The Influence of Airflow Characteristics and Accumulation Grid Velocity on the Formation of a Stone Wool Primary Layer

Peternelj, Marko; Bizjan, Benjamin; Širok, B.

doi:10.5545/sv-jme.2017.4503

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…The velocity of the coaxial blowing air flow above the center of the wheel mantle where melt film was formed was approximately 30 m/s. As the fiber-laden flow reached the collecting mesh, fibers were deposited, forming a layer of fibrous material also known as the primary layer [16] and [17].…”

Section: Fig 1 Experimental Set-up For High-speed Imaging Of Melt Fiberizationmentioning

confidence: 99%

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Analogue Experimental Study of Fiber Formation on Two-Wheel Spinner

Bizjan

Širok

Blagojevič

2020

SV-JME

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In this paper, the process of mineral fiber formation was investigated experimentally on a two-wheel spinner by means of high-speed imaging. Analogue isomalt melt was fiberized at different rotational speeds of spinner wheels, melt flow rates and impingement positions so that the fiberization process was dynamically similar to an industrial mineral wool production process. Images of fiber formation and transport reveal highly complex dynamics of these processes, as fibers mostly occur in form of 3D mutually intertwined structures such as clusters, strands and veils periodically shedding from the melt film. Despite the complexity of flow structures, there is a clear trend of increasing mean fiber length and expansion angle of the coaxial fiber-laden flow as the Weber number and the ratio of melt film velocity to blowing air velocity are increased. The fiberization efficiency (ratio of fiber mass deposited on the collecting mesh to the mass of melt poured) is affected by the impingement position and flow rate of melt as well as the Weber number of melt film. The optimum efficiency was attained at 30° (1 o’clock) impingement position and the ratio of melt film to blowing air flow velocity close to unity.

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Section: Fig 1 Experimental Set-up For High-speed Imaging Of Melt Fiberizationmentioning

confidence: 99%

“…Bizjan et al [16] modelled the bulk density and distribution of deposited sucrose fibers produced by an air-assisted cotton candy machine. A similar study was performed on the collecting drum of an industrial stone wool production line [17]. These studies did not focus on the flight of fibers between the spinner and the collecting mesh.…”

Section: Introductionmentioning

confidence: 99%

Analogue Experimental Study of Fiber Formation on Two-Wheel Spinner

Bizjan

Širok

Blagojevič

2020

SV-JME

Self Cite

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“…A pressure gauge was installed in the suction channel to regulate suction fan speed so that the pressure inside the channel remained constant during particular operating points. Suction airflow pressure was set in the range of 600-750 Pa. 18…”

Section: Materials and Fabricationmentioning

confidence: 99%

Optimization and prediction of ultra-fine glass fiber felt process parameters based on artificial neural network

Zhang

Chen

Wang

et al. 2020

Journal of Engineered Fibers and Fabrics

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Ultra-fine glass fiber felt (fiber diameter ⩽3 μm) is prepared by the flame blowing process with superior thermal insulation and sound insulation. It is widely used in construction and aerospace by improving its uniformity and fiber diameter to further enhance its thermal and acoustic insulation properties. In this article, the purpose is further to create a smart manufacturing system using artificial neural network to provide analysis, judgment, and optimization for the manufacture of aerospace-grade ultra-fine glass fiber felt. When there were 11 neurons in the hidden layer, both the relative error Z values of the uniformity and the fiber diameter were the smallest, which were 0.0382 and 0.0073, respectively. So the structure 3−[11]1–2 with the back-propagation training algorithm was the most adaptive model, which was proved by comparing the mean relative error. In addition, after comparison with the measured data, the predicted and measured values are very similar and the error between them is small, so this structure has been confirmed to have a high accuracy. Finally, three-dimensional planes for the predicted uniformity and fiber diameter as a function of each process parameters are established. The predictive quality was pretty satisfactory, which can be applied to predict new data in the same knowledge domain.

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Tensile properties of mineral fibers determined with Sentmanat extensional rheometer

Oseli

Bizjan

Król

et al. 2020

Construction and Building Materials

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The Influence of Airflow Characteristics and Accumulation Grid Velocity on the Formation of a Stone Wool Primary Layer

Abstract: In this paper, the industrial process of stone wool primary layer formation has been investigated. The blow-away

Cited by 4 publications

References 12 publications

Analogue Experimental Study of Fiber Formation on Two-Wheel Spinner

Analogue Experimental Study of Fiber Formation on Two-Wheel Spinner

Optimization and prediction of ultra-fine glass fiber felt process parameters based on artificial neural network

Tensile properties of mineral fibers determined with Sentmanat extensional rheometer

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