Abstract
Tool wear and breakage are inevitable due to the severe stress and high temperature in the cutting zone. A highly reliable tool condition monitoring system is necessary to increase productivity and quality, reduce tool costs and equipment downtime. Although many studies have been conducted, most of them focused on single-step process or continuous cutting. In this paper, a high robust milling tool wear monitoring methodology based on 2-D convolutional neural network (CNN) and derived wavelet frames (DWFs) is presented. The frequency band of high signal-to-noise ratio is extracted via derived wavelet frames, and the spectrum is further folded into a 2-D matrix to train 2-D CNN. The feature extraction ability of the 2-D CNN is fully utilized, bypassing the complex and low-portability feature engineering. The full life test of the end mill was carried out with S45C steel work piece and multiple sets of cutting conditions. The recognition accuracy of the proposed methodology reaches 98.5%, and the performance of 1-D CNN as well as the beneficial effects of the DWFs are verified.
Highlights
In the past few decades, equipment-manufacturing technology has developed rapidly.The automation level and production capacity of the machine tool are significantly improved
This paper proposes a tool condition monitoring system based on 2-D convolutional neural network and assisted by complex wavelet
Zhang W. trains 1-D convolutional neural network (CNN) based on temporal signals to identify bearing faults [60]
Summary
In the past few decades, equipment-manufacturing technology has developed rapidly.The automation level and production capacity of the machine tool are significantly improved. The industrial manufacturing processes are dynamic and complex, especially for multi-axis computer numerical control (CNC) equipment, tools often perform a variety of tasks. Work piece properties such as hardness and cutting allowance change frequently, which makes the condition of the tool difficult to predict. Translation-Invariant Signal Decomposition Using the Derived Wavelet Frames. Derived wavelet frames (DWFs) are based on dyadic doubletree complex wavelet packets and are supplemented by non-dyadic implicit wavelet packets. The latter enhances the ability of the algorithm to extract transition-band features
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