Conjugated Heat Exchange in Heat Treatment of Aluminum Ingots Simulation

Casting aluminum to obtain semi-finished products - round ingots, due to uneven cooling in the mold, leads to various defects that affect further machining. To eliminate such defects, heat treatment is carried out - homogenization annealing. One of the homogenization important stages is the cooling of the ingots after heating at a rate that does not lead to the ingot quenching. The cooling medium is air. Knowing the conditions of heat exchange between the cooling air and the high-temperature aluminum billet makes it possible to obtain the ingot’s necessary physical and mechanical properties. The article describes the developed mathematical model of conjugate heat transfer during homogenization annealing of aluminum ingot. It allows analytically calculating the temperature of the ingots depending on the cooling time. To verify the data obtained by the mathematical model, the conjugate heat transfer in the ANSYS program was simulated.

Determination of heat transfer criterial equation when cooling aluminum ingots

A big problem when casting aluminum ingots is the uneven structure formation, which leads to an increased rejection of products. Nonequilibrium structure elimination is carried out by heat treatment. To obtain the required aluminum ingots’ physicochemical properties, it is necessary to know the conditions of heat transfer between the ingots and the cooling air, i.e. a mathematical model of conjugate heat transfer is needed. The mathematical model obtained by the authors makes it possible to analytically investigate the ingots temperature and cooling air during heat treatment. This mathematical model assumes the heat transfer coefficient calculation. The existing criterion equations for determining the heat transfer coefficient have a drawback - the heat transfer coefficient according to these equations is calculated in circular channels, while heat transfer between aluminum ingots and air occurs in rectangular channels. The article describes the criterion equation identification for heat transfer, used in the analytical study, by the data of the experimental study.

Thermal Properties of 3D-Printed Sand Molds

Specific heat capacity (Cp), density (ρ), and thermal conductivity (λ) of phenolic-bonded 3D-printed sand (3DPS) molds have been determined in the temperature range of 20–1400 °C using differential scanning calorimeter (DSC), dilatometer, and hot wire method. The results have been used to simulate the thermal gradient in a sand mold during casting aluminum using a commercial simulation software. The simulated results have been compared with laboratory-measured results and simulated results using the software’s database for conventional mold making. Our results show that available database for sand thermal properties cannot explain the thermal gradient in 3DPS molds and this manufacturing process affects the thermal properties of the mold compared to traditional mold making. It is necessary to collect data for a variety of 3D-printed sand molds to ensure accurate modeling simulation in the foundry industry.

Digital Modeling of Electromagnetic Processes in Technological Induction Devices

Development of an electrical calculation method plays the leading role in simulating induction devices. In modeling electrical devices and complexes, it is often necessary to simultaneously solve both chain and field problems, i.e., to deal with both lumped and distributed parameters. The article considers the method of integral equations for induction systems with non-magnetic and ferromagnetic loading, which is based on the theory of long-range action. The method’s key statement is that the field at any point is determined as the sum of the fields produced by all sources, including primary and secondary ones. Another finite element method is based on the theory of short-range action, which describes the electromagnetic wave propagation from point to point, its refraction and reflection at the boundaries of media. The article substantiates the development of a combined method based on using the method of integral equations for calculating the input parameters of inductors (an external problem) and the finite element method for calculating the field distribution in the load (an internal problem). The combined method has well proven itself in modeling induction heating and melting of metals and oxides, heating a tape in a transverse magnetic field, induction plasmatrons, and casting aluminum into an electromagnetic crystallizer.

Features for friction stir welding of butt joints of casting VAL8 alloy with wrought 1565chN2 and AMg6M alloys

The results of technological features for friction stir welding of butt joints of sheet blanks with thickness of 3 mm made of casting aluminum V AL8 alloy with wrought magnalium group 1565chN2 and AMg6M alloys are presented. It is established that the time resistance of the joints depends on the location of the welded alloys relative to the direction of tool rotation during friction stir welding. The ultimate strength of welded joints of VAL8 alloy with 1565chN2 and AMg6 alloys in automatic argon-arc welding is 0.82...0.84 of the ultimate tensile strength of VAL8 alloy. The grain size in the stir zone practically does not depend on the initial grain size in the alloys to be joined. The destruction of the joints made of VAL8 + 1565chH2 alloys under cyclic loading has multi-focal character and is initiated from irregularities on the surface of the weld. The discrete nature of the change in the chemical composition of the weld metal along the axis of the weld is revealed. The weld is formed by alternating strips of connected alloys with width of 30...90 microns.

Study on microstructure and thermal conductivity of semi-solid die casting aluminum alloy

In order to improve the comprehensive properties of casting aluminum, and to fulfill the requirements of forming thin wall fins for communication products, mechanical stirring was employed to prepare the semi-solid aluminum alloy AlSi8. communication products were produced by the semi-solid die casting process. The microstructure and mechanical and thermal properties were studied. The test results show that the microstructure of semi-solid die-casting samples changes from dendrite to globular microstructure, and the average tensile strength, elongation and thermal conductivity are 220MPa, 7% and 170 W/(m*K), respectively, which is significantly higher than that of the common die-casting samples. It was proved that the semi-solid die casting technology can be used in actual production and improve the products quality.

Boosting for concept design of casting aluminum alloys driven by combining computational thermodynamics and machine learning techniques

Casting aluminum alloys are commonly used in industries due to their excellent comprehensive performance. Alloying/microalloying and post-solidification heat treatments are the most common measures to tune the microstructure for enhancing their mechanical properties. However, it is very challenging to achieve accurate and efficient development of novel casting aluminum alloys using the traditional trial-and-error method. With the rapid development of computer technology, the computational thermodynamics (CT) in the framework of the CALculation of PHAse Diagram approach, the data-driven machine learning (ML) technique, and also their combinations have been proved to be effective approaches for the design of casting aluminum alloys. In this review, the state-of-the-art computational alloy design approaches driven by CT and ML techniques, as well as their combinations, were comprehensively summarized. The current status of the thermodynamic database for aluminum alloys, as the core for CT, was also briefly introduced. After that, a variety of successful case studies on the design of different casting aluminum alloys driven by CT, ML, and their combinations were demonstrated, including common applications, CT-driven design of Sc-additional Al-Si-Mg series casting alloys, and design of Srmodified A356 alloys driven by combing CT and ML. Finally, the conclusions of this review were drawn, and perspectives for boosting the computational design approach driven by combining CT and ML techniques were pointed out.