Papers by Keyword: Low-Pressure Casting

Abstract: This paper presents a development methodology for a metamodel-based machine learning approach for spatiotemporal prediction of temperature and solid fraction evolution of aluminum castings during cooling under low pressure sand casting (LPSC) conditions, for various pouring temperatures (T_p) ranging from 614°C to 720°C. High-fidelity finite element (FE) simulations were performed, based on a representative case study produced by the LPSC process to generate a comprehensive database, recording nodal temperatures across the casting symmetry plane at different cooling times, and for several T_p values within the studied domain. Three different machine learning (ML) algorithms were evaluated using comparative metrics (R², MAE and MSE). Among the evaluated algorithms, the artificial neural network (ANN)-based ML model was selected for its superior predictive accuracy and robustness. The accuracy of the selected ML-model was assessed by comparing predicted and FE-simulated temperature fields. The results indicate that the predicted temperature error within the cast symmetry plane remains below 1%. Furthermore, a graphical user interface (GUI) was developed to visualize the predicted casting temperature field for different T_p values not used during the learning stage, as well as the corresponding solid fraction, which is computed based on the solidification curve of the aluminum alloy AlSi₇Mg_0.3 given by the ProCAST^® database. This methodology could provide a fast, robust, and scalable framework for extending predictive models to higher-dimensional cases and diverse LPSC casting process configurations.

Abstract: The microstructure and mechanical properties of permanent mold low-pressure casting (PMLPC) and sand mold gravity casting (SMGC) of A357 alloy were studied. The grain size of alloys formed by PMLPC is finer than that formed by sand mold gravity casting because of higher freezing rate of the former. The secondary dendrite arm spacing of PMLPC is approximately 15.2 μm (SD=4) while that of SMGC is 33.2 μm (SD=6). The ultimate tensile strength of PMLPC has a wider range from 350 MPa to 299.9 MPa and an elongation from 1.2 to 4.9. In comparison, the ultimate tensile strength of SMGC ranges from 307 MPa to 315 MPa and its elongation ranges from 2.1 to 3.7. These differences may be attributed to various factors, such as filling speed, filling pressure, and cooling rate, that affect the quality of permanent molds during the filling process.

Abstract: In order to compare the semi-solid SEED casting process to the Low Pressure Permanent Mould process (LPPM), the same part has been produced in aluminium A356 (AlSi7Mg04) using the two methods. The processes were first compared from an operational standpoint: pouring temperature, filling sequence, production rate and mould maintenance. In addition, the metallurgical quality of the castings was evaluated by measuring the tensile properties at 6 locations in the part; the metallographic structures were also compared. Filling and solidification modeling allowed the prediction of the filling sequence and local solidification times everywhere in the casting. The SEED process was generally found to deliver a finer structure, a near net shape casting and a much higher productivity. LPPM parts were more uniform in structural and mechanical properties as verified at 6 locations in the castings.

Abstract: To solve the problems of automotive wheel’ design by using traditional methods, application of finite element analysis in modern automotive wheel design is presented. Then, for example of SU0011-typed aluminum alloy wheel, this paper illustrates in detail the application in its hub structure design, mold design and casting process. By using the ANSYS software, structural design of wheel is optimized according to the CAE results of fatigue strength. In the meanwhile, the casting mold of wheel hub is also designed. Using the ProCAST software, casting process optimization and casting defects elimination can be realized based on simulating the process of filling and solidification of low-pressure casting. Experimental analysis shows that, with the help of finite element analysis, design accuracy and product reliability can be effectively improved and product development cycles can be greatly shorten. So the application has a very good prospect in the automotive industry.