Online monitoring and real-time feedback on inclusions in molten metal are essential for metal quality control. However, existing methods for detecting aluminum melt inclusions face challenges, including interference, prolonged processing times, and latency. This paper presents the design and development of an online monitoring system for molten metal inclusions. Initially, the system facilitates real-time adjustment of signal acquisition parameters through a multiplexer. Subsequently, it employs a detection algorithm capable of swiftly extracting pulse peaks, with this task integrated into our proprietary host computer software to ensure timely detection and data visualization. Ultimately, we developed a monitoring device integrated with this online monitoring system, enabling the online monitoring of the aluminum alloy filtration process. Our findings indicate that the system can accurately measure the size and concentration of inclusions during the filtration process in real time, offering enhanced detection speed and stability compared to the industrial LiMCA CM (liquid metal cleanliness analyzer continuous monitoring) standard. Furthermore, our evaluation of the filtration process demonstrates that the effectiveness of filtration significantly improves with the increase in inclusion sizes, and the synergistic effect of combining CFF (ceramic foam filter) and MCF (metallics cartridge filter) filtration methods exceeds the performance of the CFF method alone. This system thus provides valuable technical support for optimizing filtration processes and controlling inclusion quality.

Traditional methods for assessing the cleanliness of liquid metal are characterized by prolonged detection times, delays, and susceptibility to variations in sampling conditions. To address these limitations, an online cleanliness-analyzing system grounded in the method of the electrical sensing zone has been developed. This system facilitates real-time, in situ, and quantitative analysis of inclusion size and amount in liquid metal. Comprising pneumatic, embedded, and host computer modules, the system supports the continuous, online evaluation of metal cleanliness across various metallurgical processes in high-temperature environments. Tests conducted with gallium liquid at 90 °C and aluminum melt at 800 °C have validated the system’s ability to precisely and quantitatively detect inclusions in molten metal in real time. The detection procedure is stable and reliable, offering immediate data feedback that effectively captures fluctuations in inclusion amount, thereby meeting the metallurgical industry’s demand for real-time analyzing and control of inclusion cleanliness in liquid metal. Additionally, the system was used to analyze inclusion size distribution during the hot-dip galvanizing process. At a zinc melt temperature of 500 °C, it achieved a detection limit of 21 μm, simultaneously providing real-time data on the size and amount distribution of inclusions. This represents a novel strategy for the online monitoring and quality control of zinc slag throughout the hot-dip galvanizing process.

Our previous study (Zheng et al 2020 Metall. Mater. Trans. B 51 558–69; Zheng et al 2020 Acta Metall. Sin. 56 929–36) reports a non-invasive in-situ measurement technology using Lorentz force velocimetry (LFV) to quantitatively measure the meniscus velocity of molten steel online. However, effective signal recognition from complex environment noise and determination of zero-point calibration in harsh metallurgical processing is an essentially challenging task and indeed needs further exploration. In this paper, a method of combining double probe arrangement with real-time differential processing technology was proposed, the twin design structure not only enables the measurement of ∼mN Lorentz forces, but also has significant characteristics of environmental tolerance. The Lorentz force signal caused by conductor motion can be accurately calculated through a differential method, meaning that the problem of zero compensation in industrial online measurement can be effectively overcome. Moreover, based on the functional correlation between the Lorentz force and the parameter of the conductor to be measured, a method of the probes moving variably and actively and their data difference ratio processing was adopted, so as to achieve dynamic calibration during online measurement. This measurement strategy provides a new approach for LFV to achieve online dynamic measurement and online calibration, and provides technical support for electromagnetic measurement technology towards engineering applications.

In complex industrial environments, the signals generated by inclusions in molten aluminum when passing through the electric sensing zone are often temporally overlapped with strong industrial noise. This overlap reduces the detection accuracy of systems that rely solely on time-domain parameters for denoising, thereby affecting the quality and performance of metal products. To address this issue, this paper investigates signal analysis and identification for an electric-sensing-zone-based detection system. A signal processing method based on a multi-branch residual attention convolutional neural network (MRACNN) is proposed. Wavelet transform is first employed to extract the time-frequency features of transient signals, so that the differences in frequency-band distribution between inclusion signals and industrial noise can be effectively characterized. The extracted features are then fed into the MRACNN for valid signal identification and screening. Industrial experiments showed that the proposed method improved the detection range and statistical reliability of ESZ-based inclusion measurement, extending the effective detection limit approximately from 65 μm to 20 μm. Comparison of online results with offline PoDFA measurements showed consistent cleanliness grades. These results demonstrate that the proposed software-based signal analysis method can overcome the limitation of hardware measurement resolution under strong industrial noise conditions.