Because of the defect in traditional BP network of cementing quality prediction at present which is sensitive with the initial weights, easy to fall into the local least value,low forecast precision and slow convergence speed occurred. In order to overcome the shortcomings of traditional BP network, the paper introduced the particle swarm optimization (PSO) algorithm based on the random global optimization into the neural network training. The PSO is used to optimize weights of BP network. The simulation results show that this method has shorter training time and higher prediction accuracy than the BP network, and it can improve cementing quality and realize prediction and tracking analysis of cementing quality. It has good serviceability for predicting all kinds of information not known in cementing. It has provided a new method for cementing quality prediction.

Background: As an important branch of computer vision, visual measurement is a fast developing cutting-edge technology, which has been widely used in the manufacturing field. In recent years, the visual measurement of feature size of probes through small IC probes has aroused wide concern. Objective: This study aims to take small shaft parts as the research object in order to provide a full set of novel and reliable technical means for the three-dimension measurement of mechanical parts. Methods: Firstly, the trinocular vision measurement system based on the curved cantilever mechanism was designed and constructed. Secondly, the measurement system was used to collect the part images from different angles, and the images derived from the four categories of segmentation algorithms such as threshold-based, region-based segmentation algorithm were compared and analyzed. Lazy Snapping image segmentation algorithm was used to extract the foreground parts of each image. After comparing and analyzing SfM-based algorithm and Visual Hull-based algorithm, the SfM-based algorithm was adopted to reconstruct the 3D morphology of the parts. The measurement of the relevant dimensions was performed. Results: The results shows that Lazy Snapping's human-computer interaction brush function improves the accuracy and stability of image segmentation of different algorithms, such as threshold value method, regional method, Grab Cut, and Dense Cut. The SfM-based 3D reconstruction algorithm is of high robustness and fast speed. Conclusion: This study provides an effective method for measuring small mechanical parts, which will shorten the measurement cycle, improve the measurement speed, and reduce the measurement cost.

This paper proposes a circular seal identification method which based on the average relative error. First it spread out the circular seal into rectangular ones, so as to gain the seal of rectangular image, then it projects the rectangular seal to get projection curve. using projection curve of the reserved seal and the cyclic shift of detected seal projection curve to achieve registration. It calculated the average relative error that according to the registration after the reserved seal and detected seal. With the average relative error of the seal get the authenticity. The experimental results show that this method can simplify the computation and improve the seal authenticity identification effectively, which can also be applied to the actual.

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