Smart additive manufacturing: Current artificial intelligence-enabled methods and future perspectives
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https://doi.org/10.1007/s11431-020-1581-2
•Review•
Smart additive manufacturing: Current artificial intelligenceenabled methods and future perspectives 1
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WANG YuanBin , ZHENG Pai , PENG Tao , YANG HuaYong & ZOU Jun
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State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China; Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong 999077, China Received December 10, 2019; accepted March 25, 2020; published online May 15, 2020
Additive manufacturing (AM) has been increasingly used in production. Because of its rapid growth, the efficiency and robustness of AM-based product development processes should be improved. Artificial intelligence (AI) is a powerful tool that has outperformed humans in numerous complex tasks. AI-enabled intelligent agents can reduce the workforce required to scale up AM production and achieve higher resource utilization efficiency. This study provides an introduction of AI techniques. Then, the current development of AI-enabled AM product development is investigated. Existing intelligent agents are used for problems in product design, process design and production stages. Based on the review, current research gaps and future research directions are identified. To guide future development of more efficient and comprehensive intelligent agents, a smart AM framework based on cloud-edge computing is proposed. Global consideration can be realized in the cloud environment, and a fast response can be achieved at the edge nodes. additive manufacturing, artificial intelligence, product development, cloud-edge computing Citation:
Wang Y B, Zheng P, Peng T, et al. Smart additive manufacturing: Current artificial intelligence-enabled methods and future perspectives. Sci China Tech Sci, 2020, 63, https://doi.org/10.1007/s11431-020-1581-2
1 Introduction The development of additive manufacturing (AM) technologies, also known as 3D printing, has increased in recent years [1,2]. Because 3D printing can produce geometrically complex parts without accessories (e.g., fixtures and molds) [3,4], it has been applied to a wide range of applications including aerospace, medicine, footwear, etc. [5]. AM can realize mass customization and personalization without cost penalties. The customization process requires knowledge and experience to implement suitable adjustments. Currently, the success of AM highly relies on the users’ knowledge and experience to make the right decisions in the product development process [6,7]. As a complex process, product development involves multiple stages, including design,
*Corresponding author (email: [email protected])
process planning, production planning, and process monitoring. These stages are highly interrelated, and the decision makers should have sufficient knowledge of the rules of each stage. Unsuitable decisions may dramatically influence the results of AM. With the rapid growth of the AM market, the accessibility of this knowledge should be increased to
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