High-pressure Die Casting Process Optimization for ...

Jo Apr 22, 2025

The high-pressure die casting process (HPDCP), which has been widely used to manufacture various products with high dimensional accuracy and output, has much higher productivity than other manufacturing methods. Almost all major Al alloy automotive components can be manufactured by the HPDCP technology.

Die castings have usual gas porosity caused by gas in molten metal and air entrainment because molten metal fills up the die cavity at high speed. In addition, they include shrinkage porosity due to unreasonable gating conditions in the HPDCP. As a result, the internal porosity has a significant influence on the quality of castings.

There are several parameters in the HPDCP technology, and the quality of die castings can be improved when the parameters are controlled properly. The controlling parameters in the HPDCP are pouring temperature, mold temperature, filling rate in shot sleeve, piston velocity, injection pressure and holding pressure. In order to determine the reasonable HPDCP parameters, numerical simulations have been widely introduced.

Carburetor housing, one of the parts of internal combustion engine, is manufactured by HPDCP. Therefore, the numerical HPDCP simulation of the carburetor housing has to be performed to determine the optimal parameters such as pouring temperature, filling rate in shot sleeve, piston velocity and die temperature.

Kim Ryong Chol, a researcher at the Faculty of Materials Science and Technology, has proposed an HPDCP optimization method for improving the shrinkage porosity and air entrainment using Taguchi-based ProCAST simulations and MADM-based overall quality index (OQI), and determined the optimal HPDCP parameters such as pouring temperature (PT), filling rate (FR), piston velocity (PV) and preheating mold temperature (PMT) for simultaneously improving the shrinkage porosity and air entrainment in the carburetor housing made of Al alloy AlSi9Cu1Mg.

The optimal HPDCP parameters obtained through the simulations were PT of 640℃, FR of 40%, PV of 6.5m/s, and PMT of 150℃.

The proposed method could be actively applied to not only HPDCP but also to other casting processes and manufacturing processes.

For further details, please refer to his paper “High-pressure die casting process optimization for improving shrinkage porosity and air entrainment in carburetor housing with aluminum alloy using Taguchi-based ProCAST simulation and MADM-based overall quality index” in “The International Journal of Advanced Manufacturing Technology” (SCI).

A Method to Extract Image Features and Lineaments B...

Jo Apr 21, 2025

Detecting and characterizing lineaments using satellite images or digital elevation model data is a field of satellite image processing, providing a lot of useful information for geological structure analysis and mineral exploration. In recent years, many studies have been devoted to the identification of lineaments and their relationship with their distribution characteristics, geological structure and mineral deposit distribution.

Song Man Hyok, a section head at the Faculty of Earth Science and Technology, has proposed a new method for extracting the image features and lineaments related to the local extrema of an image or a digital elevation model (DEM) such as ridges and valleys, based on the continuous wavelet transform (CWT) of a set of variously illuminated hillshades.

The method is based on the principle that a hillshade can exactly reflect the lineaments nearly perpendicular to the illumination direction of the hillshade, but not other ones.

The method consists of four steps: (1) preparation of a set of differently illuminated hillshades of the input data, (2) detection of directional edges nearly perpendicular to the illumination direction from each hillshade based on the CWT, (3) a combination of multidirectional edges into an omnidirectional feature image, and (4) identification of lineaments through linkage and linearization of image feature lines.

He applied this method to the geological structure analysis of the study areas to provide a prerequisite for resource exploration and land development.

For detailed information, please refer to his paper “A Method to Extract Image Features and Lineaments Based on a Multi-hillshade Continuous Wavelet Transform” in “Mathematical Geosciences” (SCI).

Improved GM(1, 1) Model and Its Application

Jo Apr 20, 2025

In general, there are different types of uncertain systems with small samples and poor information in the real world. The grey system is a system with partially known (white) and partially unknown (black) information. Grey models play an important role in modeling, prediction, evaluation, decision making, control and system analysis in many fields because of their simple expression and computation, and excellent prediction performance with insufficient data.

GM(1, 1) model is an important part of the grey model, where ‘GM’ stands for ‘grey model’ while the first number ‘1’ in brackets indicates the first order differential equation and the second number ‘1’ indicates the differential equation of one variable. It is mathematically based on the first order linear ordinary differential equation and least square method. It requires a relatively small amount of data (four or more samples) to develop a mathematical model, and a simple calculation process to analyze the behavior of an unknown system. It has been widely used because it does not require a large number of samples and has low computational complexity and there is no limitation of statistical assumptions.

Up to now, many research works to improve the accuracy of the GM(1, 1) have been carried out on the following aspects. Most of them have used mean absolute error (MAE), mean absolute percentage error (MAPE), mean squared error (MSE) and root mean squared error (RMSE). The accuracy measures are calculated using the arithmetic average operation of errors at the fitting points, and they include no sufficient information about prediction accuracy. Consequently, most of the previous works may be regarded as the works for improving the fitting accuracy of the GM(1, 1). It may be a common drawback of the previous works for improving the performance of the GM(1, 1).

Yang Won Chol, a researcher at the Faculty of Materials Science and Technology, has proposed an improved GM(1, 1) model based on weighted mean squared error (MSE) and optimal weighted background value: OB-WMSE-GM(1, 1). He applied it to one simulation example and two application examples to verify its effectiveness.

In the simulations and applications, the errors for GM(1, 1) were much smaller than conventional GM(1, 1).

You can find the details in his paper “An improved GM(1, 1) model based on weighted MSE and optimal weighted background value and its application” in “Scientific Reports” (SCI).