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Parameters optimization of selected casting processes using teaching-learning-based optimization algorithm

机译：基于教学学习的优化算法对选定铸造工艺的参数优化

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摘要

In the present work, mathematical models of three important casting processes are considered namely squeeze casting, continuous casting and die casting for the parameters optimization of respective processes. A recently developed advanced optimization algorithm named as teaching-learning-based optimization (TLBO) is used for the parameters optimization of these casting processes. Each process is described with a suitable example which involves respective process parameters. The mathematical model related to the squeeze casting is a multi-objective problem whereas the model related to the continuous casting is multi-objective multi-constrained problem and the problem related to the die casting is a single objective problem. The mathematical models which are considered in the present work were previously attempted by genetic algorithm and simulated annealing algorithms. However, attempt is made in the present work to minimize the computational efforts using the TLBO algorithm. Considerable improvements in results are obtained in all the cases and it is believed that a global optimum solution is achieved in the case of die casting process.
机译：在本工作中，考虑了三个重要铸造工艺的数学模型，即挤压铸造，连续铸造和压铸，以优化各个过程的参数。最近开发的高级优化算法，称为基于教学学习的优化（TLBO），用于这些浇铸过程的参数优化。用适当的示例描述每个过程，该示例涉及各个过程参数。与挤压铸造有关的数学模型是一个多目标问题，而与连续铸造有关的模型是一个多目标多约束问题，而与压铸有关的问题是一个单目标问题。先前通过遗传算法和模拟退火算法尝试了本工作中考虑的数学模型。但是，在本工作中尝试使用TLBO算法使计算工作最小化。在所有情况下都能获得显着的结果改进，并且相信在压铸过程中可以实现全局最佳解决方案。

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