Numerical control device cutter wearing detection method based on cloud knowledge base and machine learning

A technology of tool wear and machine learning, which is applied in computer control, instruments, simulators, etc., can solve the problem that manual detection of tool wear cannot accurately obtain the amount of tool wear

Inactive Publication Date: 2018-06-01
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Aiming at the deficiencies of the prior art, the present invention provides a tool wear detection method for CNC equipment based on cloud knowledge base and machine learning to solve the pro

Method used

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  • Numerical control device cutter wearing detection method based on cloud knowledge base and machine learning
  • Numerical control device cutter wearing detection method based on cloud knowledge base and machine learning
  • Numerical control device cutter wearing detection method based on cloud knowledge base and machine learning

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0057] A tool wear detection method for CNC equipment based on cloud knowledge base and machine learning, such as image 3 As shown, the method is applied to the local workshop, which includes several local CNC machine tools and local workshop servers respectively connected to several CNC machine tools, including:

[0058] A. Construct and train tool wear detection model

[0059] (1) Build a cloud knowledge base to form a distributed cluster system; the distributed cluster system can have the computing power of multiple computers, and can effectively realize the storage and processing of massive data.

[0060] (2) Collect the working condition data of the workshop and transmit them to the cloud knowledge base through the network. The cloud knowledge base groups and stores the working condition data; the working condition data includes workpiece information, tool information, and cutting parameters; the workpiece information includes working condition information number, workp...

Embodiment 2

[0066] According to a method for detecting tool wear of CNC equipment based on cloud knowledge base and machine learning described in Embodiment 1, the difference is that

[0067] The cloud knowledge base is built using the Hadoop platform. The distributed cluster system includes four servers, one of which serves as the master node, and the other three servers serve as slave nodes (slave node 1, slave node 2, and slave node 3), forming a fully distributed Hadoop cluster environment such as figure 1 shown. The distributed cluster system can have the computing power of multiple computers, and can effectively realize the storage and processing of massive data. The server is a high performance computer. The cloud knowledge base is built on the workshop server, and forms a cloud knowledge base-NC machine tool system with each CNC machine tool in the workshop, such as figure 2 shown;

[0068] The cloud knowledge base includes a data preprocessing unit, a database and a reasonin...

Embodiment 3

[0073] According to a method for detecting tool wear of CNC equipment based on cloud knowledge base and machine learning described in Embodiment 2, the difference is that

[0074] Step (3), design the tool wear detection model, and use the support vector machine algorithm to train the tool wear detection model, such as Figure 4 shown, including:

[0075] a. Set input parameters, including workpiece information parameters, tool information parameters, and cutting parameters. The workpiece information parameters include workpiece material, workpiece geometric dimensions, workpiece machining surface roughness, and workpiece processing operations; the tool information parameters include tool material. , entering angle, rake angle, and relief angle; the cutting parameters include cutting speed, cutting amount, and feeding amount;

[0076] b. Set the output parameter as the tool flank wear amount;

[0077] c. According to the working condition information number, extract the data...

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Abstract

The present invention relates to a numerical control device cutter wearing detection method based on a cloud knowledge base and machine learning. The method comprises the steps of: A, constructing andtraining a cutter wearing detection model: (1) establishing a cloud knowledge base, (2) collecting condition data of a workshop and storing the condition data, (3) designing a cutter wearing detection model, and employing a support vector machine to perform training of the cutter wearing detection model; and B, performing cutter wearing detection through the cutter wearing detection model: (4) updating the cutter wearing detection model to a local numerical control machine tool, performing detection of a cutter on the local numerical control machine tool, and when the cutter wearing amount achieve a dulling standard, replacing the cutter, and (5) taking newly detected condition data and the detected cutter wearing amount as feedback information, and transmitting the feedback information to the local numerical control machine tool through a network. The cutter wearing cutter prediction accuracy rate and the detection speed are improved.

Description

technical field [0001] The invention relates to a method for detecting tool wear of numerical control equipment based on cloud knowledge base and machine learning, and belongs to the technical field of tool wear detection. Background technique [0002] Tool wear is an inevitable phenomenon in the field of mechanical processing. In the process of metal cutting, if tool wear is not discovered in time, it will lead to the interruption of the cutting process, resulting in the scrapping of the workpiece or damage to the CNC machine tool, causing great harm. Economic losses. Through the on-line detection of tool wear, the scrapping of workpieces and damage to CNC machine tools can be reduced or avoided to varying degrees. In conventional cutting, workers often estimate the degree of tool wear based on the vibration or noise of the CNC machine tool and the cutting state. This method is easily affected by subjective factors such as professional ability, and cannot accurately judge ...

Claims

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Application Information

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IPC IPC(8): G05B19/4065
CPCG05B19/4065G05B2219/37616
Inventor 胡天亮杨艳周婷婷陶飞
Owner SHANDONG UNIV
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