Automated Design for Manufacturing and Supply Chain Using Geometric Data Mining and Machine Learning

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dc.contributor.advisor Matthew C. Frank
dc.contributor.author Hoefer, Michael
dc.contributor.department Industrial and Manufacturing Systems Engineering
dc.date 2018-08-11T10:46:40.000
dc.date.accessioned 2020-06-30T03:03:02Z
dc.date.available 2020-06-30T03:03:02Z
dc.date.copyright Sun Jan 01 00:00:00 UTC 2017
dc.date.embargo 2001-01-01
dc.date.issued 2017-01-01
dc.description.abstract <p>This thesis presents an automated method for assessing conceptual designs with respect to manufacturing and supply chain, using geometric data mining and machine learning algorithms. It is important for designers to understand how design decisions will impact downstream manufacturing and sourcing. Many critical decisions are made during conceptual design that impact production cost even before detailed design is finalized; however, the effects of these decisions are not known until later. Design for manufacturing and design for supply chain are methods that provide feedback to the user in a way that enables proactive design changes.</p> <p>A conceptual design is largely defined by the geometry found in CAD files. In this work, feature-free geometric algorithms were used to extract meaningful manufacturability metrics from 3D models, which were classified as either castings or machined parts. The developed metrics serve as useful attributes for a machine learning model that can help select the manufacturing process of a conceptual design. A classification accuracy of 86% was achieved using a random forest algorithm, which is comparable to other approaches in the literature, while only using geometry as input. The work in this thesis provides methods for using geometry to evaluate a design for manufacturability and supply chain, enabling proactive design decisions early during new product development.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/etd/15320/
dc.identifier.articleid 6327
dc.identifier.contextkey 11051214
dc.identifier.doi https://doi.org/10.31274/etd-180810-4948
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath etd/15320
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/29503
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/etd/15320/Hoefer_iastate_0097M_16296.pdf|||Fri Jan 14 20:39:20 UTC 2022
dc.subject.disciplines Art and Design
dc.subject.disciplines Industrial Engineering
dc.subject.disciplines Mechanical Engineering
dc.subject.keywords conceptual design
dc.subject.keywords design for manufacturing
dc.subject.keywords design for supply chain
dc.subject.keywords geometric data mining
dc.subject.keywords machine learning
dc.subject.keywords process selection
dc.title Automated Design for Manufacturing and Supply Chain Using Geometric Data Mining and Machine Learning
dc.type article
dc.type.genre thesis
dspace.entity.type Publication
relation.isOrgUnitOfPublication 51d8b1a0-5b93-4ee8-990a-a0e04d3501b1
thesis.degree.discipline Industrial and Manufacturing Systems Engineering
thesis.degree.level thesis
thesis.degree.name Master of Science
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