An Investigation into the Effective Heat Transfer Coefficient in the Casting of Aluminum in a Green-Sand Mold

2020

J Intell Manuf

The filling stage is a critical phenomenon in sand casting for making reliable castings. Latest research has demonstrated that for most liquid engineering alloys, the critical meniscus velocity of the melt at the ingate is in the range of 0.4-0.6 m s −1 . The work described in this research paper is to use neural network (NN) technology to propose digital twin approach for gating system design that allow to understand and model its performances faster and more reliable than traditional methods. This approach was applied in the case of sand casting of liquid aluminum alloy (EN AC-44200). The approach is based first on a digital representation of filling process to perform the melt flow simulations using a combination of the gating system design parameters, selected as a training cases from Taguchi orthogonal array (OA). The second step of the approach is the data capture of functional gating design system to train up the feed-forward back-propagation NN model. The validation of the well-trained NN model is assessed by interrogating predicted ingate velocity to it and making reliable predictions with high accuracy. The claim is that such digital twin approach is an effective solution to recognize the functional design parameters from the entire filling systems used during casting process.

Section: Design Of Experiments and Fe Problem Modelingsupporting

confidence: 78%

Digital twin of functional gating system in 3D printed molds for sand casting using a neural network

Ktari

2020

J Intell Manuf

“…The result is expected and it is in good agreement with literature [41], supposing that heat transfer is unidirectional. Thus, the heat released during solidification is transferred within the mold, with a high heat transfer coefficient (supposed to be 300 W m −2 K −1 in Quikcast simulation [28]). On the opposite, and for the sand mold thickness less than 5 mm, the heat arrives to the mold walls in few seconds before the solidification beginning.…”

Section: Resultsmentioning

confidence: 99%

“…The sand density was 1590 kg m −3 and was supposed to be constant during the simulation. The heat transfer coefficient of the sand was 300 W m −2 K −1 [28] and the convective heat transfer coefficient between sand and air was 10 W m −2 K −1 [29]. The ambient temperature of the surrounding air was 20 °C.…”

Section: Numerical Simulation Proceduresmentioning

confidence: 99%

Assessment of the effect of 3D printed sand mold thickness on solidification process of AlSi13 casting alloy

Saada

Int J Adv Manuf Technol

2021

The present work addresses the printed sand mold thickness effect on the solidification process of a eutectic aluminum-silicon alloy (AlSi13). Several sand mold thicknesses (varying from 3 to 30 mm) are numerically studied using Quikcast® software. The study shows that the solidification time decreases when the sand thickness of mold increases. It is accelerated by more than 40% when the sand mold thickness increases from 3 to 30 mm. The numerical simulations are coupled with experiments. Indeed, the 3D sand printing process is used to fabricate molds presenting different thicknesses of 5 mm and 30 mm, respectively. In addition, the same printing parameters are applied for producing all sand molds. The comparison between both numerical and experimental results shows the same tendency according to the sand mold thickness. The results indicate that increasing the sand mold thickness from 5 to 30 mm allows to accelerate the solidification by 17% and 18.6%, respectively, in the numerical and experimental results. A finer microstructure is obtained when reducing the solidification time, which enhances the hardness of casting properties.

“…The casting was cooled to room temperature in air with a convection coefficient of 10 W m −2°C−1 applied to all exterior mould surfaces. In addition, a heat transfer coefficient of 350 W m −2°C−1 was applied at the metal/mould interface [41].…”

Section: Mesh Generation and Boundary Conditionsmentioning

confidence: 99%

Intelligent approach based on FEM simulations and soft computing techniques for filling system design optimisation in sand casting processes

Ktari

Int J Adv Manuf Technol

2021

This paper reports an intelligent approach for modeling and optimisation of filling system design (FSD) in the case of sand casting process of aluminium alloy. In order to achieve this purpose, physics-based process modeling using finite element method (FEM) has been integrated with artificial neural networks (ANN) and genetic algorithm (GA) soft computing techniques. A threedimensional FE model of the studied process has been developed and validated, using experimental literature data, to predict two melt flow behaviour (MFB) indexes named ingate velocity and jet high. Two feed-forward back-propagation ANN-based process models were developed and optimised to establish the relationship between the FSD input parameters and each studied MFB index. Both ANN models were trained, tested and tuned by using database generated from FE computations. It was found that both ANN models could independently predict, with a high accuracy, the values of the ingate velocity and the jet high for training and test data. The developed ANN models were coupled with an evolutionary GA to select the optimal FSD for each one. The validity of the found solutions was tested by comparing ANN-GA prediction with FE computation for both studied MFB indexes. It was found that error between predicted and simulated values does not exceed 5.61% and 6.31% respectively for the ingate velocity and the jet high, which proves that the proposed approach is reliable and robust for FSD optimisation.