首页|Data from Beihang University Provide New Insights into Machine Learning (Pore-in duced Fatigue Failure: a Prior Progressive Fatigue Life Prediction Framework of Laser-directed Energy Deposition Ti-6al-4v Based On Machine Learning)
Data from Beihang University Provide New Insights into Machine Learning (Pore-in duced Fatigue Failure: a Prior Progressive Fatigue Life Prediction Framework of Laser-directed Energy Deposition Ti-6al-4v Based On Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Machine Learning have been presented. According to news originating from Beijing, People's Republic o f China, by NewsRx correspondents, research stated, "Pores are major cause of fa tigue failure in laser-directed energy deposition (L-DED) titanium alloy. For th e safe application of L-DED titanium alloys, it is essential to establish a fati gue life prediction method based on poreinduced fatigue." Funders for this research include Special Research Project of Chinese Civil Airc raft, Aero- nautical Science Foundation of China. Our news journalists obtained a quote from the research from Beihang University, "This paper proposes a prior progressive fatigue life prediction framework base d on ridge classification and kernel ridge regression algorithms. The fatigue li fe prediction was carried out on L-DED Ti-6Al-4V alloy in three steps: critical pore identification, fine granular area existence prediction and final fatigue l ife prediction. The fatigue life prediction method adopted in the current study outperform the others with a correlation coefficient as high as 0.951, followed by a comparison with the results derived from different machine learning algorit hms. The results show that the proposed fatigue life prediction framework can pr edict the fatigue life of L-DED Ti-6Al-4V alloy based on computed tomography tes ts and microstructure features." According to the news editors, the research concluded: "Due to its strong genera lization ability and effectiveness, the proposed prediction method is expected t o be valuable for fatigueresistant design of L-DED Ti-6Al-4V alloy." This research has been peer-reviewed.
BeijingPeople's Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningBeihang University
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