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Information feedback self-adaptive harmony search algorithm for the bovine cortical bone vibration-assisted drilling optimization

•An improved algorithm is proposed to the bovine cortical bone VAD optimization.•The fluctuation mechanism is applicable to HMCR, PAR, and BW.•The deviation is within 7.63%, and the thrust force decreased by 26.5%–33.2%.•The optimized VAD thrust force decreased by 20.19%. In orthopedics and surgery,...

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Published in:Measurement : journal of the International Measurement Confederation 2020-01, Vol.149, p.107020, Article 107020
Main Authors: Li, Shaomin, Zhang, Deyuan, Shao, Zhenyu, Tang, Hui
Format: Article
Language:English
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Summary:•An improved algorithm is proposed to the bovine cortical bone VAD optimization.•The fluctuation mechanism is applicable to HMCR, PAR, and BW.•The deviation is within 7.63%, and the thrust force decreased by 26.5%–33.2%.•The optimized VAD thrust force decreased by 20.19%. In orthopedics and surgery, bone drilling is an important operation. Increasing demands call for bone drilling force prediction and minimization. Vibration-assisted drilling (VAD) provides an opportunity to reduce the force. In the study, the information feedback self-adaptive harmony search (IFSHS) algorithm is presented for the thrust force minimization in bovine cortical bone VAD. The IFSHS improves the drilling parameters which leads to the optimal cutting state. The dynamic searching parameters, optimization parameters evaluation, and evaluation information feedback are applied to search the optimal solution. This force model was developed and employed with IFSHS. The VAD drilling experiments and parameters analysis are carried out. Experimental results have shown that the VAD has the potential to reduce the thrust force. Compared with conventional drilling (CD), the thrust force decreased by 18.4%–33.2% under suitable VAD parameters. Compared with the results of the original VAD parameters, the optimized thrust force decreased by 20.19%.
ISSN:0263-2241
1873-412X
DOI:10.1016/j.measurement.2019.107020