Evolutionary learning method for intelligent monitoring of cutter states

Abstract

The invention provides an evolutionary learning method for intelligent monitoring of cutter states. A three-way acceleration sensor and a microphone are utilized to collect vibration signals and acoustic signals, the signals carries out smoothing, and the signals are divided into a training set and a test set; deep-level features of the dynamic signals are automatically extracted using a stacked auto-encoder, and extracted features are classified; according to the accuracy of a training set model, an algorithm carries out weight distribution, the final predicted cutter state is obtained through weighted averaging, and model related parameters are saved; and the real-time vibration signals and the acoustic signals in the actual machining process are preprocessed and then input into a savedmonitoring model, the cutter state of the corresponding signals is obtained, the data label with the higher confidence level is stored, and the network parameters are updated so as to realize the evolution learning of the cutter state intelligent monitoring. According to the method, manual participation can be avoided, the calculation complexity is reduced, and the influence of machine cutter performance degradation on the prediction accuracy of the cutter state monitoring model can be weakened.

Description

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AI Technical Summary

Benefits of technology

This technology allows for better understanding about how tools work without getting stuck or becoming outdated due to their deteriorating condition over time. It uses advanced techniques like artificial intelligence (Al) that learn new models based on past experience rather than relying solely upon previous ones. By updating these models, it becomes more reliable at predicting future changes made during use. Overall, this innovative approach helps improve efficiency and accuracy in machining operations.

Problems solved by technology

This patents discuss various technical problem addressed in this patented text that includes how well monitored or identifiable tool conditions can be determined without requiring human judgment and interference from operators who work closely together while working around machines. Existing techniques like sensors and cameras require frequent maintenance and replacement leading to increased costs associated with these processes. There is thus a desire towards developing more efficient ways to identify wornness states of tools overall.

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