Solid State Phenomena Vol. 347

Abstract: We propose a methodology for the rheological characterization of semisolid metal slurries using experimental squeeze flow data. The material is modelled as a structural thixotropic viscoplastic material, obeying the regularized Herschel-Bulkley constitutive equation. The yield stress and the power-law exponent are assumed to vary with the structural parameter that is governed by a first-order kinetics. The squeeze flow is simulated using finite elements in a Lagrangian framework. The evolution of the sample height has been studied for all the ranges of the Bingham and Reynolds numbers, the power-law exponent as well as the kinetics parameters of the structural parameter. Systematic comparisons have been carried out with experimental data on a semisolid aluminium alloy (A356) sample, compressed from its topside at a temperature of 582 °C under a specified load, which eventually becomes constant. Excellent agreement with the experimental data could be achieved provided that at the initial instances (up to 0.01s) of the experiment the applied load is much higher than the nominal experimental load and that the yield stress and the power-law exponent vary linearly with the structural parameter. The first requirement implies that a different model should be employed during the initial stages of the experiment. As for the second one, the evolution of the sample height can be reproduced allowing the yield stress to vary from 0 (no structure) to a maximum nominal value (full structure) and the power-law exponent from 0.2 to 1.4.

Abstract: In current work, the deformation behavior and deformed microstructures of GH3536 superalloy in semi-solid state were investigated, and the semi-solid flow stress was predicted by artificial neural network (ANN) model. The semi-solid compression deformation was carried out at 1320-1350 °C, and the solid deformation behavior at 1200 °C was tested for comparison. The peak stress under 0.01-1 s^-1 semi-solid deformation was 45.6-161.9 MPa. The peak stress decreased with the increase of deformation temperature and the decrease of strain rate. The ANN model could well describe semi-solid flow stress. During semi-solid deformation, the apparent viscosity dropped as shear rate increased. At high temperature and low strain rate, more liquid phase was distributed at grain boundaries. The solid grains coarsened as deformation temperature increased.

Abstract: Resource-efficient manufacturing is a foundation for sustainable and circular manufacturing. Semi-solid processing typically reduces material loss and improves productivity but generally requires a better understanding and control of the solidification of the cast material. Thermal analysis is commonly used in high-pressure die casting (HPDC) processes to determine casting process parameters, such as liquidus and solidus temperatures. However, this method is inadequate for semi-solid casting processes because the eutectic temperature is also a crucial parameter for successful semi-solid casting. This study explores the feasibility of using machine learning and artificial neural networks to predict fundamental values in Al-Si alloy casting. The Thermo-Calc 2022 software Scheil-Gulliver calculation function was used to generate the training and the test datasets, which included features such as melting temperature, alpha aluminium solidification temperature, eutectic temperature, and the solid fraction amounts at eutectic temperature. The results show that both models have a symmetric mean absolute percentage error (SMAPE) of less than 2 % with temperature prediction, with the machine learning model achieving a better accuracy of less than 1 %. A case study comparing practical measurements with prediction results is also discussed, demonstrating the potential of AI methods for predicting semi-solid casting processes.

Abstract: Rheocasting technology has been successfully applied to produce aluminum alloy parts of automobile and communication equipment. However, its application scope is still limited. One reason is that the strength of the applied alloys is limited. In recent years, lots of researchers have tried to use 7075 aluminum alloy in the rheocasting process because this alloy has excellent mechanical properties. In this work, the rheological behavior of 7075 aluminum alloy semi-solid slurry is studied through shear stress-controlled test and shear rate-controlled test. Then the constitutive parameters in Power-Law (PL) model or Carreau-Yasuda (CY) model of non-Newtonian fluid are determined. The models are used to simulate the flow behavior of 7075 aluminum alloy semi-solid slurry in Swirled Enthalpy Equilibration Device (SEED) rheocasting process. The simulation results indicate that the CY model derived from the shear rate sweep test is more suitable for simulating the flow behavior of 7075 aluminum alloy semi-solid slurry during rheocasting than the other models.

Abstract: In semi-solid metal casting processes a slurry, obtained operating in the temperature window between the liquidus and the solidus curve of the phase diagram of the alloy (32.7 °C for AlSi8, 11.3 °C for AlSi11), is injected into the mould. Since the control over the process parameters becomes more complicated as the involved quantities increase, to apply these processes on an industrial scale it is necessary to widen as much as possible this narrow temperature window. This goal can be obtained by tailoring the concentration of elements constituting the alloy. A EN AC-46000 secondary alloy was selected for this study, because of its wide use in foundries. Using a CALPHAD approach, various Al-Si-based pseudo-binary phase diagrams containing main constituents of the alloy (Cu, Zn, Fe, Mg, Mn, Ni, Cr and Ti) were studied, keeping the Si content fixed from 8 to 11 wt.% and the Al content as a complement. The composition limits of acceptability of the commercial alloy EN AC-46000 were used as extremes of the simulation field. The study was then proceeded investigating the pseudo-ternary diagrams, keeping silicon and a second element fixed and a third element variable for all possible combinations. Further constituent elements of the alloy were progressively added, considering all main constituents of the alloy, and evaluating how the concentration of these elements can extend the temperature window. Mg, Fe, Mn and Ti are the most promising candidates for achieving this goal. The results of this study can provide foundries with a prediction tool to define more restrictive ranges of acceptability than those of the commercial alloy, allowing them to estimate through a simple chemical analysis whether scrap of variable composition entering the plant are suitable for the SSM process or they need preliminary corrections of composition.

Abstract: Semisolid materials with a non-dendritic microstructure have a complex rheological behavior such as pseudo-plasticity and thixotropy. These properties affect filling and solidification processes during, casting; thus, simulation plays a vital role since it avoids limitations improving cast and production performance. In order to study and A356 aluminum semisolid alloy flow, a Computational Fluid Dynamics (CFD) software (ANSYS) is used to simulate the die filling process in a rectangular mold. Three non-Newtonian constitutive equations are modeled in this work: the Cross equation, the Power-law equation and the Carreau equation. The rheological parameters for each equation were obtained from experimental data reported in the literature. The results showed that the non-Newtonian models predicted better the filling behavior and the pressure distribution than the Newtonian model.

Abstract: Besides the realization of the technical specification of a casting, aspects such as resource minimization, cost awareness and robustness of the defined quality requirements are taking major priority, especially in the current global economic situation. Lightweight design, in that respect, is a central topic far beyond the automotive industry and characterizes in particular the ongoing research and development of innovative magnesium cast components. As a resource-efficient alternative to traditional die casting, magnesium-thixomolding offers the benefit of low energy consumption and eschews the necessity of protective gases. The identification of significant influencing factors between process settings, shear and time dependent material properties of semi-solid magnesium, the machine capabilities and the resulting casting quality are the basis for effective product and process design of magnesium thixomolding. Obtaining this knowledge is only possible through methodical process analysis and systematic design of experiments. YIZUMI Germany, together with MAGMA, demonstrates the methodical evaluation of a reliable process window for an AZ91D magnesium “box” component, using a demo mold on a UN1250MGII thixomolding machine. The work shows how a systematic combination of virtual and real casting experiments is used to identify a concrete process layout and generate important knowledge on process variations, robustness as well as control limits. In particular, the comparison of virtual and real casting experiments using statistical methods generates a comprehensive understanding of the impact and limits of different process variables, such as fraction solid, mold temperature and injection speeds. Furthermore it is possible to predict the influence of the gating and component design on the process. The methodological use of qualified casting simulation enables the development of an optimized component design and the determination of a suitable process window with minimized economic or productive risks long before the first component is manufactured – a step towards more resource efficiency and sustainability!

Abstract: In the actual semi-solid die-casting production, the existence of several uncertain factors can impose an effect on the final product quality, which poses a challenge to semi-solid production. However, data analysis such as machine learning (ML) can help producers eliminate this problem. In order to quickly identify defective castings, a new model of predicting quality by real-time injection pressure data will be generated in terms of ML in this research. Quality assessment will be based on non-filling defect, density and tensile properties. The result of cross-validation shows that the classifier can achieve a confidence level of 0.95 for the quality classification. In addition, this research will find key intervals by the importance given by the model and analyze the effects of process on filling pressure. According to the result of feature screening, the surface quality problems are related to speed-pressure conversion and feeding displacement of plunger, the flowing state of slurry in filling affects the formation of defects and tensile properties. This work will make semi-solid die casting more automatically and efficiently, and thus provides support for semi-solid sustainable development.

Abstract: Owing to their several attractive features such as high hardness, high elastic modulus, light weight, high strength to weight ratio, high thermal conductivity, and high temperature strength, composites from Al-Mg₂Si family offers promise towards deployment in several industries such as automobile, aerospace, marine, defence and electronic. The present molecular dynamics (employing LAMMPS) based simulation study is one of the first attempt to investigate the nucleation and grain growth mechanisms of Mg₂Si phase at atomic level in case of novel Al-15Mg₂Si-4.5Si composite, during semi-solid processing. Modified embedded atom method (MEAM) potential has been used to study the atomic interactions in the composite. Reaching the melt state at 1000 K, the temperature of the system is first decreased from 1000 K to 853 K and then the system is held at 853 K for 100 ps. The simulations are performed with three different cooling rates. With lowering of temperature, randomly distributed Mg and Si atoms form atomic clusters at arbitrary locations within the system, which is the nucleation stage for Mg₂Si phase formation. Cluster size, radial distribution function has been used to investigate the structural evolution of Mg-Si clusters. Cooling rate significantly influences the grain size as well as the grain growth kinetics. The information about the thermodynamic state of the system has been revealed by extracting the values of internal energy, enthalpy, specific heat. during the slurry preparation and isothermal holding stages. The growth mechanism of Mg₂Si nucleus has been characterized from the temporal variation of (Mg + Si) atoms taking part in the cluster formation. Power-law variation is observed in the cooling stage whereas a linear variation is observed in the isothermal stage.