Measurement of Metal Velocity in Sand Casting during Mold Filling

Abstract

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.

Modelling the Mechanical Attributes (Roughness, Strength, and Hardness) of Al-alloy A356 during Sand Casting

“…It can be observed that the pouring temperature is more effect on hardness than moisture content. Moreover, hardness is maximum at the lowest level of pouring temperature because porosity and gas shrinkages defects are minimum at a lower temperature of melt, as reported in previous work [17]. However, a further rise in temperature results in a reduction of hardness, but this pattern persists up to the middle level.…”

“…Three key parameters, namely pouring temperature, pouring velocity and moisture content, were selected as the control variables in the present research. The rationale for selecting the aforesaid variables as input parameters is their well proven significance with respect to the mechanical and surface properties of the castings [9,17,18]. The control settings of the said parameters were achieved in the following manner.…”

“…In another research reported on the same material, it was noticed that the green compression strength has more effect on percent rejection of Si Mo in SG-iron than other factors such as mold hardness, moisture content and permeability [12]. As mentioned earlier, there are many sources that contribute to determining the quality of the casted product; however, these can be categorized as [4,[12][13][14][15][16][17] (i) type of sand and sand mixture parameters, (ii) mold parameters, (iii) metal parameters, and (iv) process parameters. The detail of these categories is provided in Figure 1.…”

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

“…A sensing and control model could be interfaced during casting part with multiple sensors such capacitive or magnetic sensors used to measure the melt flow velocity in mold cavities (Sama et al 2019b) and wireless sensors for temperature measurements at several deeps inside molds (Walker et al 2018). These models can be used for online control of the filling process.…”

“…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.…”