首页|Johannes Kepler University Researchers Detail Research in Machine Learning (Polyolefin ductile-brittle transition temperature predictions by machine learning)
Johannes Kepler University Researchers Detail Research in Machine Learning (Polyolefin ductile-brittle transition temperature predictions by machine learning)
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Frontiers Media Sa
Researchers detail new data in artificial intelligence. According to news reporting out of Linz, Austria, by NewsRx editors, research stated, "Polymers show a transition from ductile-to brittle fracture behavior at decreasing temperatures." Our news editors obtained a quote from the research from Johannes Kepler University: "Consequently, the material toughness has to be determined across wide temperature ranges in order to determine the Ductile-Brittle Transition Temperature This usually necessitates multiple impact experiments. We present a machine-learning methodology for the prediction of DBTTs from single Instrumented Puncture Tests Our dataset consists of 7,587 IPTs that comprise 181 Polyethylene and Polypropylene compounds. Based on a combination of feature engineering and Principal Component Analysis, relevant information of instrumentation signals is extracted. The transformed data is explored by unsupervised machine learning algorithms and is used as input for Random Forest Regressors to predict DBTTs. The proposed methodology allows for fast screening of new materials. Additionally, it offers estimations of DBTTs without thermal specimen conditioning."
Johannes Kepler UniversityLinzAustriaEuropeCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
Frontiers Media Sa
