Abstract
Antivibration performance is one of the crucial indicators of high precision machinery, which contributes to energy efficiency simultaneously. Toward this aim, an antivibration and energy efficiency design method for large stroke additive manufacturing (AM) based on dynamic trajectory adaption (DTA) is put forward. The kinematic model of mechanical vibration in the AM system is constructed firstly. The energy consumption model of mechanical vibration whose major component is servo energy due to frequent movements along the vested trajectory is proposed, which spontaneously inspires DTA to focus on infill pattern adaption and related trajectory optimization. By physical experiment across desktop-level, large stroke, and non-contact optical profilometer measurement, the crucial metrics of antivibration and energy efficiency including mean trajectory deviation and total energy consumption are both optimized by the DTA method with an improvement rate as high as − 15.65% and − 12.28% respectively. The results show that the proposed DTA method can adapt to the layered cross-section with more optimal fusing parameters which contributes to the antivibration and energy efficiency design of the complex product for large stroke AM.
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The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.
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Funding
This research was funded by the National Natural Science Foundation of China (Nos. 51935009; 51775494;51821093), Zhejiang key research and development project (Nos. 2019C01141; LGG21E050020), National key research and development project of China (No. 2018YFB1700701), Fundamental Research Funds for the Zhejiang Provincial Universities (No. 2021XZZX008).
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KW: methodology, experiment design, and paper writing. JX: validation. SZ: supervision. JT: funding acquisition. All authors have read and agreed to the published version of the manuscript.
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Wang, K., Xu, J., Zhang, S. et al. Antivibration and energy efficiency design for large stroke additive manufacturing based on dynamic trajectory adaption. Int J Adv Manuf Technol 118, 3015–3034 (2022). https://doi.org/10.1007/s00170-021-08072-5
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DOI: https://doi.org/10.1007/s00170-021-08072-5