Gutowski, TG, Sahni, S., Allwood, JM, Ashby, MF & Worrell, E. Energy required to produce materials: Constraints on increasing energy intensity, demand parameters. Philos. transformer. R. Soc. A: Mathematics, Physics. engineering science. 37120120003 (2013).
JM Allwood, JM Cullen, RL Milford Options for achieving a 50% reduction in industrial carbon emissions by 2050. environment. Science. technology. 441888-1894 (2010).
Das, SK, Green, JAS, Kaufman, JG, Emadi, D. & Mahfoud, M. Aluminum recycling – an integrated approach across the industry. JOM 6223–26 (2010).
Dahmus, JB & Gutowski, TG What gets recycled: An information theory-based model for product recycling. environment. Science. technology. 417543–7550 (2007).
Graedel, TE et al. What do we know about metal recycling rates? J. Ind. Ecol. 15355–366 (2011).
Reck, BK & Graedel, TE Challenges in metal recycling. science 337690–695 (2012).
Gopalan, R. & Prabhu, N.K. Oxide double membranes on aluminum alloy castings – A review. meter. Science. technology. 271757–1769 (2011).
Raabe, D. et al. Sustainable aluminum production through scrap recycling: The science of “dirty” alloys. Progressive rock. meter. Science. 128100947 (2022).
Zhan, H. et al. Effect of copper content on intergranular corrosion of model almgsi(cu) alloys. meter. Koros. 59670–675 (2008).
Bacaicoa, I. et al. 3D morphology of al5fesi inclusions in al-si-cu alloys with high iron content. Procedia structure. Integrate. 22269–2276 (2016).
Samuel, A. M., Doty, H. W., Valtierra, S. & Samuel, F. H. Beta Al.5Relationship between FeSi phase platelets and pore formation in A319.2 type alloys. internal. J. Met. 1255–70 (2017).
Google Scholar
Taylor, JA Iron-bearing intermetallic phases in AI-Si-based cast alloys. Procedia meter. Science. 119–33 (2012).
Lu, L. & Dahle, AK Iron-rich intermetallic phases and their role in casting defect formation in hypoeutectic al-Si alloys. metal. meter. transformer. a 36819–835 (2005).
Google Scholar
Chen, X.-M. et al. Transformation of Fe-containing intermetallic compounds and its influence on the corrosion resistance of Al-Mg-Si alloy welded joints. J. Mater. resolution technology. 916116–16125 (2020).
Otani, LB et al. Tuning the Microstructure of Recycled 319 Aluminum Alloy Aiming for High Ductility. J. Mater. resolution technology. 83539–3549 (2019).
Fortini, A., Merlin, M., Fabbri, E., Pirletti, S. & Garagnani, GL On the influence of Mn and Mg additions on the tensile properties, microstructure and quality index of a356 aluminum casting alloy. Procedia structure. Integrate. 22238–2245 (2016).
Yuan, WH & An, BL 7075 Effect of La addition on the microstructure and mechanical properties of aluminum alloys. Advanced meter. resolution 152-1531810–1813 (2010).
Taylor, JA, Schaffer, GB & St John, DH The role of iron in the formation of porosity in al-Si-Cu based casting alloys: Part II. Phase diagram approach. metal. meter. transformer. a 301651–1655 (1999).
Moustafa, M. Effect of iron content on iron formation. β-Porosity of al5fesi and al-si eutectic alloys. J. Mater. process. technology. 209605–610 (2009).
Mbuya, TO, Odera, BO & Ng’ang’a, SP Influence of iron on castability and properties of aluminum-silicon alloys: Literature review. internal. J. Cast. We met. resolution 16451–465 (2003).
Fang, X., Shao, G., Liu, Y., and Fan, Z. Effect of intensive forced melt convection on mechanical properties of iron-bearing Al-Si based alloys. meter. Science. Engineering: A 445-44665–72 (2007).
Morphology and growth mechanism of Gao, T., Wu, Y., Li, C. & Liu, X. α-al(FeMn)si for al-si-fe-mn alloys. meter. Let. 110191–194 (2013).
Taghaddos, E., Hejazi, M., Taggiabadi, R. & Shabestari, S. 413 Effect of ferrous intermetallic compounds on the flowability of aluminum alloys. J. Alloy. Comp 468539–545 (2009).
Seifeddine, S., Johansson, S. & Svensson, IL Effect of cooling rate and manganese content. β-al5fesi phase formation and mechanical properties of Al-Si based alloys. meter. Science. engineering a 490385–390 (2008).
Castro-Román, MJ, Aguilera-Luna, I., Gaona-Coronado, AA, Herrera-Trejo, M. & Torres-Torres, J. Role of fe/mn ratio and cooling rate in the precipitation of ferrous intermetallic compounds. α-With Alfemunshi β-alfesi 356 alloy. transformer. We met at the Indian Institute. 681193–1197 (2015).
Mohammed, W., Chen, X., Ponge, D. & Raabe, D. Thermodynamics-based design of sustainable secondary al-Si alloys for enhanced Fe impurity tolerance and optimized mn doping. Acta meter. 289120932 (2025).
Raabe, D., Tasan, CC, Olivetti, EA Strategies for improving the sustainability of structural metals. nature 57564–74 (2019).
Sanders, N. CALPHAD (calculate phase diagram) (Pergamon, 1998).
Andersson, J.-O., Helander, T., Höglund, L., Shi, P. & Sundman, B. Thermo-calc & Dictra, computational tools for materials science. Calfado 26273–312 (2002).
Sarafoglou, PI and Haidemenopoulos, GN Phase fraction mapping in the as-cast microstructure of extrudable 6xxx aluminum alloys. internal. J. Mater. resolution 1051202–1209 (2014).
Wei, J. et al. Machine learning in materials science. infomat 1338–358 (2019).
Morgan, D. & Jacobs, R. Opportunities and challenges for machine learning in materials science. Anne. Pastor Mater. resolution 5071–103 (2020).
Gao, C. et al. Innovative materials science with machine learning. Advanced functions. meter. 32 2108044 (2021).
Pratap, A. & Sardana, N. Machine learning-based image processing in materials science and engineering: A review. meter. today. procedure 627341–7347 (2022).
Zhu, S., Sarıtürk, D. & Arróyave, R. Accelerating Calfado-based phase diagram prediction in complex alloys using universal machine learning potential: Opportunities and challenges. Acta meter. 286120747 (2025).
Rahman, A., Hossain, M.S., Siddique, A.-B. Review: Machine learning approaches for diverse alloy systems. J. Mater. Science. 6012189–12221 (2025).
Pihlmann, L., Rafiezadeh, S., Hofbauer, M., Ocansey, ED & Österreicher, JA Prediction of mechanical properties of aluminum alloys: A data-driven framework leveraging llm-based data extraction and physically-based feature engineering. meter. Today is community. 47112843 (2025).
Jain, S., Wagri, NK, Bhowmik, A. & Park, N. A machine learning approach to predict mechanical performance and reduce experimentation for refractory high-entropy alloys. Advanced Engineering Meter. 272403052 (2025).
Huang, S., Wang, G. & Cao, Z. Prediction of mixing enthalpy of binary alloys based on machine learning and Calfard evaluation. metal 15 https://www.mdpi.com/2075-4701/15/5/480 (2025).
Jain, S., Wagri, N.K., Arya, M., Bhowmik, A. & Park, N. Prediction of magnetic behavior of homogenized coclufenialux high-entropy alloys at different aluminum contents and temperatures: Reducing experimental dependence with a machine learning approach. meter. Chemistry. Physics. 346131386 (2025).
Strandlund, H. Fast thermodynamic calculations for dynamical simulations. Calculate. meter. Science. 29187–194 (2004).
Yi, W. et al. A new atomic mobility model for alloys under pressure by integrating Calphad and machine learning and its application to high-pressure heat-treated al-Si alloys. J. Mater. Science. technology. 217116–127 (2025).
Federation of German Industries (BDG) (ed.). Aluminum-goose: Grundlagen – Anwendungen – Regierungen – Beispiele. Githzerai Verlag (2013).
Sundman, B. & Ansara, I. Iiii.2 – Gulliver-Scheil method for coagulation path calculations. in SGTE Case Book 2nd edition, (Hack, K. ed.), Woodhead Publishing Series in Metals and Surface Engineering, 343–346 (Woodhead Publishing, 2008), 2nd edition edn. https://www.sciencedirect.com/science/article/pii/B9781845692155500322.
Kocev, D., Vens, C., Struyf, J. & Džeroski, S. Ensembles of multiobjective decision trees. in Machine learning: ECML (Kok, JN et al. eds.) 624–631 (Springer Berlin Heidelberg, 2007).
Pedregosa, F. et al. Scikit-learn: Machine learning in Python. J. Mach. learn. resolution 122825–2830 (2011).
Google Scholar
Tetsuya Ichiishi Game theory for economic analysis (Academic Press, 1983).
Lundberg, S.M. & Lee, S.-I. A unified approach to interpreting model predictions. In Guyon, I. et al. (eds.) Advanced neural information processes. system. 30, 4765–4774. http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf. (Curran Associates, 2017).
