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http://dx.doi.org/10.14775/ksmpe.2021.20.01.095

A Machine Learning Program for Impact Fracture Analysis  

Lee, Seung-Jin (Graduate Mechanical Engineering, Kumoh National Institute of Technology)
Kim, Gi-Man (Dept. Mechanical System Engineering, Kumoh National Institute of Technology)
Choi, Seong-Dae (Dept. Mechanical System Engineering, Kumoh National Institute of Technology)
Publication Information
Journal of the Korean Society of Manufacturing Process Engineers / v.20, no.1, 2021 , pp. 95-102 More about this Journal
Abstract
Analysis of the fracture surface is one of the most important methods for determining the cause of equipment structural failure. Whether structural failure is caused by impact or fatigue is necessary information in industrial fields. For ferrous and non-ferrous metal materials, two fracture phenomena are generated on the fracture surface: ductile and brittle fractures. In this study, machine learning predicts whether the fracture is based on ductile or brittle when structurural failure is caused by impact. The K-means algorithm calculates this ratio by clustering the brittle and ductile fracture data from a photograph of the impact fracture surface, unlike the existing method, which calculates the fracture surface ratio by comparison with the grid type or the reference fracture surface shape.
Keywords
Machine Learning; K-means Algorithm; Clustering; Charpy Impact test; Fracture Surface Shape;
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