Pregled bibliografske jedinice broj: 759878
Artificial Neural Network Model for Tool Condition Monitoring in Stone Drilling
Artificial Neural Network Model for Tool Condition Monitoring in Stone Drilling // Applied Mechanics and Materials, 772 (2015), 268-273 doi:10.4028/www.scientific.net/AMM.772.268 (međunarodna recenzija, članak, znanstveni)
CROSBI ID: 759878 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Artificial Neural Network Model for Tool Condition Monitoring in Stone Drilling
Autori
Brezak, Danko ; Staroveški, Tomislav ; Stiperski, Ivan ; Klaić, Miho ; Majetić, Dubravko
Izvornik
Applied Mechanics and Materials (1660-9336) 772
(2015);
268-273
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
Stone drilling; tool wear classification; neural networks
Sažetak
This paper explores the possibility of tool wear classification in stone drilling. Wear model is based on Radial Basis Function Neural Network which links tool wear features extracted from motor drive current signals and acoustic emission signals with two wear levels – sharp and worn drill. Signals were measured during stone drilling under different cutting conditions, and then filtered before tool wear features extraction. Features were obtained from time and frequency domain. They have been analyzed individually and in combinations. The results indicate tool wear monitoring capacity of the proposed model in stone drilling, and its potential for simple and cost-effective integration with CNC machine tools.
Izvorni jezik
Engleski
Znanstvena područja
Strojarstvo
POVEZANOST RADA
Projekti:
120-1201948-1938 - Napredni obradni sustavi i procesi (Udiljak, Toma, MZOS ) ( CroRIS)
120-1201948-1945 - Inteligentno vođenje obradnih sustava (Majetić, Dubravko, MZOS ) ( CroRIS)
Ustanove:
Fakultet strojarstva i brodogradnje, Zagreb
Profili:
Dubravko Majetić
(autor)
Ivan Stiperski
(autor)
Tomislav Staroveški
(autor)
Danko Brezak
(autor)
Miho Klaić
(autor)
Citiraj ovu publikaciju:
Časopis indeksira:
- Scopus
Uključenost u ostale bibliografske baze podataka::
- Compendex (EI Village)
- INSPEC
- SCOPUS, Cambridge Scientific Abstracts (CSA)