Numerical modelling of multiphase flow and heat transfer within an induction skull melting furnace

Buliński, Piotr; Smołka, Jacek; Golak, S.; Przyłucki, R.; Palacz, Michał; Siwiec, G.; Melka, Bartłomiej; Blacha, L.

doi:10.1016/j.ijheatmasstransfer.2018.06.074

Cited by 24 publications

(10 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to control the melting and thinning process, it is more convenient to do this in the pot. Therefore, induction melting furnaces are widely used in the production of materials for the latest technologies such as turbine blades used in aviation or implants and prostheses in biotechnology [26].…”

Section: Induction Furnace and Its Electrical Power Circuitmentioning

confidence: 99%

Malzeme Üretiminde Siber Güvenlik

Yener¹,

Yener²,

Mutlu³

2020

El-Cezeri Fen Ve Mühendislik Dergisi

View full text Add to dashboard Cite

Industry 4.0, a new industry revolution, is happening now and several developed countries are leading the path. Internet of things (IoT) is also encompassed by Industry 4.0. In the future, more devices in factories are to be connected to Ethernet or Internet. However, this makes the companies, devices and researchers vulnerable to cyber-attacks. Recently, some cyber-attacks which have happened to some companies or countries verify the danger. Sintering systems and furnaces are used for research by universities and for series manufacturing by factories. Arc furnaces and induction furnaces are also commonly used devices in metal factories. A sintering system, an arc furnace or an induction furnace which is connected to Internet or Ethernet may also be under cyber-attack threat. The danger may be prevented by taking necessary precautions. In this study, these threeproduction systems are first briefly introduced and then inspected assuming that they have been connected to internet and examined with considering cyber-attack point of view. Some basic solutions against cyber-attacks to the aforementioned devices are suggested.

show abstract

Section: Induction Furnace and Its Electrical Power Circuitmentioning

confidence: 99%

Malzeme Üretiminde Siber Güvenlik

Yener¹,

Yener²,

Mutlu³

2020

El-Cezeri Fen Ve Mühendislik Dergisi

View full text Add to dashboard Cite

show abstract

“…The crucible segments are separated by 1 mm slits. Bulinski et al [19] used a similar crucible design for the numerical investigation of the process. The described crucible is placed inside the inductor coil, which is also cooled by water.…”

Section: Crucible Designmentioning

confidence: 99%

“…According to the reported results, the acceptable agreement between the experimental and numerical results was achieved. Recently, Bulinski et al [19] employed the coupled CFD-EMAG model for the cold crucible furnace. That study was mostly devoted to an identification of the free surface shape and free surface area.…”

Section: Introductionmentioning

confidence: 99%

Experimental Analysis of the Aluminium Melting Process in Industrial Cold Crucible Furnaces

et al. 2019

Self Cite

View full text Add to dashboard Cite

One of the promising technology that guarantee the high-purity of the processed metal is induction skull melting. The quality of the melting process within such a system strongly depends on the cold crucible design. Hence, the experimental investigation of the cold crucible melting was performed. The experimental investigation was performed for various initial mass of the load. The results of that analysis were used for the assessment of the alloy temperature during the process, free surface shape and the area as well as the liquid metal emissivity. The collected results of the thermal analysis showed that the temperature of molten metal increased within the power input increase. Moreover, results of that analysis showed that the characteristic tooth-shaped jags were created mostly at the centre of the cold crucible segments. In addition, experimental results are suitable for the numerical models validation and further investigation of the cold crucible design.

show abstract

“…In earlier works, various models have been proposed to predict the temperature of the liquid aluminum based on the empirical [15,16] and numerical [17][18][19] approaches. Wang et al examined the computational fluid dynamics(CFD)-Taguchi combined method for the design of process parameters to optimize the aluminum melting [16].…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al examined the computational fluid dynamics(CFD)-Taguchi combined method for the design of process parameters to optimize the aluminum melting [16]. 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].…”

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

View full text Add to dashboard Cite

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.

show abstract

Numerical modelling of multiphase flow and heat transfer within an induction skull melting furnace

Cited by 24 publications

References 22 publications

Malzeme Üretiminde Siber Güvenlik

Malzeme Üretiminde Siber Güvenlik

Experimental Analysis of the Aluminium Melting Process in Industrial Cold Crucible Furnaces

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

Contact Info

Product

Resources

About