A Genetic Algorithm with Design of Experiments Approach to Predict the Optimal Process Parameters for FDM

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dc.creatorArumaikkannu, G.
dc.creatorUma Maheshwaraa, N.
dc.creatorGowri, S.
dc.date.accessioned2020-02-20T21:01:23Z
dc.date.available2020-02-20T21:01:23Z
dc.date.issued2005
dc.description.abstractThis paper describes a Genetic Algorithm (GA) with Design of Experiments (DoE) approach to predict the optimized surface roughness and porosity characteristics of the parts produced using ABS material on stratasys FDM 2000 machine. The Mathematical Model (MM) was developed by using Response Surface Methodology (RSM). It is to predict and investigate the influence of selected process parameters namely slice thickness, road width, liquefier temperature and air gap and their interactions on the surface roughness and porosity. The developed MM is the fitness function in GA in order to find out the optimal sets of process parameters and to predict the corresponding surface quality characteristics. These results have been validated and the experimental results after GA are found to be in conformance with the predicted process parameters.en_US
dc.description.departmentMechanical Engineeringen_US
dc.identifier.urihttps://hdl.handle.net/2152/80053
dc.language.isoengen_US
dc.relation.ispartof2005 International Solid Freeform Fabrication Symposiumen_US
dc.rights.restrictionOpenen_US
dc.subjectGenetic Algorithmen_US
dc.titleA Genetic Algorithm with Design of Experiments Approach to Predict the Optimal Process Parameters for FDMen_US
dc.typeConference paperen_US

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2005 International Solid Freeform Fabrication Symposium