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A Machine Learning Program for Impact Fracture AnalysisA Machine Learning Program for Impact Fracture Analysis
- Other Titles
- A Machine Learning Program for Impact Fracture Analysis
- Issue Date
- Jan-2021
- Publisher
- 한국기계가공학회
- Keywords
- Machine Learning(기계학습); K-means Algorithm(K-Means 알고리즘); Clustering(군집화); Charpy Impact test(충격시험); Fracture Surface Shape(파면형상)
- Citation
- 한국기계가공학회지, v.20, no.1, pp 95 - 102
- Pages
- 8
- Journal Title
- 한국기계가공학회지
- Volume
- 20
- Number
- 1
- Start Page
- 95
- End Page
- 102
- ISSN
- 1598-6721
2288-0771
- 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.
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Collections - School of Mechanical System Engineering > 1. Journal Articles
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