Predicting and Controlling the Thermal Part History in Powder Bed Fusion Using Neural Networks
dc.creator | Merschroth, Holger | |
dc.creator | Kniepkamp, Michael | |
dc.creator | Weigold, Matthias | |
dc.date.accessioned | 2021-11-16T16:07:30Z | |
dc.date.available | 2021-11-16T16:07:30Z | |
dc.date.issued | 2019 | |
dc.description.abstract | Laser-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.department | Mechanical Engineering | en_US |
dc.identifier.uri | https://hdl.handle.net/2152/90327 | |
dc.identifier.uri | http://dx.doi.org/10.26153/tsw/17248 | |
dc.language.iso | eng | en_US |
dc.publisher | University of Texas at Austin | en_US |
dc.relation.ispartof | 2019 International Solid Freeform Fabrication Symposium | en_US |
dc.rights.restriction | Open | en_US |
dc.subject | neural networks | en_US |
dc.subject | thermal history | en_US |
dc.subject | temperature prediction | en_US |
dc.subject | temperature control | en_US |
dc.subject | laser power | en_US |
dc.subject | laser-based powder bed fusion | en_US |
dc.title | Predicting and Controlling the Thermal Part History in Powder Bed Fusion Using Neural Networks | en_US |
dc.type | Conference paper | en_US |