Modeling of tribological properties of alumina fiber reinforced zinc–aluminum composites using artificial neural network

Genel, Kenan; Kurnaz, S. Can; Durman, M.

doi:10.1016/s0921-5093(03)00623-3

Cited by 109 publications

(56 citation statements)

References 16 publications

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“…These values are more accurate than the constitutive equations model. It is in agreement with results observed in other studies [34][35][36][37][38][39][40][41][42][43][44]. …”

Section: Artificial Neural Network Analysissupporting

confidence: 93%

“…A typical ANN model is generally constructed using various steps, such as: (i) collecting the data; (ii) determining the input/output (target) parameters; (iii) analysing and pre-processing the experimental data; (iv) training the ANN; (v) testing the trained ANN; and, finally, (vi) evaluating the performance of the constructed ANN [34][35][36][37][38][39][40][41][42][43][44]. A popular learning method for algorithms with multilayer observations is back-propagation (BP).…”

Section: Artificial Neural Network Analysismentioning

confidence: 99%

“…A popular learning method for algorithms with multilayer observations is back-propagation (BP). It is the standard method for modifying the weights and biases by utilising a gradient descent procedure to reduce the error for a particular training pattern [35]. The ANN with one hidden layer could delineate any function of interest and is truly utilised for many practical problems [38].…”

Section: Artificial Neural Network Analysismentioning

confidence: 99%

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Modelling of the Superplastic Deformation of the Near-α Titanium Alloy (Ti-2.5Al-1.8Mn) Using Arrhenius-Type Constitutive Model and Artificial Neural Network

Mosleh

Mikhaylovskaya

Котов

et al. 2017

Metals

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Abstract:The paper focuses on developing constitutive models for superplastic deformation behaviour of near-α titanium alloy (Ti-2.5Al-1.8Mn) at elevated temperatures in a range from 840 to 890 • C and in a strain rate range from 2 × 10 −4 to 8 × 10 −4 s −1 . Stress-strain experimental tensile tests data were used to develop the mathematical models. Both, hyperbolic sine Arrhenius-type constitutive model and artificial neural-network model were constructed. A comparative study on the competence of the developed models to predict the superplastic deformation behaviour of this alloy was made. The fitting results suggest that the artificial neural-network model has higher accuracy and is more efficient in fitting the superplastic deformation flow behaviour of near-α Titanium alloy (Ti-2.5Al-1.8Mn) at superplastic forming than the Arrhenius-type constitutive model. However, the tested results revealed that the error for the artificial neural-network is higher than the case of Arrhenius-type constitutive model for predicting the unmodelled conditions.

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“…These values are more accurate than the constitutive equations model. It is in agreement with results observed in other studies [34][35][36][37][38][39][40][41][42][43][44]. …”

Section: Artificial Neural Network Analysissupporting

confidence: 93%

Section: Artificial Neural Network Analysismentioning

confidence: 99%

Section: Artificial Neural Network Analysismentioning

confidence: 99%

See 1 more Smart Citation

Modelling of the Superplastic Deformation of the Near-α Titanium Alloy (Ti-2.5Al-1.8Mn) Using Arrhenius-Type Constitutive Model and Artificial Neural Network

Mosleh

Mikhaylovskaya

Котов

et al. 2017

Metals

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“…In the current decade, ANNs have been widely used in the investigations of material strength, in particular, in the field of fatigue, creep rupture strength, and fracture mechanics [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18].…”

Section: Back-propagation Artificial Neural Networkmentioning

confidence: 99%

Strain–life curves of steels and methods for determining the curve parameters. Part 2. Methods based on the use of artificial neural networks

et al. 2011

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The authors look into the possibility of using artificial neural networks for predicting the deformation characteristics of steels (the parameters of the Basquin-Manson-Coffin strain-life curve equation) based on static strength and plasticity characteristics, by constructing four independent neural networks with different configurations of input and output data. The prediction of parameters of the Basquin-Manson-Coffin equation and the fatigue life calculations by means of artificial neural networks are demonstrated to provide a better accuracy in comparison to the available conventional methods. Keywords: artificial neural networks, parameters of the Basquin-Manson-Coffin strain-life curve equation, fatigue life, static strength and plasticity characteristics.Introduction. In Part 1 [1] we reviewed the currently available methods for assessing the strain-life curves (the parameters of the Basquin-Manson-Coffin equation) and revealed some drawbacks of their definition. Here we will study the possibility of predicting the parameters of the strain-life curves by means of artificial neural networks (ANN).ANN Concept Description. The neurocomputing methodology is a relatively new area of artificial intelligence, where attempts are made to simulate the structure and operation of biological neural systems such as the human brain by constructing ANN in a computer. The use of ANN is especially helpful where one encounters the problems for which the solution is not clearly formulated or where one has to reproduce some mechanism which is otherwise difficult to describe using the available physical models.The methodology of this approach with a comprehensive description of neural networks, considering the multidiscipline nature of this subject, was detailed in [2]. Here we will just outline the ANN background and look into the possibility of using ANN in solving the applied engineering problems.Artificial neural networks consist of simple interconnected elements referred to as the processing elements or artificial neurons, which act as microprocessors. Thus, a neuron represents an information processing unit in a neural network. Figure 1 shows a model of a neuron used as a basis of ANN [2], where three main elements can be identified:1. A set of synapses (or connections), each characterized by its peculiar weight or connection strength. In particular, a signal x j at the input to synapse j connected to neuron k is multiplied by weight w kj . As opposed to brain synapses, the synaptic weight of an artificial neuron can have either positive or negative values.2. The summator adds the input signals weighted relative to the corresponding neuron synapses. This operation can be described as a linear combination.

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“…Also it was found that ANN has the ability to predict tool wear accurately from feed force. Another advantage of using ANNs in engineering materials is to model tribological behaviors of short alumina fiber reinforced zinc-aluminum composites [16]. In this study, the specific wear rate and coefficient of friction obtained from a series of the wear tests were used in the formation of training sets of ANN.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Drilling Process of Aluminum Composites Using Multiple Regression Analysis and Artificial Neural Networks

Mayyas

Qasaimeh²,

Alzoubi³

et al. 2012

JMMCE

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In recent years, aluminum-matrix composites (AMCs) have been widely used to replace cast iron in aerospace and automotive industries. Machining of these composite materials requires better understanding of cutting processes regarding accuracy and efficiency. This study addresses the modeling of the machinability of self-lubricated aluminum /alumina/graphite hybrid composites synthesized by the powder metallurgy method. In this study, multiple regression analysis (MRA) and artificial neural networks (ANN) were used to investigate the influence of some parameters on the thrust force and torque in the drilling processes of self-lubricated hybrid composite materials. The models were identified by using cutting speed, feed, and volume fraction of the reinforcement particles as input data and the thrust force and torque as the output data. A comparison between two prediction methods was developed to compare the prediction accuracy. ANNs showed better predictability results compared to MRA due to the nonlinearity nature of ANNs. The statistical analysis accompanied with artificial neural network results showed that Al 2 O 3 , Gr and cutting feed (f) were the most significant parameters on the drilling process, while spindle speed seemed insignificant. Since the spindle speed was insignificant, it directed us to set it either at the highest spindle speed to obtain high material removal rate or at the lowest spindle speed to prolong the tool life depending on the need for the application.

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Modeling of tribological properties of alumina fiber reinforced zinc–aluminum composites using artificial neural network

Cited by 109 publications

References 16 publications

Modelling of the Superplastic Deformation of the Near-α Titanium Alloy (Ti-2.5Al-1.8Mn) Using Arrhenius-Type Constitutive Model and Artificial Neural Network

Modelling of the Superplastic Deformation of the Near-α Titanium Alloy (Ti-2.5Al-1.8Mn) Using Arrhenius-Type Constitutive Model and Artificial Neural Network

Strain–life curves of steels and methods for determining the curve parameters. Part 2. Methods based on the use of artificial neural networks

Modeling the Drilling Process of Aluminum Composites Using Multiple Regression Analysis and Artificial Neural Networks

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