Feed rate control in robotic bone drilling process

Boiadjiev, Tony; Boiadjiev, George; Delchev, Kamen; Chavdarov, Ivan; Kastelov, Roumen

doi:10.1177/0954411920975890

Cited by 15 publications

(3 citation statements)

References 30 publications

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“…At automatic drilling the reasons for the negative final result caused by dulled drill bits should be minimized by feed rate control [30]. Also, it successfully solves the problem of higher drill bit penetration after the end of second cortex.…”

Section: Long Bone Fracturesmentioning

confidence: 99%

Orthopedic Bone Drilling Robot ODRO: Basic Characteristics and Areas of Applications

Boiadjiev¹,

Boiadjiev²,

Delchev³

et al. 2021

Latest Developments in Medical Robotics Systems

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The orthopedic manipulation “bone drilling” is the most executed one in the orthopedic surgery concerning the operative treatment of bone fractures. The drilling process is characterized by a number of input and output parameters. The most important input parameters are the feed rate [mm/s] and the drill speed [rpm]. They play significant role for the final result (the output parameters): thermal and mechanical damages of the bone tissue as well as hole quality. During the manual drilling these parameters are controlled by the surgeon on the base of his practical skills. But the optimal results of the manipulations can be assured only when the input parameters are under control during an automatic execution of the drilling process. This work presents the functional characteristics of the handheld robotized system ODRO (Orthopedic Drilling Robot) for automatic bone drilling. Some experimental results are also shown. A comparison is made between the similar systems which are known in the literature, some of which are available on the market. The application areas of ODRO in the orthopedic surgery practice are underlined.

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Section: Long Bone Fracturesmentioning

confidence: 99%

Orthopedic Bone Drilling Robot ODRO: Basic Characteristics and Areas of Applications

Boiadjiev¹,

Boiadjiev²,

Delchev³

et al. 2021

Latest Developments in Medical Robotics Systems

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“…Various instruments were used to result in the minimum temperature. Boiadjiev et al, 19 with a new algorithm, controlled the feed rate during different stages of the bone drilling process (an original feed rate control algorithm) using the orthopedic drilling robot and solved the main problems of bone drilling in orthopedics surgery.…”

Section: Introductionmentioning

confidence: 99%

Intelligent temperature modeling in robotic cortical bone milling process based on teaching-learning-based optimization algorithm

Tahmasbi

Rabiee

2022

Proc Inst Mech Eng H

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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.

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“…In the research studies mentioned above, the main reason for achieving drilling depth estimation is that the bone is characterised by multiple layers, and each layer has different mechanical properties: the cortical bone forms the external layer of the bone, while the cancellous bone is the inner layer of it; the cortical bone is much harder and stronger than the cancellous bone. Boiadjiev et al [22][23][24] developed a handheld drilling robot to control the feed rate, which had translational and rotational degrees of freedom. Since the robot must be kept in contact with the bone, the drilling depth is equal to the amplitude of the translational motion.…”

Section: Introductionmentioning

confidence: 99%

Vibration‐based drilling depth estimation of bone

Dai

Armand

2021

Robotics Computer Surgery

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Drilling is one of the most common forms of tissue removal procedures, and drilling to a desired depth contributes to avoid injury to the soft tissue beyond and ensure implant stability. The deformation of the human musculoskeletal system has been a common problem in many drilling processes, making it difficult to achieve accurate estimation of the drilling depth. To remedy this problem, a dynamic model is presented to describe the relationship between the axial vibration of the drill and the feed rate. During drilling process, the amplitude of the main harmonic is estimated from the high‐frequency component of the acceleration signal, while the short‐time integral of the low‐frequency part is calculated. Both the initial contact of the drilling tool to the bone and breakthrough are identified by comparing either the harmonic amplitude or the short‐time integral. The harmonic amplitude is mapped to the data from a non‐contact position sensor tracking the feed rate of the drill. Multiple drilling experiments on both a handheld device and a robotic cutting system demonstrated the effectiveness, stability and accuracy of the method when estimating depth. The mean maximum error for drilling depth estimation is less than 15% of the simulated bone thickness when using the handheld device, while the mean maximum error is less than 5% for the robotic cutting system.

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Feed rate control in robotic bone drilling process

Cited by 15 publications

References 30 publications

Orthopedic Bone Drilling Robot ODRO: Basic Characteristics and Areas of Applications

Orthopedic Bone Drilling Robot ODRO: Basic Characteristics and Areas of Applications

Intelligent temperature modeling in robotic cortical bone milling process based on teaching-learning-based optimization algorithm

Vibration‐based drilling depth estimation of bone

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