RESUMEN
Bone milling is one of the most important and sensitive biomechanical processes in the field of medical engineering. This process is used in orthopedic surgery, dentistry, treatment of fractures, and bone biopsy. The use of automatic numerical control surgical milling machines has revolutionized this procedure. The most important possible complication in bone surgery is the rise of temperature above permissible range and the formation of thermal necrosis or cell death in bone tissue. In the present article, a study on the design of experiment is first conducted by considering the rotational speed of the utilized tool, feed rate, depth of cut and tool diameter as the most important input factors of this process. Then, an adaptive neuro-fuzzy inference system (ANFIS) is developed to model and estimate the temperature behavior in the process of robotic bone milling. The optimal parameters of the ANFIS system are obtained using teaching-learning-based optimization (TLBO) algorithm. In order to model the process behavior, the results of experiments are used for the training (75% of the data) and testing (25% of the data) of the adaptive inference system. The accuracy of the obtained model is investigated via different plots, and statistical criteria, including root mean square error, correlation coefficient, and mean absolute percentage error. The findings show that the ANFIS network successfully predicts the temperature in the automatic bone milling process. In addition, the network error in estimating the temperature of the automatic bone milling process in the training and test section is equal to 1.74% and 3.17%, respectively.
Asunto(s)
Lógica Difusa , Procedimientos Quirúrgicos Robotizados , Algoritmos , Hueso Cortical , Redes Neurales de la Computación , Procedimientos Quirúrgicos Robotizados/métodos , TemperaturaRESUMEN
Machining and cutting of cortical bones are very common and important in the field of orthopedic surgeries. Considerable advances in bone machining are obtained by using computer numerical control machines and automatic surgery robots but still, researches are needed to investigate the effects of machining parameters in bone machining. In this article, for the first time, the effect of geometrical parameters of the single-tip tool on cortical bone machining is studied. The machining parameters included in the investigation are rake angle, back rake angle and side cutting edge angle and the response surface methodology is used to analyze the obtained surface quality according to a second-order regression model. The sensitivity of surface quality to the input parameters was measured by applying Sobol sensitivity analysis and the results are optimized by the Derringer algorithm. Finally, the optimum tool is determined as 15° rake angle, -5° back rake angle and 30° side cutting edge angle. Furthermore, the sensitivity of the surface quality to the input parameters is determined as 52% for rake angle, 31% for side cutting edge angle and 17% for back rake angle.
Asunto(s)
Hueso Cortical/cirugía , Fenómenos Mecánicos , Modelos Estadísticos , Procedimientos Ortopédicos , Animales , Bovinos , Fémur/cirugía , Propiedades de SuperficieRESUMEN
Bone drilling process is a prominent step of internal fixation in orthopedic surgeries. Process forces, leading to chip production, produce heat in the vicinity of the drilled bore and increase the probability of necrosis phenomenon. In this article, an analytical model to predict process temperature is presented based on Sui and Sugita model. This heat transfer model is the combination of a heat equilibrium equation for tool-chip system and a heat distribution equation for the bone itself where heat generation in tool's tip is due to cutting frictional forces. In an analytical model, it is possible to use material properties of the bone and geometry of the tool; therefore, the calibration test is not necessary. In order to validate analytical model, experiments were done using bovine bone. Using response surface method, a second-order linear regression mathematical model is derived using experimental results. The effect of each individual parameter as well as their interactions on the output of the process was investigated. Within the range of the parameters studied in this article, with an increase in rotational speed, process temperature boosts up. Effect of feed rate is complicated due to the tool-bone contact time issue. While higher temperature is achieved in lower feed rates because of higher tool-bone contact time but higher temperature is observed with high feed rates due to an increase in force and friction. Optimized combination of the parameters to minimize temperature of 35.6 °C is tool diameter of 2.5 mm, rotational speed of 500 r/min and feed rate of 30 mm/min. Good correlation was observed between analytical and experimental results.
Asunto(s)
Hueso Cortical/cirugía , Procedimientos Ortopédicos , Temperatura , RotaciónRESUMEN
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.