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Broaching tool wear and damage quantitative prediction system and method based on vibration signals

A technology of tool wear and vibration signal, applied in manufacturing tools, measuring/indicating equipment, metal processing machinery parts, etc., can solve the problems of workpiece and tool scrap economy, loss, etc.

Inactive Publication Date: 2021-08-06
SHANGHAI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This method has the potential to cause significant economic losses due to worker misinterpretation and unnecessary scrapping of workpieces and tools

Method used

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  • Broaching tool wear and damage quantitative prediction system and method based on vibration signals
  • Broaching tool wear and damage quantitative prediction system and method based on vibration signals
  • Broaching tool wear and damage quantitative prediction system and method based on vibration signals

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0029] refer to figure 1 , a quantitative prediction system for broaching tool wear and damage based on vibration signals, including broaching tool 2, acceleration sensor 3 in the direction of broaching, acceleration sensor 4 in the direction of cutting depth feed, data acquisition card 5 and computer 6, acceleration in the direction of broaching The sensor 3 and the acceleration sensor 4 in the direction of cutting depth are installed on the clamping tooling of the workpiece 1, or on the sliding plates on both sides of the broaching machine; The sensor mainly measures the acceleration signals in the broaching direction and the cutting depth feeding direction; the two acceleration signal sensors are connected with the data acquisition card 5, and the data acquisition card 5 obtains digital signals and transmits them to the computer 6, and the structural health of the broach is analyzed after spectrum analysis. Quantitative assessment of status.

[0030] In this embodiment, th...

Embodiment 2

[0032] In the above example, see figure 2 , a method for quantitatively predicting wear and damage of broaching tools based on vibration signals, using the system for quantitatively predicting wear and damage of broaching tools based on vibration signals described in claim 1 to operate, and the operating steps are as follows:

[0033] ① The broaching tool performs broaching motion at a constant speed, and its teeth will excite the workpiece according to a certain period;

[0034] ② Under the excitation of periodic load, the spectrum signal presents a multiplied frequency signal with the same period as the load;

[0035] ③ Excitation signals with different amplitudes lead to changes in the amplitude at the multiplier of the spectrum signal;

[0036] ④Analyze the amplitude at the double frequency of the cutting excitation of the spectrum signal, and quantitatively predict the wear and damage of the broach.

[0037] In this embodiment, when the acceleration sensor collects the...

Embodiment 3

[0039] In this embodiment, the vibration signal-based quantitative prediction system for broaching tool wear and damage is used in conjunction with factory automation production equipment. Such as figure 1 As shown, the acceleration sensor 3 in the broaching direction and the acceleration sensor 4 in the feeding direction of the cutting depth are installed on the slide plates on both sides of the broaching machine, and mainly measure the acceleration signals in the broaching direction and the feeding direction of the cutting depth.

[0040] When the broaching machine is broaching the workpiece, the acceleration sensor collects the acceleration change signal generated by the broaching tool during the broaching motion. Acceleration signals are read with a data acquisition card, and fast Fourier transforms are performed respectively to obtain spectrum signals. Under periodic load excitation, the spectrum signal presents a multiplied frequency signal with the same cycle as the ex...

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PUM

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Abstract

The invention relates to a broaching tool wear and damage quantitative prediction system and method based on vibration signals. The system comprises a broaching tool, a broaching direction acceleration sensor, a cutting depth feeding direction acceleration sensor, a data acquisition card and a computer. By aiming at the problem that quantitative evaluation and prediction of slight wear and damage are difficult in the broaching machine tool machining process, the tool wear and damage quantitative prediction method is provided, and quantitative health management on the broaching machine broaching tool structure is achieved. The system and method for monitoring the structural health of the broaching machine broaching tool have the advantages that the equipment cost low, the structure is simple and reliable, and whether the broaching tool is worn and damaged or not can be intuitively judged according to a spectrogram.

Description

technical field [0001] The invention relates to a system and method for quantitatively predicting wear and damage of broaching tools based on vibration signals. The application is used in the complex on-site environment of the workshop to realize the quantitative evaluation and prediction of the slight wear or damage of the broaching tool. Background technique [0002] A broach is a form tool used for broaching. There are multiple rows of teeth on the surface of the tool, and the size and shape of each row of teeth increases and changes sequentially from the cutting end to the cutting end. When the broach is broaching, each tooth cuts a certain thickness of metal from the workpiece, and finally obtains the required size and shape. Broaches are often used in batch and mass production to process round holes, spline holes, keyways, flat and formed surfaces, etc., with high productivity. Broaches are divided into internal broaches and external broaches according to the differ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): B23Q17/09
CPCB23Q17/0957
Inventor 张顺琦唐阳汪进超
Owner SHANGHAI UNIV