BURA Community:

Mathematical modeling of grain fragmentation induced by flow shearing in high-pressure die casting of light alloys

2026-05-14T00:00:00Z

Title: Mathematical modeling of grain fragmentation induced by flow shearing in high-pressure die casting of light alloys Authors: Lu, J-Z; Dou, K; Zhang, Y-J; Lordan, E; Jacot, A; Fan, Z; Wang, W-L Abstract: In the cold-chamber high-pressure die casting (CC-HPDC) process for light alloys, strong shear stress generated by the fast-flowing melt through narrow runners breaks externally solidified crystals (ESCs). Two runner configurations were applied in the CC-HPDC process of aluminum alloy to address this problem. A comprehensive finite element model was established to calculate shear stress in the runner regions during die filling, and a novel mathematical model of grain breakup was proposed to quantitatively analyze ESCs fragmentation through different runners. Particles ranging in size from 12.2 to 16.1 μm constitute a significant proportion of the ESCs and serve as the primary focus of subsequent shear fragmentation. Finally, HPDC test trials validate the mathematical model by characterizing grain morphology and size distribution in as-cast samples and the error of the model is less than 20%. The results demonstrate that the novel model is highly effective for the design of runner systems and the optimization of process parameters in the CC-HPDC process for light alloys.

Solid/liquid interface energy and its anisotropy of pure metals

2026-06-28T00:00:00Z

Title: Solid/liquid interface energy and its anisotropy of pure metals Authors: Fan, Z; Men, H Abstract: Solid/liquid (S/L) interface energy (𝛾_sl) and its anisotropy (φ) play a critical role in the understanding of nearly every single phenomenon that occurs during solidification of metals, such as nucleation, morphological instability and dendrite growth. However, due to difficulties associated with both experimental measurement and computer simulations, our current understanding of this topic is rather limited. In this work, a simple analytical model is developed to predict 𝛾_sl and φ for pure metals. This model suggests that S/L interface energy originates from atomic ordering in the S/L interface templated by the solid. 𝛾_sl can be expressed as the sum of contributions from both atomic layering (𝛾_z) and the in-plane atomic ordering ((𝛾_xy). Further analysis shows that 𝛾_sl for pure metals is determined by both heat of fusion per atom (∆𝐻_f) and their crystal structures, while anisotropy depends only on crystal structure. The analytical model reveals that the physical origin of 𝛾_sl is atomic ordering in the S/L interface templated by the solid, while the physical origin of anisotropy is the difference in structural templating power between different crystal planes. It is demonstrated that the current analytical model is capable of predicting solid/liquid interface energy (𝛾_sl) and its anisotropy (φ) for any metallic element using parameters readily available in the literature. Description: Data availability: All relevant experimental and theoretical data within the article will be provided by the corresponding author on reasonable request. This is a PDF of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability. This version will undergo additional copyediting, typesetting and review before it is published in its final form. As such, this version is no longer the Accepted Manuscript, but it is not yet the definitive Version of Record; we are providing this early version to give early visibility of the article. Please note that Elsevier’s sharing policy for the Published Journal Article applies to this version, see: https://www.elsevier.com/about/ policies-and-standards/sharing#4-published-journal-article. Please also note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Recovery of fatigue and manufacturing damages by electropulsing treatment

2023-11-23T00:00:00Z

Title: Recovery of fatigue and manufacturing damages by electropulsing treatment Authors: Chang, ITH; Cai, Q; Zhou, M; Assadi, H Abstract: Metals are vital to human society and have widespread uses across a broad spectrum of industries including but not limited to packaging, transport and construction applications. They are mainly used in load-bearing structural components. Nonetheless, they possess finite operational lifetime attributable to the dynamic interplay of environmental and mechanical stimuli, culminating in the generation of structural imperfections, deterioration, and the ultimate mechanical failure. It is estimated that metal fatigue is responsible for more than 80% of mechanical failure due to the existence of flaws such as cracks. Consequently, there is an imperative to pioneer advanced materials technology with the express aim of extending the operational lifetime of metallic components. This endeavour not only serves to increase material resource efficiency but also serves as a bulwark against environmental damage. ... Description: Meeting abstract presented at The 14th Thailand Metallurgy Conference (TMEC14), at the Amari Pattaya Hotel in Chonburi, Thailand, 23 November 2023.

Data-Driven Optimization of Size-Aware T6 Heat Treatment Parameters for A356 Aluminum Alloy

2026-06-04T00:00:00Z

Title: Data-Driven Optimization of Size-Aware T6 Heat Treatment Parameters for A356 Aluminum Alloy Authors: Tiwari, T; Gan, T-H; Patel, JB Abstract: Aluminum alloy A356 (Al-7Si-0.3Mg) is widely employed in automotive structural components due to its favorable strength-to-weight ratio, yet its mechanical performance is highly sensitive to T6 heat-treatment processes. Conventional heat-treatment schedules are typically based on uniform, empirically derived parameters and fail to consider variations in component size, geometry, or thermal mass. Consequently, applying a single schedule across all component sizes often leads to inconsistent microstructural development, energy inefficiency, and elevated scrap rates. Smaller components tend to be over-processed, while larger components may be under-processed, both resulting in suboptimal mechanical properties and increased production costs. To overcome these limitations, this study presents a scalable heat-treatment optimization framework that integrates physics-based thermal simulations with machine learning techniques. The framework combines a transient thermal simulator with Long Short-Term Memory (LSTM) networks to predict sample temperature evolution, Random Forest regressors to estimate mechanical properties such as yield strength, hardness, and modulus of toughness, and Bayesian optimization to generate size-dependent, property-compliant heat-treatment schedules. Unlike traditional methods, this approach dynamically adjusts furnace parameters to individual component characteristics, optimizing both processing time and energy consumption while minimizing scrap. Application of the framework to components ranging from 0.5 to 10 kg demonstrates internally consistent simulation-based predictions of temperature profiles, phase-fraction evolution, and mechanical-property trends within the assumed modelling framework. Optimized schedules achieved 15–25% reductions in cycle time while maintaining properties within T6 specifications. These findings underscore the potential of AI-assisted heat-treatment optimization to enhance energy efficiency, reduce material waste, and improve the consistency of mechanical performance in automotive casting operations. Description: Data Availability Statement: The original contributions presented in this study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.