|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 http://www.journals.elsevier.com/engineering/.
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
· 6 pages in general
Editorial Board of the Special Issue:
Special Issue Editor-in-Chief
Special Issue Editorial