Ultrasonic diagnosis method for stainless steel weld defects based on multi-domain feature fusion

Abstract

Purpose This paper aims to propose a new ultrasonic diagnosis method for stainless steel weld defects based on multi-domain feature fusion to solve two problems in the ultrasonic diagnosis of austenitic stainless steel weld defects. These are insufficient feature extraction and subjective dependence of diagnosis model parameters. Design/methodology/approach To express the richness of the one-dimensional (1D) signal information, the 1D ultrasonic testing signal was derived to the two-dimensional (2D) time-frequency domain. Multi-scale depthwise separable convolution was also designed to optimize the MobileNetV3 network to obtain deep convolution feature information under different receptive fields. At the same time, the time/frequent-domain feature extraction of the defect signals was carried out based on statistical analysis. The defect sensitive features were screened out through visual analysis, and the defect feature set was constructed by cascading fusion with deep convolution feature information. To improve the adaptability and generalization of the diagnostic model, the authors designed and carried out research on the hyperparameter self-optimization of the diagnostic model based on the sparrow search strategy and constructed the optimal hyperparameter combination of the model. Finally, the performance of the ultrasonic diagnosis of stainless steel weld defects was improved comprehensively through the multi-domain feature characterization model of the defect data and diagnosis optimization model. Findings The experimental results show that the diagnostic accuracy of the lightweight diagnosis model constructed in this paper can reach 96.55% for the five types of stainless steel weld defects, including cracks, porosity, inclusion, lack of fusion and incomplete penetration. These can meet the needs of practical engineering applications. Originality/value This method provides a theoretical basis and technical reference for developing and applying intelligent, efficient and accurate ultrasonic defect diagnosis technology.

Visual Communication Design of Weak and Small Target Images Based on Image Processing Model and Data Fusion

“…To solve the problem of ultrasonic diagnosis of defects in austenitic stainless steel, Zhang R. et al proposed a new defect diagnosis method based on multi-domain data FF. The experimental results showed that this method has comprehensively improved the performance of defect diagnosis and had a driving effect on ultrasonic missing element diagnosis [23]. Cui X. et al proposed a classification model by fusing image features and depth features to improve the performance of the MRI aided diagnosis model.…”

The detection and classification method of sub-macroscopic defects inside steel with an ultrasonic testing and CatBoost-based stacking model

“…The four types of sub-macroscopic defects above includes pores and slag inclusions, which may include oxides, sulfides, silicates and their combination [43,[49][50][51][52][53]. The time domain waveforms of four types of defects are distinct, which illustrates the various components of them.…”