Beneficial properties achieved by nanostructuring effects in materials have generated tremendous interests in applications in surface engineering, especially in thermal barrier coatings (TBC). Limitations in conventional TBC processing for gas turbines and aero-propulsion systems have been exposed during past decades when rapid progress was made in nano-structuring coating research and developments. The present work is a comprehensive review of the current state of progress in nanostructured TBC (Ntbc) in reference to its microstructure, damage progression, failure mechanisms and a wide range of properties. The review aims to address the comparative performance analysis between the nanostructured and conventional (microstructured) 6–8 wt.% yttrium stabilized zirconia (YSZ) TBC systems. Oxidation resistance and sintering behavior in two TBCs are considered as the central focus of discussion. A few schematics are used to represent major microstructural features and failure progression. A performance analysis is performed for standard 2-layer, as well as functionally graded multilayer, TBC systems. A comparison of TBC characteristics processed by plasma spray and vapor deposition techniques is also made as reference. Compared to the sea of R&D efforts made for conventional TBC (Ctbc), limited experimental studies on Ntbc offers conflicting data, and prediction modeling and computational research are scarce.

Artificial intelligence (AI), machine learning (ML) and deep learning (DL) along with big data (BD) management are currently viable approaches that can significantly help gas turbine components’ design and development. Optimizing microstructures of hot section components such as thermal barrier coatings (TBCs) to improve their durability has long been a challenging task in the gas turbine industry. In this paper, a literature review on ML principles and its various associated algorithms was presented first and then followed by its application to investigate thermal conductivity of TBCs. This combined approach can help better understand the physics behind thermal conductivity, and on the other hand, can also boost the design of low thermal conductivity of the TBCs system in terms of microstructure–property relationships. Several ML models and algorithms such as support vector regression (SVR), Gaussian process regression (GPR) and convolution neural network and regression algorithms were used via Python. A large volume of thermal conductivity data was compiled and extracted from the literature for TBCs using PlotDigitizer software and then used to test and validate ML models. It was found that the test data were strongly associated with five key factors as identifiers. The prediction of thermal conductivity was performed using three approaches: polynomial regression, neural network (NN) and gradient boosting regression (GBR). The results suggest that NN using the BR model and GBR have better prediction capability.

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.