Intelligent monitoring method for state of hollow drill grinding wheel

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...

AI Technical Summary

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 problems, and has strong robustness, global convergence and extensive Adaptability, provides an effective solution for intelligent monitoring of the wear state of the grinding wheel when grinding high-speed steel hollow drills, and has important practical engineering application value

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