A Novel Method for Tool Identification and Wear Condition Assessment Based on Multi-Sensor Data

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%.

Ảnh Hưởng Của Mòn Dụng Cụ Cắt Đến Rung Động Khi Tiện

“…Do những ảnh hưởng này, đã có nhiều nghiên cứu xác định, dự đoán và điều khiển tuổi bền của dụng cụ để từ đó cải thiện điều kiện làm việc nhằm nâng cao chất lượng sản phẩm cũng như hiệu quả gia công. Chẳng hạn, xác định thông qua mòn và lực tác dụng lên dụng cụ [6], sử dụng các phương pháp thống kê toán học xây dựng hàm hồi quy [7]- [9], mô phỏng [9], [10]... Gần đây, một số nghiên cứu đã bắt đầu tiến hành kiểm soát trực tiếp quá trình mòn [11], [12]. Các công cụ hiện đại như cảm biến (sensor) được gắn lên hệ thống gia công, dữ liệu được thu thập và kết nối với máy tính, qua phân tích có thể dự đoán chính xác diễn biến mòn và tuổi bền dụng cụ.…”

A new model based on belief rule base and membership function (BRB-MF) for health state prediction in sensor

“…So the datedriven-based health state prediction model cannot be used widely. The literature 11 focused on the sensor system of machine tool during the intelligent control and health management. This paper combines the neural network algorithm and data-driven methods, a typical ''black box system,'' which makes the result of the health states less interpretable.…”

Predictive Maintenance and Engineered Processes in Mechatronic Industry: An Italian Case Study

“…Some studies highlight that wear conditions can be analysed by the relationship between temperature and electrical resistance [8], or defining wear classes applying thermography combined to Convolutional Neural Network (CNN) [9]. In particular, AI Elman Adaboost approaches are used to predict wear conditions, by analysing force data, vibration data, acoustic emission signal, and other multi-sensor data [10]. Cutting forces and vibrations are surely important parameters to detect wear [11].…”