Implementation and Testing of a Genetic Algorithm for a Self-learning and Automated Parameterisation of an Aerodynamic Feeding System
Implementation and Testing of a Genetic Algorithm for a Self-learning and Automated Parameterisation of an Aerodynamic Feeding System
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气动供料系统自学习和自动参数化遗传算法的实现和测试
DOI：
10.1016/j.procir.2016.02.081
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
P. Nyhuis
中科院分区：
文献类型：
--
作者：
J. Busch;Sebastian Blankemeyer;A. Raatz;P. Nyhuis
IFA开发的主动气动送料系统在输出率、可靠性和零件几何形状中立性方面具有很大的潜力。本文介绍了将遗传算法应用于给料系统控制的过程。遗传算法自动识别最佳值的进给系统的参数，需要调整时，设置为新的工件。在遗传算法的收敛行为的测试过程中，自动参数识别的一般功能得到确认。由此，揭示了进料系统的调节时间和溶液质量之间的折衷。
An active aerodynamic feeding system developed at the IFA offers a large potential regarding output rate, reliability and neutrality towards part geometries. In this paper, the procedure of a genetic algorithm's into the feeding system's control is shown. The genetic algorithm automatically identifies optimal values for the feeding system's parameters which need to be adjusted when setting up for new workpieces. The general functioning of the automatic parameter identification is confirmed during tests on the convergence behaviour of the genetic algorithm. Thereby, a trade-off between the adjustment time of the feeding system and the solution quality is revealed.