About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Presentation Title |
Machine Learning Based Prediction of Fatigue Crack Growth Rate in Additively Manufactured Ti6Al4V Alloy
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Author(s) |
Jayaganthan Rengaswamy, Nithin Konda |
On-Site Speaker (Planned) |
Jayaganthan Rengaswamy |
Abstract Scope |
In the present work, machine learning based fatigue crack growth rate (FCGR) modelling has been carried out for Ti-6Al4V alloy fabricated through additive manufacturing (AM) technology. The FCGR data of Ti6Al4V alloy fabricated through laser powder bed fusion (LPBF) and other AM routes were utilized for fatigue life prediction using machine learning (ML) algorithms such as support vector regression, random forest, and deep neural networks. The model performances estimated using these algorithms were compared using the standard metrics such RMSE, MSE & R2. The feature importance analysis was carried out to assess the magnitude of effect of each of the post processing techniques and built orientation on FCGR behaviour. The prediction of fatigue crack growth rate in Ti-6Al-4V alloy made by ML is compared with Paris law. The microstructural characteristics of as built and post-processed LPBFed Ti-6Al-4V are used to substantiate its fatigue life predicted through ML techniques. |
Proceedings Inclusion? |
Undecided |