Optimization of Multi-Gate Systems in Casting Process: Experimental and Simulation Studies

Shaikh, Mohd Bilal Naim; Ahmad, Shazeb; Khan, Arfeen; Ali, Mohammed

doi:10.1088/1757-899x/404/1/012040

Cited by 5 publications

(2 citation statements)

References 2 publications

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“…are often used to image the fluid flow in real time. Multi-gate systems have been analyzed using acrylic molds and flow rates are calculated using tap and collect method [28]. The flow of aluminum alloy and zinc in multi-gate systems in water flow models showed that the gate closest to sprue fills first while the farthest gate exhibited the maximum volume of discharge [29].…”

Section: Water Modeling Experimentsmentioning

confidence: 99%

Measurement of Metal Velocity in Sand Casting during Mold Filling

Sama

MacDonald

Voigt

et al. 2019

Metals

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Melt turbulence during mold filling is detrimental to the quality of sand castings. In this research study, the authors present a novel method of embedding Internet of Things (IoT) sensors to monitor real-time melt flow velocity in sand molds during metal casting. Cavities are incorporated in sand molds to position the sensors with precise registration. Capacitive and magnetic sensors are embedded in the cavities where melt flow velocity is calculated by using an oscillator, the frequency of which is sensitive to changes in the close field permittivity, and change in magnetic flux, respectively. Their efficiency is investigated by integrating the sensors into 3D sand-printing (3DSP) molds for conical-helix and straight sprue configurations to measure flow velocities for aluminum alloy 319. Experimental melt flow velocities are within 5% of estimations from computational simulations. A major benefit of 3DSP is the geometrical freedom for complex gating systems necessary to reduce turbulence and access to mold volume for sensor integration during 3DSP processing. Findings from this study establish the opportunity of embedding IoT sensors in sand molds to monitor metal velocity in order to validate simulation results (2–5% error), compare gating systems performance, and improve foundry practice of manual pouring as a quality control system.

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Section: Water Modeling Experimentsmentioning

confidence: 99%

Measurement of Metal Velocity in Sand Casting during Mold Filling

Sama

MacDonald

Voigt

et al. 2019

Metals

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“…Most of the current design knowledge on gating systems are derived from trial and error approaches (Ruddle 1956), water modelling (Renukananda and Ravi 2016) and using computational simulation tools (Sama et al 2019a;Nimbulkar and Dalu 2016). Since the kinematic viscosity of most liquid metals is similar to water (Renukananda and Ravi 2016;Swift et al 1949), numerous researchers have experimented with water models using transparent molds usually made of Acrylic (Shaikh et al 2018;Renukananda and Ravi 2016) to visualize in real time the fluid flow in molds (Juretzko and Stefanescu 2005). Newly, Sama et al (2019b) have proposed a novel method of embedded Internet of Things (IoT) sensors to monitor realtime melt flow velocity in 3D sand printing mold during metal casting.…”

Section: Introductionmentioning

confidence: 99%

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

Ktari

Mansori

2020

J Intell Manuf

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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.

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

Ktari

Mansori

2021

Int J Adv Manuf Technol

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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.

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Optimization of Multi-Gate Systems in Casting Process: Experimental and Simulation Studies

Cited by 5 publications

References 2 publications

Measurement of Metal Velocity in Sand Casting during Mold Filling

Measurement of Metal Velocity in Sand Casting during Mold Filling

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

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

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