Abstract
Tool life prediction is critical to workpiece quality and machining costs. In this paper, a remaining useful life prediction method was proposed, which considering the dimension optimization accuracy of vibration features and the iterative speed of industrial models. Firstly, an optimization method for extracting spatial information of vibration signals was found. The spatial topology of high dimensional vibration information was preserved, when achieving dimension reduction of high dimensional feature. Secondly, a determination method of loss function was proposed to ensure the extraction effects of spatial information. Meanwhile, the k value selection method was proposed which considered to balance the time efficiency factor by using the extraction effect of spatial information. Thirdly, an optimal strategy and model for the remaining useful life prediction of the tool which is suitable for industry was determined, in order to ensure the accuracy and increase the speed. Finally, the superiority and effectiveness of the proposed method were verified by using the processing data of practical tool life cycle.
Highlights
Tool life management plays an important role in industrial applications
The method applied in the remaining life prediction, [1] used data fusion method in the neural network model for online monitoring of tool state in Computer numerical control (CNC) milling, the effectiveness of the method was verified; [2]–[4] used Support Vector Regression (SVR) to predict tool wear; [5] considered these common uncertainties by studying the improved
LOCALLY LINEAR EMBEDDING In the traditional RUL prediction research, the existing literatures are based on single-dimensional vibration data, without considering the multi-dimensional spatial vibration of the real vibration state, resulting in missing feature information; The three-dimensional vibration data selected in this paper takes into account the spatial information characteristics of the vibration signal, it is not possible to efficiently obtain more specific and strong association information from highdimensional space
Summary
Tool life management plays an important role in industrial applications. With the rapid development of the modern Computer numerical control (CNC) machining industry, the CNC machine tool is developing in the direction of high speed, high precision, and high intelligence. LOCALLY LINEAR EMBEDDING In the traditional RUL prediction research, the existing literatures are based on single-dimensional vibration data, without considering the multi-dimensional spatial vibration of the real vibration state, resulting in missing feature information; The three-dimensional vibration data selected in this paper takes into account the spatial information characteristics of the vibration signal, it is not possible to efficiently obtain more specific and strong association information from highdimensional space. If the individual characteristics of the vibration signals in the three directions are extracted, this does not connect them together, and the use of LLE will link the vibrations in the three directions together This method preserves the spatial topology structure of high-dimensional vibration information while achieving dimension reduction of high-dimensional feature. It is obvious that Y is a matrix composed of eigenvectors of (Ii-Wi)(Ii-Wi)T , in order to compress the data to d dimension, it is only necessary to take the feature vector corresponding to the minimum d non-zero eigenvalues of (Ii-Wi)(Ii-Wi)T , as the dimensional reduction operation is completed
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