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A Tool Wear Prediction Method

A tool wear and prediction method technology, which is applied in the direction of manufacturing tools, measuring/indicating equipment, metal processing equipment, etc., can solve problems such as insufficient prediction accuracy of the tool wear monitoring model, poor universality of the prediction model, etc., to avoid The effect of scrapping

Active Publication Date: 2021-09-21
BEIJING INSTITUTE OF TECHNOLOGYGY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In addition, the cutting process often includes multiple types of tools and workpiece materials, and it is far from enough to realize tool wear monitoring under specific tool and material combinations
It can be seen that the current research on tool wear monitoring still has limitations. The prediction accuracy of the obtained tool wear monitoring model is not high enough and the prediction model is not universal.

Method used

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  • A Tool Wear Prediction Method
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  • A Tool Wear Prediction Method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0113] This part is mainly based on the above-mentioned turning test, turning different types of workpieces under different cutting parameters and tool types, collecting cutting force and vibration signals during the turning process, and taking pictures of the surface texture of the machined surface after the test . The combination of cutting parameters is shown in Table 1:

[0114] Table 1: Combinations of cutting parameters for different tool and material types

[0115]

[0116] The materials used in this test are 42CrMo, 45CrNiMoVA, and 38CrSi. The blades of models DNMG150408GS, VBMT160408-F1 CP500, and CNMG120408FP are used for cutting tests. The test platform is TC-HAWK150CNC, and the cutting force is collected by a dynamometer of Kistler 9057B , the vibration sensor of model CT1050L ICP / IEPE collects cutting vibration, and the high-speed camera of model FR-600C captures the surface texture of the processed surface. The raw data collected are as follows: image 3 sho...

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Abstract

The invention relates to the field of mechanical processing, in particular to a tool wear prediction method based on fusion of surface texture features and multi-sensing signal features. The cutting signal acquisition system of the present invention comprises jaws arranged on the chuck, a workpiece, a turning blade arranged on the cutter bar, a hexagon socket bolt, a dynamometer sensor, a high-speed camera and a vibration sensor. It is arranged in the dynamometer sensor, the vibration sensor is arranged on the outside of the front end of the cutter bar, and the workpiece is set on the chuck through the jaws; the present invention uses the dynamometer sensor, vibration sensor, and high-speed camera to build a set of workpieces The processed surface texture and the multi-sensor cutting signal acquisition device have carried out gray-level histogram equalization processing on the processed surface texture, and calculated the gray-level co-occurrence matrix of the pre-processed surface texture, and calculated the energy and entropy of the matrix. Extraction of four feature quantities, moment of inertia and correlation, are used to monitor tool wear.

Description

technical field [0001] The invention relates to the field of mechanical processing, in particular to a tool wear prediction method based on fusion of surface texture features and multi-sensing signal features. Background technique [0002] During the cutting process, tool wear will reduce the surface quality of the workpiece, and will increase the cutting force and cutting temperature during the cutting process. In order to obtain qualified surface quality and avoid unnecessary loss, it is necessary to monitor the tool wear state during cutting. With the development of sensing technology and the rise of the tool monitoring system, the cutting signal in the cutting process is collected in real time, and the time domain and frequency domain feature extraction are performed on the cutting signal to form a feature set, and then the regression analysis is used to predict the tool wear status. a possibility. At present, most of the research on tool wear monitoring is still based...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): B23Q17/09
CPCB23Q17/09
Inventor 焦黎程明辉颜培史雪春王西彬刘志兵解丽静
Owner BEIJING INSTITUTE OF TECHNOLOGYGY