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- W4387678012 abstract "In recent years, genetic algorithms (GAs) have been combined with artificial neural networks (ANNs) to increase the search space for desired outputs. However, a research gap remains in optimising the ANN before incorporating it into the GA. In this study, the ANN was optimised through grid search, using parameters such as learning rates, number of epochs, batch sizes, number of neurons, activation functions, and optimisers. Furthermore, the optimisation approach was designed to be applied to the additive manufacturing process, where data is collected on LPBF process parameters such as laser power, scan speed and hatch distance. The results demonstrated that this approach improved the performance of GA-ANN parameter optimisation, including faster convergence and higher objective function value." @default.
- W4387678012 created "2023-10-17" @default.
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- W4387678012 date "2023-08-30" @default.
- W4387678012 modified "2023-10-17" @default.
- W4387678012 title "Enhancing Genetic Algorithm-Based Process Parameter Optimisation Through Grid Search-Optimised Artificial Neural Networks" @default.
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- W4387678012 doi "https://doi.org/10.1109/icac57885.2023.10275189" @default.
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