Data‐Driven Approaches Toward Smarter Additive Manufacturing

Advanced Intelligent Systems

Shonak Bhattacharya¹  Talia Turnham¹  Chenxi Tian¹  Atieh Moridi¹  Tianjiao Li¹  Jingjie Yeo¹  Jenniffer Bustillos¹ 

[1] Mechanical and Aerospace Engineering Cornell University 124 Hoy Rd., Upson Hall Ithaca NY 14850 USA;

关键词: additive manufacturing;    artificial intelligence;    machine learning;    material designs;    tool paths;    topology optimizations;   

DOI  :  10.1002/aisy.202100014

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown