Hydrogen defect acoustic emission recognition by deep learning neural network

Deep learning and its applications to machine health monitoring

Rui Zhao, Ruqiang Yan, Zhenghua Chen et al. · Mechanical Systems and Signal Processing · 2018 · 2.6K citations

Computing, Computer Science, Data Science +14

Observation of hydrogen trapping at dislocations, grain boundaries, and precipitates

Yi‐Sheng Chen, Hongzhou Lu, Jiangtao Liang et al. · Science · 2020 · 588 citations · Full text

Natural Sciences, Materials Engineering, Materials Science +17

Hydrogen damage of steels: A case study and hydrogen embrittlement model

Milos B. Djukic, Vera Sijacki Zeravcic, Gordana Bakić et al. · Engineering Failure Analysis · 2015 · 377 citations

Engineering, Materials Engineering, Materials Science +16

Review of tool condition monitoring in machining and opportunities for deep learning

Gökberk Serin, Batıhan Şener, Ahmet Murat Özbayoğlu et al. · The International Journal of Advanced Manufacturing Technology · 2020 · 350 citations

Engineering, Computer Science, Manufacturing Engineering +14

Convolutional neural network for ultrasonic weldment flaw classification in noisy conditions

Nauman Munir, Hak-Joon Kim, Jinhyun Park et al. · Ultrasonics · 2018 · 176 citations

Computing, Computer Science, Mechanical Engineering +15