| Download | - View final version: Principles of machine learning and its application to thermal barrier coatings (PDF, 9.4 MiB)
|
|---|
| DOI | Resolve DOI: https://doi.org/10.3390/coatings13071140 |
|---|
| Author | Search for: Liu, Yuan; Search for: Chen, Kuiying1ORCID identifier: https://orcid.org/0000-0002-0032-9389; Search for: Kumar, Amarnath; Search for: Patnaik, Prakash1ORCID identifier: https://orcid.org/0000-0002-2122-3882 |
|---|
| Affiliation | - National Research Council of Canada. Aerospace
|
|---|
| Format | Text, Article |
|---|
| Subject | artificial intelligence; algorithms; hot section components; thermal barrier coatings; thermal physical properties; gas turbine engines |
|---|
| Abstract | 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. |
|---|
| Publication date | 2023-06-23 |
|---|
| Publisher | MDPI |
|---|
| Licence | |
|---|
| In | |
|---|
| Language | English |
|---|
| Peer reviewed | Yes |
|---|
| Identifier | coatings13071140 |
|---|
| Export citation | Export as RIS |
|---|
| Report a correction | Report a correction (opens in a new tab) |
|---|
| Record identifier | f1763e4e-c00f-4476-8a33-953943bad9a4 |
|---|
| Record created | 2024-07-19 |
|---|
| Record modified | 2024-07-19 |
|---|
