About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Presentation Title |
Qualitative Assessment of Degradation and Ageing Behaviour of Epoxy-Al Nanocomposites Through Machine Learning Assisted With LIBS. |
Author(s) |
Sneha Jayaganthan, Naresh Chillu, Sarathi Ramanujam, Jayaganthan Rengaswamy |
On-Site Speaker (Planned) |
Jayaganthan Rengaswamy |
Abstract Scope |
Epoxy resin loaded with conductive nanofillers using Al below percolation threshold have improved its charge trap properties, to use as insulation structure. It is essential to monitor multi stress ageing of polymer nanocomposites for predicting it’s failure. In the present work, LIBS investigations assisted by machine learning (ML) algorithms are used to predict its ageing behaviour. Various ageing conditions such as water ageing, corona and γ irradiation were performed on the epoxy polymer along with optimized 5 wt% Al filled epoxy nanocomposite. The LIBS spectral intensities were used for detecting the levels of decompositions during ageing. The ML algorithms such as support vector machine and neural networks are adopted to LIBS spectral data for classifying the degradation level. The comparative analysis of machine learning algorithms is made with respect to their classification accuracies using confusion matrices, which helps in assessing the level of degradation for condition monitoring of power systems. |
Proceedings Inclusion? |
Undecided |