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Research Article

Collective intelligence-driven 3D printing factory for social manufacturing: implementing a testbed for industrial application

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Received 01 Jun 2023, Accepted 14 Mar 2024, Published online: 01 Apr 2024
 

ABSTRACT

The emergence of 3D printing technology has imbued the mass customization production model with novel implications. Concurrently, investigations into social manufacturing (SocialM) and collective intelligence present a fresh challenge for the 3D printing industry in their pursuit of realizing customized mass production. However, there is still a lack of investigation on the technical implementation and application scenarios of SocialM, and it hinders the development of SocialM from theory to industrial application. To mitigate this gap, firstly a five-layer framework based on collective intelligence for the configuration of design-production-service integrated 3D printing factory is established, together with the key enabling techniques that support the configuration and operation of the factory from social interaction software perspective and cyber-physical-social interconnection perspective. Secondly, the running logic of the 3D printing factory is demonstrated, and it starts from order generation to order completion. Thirdly, a testbed of the 3D printing factory is built, which contains both social interaction software and physical production hardware environments, and a production order of a 3D printed robotic arm is used to verify the feasibility of the testbed and the configuration and operation theories of SocialM. The work in this paper provides technical solutions for the industrial application of SocialM.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contribution

Haoliang Shi: conceptualization, methodology, writing – original draft. Maolin Yang: review and editing. Inno Lorren Désir Makanda: experiments and software. Wei Guo: supervising and editing. Pingyu Jiang: conceptualization, funding acquisition, supervising and editing.

Additional information

Funding

This research is supported by the National Natural Science Foundation of China (NSFC) with Grant No. 52375512 and 51975464.

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