With the demand for high-volume production of tools, the degree of automation continues to increase, bringing many industrial problems, in which real-time monitoring of abnormal conditions such as tool wear and breakage is to be solved. Real-time monitoring of the state of the cutting tool is also the basis for modernization, automation and flexibility of the manufacturing system. For decades, a lot of work has been done on the automatic monitoring of tool wear and damage at home and abroad. The measurement methods include direct measurement method based on tool wear and damage surface size, and indirect measurement by measuring the wear and damage of the tool. Measurement method. Today, we try to use artificial neural networks as pattern recognizers to imitate human pattern recognition capabilities and design intelligent tool condition monitoring systems.
Machine pattern recognition method: Using the information from the visual, auditory, and olfactory, through the synthesis of the brain, the sensory sensory information is associated with the tool state, so that the tool state can be judged extremely quickly and accurately. Based on this fact, this paper proposes an "intelligent tool state monitoring system" that simulates the operator's pattern recognition mode and sensory information fusion ability as much as possible. The multi-sensor information fusion system based on artificial neural network passes the human pattern recognition ability. Implemented on a computer-based machine.
In order for the tool condition monitoring system to work effectively, it is necessary to make comprehensive use of the information of the multi-sensor. Multi-sensor information fusion is the synthesis of processed information to form an expression of the cutting process. The integrated and fused multi-sensor information can reflect the cutting process, while a single sensor can only obtain part of the information of the cutting process.
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