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- W3097819762 abstract "The present study proposes a neural network-supported multiobjective optimization procedure for two different conformal cooling channel designs in plastic injection technology. After completing the 3D design of a plastic model, the computational heat transfer & fluid dynamics simulations find the cooling time, temperature uniformity, and pressure drop data, which are the main objectives of the minimization-based multiobjective optimization problem. The performance maps of the objectives are created via i) a classic MATLAB-based interpolation and ii) MATLAB-based neural networks. Then, multiobjective optimization is conducted. The results show that the neural networks can create the performance maps of the cooling time trends with less than 1% error whereas its best trade-off point calculation has nearly 1.1 % deviation. Also, the use of neural networks can decrease the computational time in both simulation and optimization processes." @default.
- W3097819762 created "2020-11-09" @default.
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- W3097819762 date "2020-09-23" @default.
- W3097819762 modified "2023-09-25" @default.
- W3097819762 title "Neural network-integrated multiobjective optimization of the 3D-printed conformal cooling channels" @default.
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- W3097819762 doi "https://doi.org/10.23919/splitech49282.2020.9243730" @default.
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