Tahmasbi Vahid, Ghoreishi Majid, Zolfaghari Mojtaba
1 Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran.
2 Department of Mechanical Engineering, Arak University, Arak, Iran.
Proc Inst Mech Eng H. 2017 Nov;231(11):1012-1024. doi: 10.1177/0954411917726098. Epub 2017 Aug 12.
The bone drilling process is very prominent in orthopedic surgeries and in the repair of bone fractures. It is also very common in dentistry and bone sampling operations. Due to the complexity of bone and the sensitivity of the process, bone drilling is one of the most important and sensitive processes in biomedical engineering. Orthopedic surgeries can be improved using robotic systems and mechatronic tools. The most crucial problem during drilling is an unwanted increase in process temperature (higher than 47 °C), which causes thermal osteonecrosis or cell death and local burning of the bone tissue. Moreover, imposing higher forces to the bone may lead to breaking or cracking and consequently cause serious damage. In this study, a mathematical second-order linear regression model as a function of tool drilling speed, feed rate, tool diameter, and their effective interactions is introduced to predict temperature and force during the bone drilling process. This model can determine the maximum speed of surgery that remains within an acceptable temperature range. Moreover, for the first time, using designed experiments, the bone drilling process was modeled, and the drilling speed, feed rate, and tool diameter were optimized. Then, using response surface methodology and applying a multi-objective optimization, drilling force was minimized to sustain an acceptable temperature range without damaging the bone or the surrounding tissue. In addition, for the first time, Sobol statistical sensitivity analysis is used to ascertain the effect of process input parameters on process temperature and force. The results show that among all effective input parameters, tool rotational speed, feed rate, and tool diameter have the highest influence on process temperature and force, respectively. The behavior of each output parameters with variation in each input parameter is further investigated. Finally, a multi-objective optimization has been performed considering all the aforementioned parameters. This optimization yielded a set of data that can considerably improve orthopedic osteosynthesis outcomes.
骨钻孔过程在骨科手术和骨折修复中非常突出。它在牙科和骨采样操作中也很常见。由于骨骼的复杂性和该过程的敏感性，骨钻孔是生物医学工程中最重要且最敏感的过程之一。骨科手术可通过机器人系统和机电一体化工具得到改善。钻孔过程中最关键的问题是过程温度意外升高（高于47°C），这会导致热骨坏死或细胞死亡以及骨组织局部灼伤。此外，对骨骼施加较大的力可能会导致骨折或骨裂，进而造成严重损伤。在本研究中，引入了一个作为刀具钻孔速度、进给速率、刀具直径及其有效相互作用函数的数学二阶线性回归模型，以预测骨钻孔过程中的温度和力。该模型可以确定保持在可接受温度范围内的最大手术速度。此外，首次通过设计实验对骨钻孔过程进行建模，并对钻孔速度、进给速率和刀具直径进行了优化。然后，使用响应面方法并应用多目标优化，将钻孔力最小化，以维持可接受的温度范围，同时不损坏骨骼或周围组织。此外，首次使用索博尔统计敏感性分析来确定过程输入参数对过程温度和力的影响。结果表明，在所有有效输入参数中，刀具转速、进给速率和刀具直径分别对过程温度和力的影响最大。进一步研究了每个输出参数随每个输入参数变化的行为。最后，考虑所有上述参数进行了多目标优化。这种优化产生了一组数据，可显著改善骨科骨合成效果。
