scholarly journals Deep Anomaly Detection for CNC Machine Cutting Tool Using Spindle Current Signals

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4896
Author(s):  
Guang Li ◽  
Yan Fu ◽  
Duanbing Chen ◽  
Lulu Shi ◽  
Junlin Zhou
Keyword(s):  
Anomaly Detection ◽  
Industrial Production ◽  
Production Efficiency ◽  
Cutting Tool ◽  
Current Data ◽  
Numerical Control ◽  
Cnc Machine ◽  
Class Imbalance Problem ◽  
Tool Breakage ◽  
Spindle Current

In recent years, industrial production has become more and more automated. Machine cutting tool as an important part of industrial production have a large impact on the production efficiency and costs of products. In a real manufacturing process, tool breakage often occurs in an instant without warning, which results a extremely unbalanced ratio of the tool breakage samples to the normal ones. In this case, the traditional supervised learning model can not fit the sample of tool breakage well, which results to inaccurate prediction of tool breakage. In this paper, we use the high precision Hall sensor to collect spindle current data of computer numerical control (CNC). Combining the anomaly detection and deep learning methods, we propose a simple and novel method called CNN-AD to solve the class-imbalance problem in tool breakage prediction. Compared with other prediction algorithms, the proposed method can converge faster and has better accuracy.

Reconstruction of cutting forces through fusion of accelerometer and spindle current signals

2021 ◽  
Vol 68 ◽  
pp. 990-1003
Author(s):  
Hamid Mostaghimi ◽  
Chaneel I. Park ◽  
Guseon Kang ◽  
Simon S. Park ◽  
Dong Y. Lee
Keyword(s):  
Cutting Forces ◽  
Spindle Current

On-Line Fuzzy Monitoring of Micro-Hole Drilling Based on Genetic Algorithm

2012 ◽  
Vol 184-185 ◽  
pp. 1588-1591
Author(s):  
Hui Meng Zheng ◽  
Jian Qing Chen ◽  
Shou Xin Zhu
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Control ◽  
Production Costs ◽  
Hole Drilling ◽  
Drill Bit ◽  
Bit Wear ◽  
Micro Hole ◽  
On Line ◽  
Drilling System ◽  
Spindle Current

In automated micro-hole drilling system, in order to improve the drilling performance and reduce of production costs by maximizing the use of drill life and preventing drill failures, the drill bit wear state monitoring is more important. However, drill bit wear is difficult to measure in drilling process. By observation, wear failure of the drill bit could cause related changes of the spindle current signal, so construct fuzzy control mathematical models with the relationship between drill bit wear and spindle current, genetic algorithm and fuzzy control theory are applied to micro-drilling system in this paper .The membership functions of fuzzy control model are optimized by genetic algorithms. Through calculation, we can get drill bit wear value which used as monitoring threshold value in micro-hole drilling on-line monitoring system to avoid the drill breakage and improve the monitoring reliability.

Research on Modeling Mapping Relationship between Cutting Load and Spindle Motor Current

2011 ◽  
Vol 305 ◽  
pp. 353-356
Author(s):  
Xi Li ◽  
Dan Feng Feng
Keyword(s):  
Intelligent Control ◽  
Spindle Motor ◽  
Cnc Machine Tools ◽  
Cnc Machine ◽  
Intelligent Control System ◽  
Motor Current ◽  
Spindle Motor Current ◽  
Identification Theory ◽  
The Relationship ◽  
Spindle Current

The relationship between cutting load and motor current of CNC machine tools is an important part for research in intelligent control system. In this paper, the process of processing the error spindle current signal averaging, filtering, signal preprocessing, At the base of feature analysis and extraction, using system identification theory get the mapping model from current of the spindle to cutting load. Finally, the actual cutting data used to verify the model is reasonable.

Monitoring of Drilling Tool Condition through Spindle Current

2010 ◽  
Vol 139-141 ◽  
pp. 2595-2598
Author(s):  
Yin Hui Ao
Keyword(s):  
Tool Wear ◽  
Spindle Motor ◽  
Work Piece ◽  
Drilling Tool ◽  
Signal Features ◽  
Window Technique ◽  
Low Pass ◽  
Spindle Motor Current ◽  
On Line ◽  
Spindle Current

Drill wear or breakage often damages the work piece and/or machine tool. Spindle motor current reflects the cutting process and the signal can be easily and inexpensively obtained. This paper presents a strategy for on-line drilling tool wear and breakage monitoring. It employs Wavelet Transform (WT) of the spindle current signature to perform monitoring. A moving window technique is used to extract the cutting portion of data from the entire data sequence. A low pass de-nosing filter is employed to remove noise from the current signal. Features were extracted using WT node energy and selected based on their ability to detect tool wear and chipping. The Progression of tool wear based on feature of WT detail level 4 is analyzed and pointed out status of worn or chipped tool. Experimental results validate performance of the proposed algorithm.

Sign in / Sign up

Export Citation Format

Share Document