出钢
铸铁
石墨
质量(理念)
人工神经网络
计算机科学
材料科学
冶金
工程类
人工智能
机械工程
物理
量子力学
作者
Mitsuki SHINOHARA,Nozomu UHIDA,Yuki Iwami,Yuichi Hiramoto,Masaya KATO,Toshitake Kanno
出处
期刊:Journal of Computer Chemistry, Japan
[Society of Computer Chemistry, Japan]
日期:2020-01-01
卷期号:19 (4): 164-166
标识
DOI:10.2477/jccj.2021-0016
摘要
Tapping test is an inspection method that determines the presence or absence of abnormality in a specimen based on the difference in the sound when tapping a material. This method is used to inspect buildings and railway vehicles. It is considered that this method can be used for quality evaluation of cast iron. However, although the tapping test has the advantage of being able to be performed non-destructively and simply, it also has the disadvantage of requiring a worker who can distinguish sounds. In order to solve this problem, we introduced a neural network and studied whether it is possible to judge the quality of cast iron by learning the tapping sound of cast iron.
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