Optimization of Machine Learning in Predicting Fracture Behaviour of Materials in AI - Joshi, Gaurav (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2018)
Machine learning has revolutionized various fields, including material science and engineering, by enabling accurate predictions and optimization of complex phenomena. One such area is the prediction of fracture behaviour in materials, which plays a crucial role in ensuring structural integrity and safety in numerous applications. This abstract focuses on the optimization of machine learning techniques for predicting fracture behaviour in materials using artificial intelligence (AI). The primary objective of this research is to develop a robust and efficient machine learning model that can accurately predict the fracture behaviour of materials. To achieve this, a comprehensive dataset comprising various material properties, such as strength, ductility, and microstructural features, is collected. Additionally, fracture-related data, including fracture toughness, crack propagation rates, and failure modes, are also incorporated into the dataset. Several machines learning algorithms, including decision trees, random forests, support vector machines, and neural networks, are employed to train and evaluate the predictive models. The models are optimized by tuning hyperparameters and selecting the most relevant features through feature selection techniques. Furthermore, advanced optimization algorithms, such as genetic algorithms and particle swarm optimization, are utilized to enhance the performance of the machine learning models. To ensure the generalizability and robustness of the developed models, cross-validation techniques and extensive testing on independent datasets are conducted. The accuracy and performance of the models are assessed through various evaluation metrics, such as mean squared error, accuracy, and precision-recall curves. Comparative analyses are performed to determine the most suitable machine learning algorithm for predicting fracture behaviour in materials. The results demonstrate that the optimized machine learning models exhibit high accuracy and reliability in predicting fracture behaviour. The developed models can effectively capture the complex relationships between material properties and fracture characteristics, enabling the identification of critical parameters that influence fracture behaviour. This research contributes to the advancement of AI in material science and engineering by providing valuable insights into the prediction and optimization of fracture behaviour in materials. In the optimization of machine learning techniques for predicting fracture behaviour in materials using AI. The developed models offer accurate predictions and valuable insights into the fracture characteristics of materials, aiding in the design and optimization of structural components with enhanced safety and performance. The application of machine learning in material science continues to evolve, and this research paves the way for further advancements in the field.