2835 ニューラルネットワークによる鋳鋼の鋳込みで生じる欠陥の分類(S37-3 非破壊評価とモニタリング(3),S37 非破壊評価とモニタリング)

抄録

Present paper suggests a new ultrasonic inspection technique to classify the defects occurred by casting of cast steel using the neural network. Reflected echo waves are hard to discriminate the defects by the way of wave observation because of receiving the strong effect of surface roughness. Learning and classification of neural network for the subject of both vacancy and gas defect were carried out to the reflected echo. Defects were classified by specifying the learning data, and the effect of surface roughness could be decreased