Using welding sound signals to monitor the welding pool state has potential in gas tungsten arc welding. A novel welding-penetration-monitoring method using the welding sound based on feature extraction and feature selection is proposed. In order to overcome the blindness of subjective dimensionless indicators selected as sensitive features without any experience, attempts are made to try to obtain as many relative features as possible. A feature selection technology is used to find the most effective features and to reduce the redundant features. In this paper, a wavelet packet transform method is used to decompose the welding sound signals; then the decomposed nodes are calculated and 128 features obtained through statistical processing; a genetic algorithm selects seven feature subsets from the 128 features and employs an artificial neural network to classify the different penetration states, with 85 per cent accuracy in test data.
The failure of Type 316L stainless steel expansion bellows serving in steam pipe network has been analyzed. The expansion bellows cracked after approximately 4-year service in steam network at 230°C. The caustic stress corrosion cracking (caustic-SCC) resulting from a combined function of caustic concentrating, metal temperature and residual stress was responsible for the failure. Cracking initiated on the inside surface of the bellow at the peak where served as stress raiser by observation of sample taken from failed expansion bellows. The pattern of cracking was shown as a spider web of several small cracks in photomicrographs. The metallographic examination also indicated that cracks were predominantly transgranular. The oxides on the surface of cracks can be confirmed to be caustic NaOH through X-ray diffraction (XRD) examination.
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