Application of Machine Learning to Solid Particle Erosion of APS-TBC and EB-PVD TBC at Elevated Temperatures

Coatings

Prakash Patnaik¹  Kuiying Chen¹  Yuan Liu²  Ravi Ravichandran² 

[1] Aerospace Research Center, Structures, Materials and Performance Laboratory, National Research Council Canada, Ottawa, ON K1A 0R6, Canada;Tecsis Corporation, Ottawa, ON K2E 7L5, Canada;

关键词: machine learning;    solid particle erosion;    APS-TBC;    EB-PVD TBC;    elevated temperatures;   

DOI  :  10.3390/coatings11070845

来源: DOAJ

【 摘 要 】

Machine learning (ML) and deep learning (DL) for big data (BD) management are currently viable approaches that can significantly help in high-temperature materials design and development. ML-DL can accumulate knowledge by learning from existing data generated through multi-physics modelling (MPM) and experimental tests (ETs). DL mainly involves analyzing nonlinear correlations and high-dimensional datasets implemented through specifically designed numerical algorithms. DL also makes it possible to learn from new data and modify predictive models over time, identifying anomalies, signatures, and trends in machine performance, develop an understanding of patterns of behaviour, and estimate efficiencies in a machine. Machine learning was implemented to investigate the solid particle erosion of both APS (air plasma spray) and EB-PVD (electron beam physical vapour deposition) TBCs of hot section components. Several ML models and algorithms were used such as neural networks (NNs), gradient boosting regression (GBR), decision tree regression (DTR), and random forest regression (RFR). It was found that the test data are strongly associated with five key factors as identifiers. Following test data collection, the dataset is subjected to sorting, filtering, extracting, and exploratory analysis. The training and testing, and prediction results are analysed. The results suggest that neural networks using the BR model and GBR have better prediction capability.

【 授权许可】

Unknown