Neurovision-based logic control of an experimental manufacturing plant using neural net le-net5 and automation Petri nets
Küçük Resim Yok
Tarih
2005
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
SPRINGER
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this paper, Petri nets and neural networks are used together in the development of an intelligent logic controller for an experimental manufacturing plant to provide the flexibility and intelligence required from this type of dynamic systems. In the experimental setup, among deformed and good parts to be processed, there are four different part types to be recognised and selected. To distinguish the correct part types, a convolutional neural net le-net5 based on-line image recognition system is established. Then, the necessary information to be used within the logic control system is produced by this on-line image recognition system. Using the information about the correct part types and Automation Petri nets, a logic control system is designed. To convert the resulting Automation Petri net model of the controller into the related ladder logic diagram (LLD), the token passing logic (TPL) method is used. Finally, the implementation of the control logic as an LDD for the real time control of the manufacturing system is accomplished by using a commercial programmable logic controller (PLC).
Açıklama
Anahtar Kelimeler
image recognition, neural networks, manufacturing plant control, Petri nets, ladder logic diagrams, programmable logic controller
Kaynak
JOURNAL OF INTELLIGENT MANUFACTURING
WoS Q Değeri
Q3
Scopus Q Değeri
Q1
Cilt
16
Sayı
45416
