Modeling and monitoring method of cutter abrasion loss on basis of residual error convolutional neural network

Abstract

The invention provides a modeling and monitoring method of cutter abrasion loss on the basis of a residual error convolutional neural network. A residual error convolutional network is used as a self-adaptive model, adjustment of parameters of the model is performed on this basis, and finally a model of the cutter abrasion loss is established. The model adopts time-domain signals of different sensors as input, the cutter abrasion loss is used as output, and meanwhile in consideration of uncertainty in a machining process, the signal features in the machining process are extracted through the convolutional neural network theory; then the signal features of the different dimensions are used as input of full-joint neural network, and the influence weight of the feature components on the cutter abrasion loss is obtained automatically through an adam algorithm; and finally a model from signals to the abrasion loss is established according to a supervised learning method, and the predictionof the cutter abrasion loss is achieved. The self-adaptive modeling method provided by the invention has the characteristics that the measurement process is simple and convenient, data processing is fast, and the results are accurate.

Description

technical field [0001] The invention belongs to the technical field of machine tool processing, relates to the modeling and monitoring of tool wear, and specifically relates to a tool wear modeling and monitoring method based on a residual convolutional neural network. Background technique [0002] In the process of machine tool processing, the tool will lose its cutting ability due to the wear and tear of the cutting edge when the machine tool is processing parts. The amount of tool wear plays a very important role in the remaining life of the tool and the shape of the workpiece surface; Known knowledge, tool condition supervision can be monitored indirectly using sensor methods. With the continuous improvement of the industry's requirements for machining accuracy and automation, the concept of intelligent manufacturing is more and more widely used in the industry, and the prediction of tool wear has become a huge challenge for intelligent manufacturing. In the traditional...

AI Technical Summary

Problems solved by technology

This judgment method is highly subjective, so there are inevitably two problems:

[0004] 2. If the tool has been worn or damaged without replacing the tool, it will affect the machined surface quality and dimensional accuracy, or even damage the machine tool, and the resulti

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