Abstract
Tool fault diagnosis in numerical control (NC) machines plays a significant role in ensuring manufacturing quality. Tool condition monitoring (TCM) based on multisensors can provide more information related to tool condition, but it can also increase the risk that effective information is overwhelmed by redundant information. Thus, the method of obtaining the most effective feature information from multisensor signals is currently a hot topic. However, most of the current feature selection methods take into account the correlation between the feature parameters and the tool state and do not analyze the influence of feature parameters on prediction accuracy. In this paper, a multisensor global feature extraction method for TCM in the milling process is researched. Several statistical parameters in the time, frequency, and time–frequency (Wavelet packet transform) domains of multiple sensors are selected as an alternative parameter set. The monitoring model is executed by a Kernel-based extreme learning Machine (KELM), and a modified genetic algorithm (GA) is applied in order to search the optimal parameter combinations in a two-objective optimization model to achieve the highest prediction precision. The experimental results show that the proposed method outperforms the Pearson’s correlation coefficient (PCC) based, minimal redundancy and maximal relevance (mRMR) based, and Principal component analysis (PCA)-based feature selection methods.
Highlights
Milling is a very common and efficient cutting operation that uses a rotary milling cutter with one or more teeth to intermittently cut workpieces into flat surfaces, grooves, threads, and many complex geometric components
Indirect tool condition monitoring (TCM) is a data-driven method; it uses single or multiple sensors to monitor the milling process and synthesize information provided by the sensor signal to determine the best estimates for the tool state through a mechanism based on training [13]
The advantage of multidomain methods is that they provide more candidate feature parameters related to the tool state and reduce the risk of losing important information, which is important for improving the performance of TCM
Summary
Milling is a very common and efficient cutting operation that uses a rotary milling cutter with one or more teeth to intermittently cut workpieces into flat surfaces, grooves, threads, and many complex geometric components. TCM in milling processes has been studied for over 30 years using two types of methods: Direct monitoring and indirect monitoring. Indirect TCM is a data-driven method; it uses single or multiple sensors to monitor the milling process and synthesize information provided by the sensor signal to determine the best estimates for the tool state through a mechanism based on training [13]. Indirect TCM can be divided into two phases: Model training and online monitoring. The online monitoring phase monitors the milling process and estimates the cutting tool’s condition in real time. The model training phase consists of three modules: Sensor configuration, feature extraction, and monitoring model.
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