首页|New Research on Machine Learning from Semnan University Summarized (A novel mach ine learning-based model for predicting the transition fatigue lifetime in pisto n aluminum alloys)
New Research on Machine Learning from Semnan University Summarized (A novel mach ine learning-based model for predicting the transition fatigue lifetime in pisto n aluminum alloys)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on artificial intelligence have been published. According to news reporting originating from Semnan, Iran, by NewsRx correspondents, research stated, "The estimation of transition fatigue lifetimes for piston aluminum alloys was carried out using unsupervised machine learning (ML) with the K-means algorithm." Our news editors obtained a quote from the research from Semnan University: "For this purpose, an experimental dataset representing standard ISO specimens with piston aluminum alloy material, which was subjected to rotational bending fatigu e tests under fully reversed cyclic load conditions, was utilized. Subsequently, the stress and fatigue lifetime data were employed to fit the algorithm of K-me ans clustering. Then, to enhance the K-means performance, various preprocessing methods and Kernel functions were employed to cluster fatigue lifetime and stres s data. Furthermore, following the division of the data into multiple clusters, the middle cluster, which represents fatigue lifetime and stress, was identified as the transition fatigue region, and its center defines the estimated transiti on fatigue lifetime. Ultimately, the transition fatigue lifetimes were determine d using the Coffin-Manson-Basquin equation for piston aluminum alloys and compar ed to the estimated transition fatigue lifetimes, along with the calculation of relative errors. The obtained results indicated that, among the different models employed in this study, the polynomial Kernel K-means clustering algorithm prov ed to be the most efficient for clustering data within stress and number of cycl es plots (S-N plots)."
Semnan UniversitySemnanIranAsiaA lgorithmsAlloysAluminumCyborgsEmerging TechnologiesLight MetalsMachi ne LearningMathematicsPolynomial