Predicting and Controlling the Thermal Part History in Powder Bed Fusion Using Neural Networks

dc.creatorMerschroth, Holger
dc.creatorKniepkamp, Michael
dc.creatorWeigold, Matthias
dc.date.accessioned2021-11-16T16:07:30Z
dc.date.available2021-11-16T16:07:30Z
dc.date.issued2019
dc.description.abstractLaser-based powder bed fusion of metallic parts is used widely in different branches of industry. Although there have been many investigations to improve the process stability, thermal history is rarely taken into account. The thermal history describes the parts’ thermal situation throughout the build process as a result of successive heating and cooling with each layer. This could lead to different microstructures due to different thermal boundary conditions. In this paper, a methodology based on neural networks is developed to predict and control the parts’ temperature by adjusting the laser power. A thermal imaging system is used to monitor the thermal history and to generate a training data set for the neural network. The trained network is then used to predict and control the parts temperature. Finally, tensile testing is conducted to investigate the influence of the adjusted process on the mechanical properties of the parts.en_US
dc.description.departmentMechanical Engineeringen_US
dc.identifier.urihttps://hdl.handle.net/2152/90327
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/17248
dc.language.isoengen_US
dc.publisherUniversity of Texas at Austinen_US
dc.relation.ispartof2019 International Solid Freeform Fabrication Symposiumen_US
dc.rights.restrictionOpenen_US
dc.subjectneural networksen_US
dc.subjectthermal historyen_US
dc.subjecttemperature predictionen_US
dc.subjecttemperature controlen_US
dc.subjectlaser poweren_US
dc.subjectlaser-based powder bed fusionen_US
dc.titlePredicting and Controlling the Thermal Part History in Powder Bed Fusion Using Neural Networksen_US
dc.typeConference paperen_US

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