Process planning and contour-based error compensation for precision grinding of miniature scalpels

doi:10.1007/s40436-023-00458-w

Abstract

Abstract: Miniature scalpels are mainly used in microsurgeries such as ophthalmic and cardiovascular surgeries. The size of a miniature scalpel is only a few millimeters, and the precision of the blade shape is high, which makes production of miniature scalpels extremely difficult. This study proposes a new sharpening process for grinding miniature scalpels on a four-axis machine tool. A post-processing algorithm for a four-axis grinding machine based on a kinematics model is established. We then propose a corresponding parameter calibration method for the parameters used in the kinematics model. Because of possible errors in the parameter calibration, a contour-based error compensation method is proposed for accurate adjustments to the edge shape following grinding. This can solve the problem of large deviations between the actual edge shape after grinding and the ideal edge shape. The effectiveness of the proposed process planning and error compensation method is verified experimentally, and the grinding process parameters of the miniature scalpel are optimized to improve its surface processing quality. The sharpness of the optimized miniature scalpel is less than 0.75 N, and the blade shape is symmetrical, which meets the technical requirements of miniature scalpels.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-023-00458-w

Key words: Miniature scalpel, Sharpening, Kinematics, Error compensation, Grinding

Cheng Fan, Cao-Yang Xue, Jun Zhao, Wei Jiang, Wen-Ge Han, Lei Zhang, Li-Ning Sun. Process planning and contour-based error compensation for precision grinding of miniature scalpels[J]. Advances in Manufacturing, 2024, 12(1): 108-123.

TrendMD

References

1 Dundar R, Iynen I, Buyruk A (2021) Different approach for surgery of stapes: comparison microscopic and endoscopic approach. Am J Otolaryngol 43(4):103242. https://doi.org/10.1016/j.amjoto.2021.103242
2 Marsh DJ, Fox A, Grobbelaar AO et al (2015) Abdominoplasty and seroma: a prospective randomised study comparing scalpel and handheld electrocautery dissection. J Plast Reconstr Aesthet Surg 68(2):192–196
3 Milling R, Carolan D, Pafitanis G et al (2022) Microtools: a systematic review of validated assessment tools in microsurgery. J Plast Reconstruct Aesthet Surg 75(11):4013–4022
4 Sabin LE (2010) From fingers to miniaturization and robots: an overview of the history of surgical instrumentation. Perioper Nurs Clin 5(1):1–13
5 Fan C, Liu K, Wang Y et al (2023) Nano-indentation and nano-scratch of flexible intraocular lens material at the molecular scale. Acta Mech Sin 39(1):122331. https://doi.org/10.1007/s10409-022-22321-x
6 Fan C, Liu K, Chen Y et al (2022) A new modelling method of material removal profile for electrorheological polishing with a mini annular integrated electrode. J Mater Process Technol 305:117589. https://doi.org/10.1016/j.jmatprotec.2022.117589
7 Lu J, Wang X, Huang Y et al (2020) Fabrication and cutting performance of bionic micro-serrated scalpels based on the miscanthus leaves. Tribol Int 145:106162. https://doi.org/10.1016/j.triboint.2020.106162
8 McCarthy CT, Hussey M, Gilchrist MD (2007) On the sharpness of straight edge blades in cutting soft solids: part I—indentation experiments. Eng Fract Mech 74(14):2205–2224
9 Stępień P (2010) Micro-geometrical characteristics of the cutting edge as the intersection of two rough surfaces. Wear 269(3/4):249–261
10 Reilly GA, McCormack BAO, Taylor D (2004) Cutting sharpness measurement: a critical review. J Mater Process Technol 153:261–267
11 Belkin PN, Kusmanov SA, Parfenov EV (2020) Mechanism and technological opportunity of plasma electrolytic polishing of metals and alloys surfaces. Appl Surf Sci Adv 1:100016. https://doi.org/10.1016/j.apsadv.2020.100016
12 Prescher H, Ling MX, Bigdelle V et al (2021) Scalpel edge roughness affects post-transection peripheral nerve regeneration. Surg Open Sci 4:1–6
13 Schwenke H, Knapp W, Haitjema H et al (2008) Geometric error measurement and compensation of machines—an update. CIRP Ann 57(2):660–675
14 Eastwood S, Webb P (2009) Compensation of thermal deformation of a hybrid parallel kinematic machine. Robot Comput Integr Manuf 25(1):81–90
15 Luo G, Zou L, Wang Z et al (2021) A novel kinematic parameters calibration method for industrial robot based on Levenberg-Marquardt and differential evolution hybrid algorithm. Robot Comput Integr Manuf 71:102165. https://doi.org/10.1016/j.rcim.2021.102165
16 Xia C, Wang S, Ma C et al (2020) Crucial geometric error compensation toward gear grinding accuracy enhancement based on simplified actual inverse kinematic model. Int J Mech Sci 169:105319. https://doi.org/10.1016/j.ijmecsci.2019.105319
17 Ramesh R, Mannan MA, Poo AN (2000) Error compensation in machine tools—a review: part I: geometric, cutting-force induced and fixture-dependent errors. Int J Mach Tools Manuf 40(9):1235–1256
18 Zhu S, Ding G, Qin S et al (2012) Integrated geometric error modeling, identification and compensation of CNC machine tools. Int J Mach Tools Manuf 52(1):24–29
19 Xia H, Peng W, Ouyang X et al (2017) Identification of geometric errors of rotary axis on multi-axis machine tool based on kinematic analysis method using double ball bar. Int J Mach Tools Manuf 122:161–175
20 Ibaraki S, Iritani T, Matsushita T (2012) Calibration of location errors of rotary axes on five-axis machine tools by on-the-machine measurement using a touch-trigger probe. Int J Mach Tools Manuf 58(1):44–53
21 Wan A, Song L, Xu J et al (2018) Calibration and compensation of machine tool volumetric error using a laser tracker. Int J Mach Tools Manuf 124:126–133
22 Wei X, Miao E, Wang W et al (2019) Real-time thermal deformation compensation method for active phased array antenna panels. Precis Eng 60:121–129
23 Zhang T, Ye W, Shan Y (2016) Application of sliced inverse regression with fuzzy clustering for thermal error modeling of CNC machine tool. Int J Adv Manuf Technol 85(9):2761–2771
24 Fu G, Tao C, Xie Y et al (2021) Temperature-sensitive point selection for thermal error modeling of machine tool spindle by considering heat source regions. Int J Adv Manuf Technol 112(9):2447–2460
25 Wei X, Feng X, Miao E et al (2022) Sub-regional thermal error compensation modeling for CNC machine tool worktables. Precis Eng 73:313–325

[1]	Yu-Xiang Song, Chang-He Li, Zong-Ming Zhou, Bo Liu, Shubham Sharma, Yusuf Suleiman Dambatta, Yan-Bin Zhang, Min Yang, Teng Gao, Ming-Zheng Liu, Xin Cui, Xiao-Ming Wang, Wen-Hao Xu, Run-Ze Li, Da-Zhong Wang. Nanobiolubricant grinding: a comprehensive review [J]. Advances in Manufacturing, 2025, 13(1): 1-42.
[2]	Xiao-Fei Lei, Wen-Feng Ding, Biao Zhao, Chuan Qian, Zi-Ang Liu, Qi Liu, Dong-Dong Xu, Yan-Jun Zhao, Jian-Hui Zhu. Grinding of particle-reinforced metal matrix composite materials: current status and prospects [J]. Advances in Manufacturing, 2025, 13(1): 43-68.
[3]	Xiao-Fei Lei, Wen-Feng Ding, Biao Zhao, Dao-Hui Xiang, Zi-Ang Liu, Chuan Qian, Qi Liu, Dong-Dong Xu, Yan-Jun Zhao, Jian-Hui Zhu. Surface roughness model of ultrasonic vibration-assisted grinding GCr15SiMn bearing steel and surface topography evaluation [J]. Advances in Manufacturing, 2025, 13(1): 88-104.
[4]	Yu-Long Wang, Yan-Bin Zhang, Xin Cui, Xiao-Liang Liang, Run-Ze Li, Ruo-Xin Wang, Shubham Sharma, Ming-Zheng Liu, Teng Gao, Zong-Ming Zhou, Xiao-Ming Wang, Yusuf Suleiman Dambatta, Chang-He Li. High-speed grinding: from mechanism to machine tool [J]. Advances in Manufacturing, 2025, 13(1): 105-154.
[5]	Chong-Jun Wu, Fei Liu, Jia-Zhou Wen, Pei-Yun Xia, Steven Y. Liang. Grinding defect characteristics and removal mechanism of unidirectional C_f/SiC composites [J]. Advances in Manufacturing, 2025, 13(1): 211-228.
[6]	Jin-Hao Wang, Lu Wang, Han-Song Li, Ning-Song Qu. Electrochemical grinding of honeycomb seals using sodium dodecylbenzene sulfonate as an eco-friendly inhibitor: machining principle and performance evaluation [J]. Advances in Manufacturing, 2025, 13(1): 229-244.
[7]	Jia-Hao Liu, Dong-Zhou Jia, Chang-He Li, Yan-Bin Zhang, Ying Fu, Zhen-Lin Lv, Shuo Feng. Mechanism and machinability in novel electroplastic-assisted grinding ductile iron [J]. Advances in Manufacturing, 2025, 13(1): 245-263.
[8]	Zhao-Qing Zhang, Kai-Ning Shi, Yao-Yao Shi, Yi-Hui Song, Zhe He, Ya-Song Pu. Study on a probabilistic algorithm for the forming and 3D characterization of special-shaped surfaces under profile grinding [J]. Advances in Manufacturing, 2024, 12(2): 288-299.
[9]	Zi-Shan Ding, Yun-Hui Zhao, Miao-Xian Guo, Wei-Cheng Guo, Chong-Jun Wu, Steven Y. Liang. An iterative blending integrating grinding force model considering grain size and dislocation density evolution [J]. Advances in Manufacturing, 2023, 11(3): 428-443.
[10]	Ben-Kai Li, Biao Zhao, Wen-Feng Ding, Yu-Can Fu, Chang-He Li, Rong Wang, Yan-Jun Zhao. CBN grain wear and its effects on material removal during grinding of FGH96 powder metallurgy superalloy [J]. Advances in Manufacturing, 2023, 11(1): 21-38.
[11]	Bing Cao, Guo-Long Li, Alessandro Fortunato, Heng-Xin Ni. Continuous generating grinding method for beveloid gears and analysis of grinding characteristics [J]. Advances in Manufacturing, 2022, 10(3): 459-478.
[12]	Long-Xu Yao, Zhan-Qiang Liu, Qing-Hua Song, Bing Wang, Yu-Kui Cai. Effects of process parameters on periodic impact force exerting on cutting tool in ultrasonic vibration-assisted oblique turning [J]. Advances in Manufacturing, 2022, 10(3): 411-427.
[13]	Ming-Hui Fang, Tao Yu, Feng-Feng Xi. Effect of back pressure on the grinding performance of abrasive suspension flow machining [J]. Advances in Manufacturing, 2022, 10(1): 143-157.
[14]	Roshan Lal Virdi, Sukhpal Singh Chatha, Hazoor Singh. Performance evaluation of nanofluid-based minimum quantity lubrication grinding of Ni-Cr alloy under the influence of CuO nanoparticles [J]. Advances in Manufacturing, 2021, 9(4): 580-591.
[15]	Xiang-Long Zhu, Yu Li, Zhi-Gang Dong, Ren-Ke Kang, Shang Gao. Study into grinding force in back grinding of wafer with outer rim [J]. Advances in Manufacturing, 2020, 8(3): 361-368.