Multi-Modal Sensing for Enhanced Surface Roughness Prediction in CNC Machining Using an Intelligent Vise
Journal
2024 IEEE SENSORS
Part Of
Proceedings of IEEE Sensors
Start Page
1
End Page
4
Date Issued
2024-10-20
Author(s)
Abstract
This study showcases the utilization of an intelligent vise equipped with integrated cutting force and audio sensors to predict surface roughness during the milling process. Specifically, a piezoelectric PZT-based force sensor and a MEMS microphone are embedded within the vise jaw, enabling the acquisition of cutting force and audio signals in close proximity to the workpiece without interference. The collected data is then used to train predictive models for estimating surface roughness. The results demonstrate a root mean square error (RMSE) of 0.038, which outperforms the use of either force or audio data alone, which have RMSE values of 0.072 μm and 0.103 μm, respectively.
Event(s)
2024 IEEE Sensors, SENSORS 2024, Kobe, 20 October 2024 through 23 October 2024, Code 205267
Subjects
audio sensor
CNC machining
cutting force sensor
milling
multi-modal
surface roughness prediction
Publisher
IEEE
Type
conference paper