Manufacturing Feature Instances: Which Ones to Recognize?
| dc.contributor.author | Gupta, Satyandra K. | en_US |
| dc.contributor.author | Regli, William C. | en_US |
| dc.contributor.author | Nau, Dana S. | en_US |
| dc.date.accessioned | 2004-05-31T22:28:56Z | |
| dc.date.available | 2004-05-31T22:28:56Z | |
| dc.date.created | 1994-11 | en_US |
| dc.date.issued | 1998-10-15 | en_US |
| dc.description.abstract | Manufacturing features and feature-based representations have become an integral part of research on manufacturing systems, largely due to their ability to model correspondences between design information and manufacturing operations. However, several research challenges still must be addressed in order to place feature technologies into a solid scientific and mathematical framework. One challenge is the issue of alternatives in feature-based planning. Even after one has decided upon an abstract set of features to use for representing manufacturing operations, the set of feature instances used to represent a complex part is by no means unique. For a complex part, many (sometimes infinitely many) different manufacturing operations can potentially be used to manufacture various portions of the partand thus many different feature instances can be used to represent these portions of the part. Some of these feature instances will appear in useful manufacturing plans, and others will not. If the latter feature instances can be discarded at the outset, this will reduce the number of alternative manufacturing plans to be examined in order to find a useful one. Thus, what is required is a systematic means of specifying wllich feature instances are of interest. This paper addresses the issue of alternatives by introducing the notion of primary feature instances, which we contend are sufficient to generate all manufacturing plans of interest. To substantiate our argument, we describe how various instances in the primary feature set can be used to produce the desired plans. Furthermore, we discuss how this formulation overcomes computational difficulties faced by previous work, and present some complexity results for this approach in the domain of machined parts. (Also cross-referenced as UMIACS-TR-94-127) | en_US |
| dc.format.extent | 304618 bytes | |
| dc.format.mimetype | application/postscript | |
| dc.identifier.uri | http://hdl.handle.net/1903/676 | |
| dc.language.iso | en_US | |
| dc.relation.isAvailableAt | Digital Repository at the University of Maryland | en_US |
| dc.relation.isAvailableAt | University of Maryland (College Park, Md.) | en_US |
| dc.relation.isAvailableAt | Tech Reports in Computer Science and Engineering | en_US |
| dc.relation.isAvailableAt | UMIACS Technical Reports | en_US |
| dc.relation.ispartofseries | UM Computer Science Department; CS-TR-3376 | en_US |
| dc.relation.ispartofseries | UMIACS; UMIACS-TR-94-127 | en_US |
| dc.title | Manufacturing Feature Instances: Which Ones to Recognize? | en_US |
| dc.type | Technical Report | en_US |