Aiming at the robotization of the grinding process in the steel bar finishing process, the steel bar grinding robot can achieve the goal of fast, efficient, and accurate online grinding operation, a multi-layer forward propagating deep neural network (DNN) method is proposed to efficiently predict the kinematic solution of grinding robot. The process and kinematics model of the grinding robot are introduced. Based on the proposed method, simulations of the end position and orientation, and joint angle of the grinding robot are given. Three different methods, including SGD + tanh, Nadam + tanh, Nadam + ELU, are used to test the DNN calculation process results show that the method combining Nadam with ELU function has the fastest solution speed and higher accuracy can be obtained with the increase in iteration times. Finally, the Nadam optimizer is used to optimize the calculation results of the example. The optimization results show that this method accelerates the convergence rate of trajectory prediction error and improves the accuracy of trajectory prediction. Thus, the proposed method in this paper is an effective method to predict the kinematic solution when the grinding robot works online.

An end face attitude detection system for special steel bars is designed to solve the problem of defect localization for steel bar grinding. A circle detection method based on improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is proposed for calculating special steel bars’ end face attitude. Firstly, the images are subjected to edge detection, connected region marking, and improved DBSCAN in accordance with the image characteristics. After that, the arcs belonging to the same circle are clustered into the same category to create virtual connected regions. Then, circle parameters of a virtual connected region are clustered using an improved DBSCAN algorithm. The actual circle parameter is obtained by calculating the centroid of each category. Finally, the vector is generated under the set coordinate system, passing through the center of the circumcircle of the steel bar end and one endpoint of the two-dimensional code, and the angle of the vector is calculated to determine the attitude of the special steel bar’s end face. The experimental results demonstrate that the method can obtain an attitude angle resolution of 0.2 degrees with an error range of ±0.1 degrees. This will provide accurate defect localization support for the digitization and intelligence of the grinding platform on the special steel bar production line.

In order to investigate the corrosion behavior of Mg-Gd based soluble magnesium alloy materials, two aspects, environmental factors and microstructural point of view, were discussed in this study respectively. Among them, the environmental factor aspect was carried out by immersion weight loss tests at different Cl- concentrations and different temperatures. In terms of microstructure, the influence of different initial tissue morphologies on the corrosion behavior of the alloy was mainly investigated, and the second phase Mg5Gd, prepared by melting, was subjected to immersion weight loss tests and electrochemical tests. The results show that the corrosion rate of the alloy increases with the increase of Cl- concentration and temperature, but the growth trend is not linear, in which the Cl- concentration of 1% to 3% and the temperature of 75 °C to 100 °C have a significant effect on the corrosion rate of the alloy, respectively; the dissolution rate of Mg-Gd-based soluble magnesium alloy after extrusion + perforation deformation was greatly improved from 8.37 mg/cm2/h in the as-cast state to 43.85 mg/cm2/h. Combined with the microstructure analysis, it is believed that the extrusion destroys the coarse reticulated second phase in the as-cast state organization, which reduces the corrosion-blocking network effect of the second phase; electrochemical tests showed that the second phase Mg5Gd formed after the addition of the rare earth element Gd had a high electrochemical activity.

Circle detection in images is one of the key technologies in machine vision, pattern recognition, and artificial intelligence. However, conventional circle detection methods are sensitive to complex scenes, noise, and occlusion in images. Solving the impact of these situations is still the focus of circle detection algorithm research. Therefore, a circle detection algorithm based on neighborhood density clustering (NDC) is proposed. The proposed algorithm calculates circle parameters of connected regions after extracting corners, corroding the corners, and marking the connected regions. Then, NDC of the circle parameters is executed to classify arcs belonging to the same circle into one category to acquire a virtual connected region. And the circle parameters of the virtual connected region are clustered using NDC again to obtain the circle parameter dataset of the circle to be detected. The precise circle parameters are further estimated by calculating the centroid of each category. To prevent false positives, candidate circles are verified through a ratio rule. Extensive experiments using both synthetic and real images were performed. The results compared with those of representative state-of-the-art methods demonstrate that the proposed algorithm can be applied to a variety of complex scenes and has several advantages: good anti-occlusion effect, more robustness against noise, high accuracy, and better performance.