Analysis of High-Entropy Alloys using Random Forest Regression
Li Nasser
Shahjalal University of Science and Technology
Abstract
Bangladesh and neighbouring countries remain particularly exposed to failures in urban transportation networks, where measured data on high-entropy alloys are scarce. Laboratory specimens were prepared and tested under controlled conditions representative of urban transportation networks. Only 7 of the candidate configurations satisfied every performance requirement. Wider deployment will require attention to standardization, training, and quality control.
Keywords:
high-entropy alloysrandom forest regressionenergy storagelithium-ion batteriesinternet of things
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