Cutter wear prediction method and device, equipment and storage medium

Abstract

The invention relates to a tool wear prediction method and device, equipment and a storage medium. The method comprises the following steps: collecting original cutter data, introducing a Hadamard product to improve an attention gate, fusing the improved attention gate with an independent recurrent neural network to obtain a plurality of independent recurrent network basic models with a fused attention mechanism, and stacking the plurality of independent recurrent network basic models to obtain a fused attention mechanism. According to the method, a deep independent circulation network model is constructed, the deep independent circulation network model is combined with a convolutional neural network, and a tool wear prediction model used for predicting a wear result of a tool is constructed according to original tool data. Due to the fact that the Hadamard product is introduced into the deep independent circulation network model in the tool wear prediction model for improvement, original input can be adjusted, the influence of input elements with high importance on the model can be enhanced, elements with low importance can be restrained, and the prediction accuracy of the tool wear result is improved.

Description

technical field [0001] The present application relates to the technical field of early warning of equipment status based on calculation models, and in particular to a tool wear prediction method, device, equipment and storage medium. Background technique [0002] In recent years, the rise of the "Industry 4.0" era dominated by intelligent manufacturing has triggered a new round of industrial transformation on a global scale. The level of industrialization has gradually become an important symbol for evaluating the country's comprehensive national strength. In rapid development. Among them, as an important part of industrial processing, the condition monitoring of mechanical equipment has also become an important means to ensure the quality of industrial processing and reduce processing costs. [0003] Milling is a mechanical processing method in which milling cutters are used as tools to process various parts, and it is common in modern processing and manufacturing industri...

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Problems solved by technology

[0006] At present, the tool wear prediction method based on the deep learning model has been used by a large number of research work, but the research work in this field still faces many challenges, such as the prediction accuracy of the model

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