Abstract
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.
Highlights
Thermal barrier coatings (TBCs) are applied on the surface of hot hardware parts of gas turbine engines to increase the turbine efficiency by providing thermal insulation and protection from the harsh environment
Three cases of prediction of erosion rate (ER) using the gradient boosting regression (GBR) approach were performed for all, air plasma spray (APS), and electron beam physical vapour deposition (EB-PVD) TBC data, respectively
Artificial intelligence technology comprising of machine learning (ML), deep learnmaterial designing, developments, and property prediction. This project has undertaken ing (DL), and big data (BD) treatment approaches have been evolving at a rapid pace to the prediction of erosion rate (ER) for two types of thermal barrier coatings
Summary
Thermal barrier coatings (TBCs) are applied on the surface of hot hardware parts of gas turbine engines to increase the turbine efficiency by providing thermal insulation and protection from the harsh environment. The EB-PVD is widely used to deposit ceramic coatings on combustor cans, ductwork, platforms, and other hot gas path components. TBCs exhibit two primary modes of failures, namely by oxidation (TGO, i.e., thermally grown oxide growth at the bond coat–top coat interface) and by erosion (impact by projectiles ingested into the gas stream) [1]. Turbine blades have a tip velocity in the order of 300 m/s and suffer rapid erosive wear upon impact by small hard particles (1–30 μm) entrained within the combustion gases of the turbine
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