Inference of Metal Additive Manufacturing Process States via Deep Learning Techniques

dc.creatorAnarfi, Richard
dc.creatorKwapong, Benjamin
dc.creatorFletcher, Kenneth
dc.creatorSparks, Todd
dc.creatorFlood, Aaron
dc.creatorJoshi, Mugdha
dc.date.accessioned2021-12-07T17:18:26Z
dc.date.available2021-12-07T17:18:26Z
dc.date.issued2021
dc.description.abstractNumerical simulation of metal additive processes are computationally intensive tasks. Iterative solution techniques for physics-based methods can lead to lengthy solution times and convergence problems, particularly if fluid dynamics of the melt pool are considered. Deep learning (DL) techniques offer an opportunity to infer solution results quickly. In this paper we propose a DL method based on long short term memory (LSTM), network trained on rendered images from a metal AM process simulation and CAM data. We obtained vector representations of the images by training on an autoencoder. LSTM is a memory based recurrent neural networks (RNN) that is capable of processing long sequences of data while combating temporal stability problems encountered with conventional recurrent neural networks (RNN)s. This LSTM network is used to predict images of the process given scan path and process information. This could later be used to compare with process monitoring systems as part of a quality assurance or process control schema.en_US
dc.description.departmentMechanical Engineeringen_US
dc.identifier.urihttps://hdl.handle.net/2152/90726
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/17645
dc.language.isoengen_US
dc.publisherUniversity of Texas at Austinen_US
dc.relation.ispartof2021 International Solid Freeform Fabrication Symposiumen_US
dc.rights.restrictionOpenen_US
dc.subjectadditive manufacturingen_US
dc.subjectautoencoderen_US
dc.subjectdeep learningen_US
dc.titleInference of Metal Additive Manufacturing Process States via Deep Learning Techniquesen_US
dc.typeConference paperen_US

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