TY - GEN
T1 - Cyber-coordinated Simulation Models for Multi-stage Additive Manufacturing of Energy Products
AU - Sun, Hongyue
AU - Pedrielli, Giulia
AU - Zhao, Guanglei
AU - Bragagnolo, Andrea
AU - Zhou, Chi
AU - Pan, Rong
AU - Xu, Wenyao
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/4
Y1 - 2018/12/4
N2 - This paper extends the conventional single-stage additive manufacturing (AM) processes to multi-STage distRibutEd AM systems (STREAMs). In STREAM, a batch of material produced at the pre-processing stage is jointly consumed by distributed AM printers, and then the printed parts are collected for the centralized post-processing. Such systems are widely encountered in AM processes such as energy-AM, metal-AM and bio-AM. Modeling and managing such complex systems have been challenging. We propose a novel framework for 'cyber-coordinated simulation' to manage the hierarchical information in STREAM. This is important because simulation can be used to infuse data into predictive analytics, thus providing guidance for the optimization and control of STREAM operations. The proposed framework is hierarchical in nature, where single stage, multi-stage and distributed productions are modeled through the integration of different simulators. We demonstrate the proposed framework with simulation data from freeze nano printing AM processes.
AB - This paper extends the conventional single-stage additive manufacturing (AM) processes to multi-STage distRibutEd AM systems (STREAMs). In STREAM, a batch of material produced at the pre-processing stage is jointly consumed by distributed AM printers, and then the printed parts are collected for the centralized post-processing. Such systems are widely encountered in AM processes such as energy-AM, metal-AM and bio-AM. Modeling and managing such complex systems have been challenging. We propose a novel framework for 'cyber-coordinated simulation' to manage the hierarchical information in STREAM. This is important because simulation can be used to infuse data into predictive analytics, thus providing guidance for the optimization and control of STREAM operations. The proposed framework is hierarchical in nature, where single stage, multi-stage and distributed productions are modeled through the integration of different simulators. We demonstrate the proposed framework with simulation data from freeze nano printing AM processes.
UR - http://www.scopus.com/inward/record.url?scp=85059987575&partnerID=8YFLogxK
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U2 - 10.1109/COASE.2018.8560477
DO - 10.1109/COASE.2018.8560477
M3 - Conference contribution
AN - SCOPUS:85059987575
T3 - IEEE International Conference on Automation Science and Engineering
SP - 893
EP - 898
BT - 2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018
PB - IEEE Computer Society
T2 - 14th IEEE International Conference on Automation Science and Engineering, CASE 2018
Y2 - 20 August 2018 through 24 August 2018
ER -