数据驱动的智能增材制造方法

Chenxi Tian, Tianjiao Li, Jenniffer Bustillos, Shonak Bhattacharya, Talia Turnham, J. Yeo, A. Moridi
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引用次数: 13

摘要

最新的工业革命,工业4.0,是由数字制造的出现驱动的,尤其是增材制造(AM)技术。增材制造中材料和结构的同时成形,拓宽了材料和结构的设计空间。这种扩展的设计空间在创造改进的工程材料和产品方面具有巨大的潜力,吸引了学术界和工业界越来越多的兴趣。这种日益增长的兴趣的一个主要方面反映在对数据驱动工具的不断适应上,这些工具加速了对增材制造中巨大设计空间的探索。本文回顾了数据驱动工具在增材制造各个方面的集成,从增材制造中的材料设计(即均质和复合材料设计)到增材制造的结构设计(即拓扑优化)。还讨论了利用机器学习优化增材制造刀具路径,以生产具有最佳材料和结构的最佳质量增材制造产品。最后,对AM和数据驱动方法的整体集成框架的未来发展进行了展望。
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Data‐Driven Approaches Toward Smarter Additive Manufacturing
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.
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