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[1]付佳博,王晨充,MATEO Carlos Gracia,等.物理冶金信息指導機器學習的鎳基單晶高溫合金蠕變壽命預測[J].中國材料進展,2023,42(09):722-731.[doi:10.7502/j.issn.1674-3962.202212004]
 FU Jiabo,WANG Chenchong,MATEO Carlos Gracia,et al.Creep Life Prediction of Ni-Based Single Crystal Superalloys by Physical Metallurgy Information Guided Machine Learning[J].MATERIALS CHINA,2023,42(09):722-731.[doi:10.7502/j.issn.1674-3962.202212004]
點擊復制

物理冶金信息指導機器學習的鎳基單晶高溫合金蠕變壽命預測()
分享到:

中國材料進展[ISSN:1674-3962/CN:61-1473/TG]

卷:
42
期數:
2023年第09期
頁碼:
722-731
欄目:
出版日期:
2023-09-30

文章信息/Info

Title:
Creep Life Prediction of Ni-Based Single Crystal Superalloys by Physical Metallurgy Information Guided Machine Learning
文章編號:
1674-3962(2023)09-0722-10
作者:
付佳博1王晨充1MATEO Carlos Gracia2 CARABALLO Isaac Toda2 CABALLERO Francisca Garcia2于皓1
1. 東北大學 軋制技術及連軋自動化國家重點實驗室,遼寧 沈陽 110819 2. 西班牙國家冶金研究中心物理冶金系,西班牙 馬德里 28040
Author(s):
FU Jiabo1 WANG Chenchong1 MATEO Carlos Gracia2 CARABALLO Isaac Toda2CABALLERO Francisca Garcia2 YU Hao1
1. The State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, China 2. Department of Physical Metallurgy, National Centre for Metallurgical Research (CENIM-CSIC),Avda. Gregorio del Amo, Madrid 28040, Spain
關鍵詞:
鎳基單晶高溫合金機器學習蠕變壽命高溫低應力
Keywords:
Ni-based single crystal superalloy machine learning creep life high temperature and low stress
分類號:
TP181;TG132
DOI:
10.7502/j.issn.1674-3962.202212004
文獻標志碼:
A
摘要:
蠕變壽命是影響鎳基單晶高溫合金材料服役壽命和力學性能的關鍵材料參數。因此,如何準確有效地預測合金的蠕變壽命具有重要現實意義。盡管多年來許多研究學者已經建立起多種蠕變壽命的預測模型,但是由于不同溫度應力下的蠕變機制復雜且蠕變過程涉及長時間的顯微組織演化,已有模型尚難以實現有效預測。對此,采用物理冶金原理指導下的數據挖掘結合機器學習這一研究策略,通過文獻調研建立起了高溫低應力下的鎳基單晶合金的高質量蠕變數據集,在物理冶金原理指導下對原始數據進行挖掘,提高了原始數據的內在質量,并基于Pearson系數和隨機森林平均精確度降低值分別對原始數據特征進行了相關性分析和重要性評估,表明所建立的數據集符合基本的物理冶金學機制,同時闡明了引入的三維物理冶金信息對于蠕變壽命預測的重要意義。隨后,基于機器學習方法在數據挖掘后的數據集上對合金的蠕變壽命進行了預測,并根據平方相關系數(R2)、平均絕對誤差(MAE)和過擬合程度評估了不同的機器學習模型。結果表明,支持向量回歸(SVR)模型在本研究中具有較好的泛化能力且不容易過擬合,同時結合了物理冶金信息的機器學習模型擁有更好的預測準確性和泛化能力。最終成功地建立起了高溫低應力下鎳基單晶高溫合金成分、工藝以及引入的物理冶金參數和蠕變壽命之間的關系,能夠實現對鎳基單晶高溫合金蠕變壽命的有效預測,并有望應用于基于合金服役條件的成分工藝的反向設計。
Abstract:
Creep life is a key material parameter affecting the service life and mechanical properties of Ni-based single crystal superalloys. Therefore, how to predict the creep life of alloys accurately and effectively is critically important for engineering. To address this issue, a physical metallurgy (PM)-guided machine learning (ML) model is developed. Firstly based on literature research, a high quality creep dataset of single crystal at high temperature and low stress is established. Under the guidance of the principles of physical metallurgy, three dimensional physical metallurgy information (volume fraction of γ′ phase Vf,lattice misfit δ, diffusion coefficient DL) are added to the original dataset as extra dimensions to guide the training process. Additionally, the correlation analysis and importance evaluation of the original data features are made based on the Pearson correlation coefficient and the mean accuracy decrease (MDA) value of a random forest model respectively. As a result, the dataset is basically consistent with the physical metallurgy mechanism, and the threedimensional physical metallurgy information is of great significance for the creep life prediction. The creep life of the alloy is predicted on the dataset after data mining based on the machine learning method, and different machine learning models are evaluated according to the squared correlation coefficient (R2), mean absolute error (MAE) and the degree of overfitting. Finally, the optimal model is determined as support vector regression (SVR) model. The relationship between the composition, process, physical metallurgical parameters and creep rupture life of Ni-based single crystal superalloy under high temperature and low stress is successfully established, which can effectively predict the creep life and is expected to serve the reverse design of the alloy.

參考文獻/References:

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備注/Memo

備注/Memo:
收稿日期:2022-12-05修回日期:2023-04-21 基金項目:國家重點研發計劃項目(2021YFB3702500) 第一作者:付佳博,男,1999年生,博士研究生 通訊作者:于皓,男,1991年生,博士后, Email:yuhao@ral.neu.edu.cn 王晨充,男,1988年生,副教授,博士生導師, Email:wangchenchong@ral.neu.edu.cn
更新日期/Last Update: 2023-08-28
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