System and method for intelligent movable detection of cutter damage, applied to workshop or production line

Abstract

The invention discloses a system and method for intelligent movable detection of cutter damage, applied to a workshop or a production line. The system comprises a machine tool cleaning environmental cleaning system, an online early warning system, a movable visual diagnosis system and a server, wherein the online early warning system is used for real-time detection of a main shaft power / current signal of each machine tool in the workshop or the production line and wireless transmission of the signal to the server; the movable visual diagnosis system can realize position and navigation to a machine tool detection working station and collect an image of the cutter damage; the server can judge whether the main shaft power / current value of each machine tool in the workshop is abnormal and canreport abnormal machine tool information to the movable visual diagnosis system; and the movable visual diagnosis system is controlled to collect the image and process the image, so the type and the degree of the cutter damage can be further judged. The system and method disclosed by the invention have the beneficial effects that the movable visual diagnosis system is arranged in the workshop or the production line, and the online early warning system is used for assistance, so high-precision, efficient and intelligent cutter damage detection is realized for the multiple machine tools in the workshop or the production line.

Description

technical field [0001] The invention belongs to the technical field of tool detection, in particular to an intelligent mobile monitoring system and method for tool damage in workshops or production lines. Background technique [0002] As an important part in the manufacturing process, the tool is prone to wear / breakage under the joint action of cutting force and cutting heat. If the tool damage is not detected in time, it will cause the deterioration of the processed surface quality, and in severe cases, the workpiece will be damaged. Scrapping, machine tool failure and even casualties will bring huge economic losses. In practical engineering applications, workers mostly estimate whether the tool is damaged based on experience, and it is easy to cause the tool to be replaced long before its service life, or the tool is replaced after excessive wear, which seriously restricts the development of mechanical processing, especially with the The processing workshop is developing ...

AI Technical Summary

Problems solved by technology

[0003] Chinese Patent Publication No. CN108107838A discloses a tool wear detection method for CNC equipment based on cloud knowledge base and machine learning, which can serve several machine tools in the workshop. The accuracy rate of the tool damage detection model is not high, which is easy to cause misjudgment and lead to tool waste, and the tool damage detection model established based on cutting parameters and tool parameters is a certain mapping of specific machine tool types and actual workshop working conditions parameters. When the NC program is modified or different cutting tools are used for cutting, this model will no longer be applicable; Chinese Patent Publication No. CN110340733A discloses a system and method for on-line and on-site detection of tool damage in a clean cutting environment, which takes into account both efficiency and precision. However, this detection method needs to install a tool damage monitoring device with vision system protection function and tool cleaning function inside each machine tool. It is greatly affected by the vibration of the machine tool, and the collected image is easily distorted, and it takes up the internal space of the machine tool. The interference of the machine tool is only applicable to a single machine tool, and it cannot serve multiple machine tools in the workshop, and the cost is high; Chinese Patent Publication No. CN107553219A discloses a tool wear monitoring method based on multiple types of sensor composite signals, but the structure of the machine tool needs to be changed , pre-embedded vibration sensors, cutting force sensors, etc., and due to complex and diverse cutting conditions, there are many interference factors affecting the detection results, and the monitoring accuracy is not high

In summary, the current detection methods are still not suitable for tool damage monitoring in the batch processing process of CNC machining workshops, and there is a lack of a high-precision and efficient tool damage intelligent mobile monitoring system and method for workshops or production lines.

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