20.500.12556/DiRROS-24178 Optimizing foamed glass production with machine learning Foamed glass is a lightweight material commonly used for insulation. However, optimizing its properties remains a challenge due to the large number of synthesis parameters involved in its production. While previous studies have investigated synthesis conditions, a comprehensive study applying machine learning approaches is lacking in the literature. In this paper, we apply machine learning methods, i.e., random forests of predictive clustering trees and a multilayer perceptron, training them on 124 experimental data points to accurately predict the apparent density and closed porosity of foamed glass. We then apply a multiobjective optimization algorithm together with the multilayer perceptron to find optimal values for the process parameters used in foamed glass production. Our results show that the combination of machine learning and multiobjective optimization is an effective proxy for the development of novel foamed glass materials. process optimization machine learning foamed glass true false true Elsevier Nizozemska Angleški jezik Ni določen © 2025 The Authors. Neznano 2025-11-18 13:22:26 2025-11-18 13:22:27 2025-11-19 03:42:42 0000-00-00 00:00:00 2025 0 0 Nasl. z nasl. zaslona; Opis vira z dne 21. 8. 2025; str. 1-8 Vol. 257 Sep. 2025 0000-00-00 Zaloznikova Objavljeno apc 2025-03-21 2025-07-23 2025-07-30 004.8+666.11+338.3 1873-4197 10.1016/j.matdes.2025.114459 246226691 https://www.sciencedirect.com/science/article/pii/S0264127525008792?via%3Dihub 1 4220b691-c479-11f0-94a7-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=35986 1-s2.0-S0264127525008792-main.pdf 1-s2.0-S0264127525008792-main.pdf The dataset from this study is available at https://doi.org/10.5281/zenodo.15023205. The code implementation for Task 2 and the multiobjective optimization algorithm (IBEA) are available at https://github.com/sintija-s/foaming-glass. 1 6601A430FD2C782E745268E9713F234A 65d8ae229d52dfe22d4434426648f14ae78428cb34a99004c72e62f4d2e9e48e a727d3c6-c479-11f0-94a7-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=35987 Institut Jožef Stefan 0 0 0