TY - GEN
T1 - Survey on artificial intelligence for additive manufacturing
AU - Yang, Jimeng
AU - Chen, Yi
AU - Huang, Weidong
AU - Li, Yun
N1 - Publisher Copyright:
© 2017 Chinese Automation and Computing Society in the UK - CACSUK.
PY - 2017/10/23
Y1 - 2017/10/23
N2 - Additive manufacturing of three-dimensional objects are now more and more realised through 3D printing, known as an evolutional paradigm in the manufacturing industry. Artificial intelligence is currently finding wide applications to 3D printing for an intelligent, efficient, high quality, mass customised and service-oriented production process. This paper presents a comprehensive survey of artificial intelligence in 3D printing. Before a printing task begins, the printability of given 3D objects can be determined through a printability checker using machine learning. The prefabrication of slicing is accelerated through parallel slicing algorithms and the path planning is optimised intelligently. In the aspect of service and security, intelligent demand matching and resource allocation algorithms enable a Cloud service platform and evaluation model to provide clients with an on-demand service and access to a collection of shared resources. We also present three machine learning algorithms to detect product defects in the presence of cyber-attacks. Based on the reviews on various applications, printability with multi-indicators, reduction of complexity threshold, acceleration of prefabrication, real-time control, enhancement of security and defect detection for customised designs are seen of good opportunities for further research, especially in the era of Industry 4.0.
AB - Additive manufacturing of three-dimensional objects are now more and more realised through 3D printing, known as an evolutional paradigm in the manufacturing industry. Artificial intelligence is currently finding wide applications to 3D printing for an intelligent, efficient, high quality, mass customised and service-oriented production process. This paper presents a comprehensive survey of artificial intelligence in 3D printing. Before a printing task begins, the printability of given 3D objects can be determined through a printability checker using machine learning. The prefabrication of slicing is accelerated through parallel slicing algorithms and the path planning is optimised intelligently. In the aspect of service and security, intelligent demand matching and resource allocation algorithms enable a Cloud service platform and evaluation model to provide clients with an on-demand service and access to a collection of shared resources. We also present three machine learning algorithms to detect product defects in the presence of cyber-attacks. Based on the reviews on various applications, printability with multi-indicators, reduction of complexity threshold, acceleration of prefabrication, real-time control, enhancement of security and defect detection for customised designs are seen of good opportunities for further research, especially in the era of Industry 4.0.
KW - Additive manufacturing
KW - Algorithm
KW - Artificial intelligence
KW - Attack detection
KW - Cloud service platform
KW - Machine learning
KW - Path planning
KW - Prefabrication
KW - Printability checking
KW - Real-time control
KW - SD Printing
KW - Security
UR - http://www.scopus.com/inward/record.url?scp=85040005814&partnerID=8YFLogxK
U2 - 10.23919/IConAC.2017.8082053
DO - 10.23919/IConAC.2017.8082053
M3 - 会议稿件
AN - SCOPUS:85040005814
T3 - ICAC 2017 - 2017 23rd IEEE International Conference on Automation and Computing: Addressing Global Challenges through Automation and Computing
BT - ICAC 2017 - 2017 23rd IEEE International Conference on Automation and Computing
A2 - Zhang, Jie
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd IEEE International Conference on Automation and Computing, ICAC 2017
Y2 - 7 September 2017 through 8 September 2017
ER -