of pure polymer matrix, composite material which is prepared by reinforcing a sisal fiber reinforced with a polymer matrix was studied. The effectiveness of nano reinforcement of fatigue response is identified from experiments. Later, the fatigue response of sisal and nano particle reinforced sisal fiber composites (hybrid composite) is identified with irregularities by using finite element based software ANSYS. The elastic properties of sisal fiber reinforced composite and carbon nanotube reinforced composite is estimated by using the principles of Micromechanics and Macro-mechanics. The failure mechanism of polymer, conventional sisal fiber composites and nano filled sisal fiber reinforced composites are identified. The effect of the shape of the irregularities on the fatigue response is also identified from ANSYS software. From the present work, it is observed that, the reinforcement of nano reinforcement has considerable influence on the fatigue response of the resulting composite.

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Artificial neural network for random fatigue loading analysis including the effect of mean stress

Cited by 68 publications

References 24 publications

Artificial neural network for Gaussian and non-Gaussian random fatigue loading analysis

Artificial neural network for Gaussian and non-Gaussian random fatigue loading analysis

Construction of Feed Forward MultiLayer Perceptron Model For Genetic Dataset in Leishmaniasis Using Cognitive Computing

Fatigue Characterization Of Sisal Fiber Reinforced Composites Using Experimental And Finite Element Method

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