The Scheil–Gulliver equation is essential for assessing solid fractions during alloy solidification in materials science. Despite the prevalent use of the Calculation of Phase Diagrams (CALPHAD) method, its computational intensity and time are limiting the simulation efficiency. Recently, Artificial Intelligence has emerged as a potent tool in materials science, offering robust and reliable predictive modeling capabilities. This study introduces an ensemble-based method that has the potential to enhance the prediction of the partitioning coefficient (k) in the Scheil equation by inputting various alloy compositions. The findings demonstrate that this approach can predict the temperature and solid fraction at the eutectic temperature with an accuracy exceeding 90%, while the accuracy for k prediction surpasses 70%. Additionally, a case study on a commercial alloy revealed that the model's predictions are within a 5°C deviation from experimental results, and the predicted solid fraction at the eutectic temperature is within a 15% difference of the values obtained from the CALPHAD model.

To satisfy the rising demand for higher product quality and giga-casting requirements, the casting process is undergoing significant changes. However, current control methods rely significantly on human expertise and experience, making process availability and stability difficult to ensure. The semisolid casting process is more complicated than conventional liquid casting due to the additional casting parameters incorporated during the slurry preparation, which can have an effect on the quality of the final product. Therefore, an efficient tool is required to simplify the complete process of semisolid casting. The introduction of an AI system to aid in the supervision of the casting manufacturing procedure is one potential solution. This paper introduces a new casting system named”Smart-Cast” developed for this specific purpose. The paper describes the functions of the system and its current development process. Using an AI system as an assistant can help to achieve the goal of enhancing the efficacy of casting process control, and it can also help foundries step into the Industry 4.0 era.

Electrification is a keyword for many industries today, and thermal management is essential. This paper aims to review the sustainability perspective and analyse the need for thermal conductivity. A model for thermal conductivity is developed for as-cast and heat-treated states to be used as a basis for a sustainability impact index to allow quantitative decisions on sustainable alloying for heat transfer solution alloy development and selection. In the analysis, it was necessary to consider microstructural features for a satisfactory description of the thermal conductivity measured from a set of Al-Si-based alloys in the as-cast and heat-treated states. An environmental impact index was developed for the alloying elements to gauge the effectiveness of different alloying elements, including the environmental impact. An effort to include the microstructural effects was made. Due to the low thermal conductivity of the matrix phase in the as-cast state, the eutectic regions provide a positive contribution to the correction factor developed. The heat-treated stat microstructure seems to lack importance in the ranges of Si additions used in the current study.

Semisolid casting has emerged as an attractivealternative to conventional casting methods due to its potentialto yield superior mechanical properties, reduce environmentalpollution, and decrease production costs. However, optimizingprocess parameters and controlling the casting process remainschallenging. Process control largely relies on human expertise,associated with significant time and cost expenditures. Inresponse, this study presents a third-circle research project toinvestigate the correlation between the casting process and thesolidification process. The study proposes leveraging AI technologyto digitize the entire process control, thereby increasing thereliability and stability of cast products’ quality. The researchwill focus on understanding the key factors influencing thecasting process and developing an AI-based decision supportsystem to aid in process parameter selection and optimization.The outcomes of this study are expected to contribute to thedevelopment of more reliable and efficient semisolid castingprocesses.

High-pressure die-casting (HPDC) can be a productive process for high-quality cast aluminium alloy components. However, it is also a process prone to generate defects, such as gas porosities and incomplete fillings, resulting in rejections. One way to reduce the reject rate is to employ Semi-Solid Metal processing with HPDC. The most important advantages of Semi-Solid alloys are reduced shrinkage defects, fewer gas porosities, and fewer chances of filling-related problems. To take full advantage of a semi-solid metal slurry, the casting process must be controlled meticulously to reach homogeneous casting quality and high process repeatability. A study has been conducted on cast parts composed of two-dimensional symmetrical cavities. From the mechanical tests, unexpected differences emerged in both tensile strength and fracture elongation, which were confirmed by differences in the microstructure. The paper investigates the reasons for the asymmetry in the proprieties to avoid similar problems in future studies and maximize the effectiveness and repeatability of the high-pressure die-casting process.

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.

The strive for lightweight in the automotive industry points to aluminium metal matrix composites as substitutes of cast iron in brake discs. The wear performance of the material is critical, besides suitable mechanical resistance and thermal properties. The present study investigated the wear behaviour of Al-Si alloys reinforced with silicon carbide particles. The matrix alloy was added with nickel and zirconium, and nanoindentation was performed to determine intermetallic phases' hardness and elastic modulus. The addition of 20 wt% carbides determined an elastic modulus 35–40 % higher than the matrix alloys. Wear rate was in the 2–8 * 10-5 mm3/N * m range for all materials. The tribo-layer had a critical role in the wear performance, as the coefficient of friction decreased during wear.

The present work investigates different additions of nickel and zirconium to the matrix alloy of Al-SiC metal matrix composites to enhance their high-temperature performance. These composites are promising for the demand for lightweight solutions for automotive components like brake discs. In such components, the compression behaviour at elevated temperatures is crucial. The resulting properties were combined with microstructural analysis. Ni additions led to a continuous improvement of the mechanical response, but the same result did not hold for the Zr additions. The interaction of SiC particles, eutectic silicon, and eutectic Ni-based phases led to a 44 % increment of the activation energy.