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Enriched machining feature-based reasoning for generic machining process sequencing
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AI
DOI：10.1080/00207540500354119.png)
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Abstract
En 中文
This paper presents an enriched machining feature (EMF)-based reasoning approach to generic machining process sequencing for distributed process planning (DPP). An EMF is represented by combining its machining volume with surface, geometric and volume features, as well as other technological information needed to machine the feature. The information embedded in the EMF is retrieved progressively for machining sequence generation. Following an introduction of EMF and its representation scheme, the problems in determining machine-independent feature groups (set-ups) in DPP and their machining sequences to be followed for a given part are investigated. Based on the EMF concept, five reasoning rules are formulated and the algorithms developed. As the set-ups and sequences are generated based on manufacturing constraints and datum references but separated from specific resources, they are generic and applicable to machine tools with varying configurations and capabilities. This approach is further validated through a case study.
Keywords:
machining feature
process planning
geometric reasoning
machining process sequencing
Journal
IF:
7.3
Papers:
1.1W
Citations:
3.7W
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