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A feedback-based print quality improving strategy for FDM 3D printing: an optimal design approach

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Abstract

Fused deposition modelling (FDM) 3D printing, as a supporting technology in social manufacturing and cloud manufacturing, is a rapidly growing technology in the era of industry 4.0. It produces objects with a layer-by-layer material accumulation technique. However, qualitative uncertainties are the common challenges yet. In order to assure print quality, studying the error causing parameters and minimizing their effects are important. This paper presents a feedback-based error compensation strategy, which integrates a fuzzy inference system and a grey wolf optimization algorithm. The objectives are twofold. First, the possible errors in FDM 3D printing are discussed in detail and optimal error causing parameters are obtained in percentage. This is used to understand the effects of the printing errors in every phase of the 3D printing process. From the nine optimization configuration trials used, Config-6 that has 100 number of iterations and 60 wolves is selected due to its higher convergence speed and best fitness value. The integral absolute error (IAE) is used as an objective function and the global minimum is achieved in the iteration interval \(\left [ {86,100} \right ]\). The outputs of this optimization problem are used to achieve the next objective. Second, a closed-loop quality monitoring approach comprising of inner-loops and an outer-loop is taken. The three inner-loops are used to monitor the errors during pre-printing, printing, and post-printing, respectively. The outer-loop, on the other hand, is responsible for monitoring the aggregated errors in all the three 3D printing phases. The error compensation system simulation in Matlab is run for 10 s, and the results show that the “normal” range deformation factors are reached within less than 2 s for the inner-loops, whereas the outer-loop deformation factor is achieved within 2 s. The responses are within the acceptable time range.

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Funding

This work was supported in part by the National Key Research Development Program of China (No. 2021YFE0116300); National Natural Science Foundation of China under Grants 61773382, U1909204, 61773381, 61872365 and 61701471; CAS Key Technology Talent Program (Zhen Shen); the Scientific Instrument Developing Project of the Chinese Academy of Sciences, Grant No. YZQT014; Guangdong Basic and Applied Basic Research Foundation under Grant 2021B1515140034; Foshan Science and Technology Innovation Team Project (2018IT100142); Youth Foundation of the State Key Laboratory for Management and Control of Complex Systems (Y6S9011F1G); the Collaborative Innovation Center of Intelligent Green Manufacturing Technology and Equipment, Shandong (No. IGSD-2020-014); Dongguan’s Innovation Talents Project (Gang Xiong)

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Authors and Affiliations

  1. The State Key Laboratory for Management and Control of Complex Systems, Beijing Engineering Research Center of Intelligent Systems and Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Tariku Sinshaw Tamir, Gang Xiong, Qihang Fang, Xisong Dong, Zhen Shen & Fei-Yue Wang

  2. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China

    Tariku Sinshaw Tamir & Qihang Fang

  3. Guangdong Engineering Center of 3D Printing and Intelligent Manufacturing, Cloud Computing Center, Chinese Academy of Sciences, Dongguan, 523808, China

    Gang Xiong & Xisong Dong

  4. Intelligent Manufacturing Center, Qingdao Academy of Intelligent Industries, Qingdao, 266109, China

    Zhen Shen

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  1. Tariku Sinshaw Tamir
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Correspondence to Zhen Shen.

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Tamir, T.S., Xiong, G., Fang, Q. et al. A feedback-based print quality improving strategy for FDM 3D printing: an optimal design approach. Int J Adv Manuf Technol 120, 2777–2791 (2022). https://doi.org/10.1007/s00170-021-08332-4

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