Artificial Neural Network Modeling of Grain Refinement Performance in AlSi10Mg Alloy

Kocaman, Engin; ŞİRİN, Selçuk; Dışpınar, Derya

doi:10.1007/s40962-020-00472-9

Cited by 4 publications

(4 citation statements)

References 44 publications

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“…In subsequent iterations of the algorithm, a different set of independent variables is checked for which the neural network is trained. The genetic A common solution is to calculate the relative importance of the input variables [35,47,48]. The coefficient value for each independent variable can be calculated based on the weights of connections of the input and hidden layer neurons.…”

Section: Independent Variables and Assessment Of Their Significancementioning

confidence: 99%

“…Powar and Date [52] proposed a different approach to this problem by comparing the error values obtained with different training methods for two sizes of the hidden layer. Razavi et al [63], Kocaman et al [48] and Murugesan et al [37] included different activation functions in their analysis. Wang et al [46] and Reddy et al [64] presented the error values independently for the training and test set.…”

Section: Model Selection and Overfitting Problemmentioning

confidence: 99%

“…The comparison of the error value for the training and test sets gives essential information about the quality of the model and is presented in many publications [27,34,46,55]. Such an assessment is facilitated by the scatter plots presented in many publications, where the measured and calculated values of the dependent variable are compared [28,32,33,36,41,46,48,62,[65][66][67][68][69].…”

Section: Model Selection and Overfitting Problemmentioning

confidence: 99%

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Practical Aspects of the Design and Use of the Artificial Neural Networks in Materials Engineering

Sitek

Trzaska

2021

Metals

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Artificial neural networks are an effective and frequently used modelling method in regression and classification tasks in the area of steels and metal alloys. New publications show examples of the use of artificial neural networks in this area, which appear regularly. The paper presents an overview of these publications. Attention was paid to critical issues related to the design of artificial neural networks. There have been presented our suggestions regarding the individual stages of creating and evaluating neural models. Among other things, attention was paid to the vital role of the dataset, which is used to train and test the neural network and its relationship to the artificial neural network topology. Examples of approaches to designing neural networks by other researchers in this area are presented.

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Section: Independent Variables and Assessment Of Their Significancementioning

confidence: 99%

Section: Model Selection and Overfitting Problemmentioning

confidence: 99%

Section: Model Selection and Overfitting Problemmentioning

confidence: 99%

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Practical Aspects of the Design and Use of the Artificial Neural Networks in Materials Engineering

Sitek

Trzaska

2021

Metals

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“…As a result of this process, while the grains exhibit a equaxial solidification, a more homogeneous structure as microstructural is obtained. This has a positive effect on the mechanical properties of the alloy [19,20]. Also, grain refinement is known to be effective in increasing the fatigue resistance of aluminum alloys by reducing porosity and shrinkage [21,22].…”

Section: Introductionmentioning

confidence: 99%

Effect of Al5Ti1B Grain Refiner and Al10Sr Modifier on Mechanical Properties and Corrosion Behavior of A360 Alloy

Kocaman

ŞİRİN

2023

International Journal of Automotive Science and Technology

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In this study, the effect of grain refiner and modifier on the mechanical and cor-rosion properties of the alloy, aimed at increasing the performance of A360 cast-ing alloy, which has an important place especially for the automotive industry, was investigated. For this purpose, Al5Ti1B grain refiner and Al10Sr modifier were added to A360 alloy at different rates and microstructural examinations, hardness test, tensile strength and corrosion resistance of the obtained samples were investigated. Al5Ti1B grain refiner promotes a finer grained and coaxial so-lidification. The Al10Sr modifier, on the other hand, broke down the eutectic par-ticles in the microstructure and caused it to be distributed more homogeneously in the structure. While the tensile strength and corrosion resistance of the alloy in-creased with both the grain refiner and the modifier added into the A360 alloy, a slight decrease in hardness was observed in the alloys to which only the modifier was added.

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Effect of Silicon on Machinability in AlSi6, AlSi12 and AlSi18 Alloys

Geçgel,

Altıntaş,

Arslan

et al. 2023

Silicon

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Artificial Neural Network Modeling of Grain Refinement Performance in AlSi10Mg Alloy

Cited by 4 publications

References 44 publications

Practical Aspects of the Design and Use of the Artificial Neural Networks in Materials Engineering

Practical Aspects of the Design and Use of the Artificial Neural Networks in Materials Engineering

Effect of Al5Ti1B Grain Refiner and Al10Sr Modifier on Mechanical Properties and Corrosion Behavior of A360 Alloy

Effect of Silicon on Machinability in AlSi6, AlSi12 and AlSi18 Alloys

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