Analysis of Mold Friction in a Continuous Casting Using Wavelet Transform
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MOLD friction (MDF) reflects the lubrication between mold and slab quantitatively; thus, it is a critical online monitoring parameter during the process to optimize and develop new continuous casting technologies. With the development of high-efficiency continuous casting technology, the problems of billet quality and breakout have become increasingly prominent. Hence, the lubrication between the mold and slab is understood and controlled by calculating and detecting MDF as well as by investigating MDF characteristics; these goals have become the focus of continuous casting research. Since it is difficult to detect the mechanical behavior between the mold and billet using existing testing equipment and means, MDF was measured by some specific parameters (e.g., work and pressure) and the MDF value was indirectly obtained. Yao developed the power method[1] to detect MDF based on the difference in motor power between casting and no casting under
YONG MA, BOHAN FANG, QIQI DING, and FANGYIN WANG are with the School of Materials Science and Engineering, Hefei University of Technology, Hefei, China. Contact email: [email protected] Manuscript submitted December 9, 2016.
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the mechanical vibration system. The VAI[2] developed the method to detect MDF under the hydraulic vibration system. Most published research has focused on the study of MDF in the time domain. For example, Wang et al.[3] developed a set of software programs for MDF abnormality prediction and used the software for off-line forecasting of different abnormal castings. Their results revealed that the software could predict abnormalities in advance. Based on abnormal MDF data, Wang et al.[4] predicted the abnormalities during continuous casting using an artificial neural network. The neural network model predicts not only breakout but also other abnormalities, such as the breakage of a submerged entry nozzle and mold level fluctuations. The Mold Intelligent Management System (MIMS) developed by the Chongqing Iron & Steel Designing Institute[5] monitors the MDF, heat flux, and liquid levels. In particular, MDF trends can be directly observed based on the MIMS monitoring system. However, there are obvious limitations to obtaining more information from MDF in the time domain. Therefore, MDF research has shifted to the frequency domain. Some researchers have used fast Fourier transformation to analyze MDF.[6,7] Their results indicate that the MDF frequency characteristic is sensitive to changes in the process parameters and reflects the lubrication between the mold and strand. However, their studies lost information regarding MDF in the
time domain, which is unfavorable to the breakout prediction and lubrication behavior evaluation. Therefore, it is necessary to analyze MDF both in the time and frequency domains. Wavelet transform (WT) is a new and powerful tool in the field of signal research. WT can achieve a local analysis of signals simultaneously in the time and frequency domains. The notable features of WT are
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