Wear response prediction of TiO2-polyester composites using neural networks

Satapathy, Alok; Tarkes, Dora P.; Nayak, Nadiya B.

doi:10.1007/s12588-010-0004-4

Cited by 7 publications

(1 citation statement)

References 14 publications

(8 reference statements)

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“…A novel technique such as artificial neural network (ANN), which is inspired by the biological neural system, has been used to solve a wide variety of problems in diverse fields and analyze and predict the wear response under different test conditions. ANN has been efficaciously employed by the researchers in the prediction of sliding wear behavior, erosion wear behavior, and also for abrasive wear behavior to set inputs within the experimental domain, which thereby helps in saving time and resources for a large number of experimental trials.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Possibility of Utilization of Red Mud Epoxy Based Functionally Graded Materials as Wear-Resistant Materials Using Taguchi Design of Experiment

Siddhartha

Singh

Yadav³

2015

Adv. Polym. Technol.

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The study aims for potential utilization of red mud (an industrial waste) to be utilized as a wear-resistant material.Investigations on mechanical and wear characteristics of red mud epoxy based homogeneous and their functionally graded materials (FGMs) developed with an intent of probable application in tribological systems are presented. The influence of significant operational parameters, material parameters, and their subsequent influences among themselves are explored. A specially designed experimental series is performed on a pin-on-disk machine with three different sliding velocities 105, 209, and 314 cm/s under three different loading conditions of 20, 30, and 40 N and compares the experimental results with the reported theoretical wear model. Artificial neural network approach is also applied to the wear data for subsequent validation. The comparative study indicates that although the homogeneous composites and FGMs exhibit relatively inferior mechanical properties, the sliding wear performance of the FGMs is better than homogeneous composites. It is also found that out of all synthesized composites FGMs exhibit the maximum impact strength and tensile modulus, which clearly indicates that the gradation achieved because the centrifugation technique enhances the crack arresting capability of the materials. The measured Young's moduli of homogeneous composites and FGMs were compared to standard theoretical prediction models. The measured moduli values showed good harmony with those predicted from Halpin-Tsai and Kerner models. Transmission electron microscopy microstructures confirm the continuous graded dispersion of red mud particles in the matrix. C 2015 Wiley Periodicals, Inc. Adv Polym Technol 201 , wileyonlinelibrary.com.

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Section: Introductionmentioning

confidence: 99%

Exploring the Possibility of Utilization of Red Mud Epoxy Based Functionally Graded Materials as Wear-Resistant Materials Using Taguchi Design of Experiment

Siddhartha

Singh

Yadav³

2015

Adv. Polym. Technol.

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Simultaneous prediction of delamination and surface roughness in drilling GFRP composite using ANN

Behera

Ghadai

Kalita

et al. 2016

Int J Plast Technol

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Experimental investigation and prediction of tribological behavior of unidirectional short castor oil fiber reinforced epoxy composites

Egala

Jagadeesh²,

Setti

2020

Friction

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The present study aims at introducing a newly developed natural fiber called castor oil fiber, termed ricinus communis, as a possible reinforcement in tribo-composites. Unidirectional short castor oil fiber reinforced epoxy resin composites of different fiber lengths with 40% volume fraction were fabricated using hand layup technique. Dry sliding wear tests were performed on a pin-on-disc tribometer based on full factorial design of experiments (DoE) at four fiber lengths (5, 10, 15, and 20 mm), three normal loads (15, 30, and 45 N), and three sliding distances (1,000, 2,000, and 3,000 m). The effect of individual parameters on the amount of wear, interfacial temperature, and coefficient of friction was studied using analysis of variance (ANOVA). The composite with 5 mm fiber length provided the best tribological properties than 10, 15, and 20 mm fiber length composites. The worn surfaces were analyzed under scanning electron microscope. Also, the tribological behavior of the composites was predicted using regression, artificial neural network (ANN)-single hidden layer, and ANN-multi hidden layer models. The confirmatory test results show the reliability of predicted models. ANN with multi hidden layers are found to predict the tribological performance accurately and then followed by ANN with single hidden layer and regression model.

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Wear response prediction of TiO2-polyester composites using neural networks

Cited by 7 publications

References 14 publications

Exploring the Possibility of Utilization of Red Mud Epoxy Based Functionally Graded Materials as Wear-Resistant Materials Using Taguchi Design of Experiment

Exploring the Possibility of Utilization of Red Mud Epoxy Based Functionally Graded Materials as Wear-Resistant Materials Using Taguchi Design of Experiment

Simultaneous prediction of delamination and surface roughness in drilling GFRP composite using ANN

Experimental investigation and prediction of tribological behavior of unidirectional short castor oil fiber reinforced epoxy composites

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