Identifying optimal features for cutting tool condition monitoring using recurrent neural networks

Abstract

Identification of optimal features is necessary for the decision-making models such as the artificial neural network to achieve effective and robust on-line monitoring of cutting tool condition. Most feature selection strategies proposed in the literature are for pattern recognition or classification problems, and not suitable for prognostic problems. This paper applies three parameter suitability metrics introduced in previous similar studies for failure-time analysis and modifies them for failure-process analysis which allows for the unit-wise variation of the component in a population. The suitability of a feature used for cutting tool condition monitoring is determined by its fitness value calculated based on the three metrics. Two types of recurrent neural network are employed to analyze the prognostics ability of the features extracted from multi-sensor signals (acoustics emission, motor current, and vibration) collected from a milling machine under various operating conditions. The analysis results validate that the fitness value of a feature can depict its prognostic ability. It is found that adding more features which share abundant information does not increase the prediction performance but increases the burden on the decision-marking models. In addition, adding features with low fitness values may even deteriorate the prediction.

Identification of Milling Cutter Wear State under Variable Working Conditions Based on Optimized SDP

“…Suppose the signal's discrete sampling sequence of N sampling points is F = {F 1 , F 2 , • • • , F N }, where the amplitude of the j-th sampling point is F j . These are substituted into Equations ( 1) and (2), transforming them into symmetrical polar coordinates S r j , Θ ij , Φ ij , where r j is the radial distance, and Θ ij and Φ ij are the polar angles. The principle schematic diagram of the SDP transformation is shown in Figure 1.…”

“…Cutting tools, as a critical resource in advanced manufacturing, serve as the executing end in cutting processes [1]. During these processes, the contact surface between the tool and the workpiece undergoes complex changes in stress and temperature fields, leading to tool wear [2]. When tool wear reaches a certain level, it can cause an increase in the surface roughness of the workpiece, a decrease in precision, and in severe cases, breakage or chipping of the tool, posing a danger to both the machine and the operator [3,4].…”

Chatter detection in milling processes—a review on signal processing and condition classification

“…For instance, timefrequency image analysis was done by Chen et al for a difficult-to-cut material with 82 tests; the top features were second-order statistical features [179], while for a micromilling of low carbon steel with 29 tests, the candidate features were first-order statistical features [165]. It has also been reported that the selection of optimal features and sensor type varies with the condition of interest and experimental settings [106,370,371].…”

An Optimized VMD Method for Predicting Milling Cutter Wear Using Vibration Signal

“…During the cutting process, the contact surfaces between the tool and the workpiece are subject to complex changes in the stress field and the temperature field due to the combined impact of cutting force, cutting heat, and cutting shock, thereby causing tool wear, which would degrade the quality of the machined surface and reduce the dimensional accuracy of parts and the processing efficiency of machine tools [1]. Therefore, the accurate identification and prediction of tool wear is essential to improving machining quality and efficiency, while conserving energy and abating the cost of loss [2].…”