Automatic feature recognition using artificial neural networks to integrate design and manufacturing: Review of automatic feature recognition systems
Само за регистроване кориснике
2011
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
Feature technology is considered an essential tool for integrating design and manufacturing. Automatic feature recognition (AFR) has provided the greatest contribution to fully automated computer-aided process planning system development. The objective of this paper is to review approaches based on application of artificial neural networks for solving major AFR problems. The analysis presented in this paper shows which approaches are suitable for different individual applications and how far away we are from the formation of a general AFR algorithm.
Кључне речи:
Neural Networks / ISO 10303 Standard Series / Feature Extraction / Computer-Aided Process PlanningИзвор:
Ai Edam-Artificial Intelligence For Engineering Design Analysis and Manufacturing, 2011, 25, 3, 289-304Издавач:
- Cambridge Univ Press, New York
DOI: 10.1017/S0890060410000545
ISSN: 0890-0604
WoS: 000293378000006
Scopus: 2-s2.0-80054963117
Колекције
Институција/група
Mašinski fakultetTY - JOUR AU - Babić, Bojan AU - Nesić, Nenad AU - Miljković, Zoran PY - 2011 UR - https://machinery.mas.bg.ac.rs/handle/123456789/1228 AB - Feature technology is considered an essential tool for integrating design and manufacturing. Automatic feature recognition (AFR) has provided the greatest contribution to fully automated computer-aided process planning system development. The objective of this paper is to review approaches based on application of artificial neural networks for solving major AFR problems. The analysis presented in this paper shows which approaches are suitable for different individual applications and how far away we are from the formation of a general AFR algorithm. PB - Cambridge Univ Press, New York T2 - Ai Edam-Artificial Intelligence For Engineering Design Analysis and Manufacturing T1 - Automatic feature recognition using artificial neural networks to integrate design and manufacturing: Review of automatic feature recognition systems EP - 304 IS - 3 SP - 289 VL - 25 DO - 10.1017/S0890060410000545 ER -
@article{ author = "Babić, Bojan and Nesić, Nenad and Miljković, Zoran", year = "2011", abstract = "Feature technology is considered an essential tool for integrating design and manufacturing. Automatic feature recognition (AFR) has provided the greatest contribution to fully automated computer-aided process planning system development. The objective of this paper is to review approaches based on application of artificial neural networks for solving major AFR problems. The analysis presented in this paper shows which approaches are suitable for different individual applications and how far away we are from the formation of a general AFR algorithm.", publisher = "Cambridge Univ Press, New York", journal = "Ai Edam-Artificial Intelligence For Engineering Design Analysis and Manufacturing", title = "Automatic feature recognition using artificial neural networks to integrate design and manufacturing: Review of automatic feature recognition systems", pages = "304-289", number = "3", volume = "25", doi = "10.1017/S0890060410000545" }
Babić, B., Nesić, N.,& Miljković, Z.. (2011). Automatic feature recognition using artificial neural networks to integrate design and manufacturing: Review of automatic feature recognition systems. in Ai Edam-Artificial Intelligence For Engineering Design Analysis and Manufacturing Cambridge Univ Press, New York., 25(3), 289-304. https://doi.org/10.1017/S0890060410000545
Babić B, Nesić N, Miljković Z. Automatic feature recognition using artificial neural networks to integrate design and manufacturing: Review of automatic feature recognition systems. in Ai Edam-Artificial Intelligence For Engineering Design Analysis and Manufacturing. 2011;25(3):289-304. doi:10.1017/S0890060410000545 .
Babić, Bojan, Nesić, Nenad, Miljković, Zoran, "Automatic feature recognition using artificial neural networks to integrate design and manufacturing: Review of automatic feature recognition systems" in Ai Edam-Artificial Intelligence For Engineering Design Analysis and Manufacturing, 25, no. 3 (2011):289-304, https://doi.org/10.1017/S0890060410000545 . .