Assembly Synthesis with Subassembly Partitioning for Optimal In-Process Dimensional Adjustability
dc.contributor.author | Lee, Byungwoo | en_US |
dc.contributor.author | Saitou, Kazuhiro | en_US |
dc.date.accessioned | 2011-11-14T16:30:38Z | |
dc.date.available | 2011-11-14T16:30:38Z | |
dc.date.issued | 2007-01-22 | en_US |
dc.identifier.citation | Lee, B.; Saitou, K. (2007). Assembly Synthesis with Subassembly Partitioning for Optimal In-Process Dimensional Adjustability." AI-EDAM: Artificial Intelligence in Engineering, Design, and Manufacturing 21(1): 31-43. <http://hdl.handle.net/2027.42/87240> | en_US |
dc.identifier.issn | 0890-0604 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/87240 | |
dc.description.abstract | Achieving the dimensional integrity for a complex structural assembly is a demanding task due to the manufacturing variations of parts and the tolerance relationship between them. Although assigning tight tolerances to all parts would solve the problem, an economical solution is taking advantage of small motions that joints allow, such that critical dimensions are adjusted during assembly processes. This paper presents a systematic method that decomposes product geometry at an early stage of design, selects joint types, and generates subassembly partitioning to achieve the adjustment of the critical dimensions during assembly processes. A genetic algorithm generates candidate assemblies based on a joint library specific for an application domain. Each candidate assembly is evaluated by an internal optimization routine that computes the subassembly partitioning for optimal in-process adjustability, by finding a series of minimum cuts on weighted graphs. A case study on a three-dimensional automotive space frame with the accompanying joint library is presented. | en_US |
dc.publisher | Cambridge | en_US |
dc.title | Assembly Synthesis with Subassembly Partitioning for Optimal In-Process Dimensional Adjustability | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Mechanical Engineering | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Mechanical Engineering | en_US |
dc.contributor.affiliationother | Product Realization Laboratory, GE Global Research, Niskayuna, New York, USA. | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/87240/4/Saitou11.pdf | |
dc.identifier.doi | 10.1017/S0890060407070126 | en_US |
dc.identifier.source | Artificial Intelligence in Engineering, Design, and Manufacturing | en_US |
dc.owningcollname | Mechanical Engineering, Department of |
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