Automatic Feature Recognition System for Prismatic Parts Using STEP-Based Feature Extraction and ANN-Based Pattern Recognition
Само за регистроване кориснике
2011
Конференцијски прилог (Објављена верзија)
,
Prof. K.-D. Bouzakis
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 CAPP system development. After a brief review of approaches based on application of artificial neural networks (ANN) for solving major AFR problems, this paper presents an AFR system for prismatic parts with external extraction of geometric information out of a STEP CAD model, part
representation based on B-rep coding and ANN-based pattern recognition.
Кључне речи:
Artificial neural networks / Automatic feature recognition (AFR) / STEP CAD model / Face coding / Automated CAPP system / B-rep coding / ANN-based pattern recognition / Prismatic partsИзвор:
Proceedings of the 4th International Conference on Manufacturing Engineering (ICMEN 2011), 2011, 295-304Издавач:
- The Aristotle University of Thessaloniki
Финансирање / пројекти:
Колекције
Институција/група
Mašinski fakultetTY - CONF AU - Babić, Bojan AU - Nešić, Nenad AU - Miljković, Zoran PY - 2011 UR - https://machinery.mas.bg.ac.rs/handle/123456789/4947 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 CAPP system development. After a brief review of approaches based on application of artificial neural networks (ANN) for solving major AFR problems, this paper presents an AFR system for prismatic parts with external extraction of geometric information out of a STEP CAD model, part representation based on B-rep coding and ANN-based pattern recognition. PB - The Aristotle University of Thessaloniki C3 - Proceedings of the 4th International Conference on Manufacturing Engineering (ICMEN 2011) T1 - Automatic Feature Recognition System for Prismatic Parts Using STEP-Based Feature Extraction and ANN-Based Pattern Recognition EP - 304 SP - 295 UR - https://hdl.handle.net/21.15107/rcub_machinery_4947 ER -
@conference{ author = "Babić, Bojan and Nešić, 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 CAPP system development. After a brief review of approaches based on application of artificial neural networks (ANN) for solving major AFR problems, this paper presents an AFR system for prismatic parts with external extraction of geometric information out of a STEP CAD model, part representation based on B-rep coding and ANN-based pattern recognition.", publisher = "The Aristotle University of Thessaloniki", journal = "Proceedings of the 4th International Conference on Manufacturing Engineering (ICMEN 2011)", title = "Automatic Feature Recognition System for Prismatic Parts Using STEP-Based Feature Extraction and ANN-Based Pattern Recognition", pages = "304-295", url = "https://hdl.handle.net/21.15107/rcub_machinery_4947" }
Babić, B., Nešić, N.,& Miljković, Z.. (2011). Automatic Feature Recognition System for Prismatic Parts Using STEP-Based Feature Extraction and ANN-Based Pattern Recognition. in Proceedings of the 4th International Conference on Manufacturing Engineering (ICMEN 2011) The Aristotle University of Thessaloniki., 295-304. https://hdl.handle.net/21.15107/rcub_machinery_4947
Babić B, Nešić N, Miljković Z. Automatic Feature Recognition System for Prismatic Parts Using STEP-Based Feature Extraction and ANN-Based Pattern Recognition. in Proceedings of the 4th International Conference on Manufacturing Engineering (ICMEN 2011). 2011;:295-304. https://hdl.handle.net/21.15107/rcub_machinery_4947 .
Babić, Bojan, Nešić, Nenad, Miljković, Zoran, "Automatic Feature Recognition System for Prismatic Parts Using STEP-Based Feature Extraction and ANN-Based Pattern Recognition" in Proceedings of the 4th International Conference on Manufacturing Engineering (ICMEN 2011) (2011):295-304, https://hdl.handle.net/21.15107/rcub_machinery_4947 .