コンシストレイヤー法テンパービード溶接熱影響部の靭性予測
Prediction of Toughness in HAZ produced by Temper Bead Welding of Consistent Layer Technique
著者:
于 麗娜 Lina YU Kazuyoshi SAIDA Masahito MOCHIZUKI Kazutoshi NISHIMOTO Masashi KAMEYAMA Shinro HIRANO Takehiko SERA
発刊日:
公開日:
Consistent Layer techniqueHeat affected zoneNeural networkTemper bead techniqueToughness
概要
Temper bead welding (TBW) is one effective repair welding method for the large-scale nuclear power plants instead of post weld heat treatment (PWHT). Consistent Layer (CSL) technique is the theoretically most authoritative method among the five temper bead welding techniques. For TBW, toughness is the key criteria to evaluate the tempering effect. A neural network-based method for toughness prediction in heat affected zone (HAZ) of low-alloy steel has been investigated to evaluate the tempering effect in TBW. On the basis of experimentally obtained database, the new toughness prediction system was constructed by using Radial basis function-neural network. With it, the toughness distribution in HAZ of TBW was calculated based on the thermal cycles numerically obtained by finite element method (FEM). The predicted toughness was in good accordance with the experimental results. It follows that our new prediction system is effective for estimating the tempering effect during TBW and hence enables us to assess the effectiveness of TBW before the actual repair welding.
