Contact Force Detection of Grinding Process Using Frequency Information and Differential Feature on Force Signal

Abstract

In grinding tasks, the contact force has a significant impact on product surface quality. Therefore, force-sensing technology to detect contact force is important. Although force sensors are widely used for contact force detection, the response of the force sensor includes sensor-specific errors such as offset. In this paper, we propose a contact force detection method based on the combination of frequency information and the differential feature ( $\Delta F$ ) of the force signal. The use of high-frequency information reduces the influence of force sensor-specific errors. However, contact force detection using only high-frequency information causes a time delay in the detected value relative to the measured value depending to the frame size of time window used for frequency analysis. To reduce the time delay, high-frequency information and $\Delta F$ are integrated by inputting them into an long short-term memory (LSTM)-based force detection model. To verify the effectiveness of the proposed method, we compared it with a force detection model based on an FNN and CNN on a dataset of plane grinding tasks. Consequently, the detection accuracy of the LSTM-based model was superior to that of the FNN and CNN models. Compared to the LSTM model using only high-frequency information as input, the detection accuracy was 26% higher when the error was small and 57% higher when the error was large. In addition, the time delay was reduced from 166 ms to 30 ms using $\Delta F$ as the input. The frequency information and $\Delta F$ are features calculated from the same force information dataset; therefore, no additional dataset is required.

Keywords

• Force sensing
• force control
• machine learning for robot control
• grinding