Prediction of Mg Alloy Corrosion Based on Machine Learning Models
نویسندگان
چکیده
Magnesium alloy is a potential biodegradable metallic material characterized by bone-like elastic modulus, which has great application prospects in medical, automotive, and aerospace industries owing to its biocompatibility, lightweight properties. However, the rapid corrosion rates of magnesium alloys seriously limit their applications. This study collected alloys’ data developed model predict potential, based on chemical composition alloys. We compared four machine learning algorithms: random forest (RF), multiple linear regression (MLR), support vector (SVR), extreme gradient boosting (XGBoost). The RF algorithm offered most accurate predictions than other three algorithms. input effects have been investigated. Moreover, we used feature creation (transforming component characteristics into atomic physical characteristics) so that were not limited specific compositions. From this result, model’s range was widened, verify accuracy feasibility predicting
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ژورنال
عنوان ژورنال: Advances in Materials Science and Engineering
سال: 2022
ISSN: ['1687-8434', '1687-8442']
DOI: https://doi.org/10.1155/2022/9597155