Multi-attribute target decision evaluation method for enhanced corrosion protection design of bent bolt

Through the multi-attribute target decision-making evaluation method and support vector regression algorithm, a coating anti-corrosion performance evaluation model was established, which solved the problems of coating adhesion and corrosion resistance of pipe bent bolts used in wind turbine towers under extreme environments, achieved objective evaluation and optimized design of anti-corrosion performance, and improved the service life and stability of the bolts.

CN119066802BActive Publication Date: 2025-10-14CHINA PRODUCTIVITY CENT FOR MASCH
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Patent Information

Application Number
CN202411052478.9
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-08-02
Publication Date
2025-10-14
Estimated Expiration
2044-08-02

AI Technical Summary

Technical Problem

Existing technologies make it difficult to effectively evaluate and optimize the corrosion resistance of pipe bend bolts used in wind turbine towers, especially the adhesion and corrosion resistance of the coating in extreme environments, which shortens the service life of the bolts in harsh environments.

Method used

A multi-attribute target decision-making evaluation method is adopted, combined with the support vector regression (SVR) algorithm. Through adhesion performance test, corrosion resistance performance test and comprehensive scoring, a coating anti-corrosion performance evaluation model is established, the weight of each performance indicator is determined, and objective and accurate performance evaluation is achieved.

Benefits of technology

It provides an objective and accurate evaluation method for the corrosion resistance of coatings, which can guide the design of enhanced corrosion resistance of pipe bent bolts for wind turbine towers and improve their service life and stability in extreme environments.

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Abstract

The application provides a multi-attribute target decision evaluation method for enhanced corrosion resistance design of a bent bolt, which is based on a multi-attribute decision analysis theory, and a support vector regression (SVR) machine learning algorithm is innovatively introduced to solve weight coefficients of various performance evaluation indexes, so as to obtain an objective and accurate comprehensive performance evaluation result. Compared with a traditional subjective weighting method, the biggest advantage of the method is that the method can fully explore index correlation rules and weight distribution characteristics contained in historical evaluation data, and the method realizes adaptive solving from data to weights through the machine learning algorithm, and the method has the characteristics of strong objectivity, good adaptability and strong interpretability. The method can also be widely applied to corrosion resistance performance evaluation of a new coating system, and has important guiding significance for optimization and iteration of a coating formula and process parameters.
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