Effects of Temperature, Time, Magnesium, and Copper on the Wettability of Al/TiC System

Kurt, Halil Ibrahim; Oduncuoğlu, Murat

doi:10.1155/2015/710526

Cited by 5 publications

(5 citation statements)

References 21 publications

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“…The correlation coefficient (R) is chosen to estimate the performance of the proposed model and, mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are utilized as error-evaluation criteria. The explanations and detailed formulations about statistical values are given in our works [16,17]. All analyses of the present work are performed using the Matlab program.…”

Section: Methodsmentioning

confidence: 99%

Modelling and Prediction of Covid-19 Epidemic in Turkey Comparing with USA and China

Ergül

Kurt

Oduncuoğlu

et al. 2021

Journal of Engineering Technology and Applied Sciences

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The aim of the study is to research and compare the influences of the confirmed cases, test number and time range on the death and recovery rates in the United State of America, China, and Turkey, and to find out the effect of the epidemic in the near future of Turkey. The modelling and prediction of effects of the day, case and test numbers of COVID-19 infection in the USA, China and Turkey are carried out using the artificial neural network approach (ANN). The system are trained and tested with the different numbers of neurons, hidden layers and activation functions to increase the reliability and accuracy of model. The proposed models have a high R2 value for China and Turkey. We can say according to the results that the measures taken by the USA are inadequate. The formulation is applied to predict the effect of Covid-19 infection in Turkey. The test number that is an important factor in detecting the cases should be increased. The results show a good fit between the observed data and those obtained by the ANN model. If the precautions are strictly followed, the case number will be decreased significantly after 160 days for Turkey according to result of the proposed model but due to the uncontrolled variables, this time may result in between 200 and 250 days.

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Section: Methodsmentioning

confidence: 99%

Modelling and Prediction of Covid-19 Epidemic in Turkey Comparing with USA and China

Ergül

Kurt

Oduncuoğlu

et al. 2021

Journal of Engineering Technology and Applied Sciences

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“…Over the last decades, the interest of the modeling techniques in different fields of materials science has been increased [1][2][3][4][5][6][7]. It is aimed to find the optimum solution with better performance by using human intuition, thinking and decisionmaking ability, eliminating uncertainties with simple and low-cost solutions and in solving complex and difficult to solve problems.…”

Section: Production Methods and Data Collectionmentioning

confidence: 99%

“…The effects of temperature, time, and the additions of magnesium and copper on the wetting behavior of Al/TiC are studied theoretically [7]. The R values of training and test sets are 0.911 and 0.903, respectively.…”

Section: Potential Application Of Ann Anfis and Taguchi Approaches For Aluminum Alloys And Aluminum Matrix Compositesmentioning

confidence: 99%

“…The UTS of unrefined Al-Zn-Mg-Cu alloys and refined the alloys by Al-5Ti-1B and Al-5Zr master alloys are calculated with ANN [14]. There is no well-defined procedure to determine the optimal model structure, so the different neuron numbers in one hidden layer (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) are used with the trial and error approach. The optimal structure for this works is the 15-17-1 with logistic sigmoid transfer function.…”

Section: Potential Application Of Ann Anfis and Taguchi Approaches For Aluminum Alloys And Aluminum Matrix Compositesmentioning

confidence: 99%

See 1 more Smart Citation

The Theoretical Overview of the Selected Optimization and Prediction Models Useful in the Design of Aluminum Alloys and Aluminum Matrix Composites

Kurt¹,

Ergül²,

Yilmaz³

et al. 2021

Advanced Aluminium Composites and Alloys

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The growing attention regarding aluminum alloy matrix composites within the aerospace, automotive, defense, and transportation industries make the development of new engineering materials with the improved mechanical properties. Currently, materials are selected because of their abilities to satisfy engineering demands high for strength-to-weight ratio, tensile strength, corrosion resistance, and workability. These properties make aluminum alloys and aluminum matrix composites (AMCs) an excellent option for various industrial applications. Soft computing methods such as the artificial neural network (ANN), adaptive-neuro fuzzy inference systems (ANFIS), and Taguchi with ANOVA are the most important approaches to solve the details of the mechanism and structure of materials. The optimal selection of variables has important effects on the final properties of the alloys and composites. The chapter presents original research papers from our works and taken from literature studies dealing with the theory of ANN, ANFIS, and Taguchi, and their applications in engineering design and manufacturing of aluminum alloys and AMCs. Also, the chapter identifies the strengths and limitations of the techniques. The ANFIS and ANN approaches stand out with wide properties, optimization, and prediction, and to solving the complex problems while the Taguchi experimental design technique provides the optimum results with fewer experiments.

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“…The ANNs are a methodology in different applications of materials including prediction of tribological and mechanical properties and were used by many researchers [19][20][21][22][23][24]. Venkata Rao et al [25] used ANN to predict surface roughness, tool wear, and amplitude of work piece vibration.…”

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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Effects of Temperature, Time, Magnesium, and Copper on the Wettability of Al/TiC System

Cited by 5 publications

References 21 publications

Modelling and Prediction of Covid-19 Epidemic in Turkey Comparing with USA and China

Modelling and Prediction of Covid-19 Epidemic in Turkey Comparing with USA and China

The Theoretical Overview of the Selected Optimization and Prediction Models Useful in the Design of Aluminum Alloys and Aluminum Matrix Composites

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

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