Skip to Main Content (Press Enter)

Logo UNIBG
  • ×
  • Home
  • Corsi
  • Insegnamenti
  • Persone
  • Pubblicazioni
  • Strutture
  • Attività
  • Competenze

UNI-FIND
Logo UNIBG

|

UNI-FIND

unibg.it
  • ×
  • Home
  • Corsi
  • Insegnamenti
  • Persone
  • Pubblicazioni
  • Strutture
  • Attività
  • Competenze
  1. Pubblicazioni

Bayesian identification of energy models for industrial machinery controlled rotary axes

Articolo
Data di Pubblicazione:
2021
Citazione:
(2021). Bayesian identification of energy models for industrial machinery controlled rotary axes [journal article - articolo]. In JOURNAL OF CLEANER PRODUCTION. Retrieved from http://hdl.handle.net/10446/191957
Abstract:
A fair prediction of energy consumption in machine tools is an essential requirement to optimize work cycles, processes and equipments also in the light of energy efficiency, as promoted by Industry 4.0. In this work, we focus on the energy behavior of machinery controlled rotary axes commanded by brushless electric motors, and propose a framework to predict their energy consumption. It consists of a model-based energy representation of these axes, combined with experimental procedures and statistical techniques to characterize the model. In particular, we select a proper model from a number of candidates, and estimate its coefficients including their variability based on specific movement conditions. Model fitting is performed through a Bayesian approach, while the dependency of the coefficients on movement conditions is characterized with a K-Nearest Neighbors technique. The overall framework has been validated on the six axes of the pick and place anthropomorphic Comau NS16 robot. The results of this application confirm the effectiveness of the approach, which can therefore be considered as a valid tool for assessing the energy consumption of a machinery axis during the execution of a specific work cycle.
Tipologia CRIS:
1.1.01 Articoli/Saggi in rivista - Journal Articles/Essays
Elenco autori:
Lanzarone, Ettore; Borgia, Stefano
Autori di Ateneo:
LANZARONE Ettore
Link alla scheda completa:
https://aisberg.unibg.it/handle/10446/191957
Pubblicato in:
JOURNAL OF CLEANER PRODUCTION
Journal
  • Ricerca

Ricerca

Settori (2)


Settore ING-IND/16 - Tecnologie e Sistemi di Lavorazione

Settore MAT/06 - Probabilita' e Statistica Matematica
  • Utilizzo dei cookie

Realizzato con VIVO | Designed by Cineca | 25.6.1.0