A Multisensor Fusion Method for Tool Condition Monitoring in Milling

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.

A review on multi sensor data fusion technique in CNC machining of tailor-made nanocomposites

“…Diagnosing the fault in tool plays an important role in defining the quality of the product. Assigning multiple sensors in acquiring information about several time-frequency domains (wavelet transforms) which are also selected as an alternative during machining [24]. Understanding the fundamentals of complex means of tool failure in monitoring machine condition and embedding new methods of finding aimed at high-quality research [25].…”

Multi-Sensor Data Fusion for Remaining Useful Life Prediction of Machining Tools by IABC-BPNN in Dry Milling Operations

“…In summary, the solution search equation described in Equation 24is more exploratory but insufficiently exploitable. In order to improve the exploitation ability of ABC in the process of optimization, many scholars have rewritten Equation (24) in the form of Equation (26), by adding a term called global-best term close to or far away the global optimal solution (X g ) [41][42][43].…”

“…By adjusting the value of β, the exploration and exploitation ability of the algorithm can be well balanced, but the global optimization ability can also be reduced in a certain degree. In this paper, we improve ABC algorithm by replacing Equation (24) with Equation (27), which combines two search strategies form Equation (24) and Equation (26). In the early stage of the iteration, the algorithm is mainly based on the exploration efficiency, which can quickly find the global optimization, and also has a certain local exploitation ability.…”

Process monitoring in end milling using polar figures

“…Newer approaches use a multi-sensory approach. Zhou et al use five different sensors to create eight time-domain and seven frequency-domain features that are combined into one global feature which correlates with tool wear [16]. Apart from those feature-based approaches, other research uses models to predict an expected force or power signal.…”