Abstract
Researchers detail new data in Machine Learning. According to news reporting out of Beijing, People's Republic of China, by NewsRx editors, research stated, "Calculation of the stress intensity factor K is a crucial and difficult task in linear elastic fracture mechanics. With the capacity to solve complex input-output problems of an underlying system, machine learning is especially useful in the calculation of K. However, when faced with complex systems, such as the firtree groove structure of a turbine disk, the data-consuming issue has always been a thorny problem in K -solutions combined with machine learning studies for a long time." Funders for this research include National Major Science and Technology Project, Fundamental Research Funds for the Central Universities. Our news journalists obtained a quote from the research from Beihang University, "In this paper, a novel K -solution method called PA-GPR (Pool -based Active learning with Gaussian Process Regression) for the calculation of the stress intensity factor for surface cracks in the firtree groove structure of a turbine disk is proposed. Using the pool -based active learning strategy, the proposed K -solution method could make the GPR model have a great regression performance with a few samples required. In the pool -based active learning strategy analysis, the learning function based on greedy sampling is proposed to select samples with a high contribution to the training of the GPR model. The calculation of K for a semi -elliptical surface crack in the firtree groove structure is evaluated to verify the accuracy and effectiveness of the proposed method."
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