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Call for Papers: Special Issue on Additive Manufacturing

Deadline for Paper Submission: 30 June 2018

Engineering is an international peer-reviewed academic journal sponsored by Chinese Academy of Engineering. The journal is published on a bimonthly basis in English. Online versions are available through

Additive Manufacturing (AM), also known as three-dimensional (3D) printing, refers to processes that allow for the direct fabrication of physical products from Computer-Aided Design (CAD) models through the repetitious deposition of materials layers. Compared with traditional manufacturing processes, AM provides many advantages, i.e. geometric flexibility, no assembly required, supply chain efficiencies, shortened time-to-market, environmental sustainability, etc.  These advantages make AM a major player in the next industrial revolution.

Recent emphasis on AM quality assurance has highlighted the need for systematic integration, management and analysis of the data/information associated with the AM process: from design, to simulation, to build plan, to process monitoring and control, to verification.  With this special issue, we hope to draw attention to additive manufacturing from ‘big data’ point of view, and bring together experts from various aspects of additive manufacturing to share their knowledge and perspective regarding AM data characteristics, integration, management, and analytics.


This issue will publish original research papers and review including but not limited to the following topics:

·         AM Design-to-Product Digital Implementation, i.e. AM digital thread, AM data structures and interfaces, AM Data management, AM Data traceability, etc.

·         AM data varieties, including Materials and Powders Characteristics, Topology Optimization, Energy Beam-Material Interactions: Modeling and Simulation, Virtual AM, Pre- and Post-Processing, etc.

·         AM Data Characteristics, i.e. Four V’s in AM Big Data: Volume, Velocity, Variety, and Veracity

·         AM Data Detection, Collection, Classification, Prioritization and Reduction, i.e. In-situ process monitoring and sensing technologies, Reduce terabytes of raw data to megabytes of useful information, Uncertainty Quantification (UQ), Verification and Validation (V&V)

·         AM Data Analytics and Machine Learning Techniques, i.e. Data mining algorithms for extracting useful information directly from physical measurements, High fidelity-data deficiency and incompleteness challenges, Data analytics that combines data produced from experiments, physical models and high-fidelity process simulations to identify the relation of process-microstructure-properties-performance

·         AM Data Standardization

·         Iot and Infrastructure

Paper Length:

·         6 pages in general

Editorial Board of the Special Issue:

Special Issue Editor-in-Chief
Bingheng Lu, Xi’an Jiaotong University, CHINA

Executive Editor-in-Chief
Lijuan Zhang, National Innovation Institute of Additive Manufacturing, CHINA

Special Issue Editorial Board
Litong Zhang, Northwestern Polytechnical University, CHINA
Zongben Xu, Xi’an Jiaotong University, CHINA
Huaming Wang, Beihang University, CHINA
Dichen Li, Xi’an Jiaotong University, CHINA
Weidong Huang, Northwestern Polytechnical University, CHINA
Yusheng Shi, Huazhong University of Science and Technology, CHINA
Lin Feng, Tsinghua University, CHINA
Dongdong Gu, Nanjing University of Aeronautics and Astronautics, CHINA
Ting Wen, National Innovation Institute of Additive Manufacturing, CHINA
Jay Lee, University of Cincinnati, USA
Jun Wei, Singapore Institute of Manufacturing Technology, SINGAPORE
Bo Li, Case Western Reserve University, USA
Reinhart Poprawe, Fraunhofer Institute for Laser Technology ILT, GERMANY
Wing Kam Liu, Northwestern University, USA
Richard Leach, University of Nottingham, UK   

ISSN Print: 2095-8099
ISSN Online: 2096-0026
CN: 10-1244/N
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