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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.10128 |
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| _version_ | 1866914087680606208 |
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| author | Khod, Sunita Kamma, Vinay Verma, Ravi Kumar Goswami, Mayank |
| author_facet | Khod, Sunita Kamma, Vinay Verma, Ravi Kumar Goswami, Mayank |
| contents | This study proposes an Artificial Intelligence (AI) driven methodology for predicting a combination of brazed ceramic-metal composite materials. Multiple machine learning (ML) algorithms are compared with the deep learning (DL) model. The developed models are tested using k-fold validation. Nine different input-output feature configurations are evaluated to assess the model performance. The input-output feature comprises material properties, namely, the coefficient of thermal expansion (CTE) and molecular mass of brazed ceramic-metal composite materials obtained from literature and the strength parameter (average Von Mises Stress (VMS)) estimated from Finite Element Method (FEM) simulation for joint assembly structure. A multi-output model, Autoencoder (AE), has also been developed and tested to predict various features. The ML model, namely the polynomial regression (PR), outperforms the other ML/DL models with a Mean square Error (MSE) of 0.01 for the test data. The autoencoder model with a 32-16-32 structure outperforms LR, PR, RF, and ANN with an MSE of 0.04% for the prediction of unseen data. The developed multi-output model accurately predicts all the features (single and multiple), while PR fails to accurately predict multi-output features of low importance. The developed AE model predicts the different material properties with an average error of ~0.16-3.78% with literature-reported values. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_10128 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Material combination optimization for brazed ceramic-metal composites using Artificial Intelligence Khod, Sunita Kamma, Vinay Verma, Ravi Kumar Goswami, Mayank Applied Physics Materials Science This study proposes an Artificial Intelligence (AI) driven methodology for predicting a combination of brazed ceramic-metal composite materials. Multiple machine learning (ML) algorithms are compared with the deep learning (DL) model. The developed models are tested using k-fold validation. Nine different input-output feature configurations are evaluated to assess the model performance. The input-output feature comprises material properties, namely, the coefficient of thermal expansion (CTE) and molecular mass of brazed ceramic-metal composite materials obtained from literature and the strength parameter (average Von Mises Stress (VMS)) estimated from Finite Element Method (FEM) simulation for joint assembly structure. A multi-output model, Autoencoder (AE), has also been developed and tested to predict various features. The ML model, namely the polynomial regression (PR), outperforms the other ML/DL models with a Mean square Error (MSE) of 0.01 for the test data. The autoencoder model with a 32-16-32 structure outperforms LR, PR, RF, and ANN with an MSE of 0.04% for the prediction of unseen data. The developed multi-output model accurately predicts all the features (single and multiple), while PR fails to accurately predict multi-output features of low importance. The developed AE model predicts the different material properties with an average error of ~0.16-3.78% with literature-reported values. |
| title | Material combination optimization for brazed ceramic-metal composites using Artificial Intelligence |
| topic | Applied Physics Materials Science |
| url | https://arxiv.org/abs/2510.10128 |