Effects of Degassing and Fluxing on the Quality of Al-7%Si and A356.2 Alloys

Shih, Teng Shih; Wen, Kon Yia

doi:10.2320/matertrans.46.263

Cited by 17 publications

(8 citation statements)

References 6 publications

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“…Dissolved hydrogen gas cannot get out because the pressure inside the molten aluminum is less than 1 atm, while the outside pressure is 1 Atm [7]. Shih and Wen (2005) [26] said that if the molten aluminum is in a vacuum environment, the hydrogen partial pressure will drop dramatically to near zero. In this case, it diffuses from the molten aluminum into the bubbles.…”

Section: Resultsmentioning

confidence: 99%

Effect of Degassing Treatment on the Interfacial Reaction of Molten Aluminum and Solid Steel

Triyono

Muhayat

Supriyanto

et al. 2017

Archives of Foundry Engineering

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The gas porosity is one of the most serious problems in the casting of aluminum. There are several degassing methods that have been studied. During smelting of aluminum, the intermetallic compound (IMC) may be formed at the interface between molten aluminum and solid steel of crucible furnace lining. In this study, the effect of degassing treatment on the formations of IMC has been investigated. The rectangular substrate specimens were immersed in a molten aluminum bath. The holding times of the substrate immersions were in the range from 300 s to 1500 s. Two degassing treatments, argon degassing and hexachloroethane tablet degassing, were conducted to investigate their effect on the IMC formation. The IMC was examined under scanning electron microscope with EDX attachment. The thickness of the IMC layer increased with increasing immersion time for all treatments. Due to the high content of hydrogen, substrate specimens immersed in molten aluminum without degasser had IMC layer which was thicker than others. Argon degassing treatment was more effective than tablet degassing to reduce the IMC growth. Furthermore, the hard and brittle phase of IMC, FeAl3, was formed dominantly in specimens immersed for 900 s without degasser while in argon and tablet degasser specimens, it was formed partially.

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Section: Resultsmentioning

confidence: 99%

Effect of Degassing Treatment on the Interfacial Reaction of Molten Aluminum and Solid Steel

Triyono

Muhayat

Supriyanto

et al. 2017

Archives of Foundry Engineering

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“…On the other hand, the process window, in which the processing produces a better melt quality, is small and particularly sensitive to small changes in process parameters. When fluxes are not used, the operating parameters of rotary degassing units are usually optimized for efficient removal of dissolved hydrogen [ 23 , 24 , 25 , 26 ]. These parameters include (and are not limited to) rotor geometry, retaining vessel and rotor dimensions, amount of metal, rinsing gas flow rate, impeller speed, treatment time, use of deflectors, initial and planned volume of hydrogen, melt temperature, chemical composition of melt and other environmental factors (such as atmospheric humidity and gas pipelines used) [ 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

Aluminum Melt Degassing Process Evaluation Depending on the Design and the Degree of the FDU Unit Graphite Rotor Wear

et al. 2022

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One of the most important indicators of casting quality is porosity. The formation of pores is largely conditioned by the presence of hydrogen in the batch and subsequently in the melt. The gasification of the melt is the primary factor increasing the porosity of casts. This paper addresses the issue of reducing the melt gasification by using FDU (Foundry Degassing Unit) unit. The gas content in the melt is evaluated by determining the Dichte Index depending on the geometry and the degree of the FDU unit rotor wear. For experiments performed under the operating conditions, three types of graphite rotors with different geometries are used. The extent of melt gasification and the Dichte Index are monitored during the rotor wear, at a rate of 0%, 25%, 50%, 75% and 100% rotor wear. Secondly, the chemical composition of the melt is monitored depending on the design and wear of the rotor. It is proven that the design and the degree of rotor wear do not have significant effect on the chemical composition of the melt and all evaluated samples fell within the prescribed quality in accordance with EN 1706. With regard to the overall comparison of the geometry and wear of individual rotor types, it has been proven that, in terms of efficiency, the individual rotors are mutually equivalent and meet the requirements for melt degassing throughout the service life.

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“…To understand the melting behavior of the liquid aluminum by each process parameter, Quintana et al [17], Nieckele et al [18], and Bulin' ski et al [19] proposed the numerical solutions considering the heat transfer and the flow behavior based on the process parameters. Numerous studies on the quantification of the dissolved hydrogen content in the liquid aluminum have also attracted much attention from researchers for a long time [11,[20][21][22][23][24][25][26]. Various melt treatments to control the dissolved hydrogen content in the liquid aluminum such as gas bubbling filtration (GBF) [20,21], degassing agent [22,26], and ultrasonication [24,25,27] have been introduced.…”

Section: Introductionmentioning

confidence: 99%

“…Numerous studies on the quantification of the dissolved hydrogen content in the liquid aluminum have also attracted much attention from researchers for a long time [11,[20][21][22][23][24][25][26]. Various melt treatments to control the dissolved hydrogen content in the liquid aluminum such as gas bubbling filtration (GBF) [20,21], degassing agent [22,26], and ultrasonication [24,25,27] have been introduced. Among these techniques, the GBF is one of the most effective processes, which has an obvious effect on degassing of hydrogen in the liquid aluminum with minimizing the melt loss and the dross formation.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Temperature of Liquid Aluminum and the Dissolved Hydrogen Content in Liquid Aluminum with a Machine Learning Approach

Kim

Yun

Yang

et al. 2020

Metals

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In aluminum casting, the temperature of liquid aluminum and the dissolved hydrogen density are crucial factors to be controlled for the purpose of both quality control of molten metal and cost efficiency. However, the empirical and numerical approaches to predict these parameters are quite complex and time consuming, and it is necessary to develop an alternative method for rapid prediction with a small number of experiments. In this study, the machine learning models were developed to predict the temperature of liquid aluminum and the dissolved hydrogen content in liquid aluminum. The obtained experimental data was preprocessed to be used for constructing the machine learning models by the sliding time window method. The machine learning models of linear regression, regression tree, Gaussian process regression (GPR), Support vector machine (SVM), and ensembles of regression trees were compared to find the model with the highest performance to predict the target properties. For the prediction of the temperature of liquid aluminum and the dissolved hydrogen content in liquid aluminum, the linear regression and GPR models were selected with the high accuracy of prediction, respectively. In comparison to the numerical modeling, the machine learning modeling had better performance, and was more effective for predicting the target property even with the limited data set when the characteristics of the data were properly considered in data preprocessing.

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Effects of Degassing and Fluxing on the Quality of Al-7%Si and A356.2 Alloys

Cited by 17 publications

References 6 publications

Effect of Degassing Treatment on the Interfacial Reaction of Molten Aluminum and Solid Steel

Effect of Degassing Treatment on the Interfacial Reaction of Molten Aluminum and Solid Steel

Aluminum Melt Degassing Process Evaluation Depending on the Design and the Degree of the FDU Unit Graphite Rotor Wear

Prediction of the Temperature of Liquid Aluminum and the Dissolved Hydrogen Content in Liquid Aluminum with a Machine Learning Approach

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