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Ontology-based modeling of process selection knowledge for machining feature
- Source :
- International Journal of Precision Engineering and Manufacturing. 14:1719-1726
- Publication Year :
- 2013
- Publisher :
- Springer Science and Business Media LLC, 2013.
-
Abstract
- One of the major activities in process planning is to decide the most appropriate machining methods for a part to be manufactured. Above all it requires the knowledge on feature, manufacturing capability of machining process and their relationships. As new technologies emerge, the process selection knowledge needs to be updated accordingly. Most of the systems dealing with process knowledge are not flexible enough to accommodate the relevant changes within an acceptable cost. In this paper, ontology based modeling of the process planning knowledge is presented. The core process ontology represents the process planning knowledge for machining operation selection regarding multi-axis machining feature. Firstly the concepts such as features, machining methods and process capability are modeled with relevant properties. Secondly the causal relationships between these concepts are modeled. In addition, the process selection logic is modeled by using rules which describe the match between machining requirements of a feature and process capability of a machining method. An example shows how the process ontology can be used in a reasoning mechanism for operations selection.
- Subjects :
- Engineering
business.industry
Process (engineering)
Mechanical Engineering
Process capability
Process ontology
Ontology (information science)
Work in process
computer.software_genre
Industrial engineering
Industrial and Manufacturing Engineering
Machining
Feature (computer vision)
Data mining
Electrical and Electronic Engineering
business
computer
Selection (genetic algorithm)
Subjects
Details
- ISSN :
- 20054602 and 22347593
- Volume :
- 14
- Database :
- OpenAIRE
- Journal :
- International Journal of Precision Engineering and Manufacturing
- Accession number :
- edsair.doi...........6bb2c393ba2fdd0c0989719166861f15
- Full Text :
- https://doi.org/10.1007/s12541-013-0231-7