Artificial Neural Network based Prediction of Tensile Strength of Hybrid Composites

Vineela, M. Gayatri; Dave, Abhishek; Chaganti, Phaneendra Kiran

doi:10.1016/j.matpr.2018.06.356

Cited by 28 publications

(17 citation statements)

References 7 publications

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“…However, ANN model provided higher accuracy and less error than MLR. Gayatri et al employed ANN to predict the tensile strength of hybrid composites that made of carbon fiber, epoxy resin and glass fiber [ 17 ]. Experimental results showed that ANN was able to predict the tensile strength parameters with high accuracy as compared to MLR.…”

Section: Introductionmentioning

confidence: 99%

Use of an Artificial Neural Network for Tensile Strength Prediction of Nano Titanium Dioxide Coated Cotton

et al. 2022

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In this study, an artificial neural network (ANN) is used for the prediction of tensile strength of nano titanium dioxide (TiO2) coated cotton. The coating process was performed by ultraviolet (UV) radiations. Later on, a backpropagation ANN algorithm trained with Bayesian regularization was applied to predict the tensile strength. For a comparative study, ANN results were compared with traditional methods including multiple linear regression (MLR) and polynomial regression analysis (PRA). The input conditions for the experiment were dosage of TiO2, UV irradiation time and temperature of the system. Simulation results elucidated that ANN model provides high performance accuracy than MLR and PRA. In addition, statistical analysis was also performed to check the significance of this study. The results show a strong correlation between predicted and measured tensile strength of nano TiO2-coated cotton with small error values.

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Section: Introductionmentioning

confidence: 99%

Use of an Artificial Neural Network for Tensile Strength Prediction of Nano Titanium Dioxide Coated Cotton

et al. 2022

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“…Disadvantages are sensitivity to dataset, iterative process of determining the optimal structure, and hardware dependence [29]. ANNs are used in concrete mix design to predict optimal mix proportions or properties such as compressive and tensile strength [25,[34][35][36][37], modulus of elasticity [38], slump [2,[39][40][41], drying shrinkage [42], etc.…”

Section: Prediction Methods Referencementioning

confidence: 99%

“…Although most research work is focused on predicting the compressive strength, there are notable works handling other properties of concrete. Predictions of mechanical properties of hardened concrete such as flexural strength [34] for modified zeolite additive mortar, or [36] for hybrid composites, elastic modulus of recycled aggregate concrete [70], Poisson's ratio of lightweight concrete [71], fatigue strength [72], freeze-thaw durability [73], and electrical property prediction [74], showed to be useful. There have also been investigations focused on the properties of fresh concrete such as drying shrinkage [42], structural properties such as chloride permeability [75,76] and diffusivity [77], air void content [78], as well as the dependency of compressive strength on the concrete microstructure [79].…”

Section: Anns For Prediction Of Concrete Materials Behaviormentioning

confidence: 99%

Application of Artificial Neural Networks for Prediction of Mechanical Properties of CNT/CNF Reinforced Concrete

Kekez

Kubica

2021

Materials

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Prominence of concrete is characterized by its high mechanical properties and durability, combined with multifunctionality and aesthetic appeal. Development of alternative eco-friendly or multipurpose materials has conditioned improvements in concrete mix design to optimize concrete production speed and price, as well as carbon footprint. Artificial neural networks represent a new and efficient tool in achieving optimal concrete mixtures according to its intended function. This paper addresses concrete mix design and the application of artificial neural networks (ANNs) for self-sensing concrete. The authors review concrete mix design methods and the development of ANNs for prediction of properties for various types of concrete. Furthermore, the authors present developments and applications of ANNs for prediction of compressive strength and flexural strength of carbon nanotubes/carbon nanofibers (CNT/CNF) reinforced concrete using experimental results for the learning process. The goal is to bring the ANN approach closer to a variety of concrete researchers and possibly propose the implementation of ANNs in the civil engineering practice.

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“…. purposes and typically requires more memory, but less time [36]. The training automatically stops when generalization stops improving, as indicated by an increase in the mean square error of the validation samples.…”

Section: S/n Propertiesmentioning

confidence: 99%

“…The trainlm network training function, which updates the weights and biases according to Levenberg-Marquardt optimization, was employed. This is because it is often the fastest backpropagation algorithm in the Neural Network toolbox, and is highly suitable for supervised learning, although it requires more memory than other algorithms [36].…”

Section: S/n Propertiesmentioning

confidence: 99%

DEVELOPMENT OF NUMERICAL MODELS FOR THE PREDICTION OF TEMPERATURE AND SURFACE ROUGHNESS DURING THE MACHINING OPERATION OF TITANIUM ALLOY (Ti6Al14V)

et al. 2020

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Temperature and surface roughness are important factors, which determine the degree of machinability and the performance of both the cutting tool and the work piece material. In this study, numerical models obtained from the Response Surface Methodology (RSM) and Artificial Neural Network (ANN) techniques were used for predicting the magnitude of the temperature and surface roughness during the machining operation of titanium alloy (Ti6Al4V). The design of the numerical experiment was carried out using the Response Surface Methodology (RSM) for the combination of the process parameters while the Artificial Neural Network (ANN) with 3 input layers, 10 sigmoid hidden neurons and 3 linear output neurons were employed for the prediction of the values of temperature. The ANN was iteratively trained using the Levenberg-Marquardt backpropagation algorithm. The physical experiments were carried out using a DMU80monoBLOCK Deckel Maho 5-axis CNC milling machine with a maximum spindle speed of 18 000 rpm. A carbide-cutting insert (RCKT1204MO-PM S40T) was used for the machining operation. A professional infrared video thermometer with an LCD display and camera function (MT 696) with infrared temperature range of −50−1000 °C, was employed for the temperature measurement while the surface roughness of the work pieces were measured using the Mitutoyo SJ – 201, surface roughness machine. The results obtained indicate that there is high degree of agreement between the values of temperature and surface roughness measured from the physical experiments and the predicted values obtained using the ANN and RSM. This signifies that the developed RSM and ANN models are highly suitable for predictive purposes. This work can find application in the production and manufacturing industries especially for the control, optimization and process monitoring of process parameters.

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Artificial Neural Network based Prediction of Tensile Strength of Hybrid Composites

Cited by 28 publications

References 7 publications

Use of an Artificial Neural Network for Tensile Strength Prediction of Nano Titanium Dioxide Coated Cotton

Use of an Artificial Neural Network for Tensile Strength Prediction of Nano Titanium Dioxide Coated Cotton

Application of Artificial Neural Networks for Prediction of Mechanical Properties of CNT/CNF Reinforced Concrete

DEVELOPMENT OF NUMERICAL MODELS FOR THE PREDICTION OF TEMPERATURE AND SURFACE ROUGHNESS DURING THE MACHINING OPERATION OF TITANIUM ALLOY (Ti6Al14V)

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