Multi-objective optimization of CFRP drilling parameters with a hybrid method integrating the ANN, NSGA-II and fuzzy C-means

A full factorial experiment is performed for the conventional dry drilling of CFRP with spindle speed, feed rate and point angle as drilling parameters, response variables are thrust force and exit-delamination. Artificial neural network (ANN) is developed to express thrust force and delamination fa...

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Vydáno v:	Composite structures Ročník 235; s. 111803
Hlavní autoři:	Wang, Qian, Jia, Xiaoliang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.03.2020
Témata:	Artificial neural network (ANN) Carbon fiber reinforced polymer Fuzzy C-means clustering algorithm Multi-objective optimization Non-dominated Sorting Genetic Algorithm (NSGA-II) Fuzzy C-means clustering algorithm Multi-objective optimization Artificial neural network (ANN) Carbon fiber reinforced polymer Non-dominated Sorting Genetic Algorithm (NSGA-II)
ISSN:	0263-8223, 1879-1085
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