基于机器学习的Inconel 617热腐蚀行为预测

IF 2.3 4区材料科学 Q2 METALLURGY & METALLURGICAL ENGINEERING

Anti-corrosion Methods and Materials Pub Date : 2023-12-01 DOI:10.1108/acmm-07-2023-2854

Amir Rezaei

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引用次数: 0

摘要

目的研究机器学习在Inconel 617合金热腐蚀预测中的可行性。设计/方法/方法通过对Inconel 617热腐蚀的实验研究，建立了一个用于机器学习模型的数据集。除合金成分外，本文还将热腐蚀条件(如时间、温度)和含盐介质成分作为独立特征，而将比质量变化作为目标特征。本文采用线性回归、随机森林和XGBoost来预测Inconel 617的比质量增益。发现sxgboost的决定系数(R2)为0.98，是所有模型中最高的。该模型的平均绝对误差最小(0.20)。在900℃温度下，XGBoost对合金在不同时间的比质量增益的预测效果最好。总之，XGBoost在预测Inconel 617的比质量增益方面显示出最高的准确性。独创性/价值使用机器学习来预测Inconel 617的热腐蚀标志着该领域的重大进展，并有望简化具有增强热腐蚀弹性的新材料的开发和评估。

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Prediction of hot corrosion behavior of Inconel 617 via machine learning

Purpose

This paper aims to study the feasibility of using machine learning in hot corrosion prediction of Inconel 617 alloy.

Design/methodology/approach

By examination of the experimental studies on hot corrosion of Inconel 617, a data set was built for machine learning models. Apart from the alloy composition, this paper included the condition of hot corrosion like time and temperature, and the composition of the saline medium as independent features, while the specific mass change is set as the target feature. In this paper, linear regression, random forest and XGBoost are used to predict the specific mass gain of Inconel 617.

Findings

XGBoost yields the coefficient of determination (R²) of 0.98, which was highest among models. Also, this model recorded the lowest value of mean absolute error (0.20). XGBoost had the best performance in predicting specific mass gain of the alloy in different times at temperature of 900°C. In sum, XGBoost shows highest accuracy in predicting specific mass gain for Inconel 617.

Originality/value

Using machine learning to predict hot corrosion in Inconel 617 marks a substantial progress in this domain and holds promise for simplifying the development and evaluation of novel materials featuring enhanced hot corrosion resilience.

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来源期刊

Anti-corrosion Methods and Materials 工程技术-冶金工程

CiteScore

2.80

自引率

16.70%

发文量

审稿时长

13.5 months

期刊介绍： Anti-Corrosion Methods and Materials publishes a broad coverage of the materials and techniques employed in corrosion prevention. Coverage is essentially of a practical nature and designed to be of material benefit to those working in the field. Proven applications are covered together with company news and new product information. Anti-Corrosion Methods and Materials now also includes research articles that reflect the most interesting and strategically important research and development activities from around the world. Every year, industry pays a massive and rising cost for its corrosion problems. Research and development into new materials, processes and initiatives to combat this loss is increasing, and new findings are constantly coming to light which can help to beat corrosion problems throughout industry. This journal uniquely focuses on these exciting developments to make essential reading for anyone aiming to regain profits lost through corrosion difficulties. • New methods, materials and software • New developments in research and industry • Stainless steels • Protection of structural steelwork • Industry update, conference news, dates and events • Environmental issues • Health & safety, including EC regulations • Corrosion monitoring and plant health assessment • The latest equipment and processes • Corrosion cost and corrosion risk management.