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• W4225607550 abstract "An accurate thermal simulation of direct laser deposition (DLD) provides a basis for optimizing DLD process parameters. However, the input thermal parameters that significantly affect the simulation performance are difficult to be measured directly through experimental methods. This paper proposes an inverse method to identify the heat source parameters for DLD thermal simulations using support vector regression and genetic algorithm. Firstly, forward simulation errors of a single-track deposition under different heat source parameters are obtained. Secondly, a quantitative relationship between the heat source parameters and simulation errors is established using support vector regression (SVR), and the suboptimal heat source parameters are inversely identified by genetic algorithm (GA). Finally, a closed-loop forward-inverse iteration is further developed to improve the accuracy of the identified heat source parameters. The results indicate that the proposed inverse identification method gives a good agreement between the predicted and measured temperatures during DLD, and is an efficient method to accurately extract unknown input parameters." @default.
• W4225607550 created "2022-05-05" @default.
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• W4225607550 date "2022-01-01" @default.
• W4225607550 modified "2023-09-24" @default.
• W4225607550 title "Parameter Inverse Identification for Direct Laser Deposition Simulation Using Support Vector Regression and Genetic Algorithm" @default.
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• W4225607550 doi "https://doi.org/10.1007/978-981-16-7381-8_100" @default.
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