This paper proposed an instrument target detection algorithm based on yolov3 network for the drawbacks caused by manual inspection of pointer instruments in complex industrial environments. Firstly, the algorithm improved the model convergence speed by introducing the k-means++ algorithm to cluster out 9 sets of initial anchor boxes suitable for the pointer meter data set. Moreover, by combining the channel attention mechanism with spatial attention mechanism in the yolov3 backbone network, the extraction of shallow features was further improved by adding two residual blocks to the second residual block, then a new model yolov3-CBAM (Convolutional Block Attention Module) was formed. In addition, the mean average accuracy (map) of the training and testing of the three types of instruments on the data set reaches 90.8% by the results, which is about 2.1% higher than the original yolov3. This algorithm has obvious advantages in the patrol inspection and identification of industrial instruments.
Focus on the accuracy problem in machining process of SK21 NC cam grinder, the precision-machining restriction condition equation and the calculation method of precision NC instruction are deduced. The reversible calculation model from NC instruction to the cam’s outline is given and a software package of error compensation is developed. Many error parameters which include the abrasion of the grinding wheel, load distortion of the grinding wheel axis, rotation precision of the cam axis and movement precision of the worktable are measured through experiments. The finally grinding experiments results show that the cam’s machining accuracy can be improved more than 50% by the error compensation method in this paper.
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