Application of artificial neural networks to predict sliding wear resistance of Ni–TiN nanocomposite coatings deposited by pulse electrodeposition

Li, Xingyuan; Zhu, Ya-Ru; Xiao, Guorong

doi:10.1016/j.ceramint.2014.04.005

Cited by 41 publications

(9 citation statements)

References 11 publications

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“…The error rate they predicted is lower than 10% (Rashidi et al, 2013). Li et al (2014) effectively predict the sliding wear resistance of Ni-TiN nanocomposite coatings with the ANN model, which shows an error of approximately 4.2%. Liu et al (2012) predict the microhardnesses of the Ni-TiN nanocomposite coatings with radical basis function (RBF) neural network.…”

Section: Introductionmentioning

confidence: 92%

A predictive modelling of nanocomposite coating microhardness based on extremely randomised trees

Guo

Zhao

Xiaoniu

2019

IJMPT

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Nanocomposite coating is a coating made of particles whose sizes are of nanoscale. The microhardness of the coating is an importance parameter. Currently, experimental method is mainly adopted in the coating's microhardness and performance research, with high research cost and long time period. In this paper, the content of the nano-particles in the plating liquid, current density, duty ratio, addition of additives and ultrasonic power are set as inputs; the micro hardness of the nanocomposite coating is set as output. Extremely randomised trees (ERT) are used to establish a strong prediction model. The prediction performance is the ERT model is superior to that of the single models such as linear regression, back-propagation neural network and radial basis function neural network, etc. and other ensemble learning methods. ERT model can be used for predicting the microhardness of nanocomposite coating, providing an efficient and highly reliable method for new material performance prediction.

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Section: Introductionmentioning

confidence: 92%

A predictive modelling of nanocomposite coating microhardness based on extremely randomised trees

Guo

Zhao

Xiaoniu

2019

IJMPT

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“…Gyurova and Friedrich [26] for the prediction of sliding friction and wear properties of polymer composites used ANN and stated that the prediction profiles for the characteristic tribological properties of the ANN exhibited very good agreement with the measured results. Li et al [27] modeled the sliding wear resistance of the Ni-TiN coatings by using ANN. They explained that the proposed ANN model shows an error of approximately 4.2% and can effectively predict sliding wear resistance of Ni-TiN nanocomposite coatings.…”

Section: Neural Networkmentioning

confidence: 99%

Application of a Neural Network Model for Prediction of Wear Properties of Ultrahigh Molecular Weight Polyethylene Composites

Kurt

Oduncuoğlu

2015

International Journal of Polymer Science

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In the current study, the effect of applied load, sliding speed, and type and weight percentages of reinforcements on the wear properties of ultrahigh molecular weight polyethylene (UHMWPE) was theoretically studied. The extensive experimental results were taken from literature and modeled with artificial neural network (ANN). The feed forward (FF) back-propagation (BP) neural network (NN) was used to predict the dry sliding wear behavior of UHMWPE composites. Eleven input vectors were used in the construction of the proposed NN. The carbon nanotube (CNT), carbon fiber (CF), graphene oxide (GO), and wollastonite additives are the main input parameters and the volume loss is the output parameter for the developed NN. It was observed that the sliding speed and applied load have a stronger effect on the volume loss of UHMWPE composites in comparison to other input parameters. The proper condition for achieving the desired wear behaviors of UHMWPE by tailoring the weight percentage and reinforcement particle size and composition was presented. The proposed NN model and the derived explicit form of mathematical formulation show good agreement with test results and can be used to predict the volume loss of UHMWPE composites.

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“…The average grain size of the optimized nanocomposite film is about 50 nm. Li et al [8] reported that Ni-TiN composite coatings were prepared on 45 steel substrates by pulse electrodeposition. The effect of plating parameters on the sliding wear resistance of the Ni-TiN nanocomposite coatings was investigated using transmission electron microscopy (TEM), scanning electron microscopy (SEM) and X-ray diffraction (XRD).…”

Section: Introductionmentioning

confidence: 99%

Microstructural, nanomechanical and wear properties of magnetic pulse electrodeposited Ni-TiN composite coatings

Xia

Tian

et al. 2015

Science and Engineering of Composite Materials

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The current paper reports successful syntheses of Ni-TiN composite coatings via pulse current (PC) and magnetic PC (MPC) depositions. The microstructural, nanomechanical, wear properties and wear mechanism of the Ni-TiN composite coatings were investigated via scanning electron microscopy (SEM), atomic force microscopy (AFM), transmission electron microscopy (TEM), X-ray diffraction (XRD), a nanoindenter (NI) and a high frequency reciprocating wear test instrument. The results showed that the Ni-TiN composite coating deposited at magnetic intensity of 0.3 T had numerous homogeneously dispersed TiN particles. The size of the Ni grains and the TiN particles in the coating were of nanometer size, with an average diameter of ∼ 65 nm and ∼ 25 nm, respectively. The maximum hardness and Young ' s modulus values for the Ni-TiN composite coatings, deposited at magnetic intensity of 0.3 T, were 34.85 GPa and 165.2 GPa, respectively. The wear results showed that the weight loss of the Ni-TiN composite coating was approximately 47.4 mg at a magnetic density of 0.3 T. Furthermore, the coatings deposited at 0.3 T presented low friction coefficients, with an average value of about 0.43.

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Application of artificial neural networks to predict sliding wear resistance of Ni–TiN nanocomposite coatings deposited by pulse electrodeposition

Cited by 41 publications

References 11 publications

A predictive modelling of nanocomposite coating microhardness based on extremely randomised trees

A predictive modelling of nanocomposite coating microhardness based on extremely randomised trees

Application of a Neural Network Model for Prediction of Wear Properties of Ultrahigh Molecular Weight Polyethylene Composites

Microstructural, nanomechanical and wear properties of magnetic pulse electrodeposited Ni-TiN composite coatings

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