首页|Studies from Polytechnic University Torino in the Area of Machine Learning Descr ibed (Stress, Strain, or Energy? Which One Is Superior Predictor of Fatigue Life In Notched Components? a Novel Machine Learning-based Framework)
Studies from Polytechnic University Torino in the Area of Machine Learning Descr ibed (Stress, Strain, or Energy? Which One Is Superior Predictor of Fatigue Life In Notched Components? a Novel Machine Learning-based Framework)
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2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on Machine Learning are pre sented in a new report. According to news reporting originating from Turin, Ital y, by NewsRx editors, the research stated, "This paper introduces an efficient f ramework for accurately predicting the fatigue lifetime of notched components un der uniaxial loading within the high-cycle fatigue regime. For this purpose, var ious machine learning algorithms are applied to a wide range of materials, loadi ng conditions, notch geometries, and fatigue lives." Financial support for this research came from European Union (EU). Our news editors obtained a quote from the research from Polytechnic University Torino, "Traditional approaches for this task have mostly relied on one of the m echanical response parameters, such as stress, strain, or energy. This study als o concludes which of these parameters serves as a better measure. The key idea o f the framework is to use the profile (field distribution represented by some po ints) of the mechanical response parameters (stress, strain, and energy release rate) to distinguish between different notch geometries. To demonstrate the accu racy and broad applicability of the framework, it is initially validated using m etal materials, subsequently applied to specimens produced through additive manu facturing techniques, and ultimately tested on carbon fiber laminated composites . This research demonstrates the effective use of all three parameters in estima ting fatigue lifetime, while stress-based predictions exhibit the highest accura cy. Among the machine learning algorithms investigated, Gradient Boosting and Ra ndom Forest yield the most successful results."
TurinItalyEuropeCyborgsEmerging TechnologiesMachine LearningPolytechnic University Torino
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