Browse Topic: Casting
Aluminum alloy wheels have become the preferred choice over steel wheels due to their lightweight nature, enhanced aesthetics, and contribution to improved fuel efficiency. Traditionally, these wheels are manufactured using methods such as Gravity Die Casting (GDC) [1] or Low Pressure Die Casting (LPDC) [2]. As vehicle dynamics engineers continue to increase tire sizes to optimize handling performance, the corresponding increase in wheel rim size and weight poses a challenge for maintaining low unsprung mass, which is critical for ride quality. To address this, weight reduction has become a priority. Flow forming [3,4], an advanced wheel rim production technique, which offers a solution for reducing rim weight. This process employs high-pressure rollers to shape a metal disc into a wheel, specifically deforming the rim section while leaving the spoke and hub regions unaffected. By decreasing rim thickness, flow forming not only enhances strength and durability but also reduces overall wheel weight. This study investigates and compares the mechanical properties of conventional GDC and LPDC cast alloy wheels with flow-formed counterparts, focusing on the rim region. Results reveal that the flow-forming process facilitates a 30% thickness reduction in the rim section. Furthermore, it leads to a slight increase in yield and tensile strength while significantly improving elongation in parallel to the flow-forming direction. The study also examines microstructural changes, including the deformation behavior of silicon dendrites [5].
Image-based machine learning (ML) methods are increasingly transforming the field of materials science, offering powerful tools for automatic analysis of microstructures and failure mechanisms. This paper provides an overview of the latest advancements in ML techniques applied to materials microstructure and failure analysis, with a particular focus on the automatic detection of porosity and oxide defects and microstructure features such as dendritic arms and eutectic phase in aluminum casting. By leveraging image-based data, such as metallographic and fractographic images, ML models can identify patterns that are difficult to detect through conventional methods. The integration of convolutional neural networks (CNNs) and advanced image processing algorithms not only accelerates the analysis process but also improves accuracy by reducing subjectivity in interpretation. Key studies and applications are further reviewed to highlight the benefits, challenges, and future directions of using ML in material failure analysis through image processing.
The initial powder used for the manufacturing of NdFeB permanent magnets is usually prepared through rapid cooling, either by melt spinning or strip casting. The powders produced by these two methods are suitable for different applications: while melt-spun powder is a good initial material for bonded and hot-deformed magnets, strip-cast powder is normally used for sintered magnets. To investigate the suitability of using strip-cast powder to manufacture hot-deformed magnets, NdFeB powder prepared by strip casting was hot pressed (without particle alignment) and compared with melt-spun powder prepared under the same conditions (700 °C, 45 MPa, 90 min). Although the processing parameters are the same (pressed in the same mold), the magnetic properties of the magnets made from the two powders are significantly different. Surprisingly, the magnet made from the strip-cast powder (after ball milling) shows comparable magnetic properties to those of isotropic magnets, with coercivity (HcJ) of 1270 kA/m and remanence (Br) of 0.7 T, while that made from the melt-spun powder exhibits much lower properties: HcJ = 480 kA/m, Br = 0.5 T, although the melt-spun powder initially shows much better magnetic properties than the strip-cast powder. Possible reasons for such a difference in magnetic properties are discussed. It was shown that the particle size of the initial powder plays an important role in determining the final magnetic properties of the hot-pressed magnets.
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