Use of ultrasonics for the examination of hot billets and for cleanness assessment

Andrews, Kevin; Druce, M.; Russell, James E.

doi:10.1179/030716974803288338

Cited by 5 publications

(1 citation statement)

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“…With high precision, high detection efficiency and harmlessness to the human body, ultrasonic non-destructive testing is widely used in the evaluation of metal internal cleanliness. Andrew et al [18] proposed an ultrasonic testing method to detect internal pores of steel materials, and found that the selection of ultrasonic transducer parameters can strongly affect the experimental results. Bifulco et al [19] used a broadband-pulsed ultrasound and time-frequency analytical techniques of defects echo signals to test the internal cleanliness of aluminum specimens.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Wang

Chen

2023

Meas. Sci. Technol.

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With the increasing demand for advanced steel is increasing year by year, and the internal cleanness content of steel inclusions becomesis an important evaluation indicator for the evaluation of material material quality. Sub-macroscopicInclusions defects are randomly distributed inside the steel materials, which has a great impact on the performance and quality safety of the steel. In especial, sub-macroscopic inclusions with sizes ranging from 50μm to 400μm have seriously affected material stability and fatigue life because they are not covered by existing testing standards. In addition,Besides, the existing current detection methods for inclusions in steel generally have problems such as low efficiency and complexity process. In this paper, we propose a non-destructive inclusion testing and classification framework basing on ultrasonic testing experiments, signal feature extraction and machine learning methods. Under the optimal experimental detection conditions we found through experiments, a large-scale sub-macroscopic inclusion signal data set is established to realize the classification of defects. Moreover, Empirical Mode Decomposition (EMD) and other feature extraction algorithms are applied to further boost the model performance. We propose a Catboost-based stacking fused model named Stacked-CBT, which obtains state-of-the-art experimental result with accuracy rate of 86.65% and demonstrates that the proposed framework is feasible to classify the sub-macroscopic inclusion signals. To the best of our knowledge, there is no previous study in this field has acquire such large amount of experimental sub-macroscopic signal data while taking into consideration classification-specific designs.

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Section: Introductionmentioning

confidence: 99%