After injection molding, plastic gears often exhibit surface defects, including those on end faces and tooth surfaces. These defects encompass a wide range of types and possess complex characteristics, which pose challenges for inspection. Current visual inspection systems for plastic gears suffer from limitations such as single-category defect inspection and low accuracy. There is an urgent industry need for a comprehensive and accurate method and system for inspecting defects on plastic gears, with improved inspection capability and higher accuracy. This paper presents an intelligent inspection algorithm network for plastic gear defects (PGD-net), which effectively captures subtle defect features at arbitrary locations on the surface compared to other models. An adaptive sample weighting method is proposed and integrated into an improved Focal-IoU loss function to address the issue of low inspection accuracy caused by imbalanced defect dataset distributions, thus enhancing the regression accuracy for difficult defect categories. CoordConv layers are incorporated into each inspection head to improve the model’s generalization capability. Furthermore, a dataset of plastic gear surface defects comprising 16 types of defects is constructed, and our algorithm is trained and tested on this dataset. The PGD-net achieves a comprehensive mean average precision (mAP) value of 95.6% for the 16 defect types. Additionally, an online inspection system is developed based on the PGD-net algorithm, which can be integrated with plastic gear production lines to achieve online full inspection and automatic sorting of plastic gear defects. The entire system has been successfully applied in plastic gear production lines, conducting daily inspections of over 60,000 gears.

Line laser measurement technology is effective for obtaining the 3D point clouds of complex surfaces. Thus, a 3D gear measurement model is established in this study. All the point clouds of gear surfaces are rapidly obtained using a line laser sensor, and the holistic deviations of gear surfaces are acquired by calculating the normal errors of the gear surface. These informative and complete data include traditional gear error items and considerable valuable information that is not deconstructed. To exploit the acquired gear holistic deviations, the structured 3D model is constructed to express tooth surface errors. In this model, the control method of the statistical process is used to define highly representative assessment indicators, and a new gear accuracy assessment system is developed by selecting reasonable indicators. The measurement practice shows that this method exploits the complete information of the gear surface, including all the current accuracy assessment indicators, which can be used to expand the new assessment indicators. This method can be used to characterize the 3D topological tooth surface completely and comprehensively and realize the deep mining and extended applications of the 3D full information of gear surfaces.

In a laser tracking control system, a motor drives the load to achieve continuous forward and reverse rotation speed control, and the magnitude of the load changes periodically during the motor rotation to realize tracking. Based on these characteristics, we propose an integral-separated improved variable universe fuzzy proportional-integral (PI) control method with torque feedforward compensation method in this study. The proposed method achieves the control performances of fast response, high steady-state accuracy, and strong anti-interference. The conventional fuzzy PI control method achieves good performance only in the unidirectional acceleration of a motor; therefore, we have improved its fuzzy rules, and designed a novel universe contraction–expansion factor. Additionally, a load torque observer is introduced to observe and compensate for the external load and to further improve the anti-interference performance of the tracking control system. The experimental results demonstrate that the integral-separated improved variable-universe fuzzy PI control method achieves better performance in the forward and reverse speed regulation processes of the motor. The overshoot is 92% less than that of the conventional PI control when the target speed is set to ±500 rpm. The Luenberger observer is introduced to observe and compensate for the load torque. After the stabilizing the speed and applying an external load of 0.2 N m to the motor, the maximum speed reduction is observed to be 59% less than that obtained using the conventional PI control. The improved PI control method satisfies the speed loop control requirements of the laser tracking control system and can improve the steady-state tracking accuracy and robustness of the system.