SPATIAL-TEMPORAL MODELING USING DEEP LEARNING FOR REAL-TIME MONITORING OF ADDITIVE MANUFACTURING
Jaehyuk Kim, Yan Lu, Zhuo Yang, Hyunwoong Ko, Dongmin Shin, Yosep Oh
Real-time monitoring for Additive Manufacturing (AM) processes can greatly benefit from spatial-temporal modeling using deep learning. However, existing, deep-learning approaches in AM are case-dependent, and therefore not robust to changes of control inputs and data types. As AM is dynamic and complex, this limitation leads to a lack of systematic, DL approaches for real-time monitoring of AM, which involves a large number of varying control parameters and monitoring data. To address the challenge, this paper introduces a novel approach for developing spatial-temporal models to monitor Laser Powder Bed Fusion (LPBF) processes using deep learning on real-time, monitoring data. First, we present a novel model for representing in-situ-monitoring and control data of LPBF at multiple scales. Second, from the model, we extract spatial-temporal relationships for in-situ monitoring of LPBF processes. Third, we present a spatial-temporal, modeling approach using the architecture of convolutional long short-term memory (LSTM) to monitor the spatial-temporal relationships and detect anomalies. A case study used convolutional LSTM Autoencoder on optical, melt-pool-monitoring data, one of the most widely adopted data types in in-situ monitoring of LPBF. The data was generated from a LPBF testbed called the Additive Manufacturing Metrology Testbed. The novel, learning approach enables spatial-temporal modeling of AM dynamics directly from real-time data for the monitoring of varying AM environments. The non-ad-hocness of the approach provides potential to fuse real-time data at multiple, spatial-temporal scales.
Proceedings of the ASME 2022
August 14-17, 2022
St. Louis, MO, US
International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE2022)
, Lu, Y.
, Yang, Z.
, Ko, H.
, Shin, D.
and Oh, Y.
SPATIAL-TEMPORAL MODELING USING DEEP LEARNING FOR REAL-TIME MONITORING OF ADDITIVE MANUFACTURING, Proceedings of the ASME 2022, St. Louis, MO, US, [online], https://doi.org/10.1115/DETC2022-91021, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=934817
(Accessed May 28, 2023)