An integration of neural network and shuffled frog-leaping algorithm for CNC machining monitoring
Yükleniyor...
Dosyalar
Tarih
2021
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Sciendo
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
This paper addresses Acoustic Emission (AE) from Computer Numerical Control (CNC) machining operations. Experimental measurements are performed on the CNC lathe sensors to provide the power consumption data. To this end, a hybrid methodology based on the integration of an Artificial Neural Network (ANN) and a Shuffled Frog-Leaping Algorithm (SFLA) is applied to the data resulting from these measurements for data fusion from the sensors which is called SFLA-ANN. The initial weights of ANN are selected using SFLA. The goal is to assess the potency of the signal periodic component among these sensors. The efficiency of the proposed SFLA-ANN method is analyzed compared to hybrid methodologies of Simulated Annealing (SA) algorithm and ANN (SA-ANN) and Genetic Algorithm (GA) and ANN (GA-ANN).
Açıklama
Anahtar Kelimeler
Artificial Neural Network, CNC Machining; Multi-Sensor Data Fusion, Genetic Algorithm, Shuffled Frog-Leaping Algorithm, Simulated Annealing
Kaynak
Foundations of Computing and Decision Sciences
WoS Q Değeri
N/A
Scopus Q Değeri
Q3
Cilt
46
Sayı
1
Künye
Goli, A., Tirkolaee, E. B., & Weber, G. W. (2021). An Integration of Neural Network and Shuffled Frog-Leaping Algorithm for CNC Machining Monitoring. Foundations of Computing and Decision Sciences, 46(1), 27-42.
