Specific total loss is one of the most important evaluation indexes for the magnetic properties of non-oriented electrical steel sheets. The aim of this study is to investigate the influencing mechanisms of laser cutting parameters as well as the sample characteristics on the specific total loss of thin non-oriented electrical steel sheets processed by laser cutting using a machine learning method. Eight input parameters were finally considered; namely, silicon and manganese contents, thickness of the steel sheets, laser nozzle diameter, laser power, cutting speed, the pressure of process gas, and laser defocus, while one output parameter, the specific total loss, was evaluated. It was found that the specific total loss was positively correlated with the sample thickness, but negatively correlated with silicon and manganese contents, the process gas pressure and laser nozzle diameter. In addition, laser power and cutting speed exhibit complicated non-linear relationships with the specific total loss.

Based on the application demand of laser cutting technology in non-oriented electrical steel, the influencing mechanisms of laser cutting parameters on the magnetic properties of 50W350 high-grade non-oriented electrical steel were investigated in this work. The specific total loss was utilized to evaluate the quality of cutting methods and the cutting parameter combinations. The results showed that the deterioration of the specific total loss was mainly due to the increase in hysteresis loss. Compared to traditional mechanic shearing, laser cutting generally degrades the magnetic properties under the evaluation index ΔP1.0/50. However, in some cases, laser cutting is superior to the mechanic shearing method under the evaluation index ΔP1.5/50. The main parameters related to laser cutting exhibited complicated influencing mechanisms on the specific total loss of 50W350 high-grade non-oriented electrical steel. However, based on the results of the experiments designed using the Box–Behnken model, the laser cutting parameters were optimized and the evaluation indexes have been significantly improved.

To quickly and accurately measure the AC magnetic properties of grain-oriented electrical steel by means of the existing measuring system designed for the magnetizing current method (MC), specifically the SST (92) single sheet method, in this work, the H-coil (HC) measuring system, which directly senses the magnetic field strength of the tested sample, was designed to measure the AC magnetic properties of the grain-oriented electrical steel. The assumed effective magnetic path length introduced in the MC method was corrected by comparing the measurement results obtained by means of HC and MC methods. The results found that specific total loss measured by the HC method was significantly lower than that measured by the classical magnetizing current (MC) method. Taking the HC method as the reference, the influencing factors of the effective magnetic path length was studied. It was found that the actual effective magnetic path length depends on the investigated sample characteristics, the measurement conditions, as well as yoke characteristics. The actual effective magnetic path length introduced in the MC method is examined to be more than 450 mm, fluctuating around 468 mm.

BackgroundThe pathogenesis of myocardial infarction complicating depression is still not fully understood. Bioinformatics is an effective method to study the shared pathogenesis of multiple diseases and has important application value in myocardial infarction complicating depression.MethodsThe differentially expressed genes (DEGs) between control group and myocardial infarction group (M-DEGs), control group and depression group (D-DEGs) were identified in the training set. M-DEGs and D-DEGs were intersected to obtain DEGs shared by the two diseases (S-DEGs). The GO, KEGG, GSEA and correlation analysis were conducted to analyze the function of DEGs. The biological function differences of myocardial infarction and depression were analyzed by GSVA and immune cell infiltration analysis. Four machine learning methods, nomogram, ROC analysis, calibration curve and decision curve were conducted to identify hub S-DEGs and predict depression risk. The unsupervised cluster analysis was constructed to identify myocardial infarction molecular subtype clusters based on hub S-DEGs. Finally, the value of these genes was verified in the validation set, and blood samples were collected for RT-qPCR experiments to further verify the changes in expression levels of these genes in myocardial infarction and depression.ResultsA total of 803 M-DEGs, 214 D-DEGs, 13 S-DEGs and 6 hub S-DEGs (CD24, CSTA, EXTL3, RPS7, SLC25A5 and ZMAT3) were obtained in the training set and they were all involved in immune inflammatory response. The GSVA and immune cell infiltration analysis results also suggested that immune inflammation may be the shared pathogenesis of myocardial infarction and depression. The diagnostic models based on 6 hub S-DEGs found that these genes showed satisfactory combined diagnostic performance for depression. Then, two molecular subtypes clusters of myocardial infarction were identified, many differences in immune inflammation related-biological functions were found between them, and the hub S-DEGs had satisfactory molecular subtypes identification performance. Finally, the analysis results of the validation set further confirmed the value of these hub genes, and the RT-qPCR results of blood samples further confirmed the expression levels of these hub genes in myocardial infarction and depression.ConclusionImmune inflammation may be the shared pathogenesis of myocardial infarction and depression. Meanwhile, hub S-DEGs may be potential biomarkers for the diagnosis and molecular subtype identification of myocardial infarction and depression.