Intelligent monitoring method for state of hollow drill grinding wheel

A technology of intelligent monitoring and hollow drilling, applied in nuclear methods, measuring devices, processing detection response signals, etc., can solve problems such as difficulty in determining the empirical parameters of support vector machines, and achieve the effect of improving the level of machine tool processing technology

Active Publication Date: 2020-09-29
UNIV OF SHANGHAI FOR SCI & TECH +1
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Problems solved by technology

Aiming at the problem that the empirical parameters of traditional support vector machines are difficult to determine, a simulated annealing algorithm is proposed to optimize the selection of parameters. This optimization algorithm is suitable for solving various nonlinear proble

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  • Intelligent monitoring method for state of hollow drill grinding wheel
  • Intelligent monitoring method for state of hollow drill grinding wheel
  • Intelligent monitoring method for state of hollow drill grinding wheel

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Embodiment Construction

[0037] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0038]An intelligent monitoring method for the grinding wheel state of hollow drills. The grinding wheel wear state monitoring system adopted is mainly divided into three parts: signal monitoring and characteristic parameter extraction, SA-SVM model and model prediction results. The first part monitors the acoustic emission signal, power signal and vibration signal in the grinding process, and extracts the time-domain parameters of the acoustic emission signal and power signal and the high-frequency characteristic information of the vibration signal as characteristic parameters, and then normalizes these characteristic parameters Normalize and extract the principal components as the input samples of the SA-SVM model. In the second part, the simulated annealing algorithm is used to optimize the selection of support vector machine parameters, and the samp...

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Abstract

The invention relates to an intelligent monitoring method for the state of a hollow drill grinding wheel. Firstly, sensors are installed and signals are monitored; an acoustic emission signal, a powersignal and a vibration signal in the grinding process are monitored; signal characteristic parameter extraction is carried out; time domain parameters of the acoustic emission signal and the power signal and high-frequency characteristic information of the vibration signal are extracted as characteristic parameters; the characteristic parameters are normalized, main components are extracted as input samples of an SA-SVM model, selection of support vector machine parameters is optimized by using a simulated annealing algorithm, and training learning is performed on the samples; and finally, anSA-SVM model is adopted for intelligent prediction, a system analysis result is compared with the actual grinding state of a grinding wheel, and the prediction performance of the model is judged.

Description

technical field [0001] The invention relates to an intelligent monitoring method for the state of a hollow drill grinding wheel, in particular to an intelligent monitoring method for the state of a hollow drill grinding wheel using a simulated annealing optimization support vector machine. Background technique [0002] With the development of science and technology, higher and higher requirements are put forward for engineering materials. Various high-strength, high-hardness, corrosion-resistant and high-temperature-resistant engineering materials are used more and more. Most of them belong to Difficult to machine materials. In order to meet the processing requirements of difficult-to-machine materials and new materials, and to ensure high-quality and efficient completion of processing tasks, higher requirements are put forward for the wear resistance, reliability, precision, and size of cutting tools. The hollow drill is mainly composed of two parts, the blade and the hand...

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Application Information

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IPC IPC(8): G01N29/04G01N29/14G01N29/46G01N29/44G06N20/10
CPCG01N29/04G01N29/14G01N29/46G01N29/4418G06N20/10G01N2291/0234
Inventor 迟玉伦江欢李郝林王赟
Owner UNIV OF SHANGHAI FOR SCI & TECH
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