BCAST is striving for international excellence on both fundamental and applied research on solidification of metallic materials. BCAST sees itself as a reliable source of both new knowledge and new solidification technologies for the metallurgical industry. https://bura.brunel.ac.uk/handle/2438/155 2026-10-27T03:08:23Z 2026-10-27T03:08:23Z Subroto, Tungky Miroux, Alexis Eskin, Dmitry G Katgerman, Laurens https://bura.brunel.ac.uk/handle/2438/34084 2026-10-25T03:01:33Z 2026-09-10T00:00:00Z

Title: Sensitivity of a Multi-Physics DC Casting Model to Material Database Selection: Implications on Hot-Tearing and Cold-Cracking Prediction in an AA7050 Alloy Authors: Subroto, Tungky; Miroux, Alexis; Eskin, Dmitry G; Katgerman, Laurens Abstract: Numerical process simulation is increasingly used to optimize direct-chill (DC) casting of aluminum alloys, yet crack-susceptibility predictions depend critically on the constitutive–property databases employed. This study presents a sensitivity analysis of the ALSIM DC casting model with respect to material database selection, using AA7050 billet (315 mm diameter) casting as a benchmark. Three casting scenarios—hot tearing (HT), cold cracking (CC), and healthy billets—were simulated using five databases spanning semi-solid and fully-solid regimes: Al-2 wt.% Cu, AA7050, and artificially strengthened AA7050XTR semi-solid databases, alongside AA1050 and AA7050 fully-solid (hardening) databases. The results show that the fully-solid database dominates the first principal-stress field, which governs the critical crack size (CCS) used as the CC susceptibility indicator, while the semi-solid database has limited effect on the integrated critical strain (ICS) used as the HT indicator. This insensitivity may arise because accumulated strain in DC casting is governed primarily by process conditions rather than material properties, revealing a fundamental limitation of strain-based HT criteria for alloy-to-alloy comparison. To address this limitation, an improved version of reserve strain value (RSV) concept is discussed as a complementary, material-sensitive HT indicator that may support future development of coupled HT-CC assessment methods. Practical recommendations for industrial database development are provided.

2026-09-10T00:00:00Z Don Merenchige, Upendra Arjeewani Weerathunga Ji, Shouxun https://bura.brunel.ac.uk/handle/2438/34033 2026-10-20T02:01:35Z 2026-08-11T00:00:00Z

Title: Assessing the Materiality of Value-Chain Emissios for Organizational Decarbonization Strategy Development: a Case Study of the Automotive Industry Authors: Don Merenchige, Upendra Arjeewani Weerathunga; Ji, Shouxun Abstract: Industrial decarbonization strategies increasingly require organizations to measure emissions throughout their entire value chain, not just from direct operational sources. While corporate greenhouse gas accounting frameworks offer structured reporting methods, the significance of upstream supply-chain emissions is often poorly understood across many industrial sectors, especially in material-intensive manufacturing. This study evaluates the greenhouse gas inventory of an automotive parts manufacturer to assess the relative impact of value-chain emissions on organizational decarbonization efforts. By combining company-specific operational data with established emission factors, emissions were calculated across Scopes 1–3 to pinpoint the main sources within production and supply networks. Results reveal that over 90% of total emissions come from upstream supply-chain activities, with raw material production being the largest contributor to climate impact. Direct manufacturing processes and electricity use have smaller shares. These results emphasize that improving operational efficiency alone is not enough for significant emissions reductions. Instead, supply-chain strategies, material choices and procurement policies are the most effective leverage points for decarbonization. The study underscores the importance of understanding value-chain emissions for industrial climate management and provides evidence supporting increased focus on supplier engagement and material decarbonization in corporate sustainability plans. This approach offers practical insights for organizations with complex manufacturing supply chains looking to align operational decisions with long-term climate goals.

2026-08-11T00:00:00Z Chidara, Alexander Chinaka Ji, Shouxun Awak, Anietie Austin https://bura.brunel.ac.uk/handle/2438/34032 2026-10-20T02:00:59Z 2026-10-14T00:00:00Z

Title: Ontology- and Data-Driven Circularity Assessment of Waste-Derived Rigid PVC-u: A Micro–Macro Decision Framework for Sustainable Recycling and Smart Manufacturing Authors: Chidara, Alexander Chinaka; Ji, Shouxun; Awak, Anietie Austin Abstract: The circularity of waste-derived rigid unplasticised poly (vinyl chloride) (PVC-u) is constrained by heterogeneous waste-stream sources, degraded material quality, legacy additives, and inconsistent decision criteria spanning technical, environmental, and economic aspects. In this work, we developed an ontology- and data-driven circularity assessment framework for waste-derived rigid PVC-u recycling systems, built on a Protégé ontology coupled with big data analytics. The framework structures knowledge on materials, recycling processes, process parameters, quality aspects, sustainability measures, carbon aspects, and cost aspects, and links it to process, operational, and business data. Semantic reasoning, based on rule-based and query-based logics, derives circularity, environmental, and economic measures and improves assessment consistency across different recycling cases. The assessment is carried out from two interconnected perspectives: a micro-level perspective focused on efficiency, quality, waste, energy, cost, and emissions at the plant level, and a macro-level perspective focused on circularity, resource retention, industrial value recovery, and net-zero implications at the system level. The combination of ontological modelling and analytics-driven assessment facilitates transparent, rule-based and traceable circularity assessment of recycling, processing and value recovery pathways for waste-derived rigid PVC-u. Applying the framework to three illustrative, literature-informed synthetic scenarios illustrates its ability to translate plant-level data into consistent, traceable micro- and macro-level circularity assessments aligned with circular economy and net-zero goals. Monte Carlo propagation of the scenario parameter ranges shows that every recycling route remains a net emission saver, whereas cost recovery and carbon-price viability differ strongly between routes, and that the 2030 circularity targets are met only if about 60% (circular material use) to 81% (end-of-life recycling) of the projected improvement is realised. These results are illustrative: we have not yet used industrial plant data, and empirical validation with recyclers is a priority for future work. This paper contributes an ontology- and data-driven circularity assessment framework for the sustainable recycling and smart manufacturing of waste-derived rigid PVC-u and outlines an approach to combine semantic intelligence with industrial analytics for circularity assessment. Description: Data Availability Statement: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.

2026-10-14T00:00:00Z Han, Qixiu Yu, Cheng Dong, Xixi Yang, Hailin Liu, Jianping Lu, Shiping Ji, Shouxun https://bura.brunel.ac.uk/handle/2438/34031 2026-10-20T02:01:36Z 2026-08-17T00:00:00Z

Title: Effects of Key Processing Parameters on Microstructure Features and Mechanical Properties of Die-Cast Aluminium Alloy Wheels Assisted with AI and Intelligent Prediction Authors: Han, Qixiu; Yu, Cheng; Dong, Xixi; Yang, Hailin; Liu, Jianping; Lu, Shiping; Ji, Shouxun Abstract: Effects of key processing parameters including ingate velocity (IV) and intensification pressure (IP), on the average/maximum sizes, volume fraction and fractal dimension of defects/porosities and grain size, and mechanical properties of die-cast Al7SiMnMg alloy wheels, were investigated by experiments and assisted with AI and intelligent prediction. Experiments show that IV considerably influences the maximum size of defects/porosities, which decreases considerably from 35.3 μm to 18μm with increasing IVs from 15 m/s to 35 m/s, afterwards remains constant. The average size and volume fraction of defects/porosities can be minimized under IVs of 35–50m/s. The grain size decreases slightly from 18 μm to 15 μm with increasing IVs from 15 m/s to 75 m/s. Microstructure features excluding fractal dimension exhibit similar reduction trends at low IPs of 300–600 bar, afterwards the effect of IP becomes not evident. IV substantially influences elongation, which reaches a high level of 10% at 35–50 m/s, but decreases remarkably to 5% at 15 m/s and 6.8% at 75m/s. IP clearly affects UTS and elongation, and UTS increases by 20MPa to 260MPa, while elongation increases absolutely from 4% to 12%, as IPs increase from 300 to 1500 bar. The yield strength is not remarkably affected by both IV and IP. Dataset of processing parameters–microstructure features–mechanical properties correlation was constructed, and XGBoost was confirmed as the most robust AI model for intelligently predicting mechanical properties within 10% error. SHAP analysis quantified underlying contributions of processing parameters to mechanical properties, and IV and IP account for 45.0% and 55.6% of the total feature impact to consequentially affect elongation.

2026-08-17T00:00:00Z