The evaluation of corrosion risk of automotive materials refers to the assessment of the potential risk of corrosion of automotive materials during use, in order to determine their impact on automotive performance and safety. At present, the methods for assessing and managing the corrosion risk of automotive materials are mainly based on expert experience and manual measurement. These methods have problems such as inaccurate evaluation results and low efficiency. With the development of artificial intelligence technology, intelligent algorithms are gradually being applied in the automotive field. Therefore, this article proposed a risk assessment model for automotive material corrosion based on intelligent algorithms, aiming to predict risks in advance and reduce unnecessary losses. This article mainly applied experimental analysis and algorithm comparison to analyze the influencing factors of automotive material corrosion, and compared the performance of different intelligent algorithms. The experimental results showed that the minimum error rate of the support vector machine algorithm was 9.8%, and further improvement of the intelligent algorithm was needed.

Xin Wang, Kaixu Ren, Mengmeng Zhuang, Xiuxu Wang, Jiayu Wang, Peng Liu, Jiawei Zhao. Intelligent Algorithms for Air Pollution Corrosion Risk Assessment and Management of Automotive Materials. Academic Journal of Materials & Chemistry (2023) Vol. 4, Issue 6: 53-60. https://doi.org/10.25236/AJMC.2023.040610.

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