Over the past decades, demand for high-purity aluminium (Al) has increased in many sectors, like aerospace and automotive sectors, since it combines a high level of purity with the flexibility of controlled alloying, which allows for tailored enhancements of material properties. To accommodate the rising demand, primary Al production has significantly increased since the refining of secondary Al is constrained by high impurity levels, especially iron (Fe). A way to mitigate this problem is to add Fe-bearing intermetallic particle formers, like manganese (Mn). This paper investigates the influence of different Mn additions for low-Fe composition aluminium melts at a cooling rate of 3 °C/min, as the primary Fe-rich phases may differ and cannot be extrapolated. More specifically, the impact of filters, the Fe removal efficiency for different Mn additions, and the Fe-bearing intermetallic particles’ Fe removal potential. Fe removal potential was evaluated by combining intermetallic particle area fraction with their average Fe content. This was done by running Thermo-Calc equilibrium calculations to guide the planning of the experimental work. Then, running small-scale experiments with 8 kg of Al-11Si-0.5Fe alloy. The study concludes that the Fe-bearing intermetallic parties sedimented at the bottom of the furnace since the composition of the filtered and unfiltered samples from the top part of the melt was similar. Additionally, larger amounts of Mn are required to improve the Fe removal efficiency for low-Fe concentration Al-Si cast alloys since it improves the Fe removal potential and increases the amount of Fe-bearing intermetallic particles in the melt.

Three significant changes are driving the use of materials in the automotive industry today. First, the direct environmental load of materials drives the issue of climate change through the associated carbon footprint of the car from manufacturing to use and end-of-life phases. The new consumer attitudes and legislation force new requirements on the automotive industry. These requirements constitute the second driver, pushing the electrification of the drive line and the use of batteries. The electrification significantly simplifies the car's architecture and allows for a more significant functional integration of the automotive components. This leads to functional integration in component design, considerably changing the conditions to the third driver, consisting of reduced raw material use, material efficiency and recycling and how to achieve cost-effectiveness and resource efficiency. Closing the circle to the climate impact and the carbon footprint changes dramatically. The current paper reviews and analyses the consequences of electrification and the use of Giga casting on aluminium alloys, especially alloying element streams, for recycling in the automotive industry, targeting a near-closed-loop approach. This analysis is made to identify potential resource quality and availability issues for the aluminium alloys and the alloying elements used. It was concluded that there would be a significant need for primary or non-automotive aluminium scrap to be introduced into the flow. All electrified drivelines will allow for a closed-loop scenario where Mg, Si and Mn are the first to reach surplus and Fe, Zn, and Cu are the last. Critical is that the additions of Si made in the recycling process can, in theory, be eliminated. Si is responsible for more than half the carbon footprint of aluminium alloy recycling.

Recently, it has become a focus to reduce non-exhaust aerosol emissions from the transport sector. One possible way to decrease brake particle emissions is by laser-cladding coatings onto brake discs. This work tested five different laser-cladding coated brake discs against a non-asbestos organic brake pad. The performance of these coatings was compared to a commercial grey cast iron brake disc tested against both low-metallic and non-asbestos organic brake pads. A specialized experimental system composed of a pin-on-disc tribometer, an aerodynamic particle sizer (APS) spectrometer, and a condensation particle counter (CPC) was employed to compare wear mass loss, coefficient of friction, particle number and mass concentration, and size distribution. The worn surfaces of pads and discs were analysed using scanning electron microscopy and energy-dispersive X-ray spectroscopy. The results showed that the tungsten carbide-reinforced laser-cladded coating exhibited the lowest wear and particle number concentration compared to the other coatings and the commercial references.

Aluminium metal matrix composites are promising materials for automotive brake discs, and it is critical to assess their wear performance in different braking conditions. This article presents the wear behaviour of aluminium-based composites with different Al-Si matrix alloys added with nickel and copper to retain mechanical strength at high temperatures. The wear tests were conducted at room and high temperatures (250 and 400 °C) to simulate different braking conditions on a pin-on-plate tribometer. The coefficient of friction is in the range of 0.15–0.17 for all materials at room temperature. The specific wear rates of the brake pad and the disc materials indicate that material transfer occurs from the brake pad to the metal counterpart. Microscopy investigations of the wear tracks confirm the material transfer on the composites. It protects the composite surface from wear damage and maintains a stable coefficient of friction. To translate these results into real-world scenarios, the findings of this study suggest that aluminium-based metal matrix composite brake discs have a longer product lifespan compared to the grey cast iron brake discs; the brake pads for the composites would be the components to need replacement due to wear during the product life instead of the brake discs.

Over the past years, the demand for high-purity aluminium has increased in many sectors, like the aerospace and automotive sectors. This is because aluminium has excellent corrosion resistance and a high strength-to-weight ratio. To cope with this significant increase in demand, the production of primary aluminium has increased since the refining processes of secondary aluminium are limited by high impurities, mainly iron. The iron-rich intermetallic compounds (β-Fe phase) in Al-Si aluminium alloy negatively affect the mechanical properties of the aluminium from its sharp-edged coarse plate structure. In order to mitigate this problem and reduce the iron content in the melt, one way is to add Fe-bearing intermetallic particle formers, like Cr, Mn and Sr. This paper aims to investigate the influence of different Mn additions forlow Fe composition aluminium melt at a constant cooling rate. Specifically, the impact of usingfilters, the Fe removal efficiency for different Mn additions, and the Fe-bearing intermetallic particle’s Fe removal potential. This was done by running small-scale experiments with 8kg/experiment of Al-10Si-0.5Fe (wt%) alloy. The main parameter that varied was the amount of Fe-bearing intermetallic particle formers added to the melt. This report concludes that the Fe-bearing intermetallic parties have mostly sedimented from the top surface of the melt since the composition of the filtered and unfiltered samples were similar. Additionally, larger amounts of Mn are required to improve the Fe removal efficiency for low Fe concentration melt since it improves the Fe removal potential and increases both the size and amount of Fe-bearing intermetallic particles in the melt.

The simulation of casting processes is a powerful tool that helps predict the defects in the final component. In practice, material data are often extracted from the literature, and significant deviations can occur between the simulation and real casting. Thermal analysis-driven data are a useful strategy to acquire reliable material data, even more so in the case of aluminium metal matrix composites (MMCs) reinforced with silicon carbide particles (SiCp).  High processing costs are a drawback of composites in automotive applications, highlighting the need for simulation techniques based on reliable datasets. The castability and solidification behaviour of aluminium-based composites depend on the reinforcement particle shape, size, and content, among other factors. The calibration of the material dataset for the simulation maximises the defect prediction accuracy and minimises the production costs. The present study investigates the castability and thermophysical properties of aluminiumbased composites reinforced with different SiCp contents ranging from 0 to 30 wt.%. The materials were produced by casting to gather relevant data as input for the material database of the casting simulation of the brake rotor. The simulation model predicted shrinkage defects by dividing the casting into zones with different liquid-phase fractions. The shrinkage porosities were caused by changing the melt and solid phase densities at the temperature change. The laws of heat and mass transfer between the different casting phases and moulds were used to forecast the shrinkage porosity, cold shuts, and hotspots. Material properties, such as thermal diffusivity, thermal expansion, and specific heat capacity, were evaluated as a function of temperature and simulation software with density and thermal conductivity. Computer-aided cooling curves were imported to create a new dataset of aluminium-based composites with different reinforcement additions. A simulation based on the adapted material database was validated in terms of solidification and defect prediction. 

Aluminum-based composites provide tribological performance and thermophysical properties that, combined with being lightweight, are suitable for their application in automotive brake discs. Aluminum alloys allow the use of secondary materials to produce composites, with the drawback of several elements, impurities, and oxides that can harm the mechanical and thermophysical properties. This preliminary study explored the mechanical and thermophysical performance of a composite material produced with a secondary matrix alloy. Overall, the results are promising, with a minimal decrease in mechanical and thermophysical properties despite clustered silicon carbide particles in the composite with the secondary matrix. The challenges in effectively dispersing carbides in the melt seem linked to aluminum oxides, and future microstructural investigations will aim to clarify this aspect.

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.