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Exact(5)
The algorithm takes multiple feature interpretations of a part as input.
Multiple feature interpretations and lack of scalability have been issues in the latest feature recognition research.
We present an algorithm that generates multiple feature interpretations within and across domains.
We define three types of multiple feature interpretations or models, (1) only positive, (2) mixed and (3) only negative.
The algorithm generates multiple feature interpretations for parts, which have a through feature in any one domain.
Similar(55)
By performing process planning with maximal features, the optimal or satisfactory feature interpretation will be found.
Each axis is described by multiple features.
Multiple features of each object were quantified.
Verification item 11: Does the content of the interactions between the features provide multiple interpretations?
Moreover, multiple interpretations of an interacting feature can be easily generated by simply subtracting potential external undercut features from each other in different orders.
In this form, m has multiple equivalent interpretations.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com