Temperature Prediction Model of Bone Material Micro-milling Cutting Edges

doi:10.12068/j.issn.1005-3026.2020.11.015

Abstract

Abstract: Due to the brittleness of bone materials and micro-milling characteristics， there is no effective theoretical prediction model for bone milling cutting edge temperature currently. A novel temperature prediction model of bone material micro-milling cutting edges was proposed. Considering the characteristics of generation， transmission and distribution of heat in bone material high-speed micro-milling， the prediction model was divided into two sub-models:milling heat generation model and cutting edge temperature model. Then， a bone milling experimental platform was established， and the cutting edge temperatures during the milling process were collected by a thermal imager. Finally， by comparing the experimental results with the theoretical predictions， the rationality of the built temperature prediction model of bone material micro-milling cutting edges was verified. The model is helpful to guide doctors to select appropriate processing parameters according to different requirements， and assist engineers in tool optimization design.

Key words: bone material, temperature prediction, micro-milling, cutting edge temperature, static plane heat source

CLC Number:

TG54

LIU Yu， CHEN Qi-sen， DONG Qiu-shi. Temperature Prediction Model of Bone Material Micro-milling Cutting Edges[J]. Journal of Northeastern University Natural Science, 2020, 41(11): 1615-1622.

References

[1]Lee J，Rabin Y，Ozdoganlar O B.A new thermal model for bone drilling with applications to orthopaedic surgery［J］.Medical Engineering & Physics，2011，33(10):1234-1244.
[2]Tawy G F，Rowe P J，Riches P E.Thermal damage done to bone by burring and sawing with and without irrigation in knee arthroplasty［J］.Journal of Arthroplasty，2016，31(5):1102-1108.
[3]Denis K，Van Ham G，Vander S J，et al.Influence of bone milling parameters on the temperature rise，milling forces and surface flatness in view of robot-assisted total knee arthroplasty［J］.International Congress Series，2001，1230:300-306.
[4]Sugita N，Warisawa S，Mitsuishi M.A cutting temperature study of bone machining for orthopaedic robotic surgery［C］//Proceedings of the 20th Annual Meeting of the ASPE.Norfolk，2005:142-145.
[5]Sugita N，Ishii K，Sui J，et al.Multi-grooved cutting tool to reduce cutting force and temperature during bone machining［J］.CIRP Annals，2014，63(1):101-104.
[6]Shin H C，Yoon Y S.Bone temperature estimation during orthopaedic round bur milling operations［J］.Journal of Biomechanics，2006，39(1):33-39.
[7]Sugita N，Osa T，Mitsuishi M.Analysis and estimation of cutting-temperature distribution during end milling in relation to orthopedic surgery［J］.Medical Engineering & Physics，2009，31(1):101-107.
[8]Liao Z，Axinte D，Gao D.On modelling of cutting force and temperature in bone milling［J］.Journal of Materials Processing Technology，2019，266:627-638.
[9]Nasri A，Tsoumarev O，Ben S M，et al.Numerical simulation of temperature distribution in a 3D ball end milling model［J］.International Journal of Machining and Machinability of Materials，2011，9(3/4):209-222.
[10]Al-Abdullah K I A，Abdi H，Lim C P，et al.Force and temperature modelling of bone milling using artificial neural networks［J］.Measurement，2018，116:25-37.
[11]Feldmann A，Ganser P，Nolte L，et al.Orthogonal cutting of cortical bone:temperature elevation and fracture toughness［J］.International Journal of Machine Tools and Manufacture，2017，118/119:1-11.
[12]Berliner E M，Krainov V P.Analytic calculations of the temperature field and heat flows on the tool surface in metal cutting due to sliding friction［J］.Wear，1991，143(2):379-395.
[15]关守平，房少纯.一种新型的区间-粒子群优化算法［J］.东北大学学报(自然科学版)，2012，33(10):1381-1384.(Guan Shou-ping，Fang Shao-chun.A new interval particle swarm optimization algorithm［J］.Journal of Northeastern University(Natural Science)，2012，33(10):1381-1384.)(上接第1601页)
[6]Kim W S，Raman S.On the selection of flatness measurement points in coordinate measuring machine inspection［J］.International Journal of Machine Tools and Manufacture，2000，40(3):427-443.
[7]Li P，Ding X M，Tan J B，et al.A hybrid method based on reduced constraint region and convex-hull edge for flatness error evaluation［J］.Precision Engineering，2016，45:168-175.
[8]Hwang J，Park C H，Gao W，et al.A three-probe system for measuring the parallelism and straightness of a pair of rails for ultra-precision guideways［J］.International Journal of Machine Tools & Manufacture，2007，47(7/8):1053-1058.
[9]Bhattacharya J C.Measurement of parallelism of the surfaces of a transparent sample［J］.Optics and Lasers in Engineering，2001，35(1):27-31.
[10]Lee K I，Shin D H，Yang S H.Parallelism error measurement for the spindle axis of machine tools by two circular tests with different tool lengths［J］.International Journal of Advanced Manufacturing Technology，2017，88:2883-2887.
[11]Maurizio V，Bertozzi R.Parallelism error characterization with mechanical and interferometric methods［J］.Optics and Lasers in Engineering，2007，45:719-722.
[15]关守平，房少纯.一种新型的区间-粒子群优化算法［J］.东北大学学报(自然科学版)，2012，33(10):1381-1384.(Guan Shou-ping，Fang Shao-chun.A new interval particle swarm optimization algorithm［J］.Journal of Northeastern University(Natural Science)，2012，33(10):1381-1384.)