Machine Learning-Driven Optimization of TPMS Architected Materials Using Simulated Annealing
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911910510723072 |
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| author | Mishra, Akshansh |
| author_facet | Mishra, Akshansh |
| contents | The research paper presents a novel approach to optimizing the tensile stress of Triply Periodic Minimal Surface (TPMS) structures through machine learning and Simulated Annealing (SA). The study evaluates the performance of Random Forest, Decision Tree, and XGBoost models in predicting tensile stress, using a dataset generated from finite element analysis of TPMS models. The objective function minimized the negative R-squared value on the validation set to enhance model accuracy. The SA-XGBoost model outperformed the others, achieving an R-squared value of 0.96. In contrast, the SA-Random Forest model achieved an R squared value of 0.89 while the SA-Decision Tree model exhibited greater fluctuations in validation scores. This demonstrates that the SA-XGBoost model is most effective in capturing the complex relationships within the data. The integration of SA helps in optimizing the hyperparameters of these machine learning models, thereby enhancing their predictive capabilities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_05142 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine Learning-Driven Optimization of TPMS Architected Materials Using Simulated Annealing Mishra, Akshansh Materials Science Artificial Intelligence Machine Learning Optimization and Control The research paper presents a novel approach to optimizing the tensile stress of Triply Periodic Minimal Surface (TPMS) structures through machine learning and Simulated Annealing (SA). The study evaluates the performance of Random Forest, Decision Tree, and XGBoost models in predicting tensile stress, using a dataset generated from finite element analysis of TPMS models. The objective function minimized the negative R-squared value on the validation set to enhance model accuracy. The SA-XGBoost model outperformed the others, achieving an R-squared value of 0.96. In contrast, the SA-Random Forest model achieved an R squared value of 0.89 while the SA-Decision Tree model exhibited greater fluctuations in validation scores. This demonstrates that the SA-XGBoost model is most effective in capturing the complex relationships within the data. The integration of SA helps in optimizing the hyperparameters of these machine learning models, thereby enhancing their predictive capabilities. |
| title | Machine Learning-Driven Optimization of TPMS Architected Materials Using Simulated Annealing |
| topic | Materials Science Artificial Intelligence Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2406.05142 |