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Tool wear prediction method, device, equipment and storage medium

A tool wear and prediction method technology, which is applied in the direction of measuring/indicating equipment, metal processing equipment, neural learning methods, etc., to improve the accuracy of prediction and avoid the loss of data information

Active Publication Date: 2022-06-21
HUNAN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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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

Method used

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  • Tool wear prediction method, device, equipment and storage medium
  • Tool wear prediction method, device, equipment and storage medium
  • Tool wear prediction method, device, equipment and storage medium

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Embodiment Construction

[0060] Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the scope of this application to those skilled in the art.

[0061] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to limit the application. As used in this application and the appended claims, the singular forms "a," "the," and "the" are intended to include the plural forms as well, unless the context clearly dictates otherwise. It will also be understood that the term "and / or" as used herein refers to and includes any and all possible ...

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Abstract

The present application relates to a tool wear prediction method, device, equipment and storage medium. This method collects the original tool data, introduces the Hadamard product to improve the attention gate, and fuses the improved attention gate with the independent cyclic neural network to obtain multiple independent cyclic network basic models with a fusion attention mechanism. Stack multiple independent cyclic network basic models to build a deep independent cyclic network model, combine the deep independent cyclic network model with a convolutional neural network, and construct a tool for predicting tool wear results based on the original tool data Wear Prediction Model. Since the deep independent cyclic network model in the tool wear prediction model is improved by introducing the Hadamard product, it can adjust the original input, strengthen the influence of the more important input elements on the model, suppress the less important elements, and improve the tool wear results. prediction accuracy.

Description

technical field [0001] The present application relates to the technical field of equipment state early warning based on a computing model, 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 led 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 a country's comprehensive national strength. rapidly developing. Among them, as an important part of industrial processing machinery and equipment, its condition monitoring has also become an important means to ensure industrial processing quality and reduce processing costs. [0003] Milling is a machining method that uses a milling cutter as a tool to process various parts, which is common in modern manufacturing. As a key component of milling machine cutting, the state of t...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): B23Q17/09G06F30/27G06N3/04G06N3/08G06F119/04
CPCB23Q17/09B23Q17/0914B23Q17/0957G06F30/27G06N3/08G06F2119/04G06N3/044G06N3/045
Inventor 陆绍飞朱雅君杨贯中李军义
Owner HUNAN UNIV
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