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机器学习在高温合金粉末盘构件疲劳寿命预测中的应用
Prediction of Fatigue Life of Powder Metallurgy Superalloy Disk via Machine Learning
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- DOI:
- 作者:
- 张国栋 1 ，苏宝龙 1 ，廖玮杰 1 ，王晓峰 2 ，邹金文 2 ，袁睿豪 1 ，李金山 1，3
ZHANG Guodong1 , SU Baolong1 , LIAO Weijie1 , WANG Xiaofeng2 , ZOU Jinwen2 , YUAN Ruihao1 , LI Jinsh
- 作者单位:
- 1. 西北工业大学 凝固技术国家重点实验室，陕西 西安 710072；2.中国航发北京航空材料研究院 先进高温结构材料重 点试验室，北京 100095；3. 西北工业大学 重庆科创中心，重庆 401135
1. State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi' an 710072, China; 2.Key Laboratory of Advanced High Temperature Structural Materials, Beijing Institute of Aeronautical Materials of AECC, Beijing 100095, China; 3. Innovation Center NPU Chongqing, Chongqing 401135, China
- 关键词:
- 粉末冶金；机器学习；寿命预测；高温合金
powder metallurgy; machine learning; life prediction; superalloy
- 摘要:
- 由于制粉的污染、容器材料剥落等原因，采用粉末冶金工艺制备高温合金，不可避免地将引入非金属夹杂
物。 这些夹杂物的存在严重危害高温合金涡轮盘的力学性能，并可能导致涡轮盘低周疲劳失效。 目前，疲劳寿命模型主
要是由 Manson-Coffin 公式发展出来的，均未考虑疲劳过程中弹性模量的变化和合金缺陷特征因素的影响，这些导致预
测结果与实际存在较大误差。 本文利用机器学习算法建立了涡轮盘低周疲劳循环次数与夹杂物距涡轮盘表面的距离以
及夹杂物的尺寸之间的定量预测模型。 结果表明，通过支持向量回归、随机森林、梯度提升机、核岭回归和套索回归法等
不同模型的计算对比发现，梯度提升树算法能够更好地预测疲劳寿命。
Due to the pollution of powder making and spalling of container materials, non-metallic inclusions will inevitably be introduced in the preparation of superalloy by powder metallurgy. These inclusions seriously harm the mechanical properties of superalloy turbine disks and may lead to low cycle fatigue failure of turbine disks. At present, the fatigue life model is mainly developed by Manson-Coffin's formula, which does not take into account the change of elastic modulus during fatigue and the influence of alloy defect characteristics, which results in a large error between the predicted results and the actual results. In this paper, five quantitative prediction models between the low cycle fatigue numbers of turbine disk with the distance of inclusions to disk surface and the inclusion sizes were established by using machine learning models. The results showed that compared to support vector machine, random forest, kernel ridge regression and lasso algorithms, the gradient lifting machine algorithm can better predict the fatigue life.