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Abstract
The development of industry 4.0 has put forward higher requirements for modern milling technology. Monitoring the degree of milling tool wear during machine tool processing can improve product quality and reduce production losses. In the machining process of machine tools, many kinds of tools are usually used, and the signal characteristics of various sensors of different tools are different. Therefore, before the tool wear assessment, this paper identified the tool type according to the spindle current data. After the tool type recognition, this paper evaluates the tool wear degree according to the tool force data, vibration data, acoustic emission signal, and other multi-sensor data. Firstly, the Elman neural network and Adaboost algorithm are combined to construct the Elman_Adaboost strong predictor. Then, the variance and mean of seven sensor data were selected as the characteristic quantities to input the strong predictor. Finally, three wear quantities were obtained to measure the wear degree of the tool. The method proposed in this paper is implemented by Matlab, and the validity of this method is verified using the competition data provided by PHM (Prognostics and Health Management) Society. The results show that the average evaluation accuracy of the same tool wear is more than 92%, and that of the similar tool wear is more than 85%.
Highlights
HSC is one of the representatives of advanced manufacturing technology, which has the advantages of high precision, high speed, and high quality [1,2]
According to the statistical research, when the automatic production equipment is equipped with a tool state detection system, the downtime will be reduced by 75%, the utilization rate of machine tools will be increased by at least 50%, and the total production efficiency will be increased by 10%–60% [7]
Since the data characteristics of different tools are different, it is necessary to identify the types before the tool wear prediction
Summary
HSC (high speed cutting) is one of the representatives of advanced manufacturing technology, which has the advantages of high precision, high speed, and high quality [1,2]. Driven by the monitored big data, Kunpeng Zhu systematically investigates the key issues for tool condition monitoring, such as machining dynamics, intelligent tool wear monitoring and compensation algorithms, heterogeneous big data fusion, and deep learning methods. Under this scheme, it develops the smart tool condition monitoring system that could improve the computer numerical control machining precision and productivity significantly [14]. In order to solve the problem of tool wear evaluation, this paper proposes a tool wear prediction model based on the multi-sensor.
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