In this paper, the influence of bismuth content and heating rate on the morphology of MnS inclusions in bismuth-containing free-cutting steel during heating was investigated through in situ observation experiments and 3D electrolytic corrosion experiments. By observing the microscopic morphology of inclusions in the original sample, it was found that MnS inclusions in the sample were rod-shaped, spherical, irregular, small in size, and mostly clustered at the grain boundary in the form of chains and divergences. With the addition of bismuth, MnS inclusions of a larger size appear in the steel, and the inclusions distributed at grain boundaries are also reduced. When bismuth (0.010~0.020%) is added to the steel, MnS is mainly spherical and uniformly dispersed in the steel matrix. If the bismuth content is too high, the inclusions aggregate. Through in situ observations of the inclusions in the sample, it was found that the addition of bismuth in the heating process delays the appearance of ferrite grain boundaries and contributes to the spheroidization of MnS inclusions. Mn and S elements can fully diffuse slowly in the matrix with a heating rate below 1 °C/s and a long holding time (300 s), which provides the possibility for the spheroidization of MnS inclusions.

Pearlitic steel rods are subjected to cold-drawing processes to produce pearlitic steel wires with true strains ranging from 0.81 to 2.18. Tensile tests are utilized to attain mechanical properties of cold-drawn pearlitic steel wires. TEM and XRD investigations were performed on the microstructure of the cold-drawn steel wires. With an increasing cold-drawn strain, both the interlamellar spacing and cementite lamellae thickness decrease, while the dislocation density significantly increases. The drawn wire has a tensile strength of 2170 MPa when the true stain reaches 2.18. Deformation-induced cementite dissolution occurs during cold-drawing progress, which releases many C atoms. The findings indicate that the supersaturation of C is heterogeneously distributed in the ferrite matrix. The ordered distribution of the released C in ferrite phases creates short-range order (SRO). SRO clusters and disordered Cottrell atmospheres contribute to solution strengthening, which, together with dislocation strengthening and interlamellar boundary strengthening, form an effective strengthening mechanism in cold-drawn pearlitic steel wires. Our work provides new insights into carbon redistribution and the mechanism of solution strengthening within ferrous phases.

It is very important to carry out effective safety inspections on suppression rigging because of the bad service environment of suppression rigging: marine environments. In this paper, the multi-parameter simulation method in ANSYS and ANSYS Electronics Suite simulation software is used to simulate the effect of geomagnetic fields on the magnetic induction intensity of defective pressed rigging under the variable stress in marine environments. The results of the ANSYS simulation and geomagnetic flaw detection equipment are verified. The simulation results show that, according to the multi-parameter simulation results of ANSYS and ANSYS Electronics Suite simulation software, it can be found that, under the action of transverse force, the internal stress of the pressed rigging will affect the magnetic field around pressed rigging with defects. With an increase in internal stress in the range of 0~20 MPa, the magnetic induction intensity increases to 0.55 A/m, and with an increase in internal stress in the range of 20~150 MPa, the magnetic induction intensity decreases to 0.06 A/m. From the use of a force magnetic coupling analysis method, it can be obtained, under the lateral force of the defects in suppressing rigging, that magnetic flux leakage signals decrease with an increase in the rigging’s radial distance. The experiment results show that the difference between the peak and trough of the magnetic induction intensity at the pressed rigging defect calculated by the ANSYS simulation is very consistent with the results measured by the geomagnetic flaw detection equipment.

In this paper, technical code analysis and recognition of the defect signal of the suppression rigging based on a convolutional neural network are carried out given the difficulty and low recognition rate of the defect detection and recognition of the suppression rigging. Firstly, the magnetic induction signal of the suppression rigging defects is collected using CM-801 (Anshan, China), Kalman filtering is used to screen and pre-process the collected data, and the noise reduction data are presented in the form of a cloud image. The pressed rigging defect data set is constructed, and the region of broken wire defect and stress in the image is calibrated. The single-stage object detection algorithm YOLOv5 (You Only Look Once) based on convolutional neural network model calculation is used, the scale detection layer and positioning loss function of the YOLOv5 algorithm are improved and optimized, and the improved YOLOv5 algorithm is used for experiments. The experimental results show that the detection accuracy of the convolution neural network model can reach 97.1%, which can effectively identify the defect signal of the suppressed rigging.