Abstract
Intelligent machining has become an important part of manufacturing systems because of the increased demand for productivity. Tool condition monitoring is an integral part of these systems. Airborne acoustic emission from the machining process is a vital indicator of tool health, however, it is highly affected by background noise. Reducing the background noise helps in developing a low-cost system. In this research work, a feedforward neural network is used as an adaptive filter to reduce the background noise. Acoustic signals from four different machines in the background are acquired and are introduced to a machining signal at different speeds and feed-rates at a constant depth of cut. These four machines are a three-axis milling machine, a four-axis mini-milling machine, a variable speed DC motor, and a grinding machine. The backpropagation neural network shows an accuracy of 75.82% in classifying the background noise. To reconstruct the filtered signal, a novel autoregressive moving average (ARMA)-based algorithm is proposed. An average increase of 71.3% in signal-to-noise ratio (SNR) is found before and after signal reconstruction. The proposed technique shows promising results for signal reconstruction for the machining process.
Highlights
Published: 1 December 2021With the increase in demand for unmanned manufacturing processes, intelligent machining has become indispensable to avoid breakdown maintenance [1]
Indirect methods are based on the signals that may generate from the machining process
The aim of this paper is to develop a neural network and autoregressive moving average (ARMA)-based algorithm to reduce background noise for a machining signal
Summary
Published: 1 December 2021With the increase in demand for unmanned manufacturing processes, intelligent machining has become indispensable to avoid breakdown maintenance [1]. Direct methods are based on the physical geometry of a tool that directly indicates the tool condition These methods involve image processing, electrical resistance, and radioactive based techniques to evaluate tool health. These methods are generally accurate in predicting tool health; they require stopping the machine which interrupts production, but may increase the production cost. To avoid these issues, indirect methods are introduced. Set of feature vector is taken as input to the neural network and their response is taken as output of the network. The number of neurons in input and output layers are equal to feature vector dimensions and the response respectively
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
