Back to Search
Start Over
A customizable process planning approach for rotational parts based on multi-level machining features and ontology
A customizable process planning approach for rotational parts based on multi-level machining features and ontology
- Source :
- The International Journal of Advanced Manufacturing Technology. 108:647-669
- Publication Year :
- 2020
- Publisher :
- Springer Science and Business Media LLC, 2020.
-
Abstract
- Turning is the one of the most commonly available and least expensive machining operations. Confronted with the increased number of part variants and the requirement for fast system responsiveness in dynamic environments, traditional methods for building a computer-aided process planning (CAPP) system for turning are infeasible due to the fixed feature library and hard-coded heuristic rules. In this paper, a customizable approach for automatic process planning of rotational parts is proposed to boost the productivity of enterprises by realizing multi-level machining feature recognition and knowledge-based machining activity/resource selection. First, from the decomposed cells of the turning area, basic turning features are successively recognized based on a novel cell machinability analysis. The high-level custom features are define based on the directed acyclic graph and recognized from the basic feature precedence graph using the subgraph matching algorithm. To facilitate the knowledge-based process planning, a knowledge base is then established utilizing ontology, in which the taxonomies, properties, and causal relationships among the core concepts, namely, machining feature, machining operation, cutting tool, and machine tool, are formally defined. Finally, a five-step procedure is proposed to automatically infer manufacturing activity/resource for multi-level features through rule-based reasoning. The effectiveness and extensibility of the proposed approach are validated through two case studies on complex rotational parts.
- Subjects :
- 0209 industrial biotechnology
business.product_category
Computer science
Mechanical Engineering
Feature recognition
02 engineering and technology
Ontology (information science)
computer.software_genre
Directed acyclic graph
Industrial and Manufacturing Engineering
Computer Science Applications
Machine tool
020901 industrial engineering & automation
Machining
Control and Systems Engineering
Feature (machine learning)
Data mining
Precedence graph
business
computer
Software
Blossom algorithm
Subjects
Details
- ISSN :
- 14333015 and 02683768
- Volume :
- 108
- Database :
- OpenAIRE
- Journal :
- The International Journal of Advanced Manufacturing Technology
- Accession number :
- edsair.doi...........f5c8e79bc8f391383db37f97ee2eca8b
- Full Text :
- https://doi.org/10.1007/s00170-020-05437-0