A graph grammar based approach to automated manufacturing planning

dc.contributor.advisorCampbell, Matthew I.en
dc.contributor.committeeMemberEftekharian, Ata A.en
dc.creatorFu, Wentaoen
dc.date.accessioned2012-07-26T16:24:14Zen
dc.date.available2012-07-26T16:24:14Zen
dc.date.issued2012-05en
dc.date.submittedMay 2012en
dc.date.updated2012-07-26T16:24:22Zen
dc.descriptiontexten
dc.description.abstractIn this thesis, a new graph grammar representation is proposed to reason about the manufacturability of solid models. The knowledge captured in the graph grammar rules serves as a virtual machinist in its ability to recognize arbitrary geometries and match them to various machine operations. Firstly, a novel convex decomposition algorithm has been developed to decompose a given part into multiple sub-volumes, where each sub-volume is assumed to be machined in one operation or to be non-machinable. Then the decomposed part is converted into a graph so that graph grammar rules can determine the machining details. A candidate plan is a feasible sequence of all of the necessary machining operations needed to manufacture this part. If a given geometry is not machinable, the rules will fail to find a complete manufacturing plan for all of the sub-volumes. As a result of this representation, designers can quickly get insights into how a part can be made and how it can be improved based upon the feedback of the rules. A variety of tests of this algorithm on both simple and complex engineering parts show its effectiveness and efficiency.en
dc.description.departmentMechanical Engineeringen
dc.format.mimetypeapplication/pdfen
dc.identifier.slug2152/ETD-UT-2012-05-5342en
dc.identifier.urihttp://hdl.handle.net/2152/ETD-UT-2012-05-5342en
dc.language.isoengen
dc.subjectGraph grammaren
dc.subjectManufacturing process planningen
dc.titleA graph grammar based approach to automated manufacturing planningen
dc.type.genrethesisen
thesis.degree.departmentMechanical Engineeringen
thesis.degree.disciplineMechanical Engineeringen
thesis.degree.grantorUniversity of Texas at Austinen
thesis.degree.levelMastersen
thesis.degree.nameMaster of Science in Engineeringen

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