Open AccessData‐Driven Approaches Toward Smarter Additive Manufacturing
Author(s) -
Tian Chenxi,
Li Tianjiao,
Bustillos Jenniffer,
Bhattacharya Shonak,
Turnham Talia,
Yeo Jingjie,
Moridi Atieh
Publication year - 2021
Publication title -
advanced intelligent systems
Language(s) - English
Resource type - Journals
ISSN - 2640-4567
DOI - 10.1002/aisy.202100014
Subject(s) - adaptation (eye) , quality (philosophy) , space (punctuation) , computer science , manufacturing engineering , homogeneous , topology optimization , space industry , industrial engineering , systems engineering , engineering , mathematics , philosophy , physics , structural engineering , epistemology , combinatorics , finite element method , optics , operating system
The latest industrial revolution, Industry 4.0, is driven by the emergence of digital manufacturing and, most notably, additive manufacturing (AM) technologies. The simultaneous material and structure forming in AM broadens the material and structural design space. This expanded design space holds a great potential in creating improved engineering materials and products that attract growing interests from both academia and industry. A major aspect of this growing interest is reflected in the increased adaptation of data‐driven tools that accelerate the exploration of the vast design space in AM. Herein, the integration of data‐driven tools in various aspects of AM is reviewed, from materials design in AM (i.e., homogeneous and composite material design) to structure design for AM (i.e., topology optimization). The optimization of AM tool path using machine learning for producing best‐quality AM products with optimal material and structure is also discussed. Finally, the perspectives on the future development of holistically integrated frameworks of AM and data‐driven methods are provided.