A milling cutter wear monitoring method based on order spectrum and dynamic immune fuzzy clustering

By using the method based on order spectrum and dynamic immune fuzzy clustering, the milling cutter spindle current signal is used, combined with fuzzy clustering and immune algorithm optimization, the accuracy problem of milling cutter wear status monitoring in multiple operating conditions is solved, and efficient wear status recognition and evaluation is achieved.

CN117182654BActive Publication Date: 2025-08-26DALIAN UNIV OF TECH
2 Cites 0 Cited by

Patent Information

Application Number
CN202311292041.8
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-10-08
Publication Date
2025-08-26
Estimated Expiration
2043-10-08

AI Technical Summary

Technical Problem

The existing milling cutter wear status monitoring methods require a lot of prior knowledge and complex data preprocessing, and the recognition effect is poor in multiple operating conditions, making it difficult to adapt to process changes in actual engineering.

Method used

Using the method based on order spectrum and dynamic immune fuzzy clustering, the original time domain signal of the spindle current during milling cutter is used, combined with the fuzzy clustering algorithm and the immune algorithm, the optimal clustering division is obtained by optimizing the threshold λ, and a milling cutter wear monitoring model is established.

Benefits of technology

The accurate identification of the wear status of the milling cutter under multiple operating conditions is achieved. The classification accuracy in the test sample data is 95%, providing quantitative evaluation for the assessment of the wear status of the milling cutter in actual projects.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The present invention discloses a milling cutter wear monitoring method based on order spectrum and dynamic immune fuzzy clustering, which belongs to the field of intelligent manufacturing and processing technology. The method comprises the following steps: taking the original time domain signal of the spindle current during the milling process of the milling cutter as the model input, referring to the synchronous observation results and quantitative standards of a super-depth-of-field three-dimensional microscope, extracting the characteristic parameter vectors of the current signals of the milling cutter at different wear levels, considering the fuzziness and uncertainty of experimental data in the identification of critical states of each level of milling cutter wear, introducing a dynamic fuzzy clustering algorithm, obtaining the initial fuzzy clustering division by outputting a threshold λ, further considering the problem that the dynamic fuzzy clustering algorithm is prone to fall into local optimal values, adopting an immune algorithm with global search and parallel capabilities for optimization, obtaining the optimal threshold λ, and finally establishing an immune-optimized dynamic fuzzy clustering model, which provides new ideas for the actual engineering milling cutter wear state level assessment and equipment safety quantitative evaluation.
Need to check novelty before this filing date? Find Prior Art