Online Process Monitoring Based on Vibration-Surface Quality Map for Robotic Grinding

Abstract

In robotic manufacturing, the machining vibration can easily affect the quality of the processed surfaces due to poor stiffness and variable stiffness of the robot arm. It is important to monitor the processing quality. Therefore, an online process monitor method for robotic grinding is proposed in this article. Since the surface quality is difficult to be measured directly online, a vibration-surface quality map is proposed to show the mapping relationship between the vibration signals and the surface images. Based on this map, the grinding states are classified to lack, normal, and overgrinding, which formed the training set data. Then a convolutional neural network classifier is trained to detect the grinding states during robotic machining only with the vibration signals. To establish the vibration-surface quality map, the symmetrized dot pattern and sparse autoencoder methods are utilized to extract the features from the raw vibrations and images data, and a gray-level co-occurrence matrix index is further used to build the bridge between them. The experimental results in this article have proved the effectiveness and potential of our method for online robotic grinding quality monitoring.