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Polishing and grinding are crucial in the fabrication processes of industrial and commercial products. While the fabrication process can be automated using robots or specialized machines, experienced workers are still needed for the subsequent quality inspection. Here, we report the development of an automatic defect detection system, which is capable of detecting the defects of grinded and polished faucets due to its Faster Region-based Convolutional Neural Networks (Faster RCNN) architecture. The images of the workpieces were taken using a manipulator with a preset trajectory to cover all the surfaces of the workpieces. After labeling, the data were augmented to the trainable level. Three pretrained CNN-based models were utilized and evaluated. The hyperparameters were analyzed to validate their effect on the performance of the model. The mean average precision, using the tuned hyperparameters, was 80.26%.

Industrial robots play potential and important roles on labor-intensive and high-risk jobs. For example, typical industrial robots have been used in grinding process. However, the automatic grinding process by robots is a complex process because it still relies on skillful engineers to adaptively adjust several key parameters. Moreover, it might take a lot of time and effort to yield better grinding quality. Hence, this paper proposed a new framework of cyber-physical robot system with automatic zero-tuning optimization of the process parameters to achieve the desired quality. To overcome the unexpected difference between reality and simulation, proper system calibration can help in precise positioning in real environment, and the cloud database is constructed to record the relative data during the grinding process simultaneously. The proposed zero-tuning methodology combines both neural network (NN) model and genetic algorithm (GA) to generate the best combination of corresponding parameters to meet the desired quality. Experimental results showed that the average error of the output result was 8.93%. To compare the CNC machine, our solution shows more prominent role and potential in plumbing industry.

Industrial robots have been applied in labor-intensive and high-risk jobs such as grinding, polishing, deburring, etc. However, traditional robotic manufacturing systems require skillful experts to advise the robot programming for controlling manufacturing efficiency and quality. Based on the level of automation, an advanced cyber-physical robot grinding system has been developed to increase efficiency, improve quality, and save human effort. The proposed system consists of robot trajectory generation and optimization, force-based trajectory modification, vision-based grinding machine localization, and acoustic emission based abrasive belt wear-life analysis. Compared with traditional robot tuning processes, the process quality of the proposed system is better than that, and the tuning time is shortened from 14 days to 1-2 days.