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

Cheng Fan , Cao-Yang Xue , Jun Zhao , Wei Jiang , Wen-Ge Han , Lei Zhang , Li-Ning Sun

Advances in Manufacturing ›› 2024, Vol. 12 ›› Issue (1) : 108 -123.

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Advances in Manufacturing ›› 2024, Vol. 12 ›› Issue (1) : 108 -123. DOI: 10.1007/s40436-023-00458-w
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Process planning and contour-based error compensation for precision grinding of miniature scalpels

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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.

Keywords

Miniature scalpel / Sharpening / Kinematics / Error compensation / Grinding

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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. Advances in Manufacturing, 2024, 12(1): 108-123 DOI:10.1007/s40436-023-00458-w

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References

[1]

Dundar R, Iynen I, Buyruk A. Different approach for surgery of stapes: comparison microscopic and endoscopic approach. Am J Otolaryngol, 2021, 43(4): 103242.

[2]

Marsh DJ, Fox A, Grobbelaar AO, et al. Abdominoplasty and seroma: a prospective randomised study comparing scalpel and handheld electrocautery dissection. J Plast Reconstr Aesthet Surg, 2015, 68(2): 192-196.

[3]

Milling R, Carolan D, Pafitanis G, et al. Microtools: a systematic review of validated assessment tools in microsurgery. J Plast Reconstruct Aesthet Surg, 2022, 75: 4013-4022.

[4]

Sabin LE. From fingers to miniaturization and robots: an overview of the history of surgical instrumentation. Perioper Nurs Clin, 2010, 5(1): 1-13.

[5]

Fan C, Liu K, Wang Y, et al. Nano-indentation and nano-scratch of flexible intraocular lens material at the molecular scale. Acta Mech Sin, 2023, 39(1): 122331.

[6]

Fan C, Liu K, Chen Y, et al. A new modelling method of material removal profile for electrorheological polishing with a mini annular integrated electrode. J Mater Process Technol, 2022, 305: 117589.

[7]

Lu J, Wang X, Huang Y, et al. Fabrication and cutting performance of bionic micro-serrated scalpels based on the miscanthus leaves. Tribol Int, 2020, 145: 106162.

[8]

McCarthy CT, Hussey M, Gilchrist MD. On the sharpness of straight edge blades in cutting soft solids: part I—indentation experiments. Eng Fract Mech, 2007, 74(14): 2205-2224.

[9]

Stępień P. Micro-geometrical characteristics of the cutting edge as the intersection of two rough surfaces. Wear, 2010, 269(3/4): 249-261.

[10]

Reilly GA, McCormack BAO, Taylor D. Cutting sharpness measurement: a critical review. J Mater Process Technol, 2004, 153: 261-267.

[11]

Belkin PN, Kusmanov SA, Parfenov EV. Mechanism and technological opportunity of plasma electrolytic polishing of metals and alloys surfaces. Appl Surf Sci Adv, 2020, 1: 100016.

[12]

Prescher H, Ling MX, Bigdelle V, et al. Scalpel edge roughness affects post-transection peripheral nerve regeneration. Surg Open Sci, 2021, 4: 1-6.

[13]

Schwenke H, Knapp W, Haitjema H, et al. Geometric error measurement and compensation of machines—an update. CIRP Ann, 2008, 57(2): 660-675.

[14]

Eastwood S, Webb P. Compensation of thermal deformation of a hybrid parallel kinematic machine. Robot Comput Integr Manuf, 2009, 25(1): 81-90.

[15]

Luo G, Zou L, Wang Z, et al. A novel kinematic parameters calibration method for industrial robot based on Levenberg-Marquardt and differential evolution hybrid algorithm. Robot Comput Integr Manuf, 2021, 71: 102165.

[16]

Xia C, Wang S, Ma C, et al. Crucial geometric error compensation toward gear grinding accuracy enhancement based on simplified actual inverse kinematic model. Int J Mech Sci, 2020, 169: 105319.

[17]

Ramesh R, Mannan MA, Poo AN. Error compensation in machine tools—a review: part I: geometric, cutting-force induced and fixture-dependent errors. Int J Mach Tools Manuf, 2000, 40(9): 1235-1256.

[18]

Zhu S, Ding G, Qin S, et al. Integrated geometric error modeling, identification and compensation of CNC machine tools. Int J Mach Tools Manuf, 2012, 52(1): 24-29.

[19]

Xia H, Peng W, Ouyang X, et al. 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, 2017, 122: 161-175.

[20]

Ibaraki S, Iritani T, Matsushita T. 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, 2012, 58(1): 44-53.

[21]

Wan A, Song L, Xu J, et al. Calibration and compensation of machine tool volumetric error using a laser tracker. Int J Mach Tools Manuf, 2018, 124: 126-133.

[22]

Wei X, Miao E, Wang W, et al. Real-time thermal deformation compensation method for active phased array antenna panels. Precis Eng, 2019, 60: 121-129.

[23]

Zhang T, Ye W, Shan Y. Application of sliced inverse regression with fuzzy clustering for thermal error modeling of CNC machine tool. Int J Adv Manuf Technol, 2016, 85(9): 2761-2771.

[24]

Fu G, Tao C, Xie Y, et al. Temperature-sensitive point selection for thermal error modeling of machine tool spindle by considering heat source regions. Int J Adv Manuf Technol, 2021, 112(9): 2447-2460.

[25]

Wei X, Feng X, Miao E, et al. Sub-regional thermal error compensation modeling for CNC machine tool worktables. Precis Eng, 2022, 73: 313-325.

Funding

Key Technologies Research and Development Program http://dx.doi.org/10.13039/501100012165(2021YFB3400300)

National Natural Science Foundation of China http://dx.doi.org/10.13039/501100001809(51975392)

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