Frequency Domain Analysis of Sensor Data for Event Classification in Real-Time Robot Assisted Deburring

Abstract

Process monitoring using indirect methods relies on the usage of sensors. Using sensors to acquire vital process related information also presents itself with the problem of big data management and analysis. Due to uncertainty in the frequency of events occurring, a higher sampling rate is often used in real-time monitoring applications to increase the chances of capturing and understanding all possible events related to the process. Advanced signal processing methods are used to further decipher meaningful information from the acquired data. In this research work, power spectrum density (PSD) of sensor data acquired at sampling rates between 40–51.2 kHz was calculated and the corelation between PSD and completed number of cycles/passes is presented. Here, the progress in number of cycles/passes is the event this research work intends to classify and the algorithm used to compute PSD is Welch’s estimate method. A comparison between Welch’s estimate method and statistical methods is also discussed. A clear co-relation was observed using Welch’s estimate to classify the number of cycles/passes. The paper also succeeds in classifying vibration signal generated by the spindle from the vibration signal acquired during finishing process.

Adaptive neuro-fuzzy inference system for deburring stage classification and prediction for indirect quality monitoring

“…We used wavelet decomposition and Welch spectrum estimate as a signal processing and feature extraction method, and ANFIS as a machine learning method. The combination of wavelet decomposition and Welch's method is selected to improve the accuracy in the previous study [10]. As shown in [10], it is proven that Welch's method can identify the event classification of deburring vibration data at specific frequency (i.e.…”

Variable Stiffness Mechanism for the Reduction of Cutting Forces in Robotic Deburring

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Block cosparsity overcomplete learning transform image segmentation algorithm based on burr model

“…Therefore, it is necessary to remove the burrs on the high‐voltage copper contact. Robot deburring is the most suitable way for manufacturing plant and the first step is to acquire the burr image and then process it [3, 4]. Image segmentation is an important research direction in image processing [5] and target recognition [6], and it is the foundation of machine vision.…”