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dc.contributor.advisorLilly, Blaine
dc.creatorCastro, Carlos
dc.description.abstractInjection molding (IM) is considered the most prominent processes for mass-producing plastic products. One of the biggest challenges facing injection molders today is to determine the proper settings for the IM process variables. Selecting the proper settings for an IM process is crucial because the behavior of the polymeric material during shaping is highly influenced by the process variables. Consequently, the process variables govern the quality of the part produced. The difficulty of optimizing an IM process is that the performance measures-quantities that characterize the adequacy of part, process, or machine to intended purposes such, i.e. surface quality or cycle time- usually show conflicting behavior. Therefore, a compromise must be found between all of the performance measures of interest. This thesis demonstrates a method incorporating Computer Aided Engineering, Artificial Neural Networks, and Data Envelopment Analysis (DEA) that can be used to find the best compromises between performance measures in IM, and potentially other polymer processes.en
dc.format.extent552449 bytes
dc.publisherThe Ohio State Universityen
dc.relation.ispartofseriesThe Ohio State University. Department of Mechanical Engineering Honors Theses;2005
dc.subjectData Envelopment Analysisen
dc.subjectInjection Moldingen
dc.subjectComputer Aided Engineeringen
dc.subjectArtificial Neural Networksen
dc.titleMultiple criteria optimization in injection moldingen

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