Computational Modeling of Bone Fracture Healing Under Different Initial Conditions and Mechanical Load.

OBJECTIVE: Computational model of bone healing can replace animal experiments to study the parameters affecting the bone healing process, thus reducing the damage to experimental animals and saving a lot of time. We propose a computational model for continuous simulation of four phases of bone healing to study the effects of mechanical environmental and biological factors, including initial conditions at the fracture site, mechanical stimulus loading, and vascular growth rate. METHODS: A finite element model of mechanobiological fracture healing containing several pre-determined variables was developed for bone healing after fracture in sheep, which included many relevant parameters and biological effects during fracture healing, such as the effects of mechanical environment, blood supply level in the local fracture area, cell migration and diffusion, and resorption effects of fracture healing. The effects of several parameters on indices such as Young's modulus of the callus during bone healing were obtained by simulation. RESULTS: The initial geometry of the healing tissue and mechanical loading had the greatest effect on fracture healing, and different preset values were likely to cause delayed or non-healing fractures. Changed initial tissue properties of the healing tissue showed a nonlinear effect on fracture healing rather than a linear delay or advancement. Parameters related to angiogenesis had a greater effect on fracture healing compared to those related to cell migration. CONCLUSION: This paper quantified the effect of fracture healing pre-determined variables on fracture healing to better understand the application of mechanobiology in fracture healing simulation models and optimization of treatment strategies. SIGNIFICANCE: The importance of initial conditions and loads on fracture healing has been shown to help physicians treat bone nonunion or delayed bone healing after a fracture.

Role of Altitude in Influencing the Spray Combustion Characteristics of a Heavy-Duty Diesel Engine in a Constant Volume Combustion Chamber. Part II: Impinging Diesel Jet.

Wall impingement, particularly liquid-wall impingement, has been demonstrated to be one of the critical causes of combustion deterioration in plateau diesel engines. Obviously, the complexity of wall impingement is exacerbated by the plateau scenario. However, fundamental studies specifically dedicated to this phenomenon are still inconclusive and insufficiently detailed, obviating the feasibility of the targeted design and optimization of diesel engines operating in regions with different altitudes. Consequently, the second part of this investigation, presented in this work, focused on the detailed physical and chemical processes of impinging spray combustion under different altitude conditions. A wall impingement system was designed to generate an impinging spray flame. The impingement distance was varied from 77 to 37 mm to cover different situations of wall impingement. The liquid spray, ignition, and combustion processes were visualized in detail by using different optical diagnostics. The results showed that the variation of the liquid length with the impingement distance was mainly dependent on the liquid impingement under the same altitude condition. The effect of the impingement distance on the ignition distance was more sensitive to the altitude. The quantitative analysis of the flame natural luminosity confirmed the decisive effect of the impinging flame morphology on the ambient entrainment and fuel-air mixing under different altitude conditions, and it also revealed that there was an optimal impingement distance under identical altitude conditions to achieve minimum soot emissions. And interestingly, the optimal impingement distance increased with altitude. Finally, the spray combustion processes of an impinging diesel jet were determined to occur in four typical regions, upon which a schematic diagram depicting the flame structure of an impinging diesel jet was proposed to phenomenologically describe the role of altitude in impinging spray combustion processes. Based on this, an attempt was made to explore some new perspectives beyond the popular solutions to recover and improve the performance of plateau diesel engines.

Force-Position Hybrid Compensation Control for Path Deviation in Robot-Assisted Bone Drilling.

Bone drilling is a common procedure in orthopedic surgery and is frequently attempted using robot-assisted techniques. However, drilling on rigid, slippery, and steep cortical surfaces, which are frequently encountered in robot-assisted operations due to limited workspace, can lead to tool path deviation. Path deviation can have significant impacts on positioning accuracy, hole quality, and surgical safety. In this paper, we consider the deformation of the tool and the robot as the main factors contributing to path deviation. To address this issue, we establish a multi-stage mechanistic model of tool-bone interaction and develop a stiffness model of the robot. Additionally, a joint stiffness identification method is proposed. To compensate for path deviation in robot-assisted bone drilling, a force-position hybrid compensation control framework is proposed based on the derived models and a compensation strategy of path prediction. Our experimental results validate the effectiveness of the proposed compensation control method. Specifically, the path deviation is significantly reduced by 56.6%, the force of the tool is reduced by 38.5%, and the hole quality is substantially improved. The proposed compensation control method based on a multi-stage mechanistic model and joint stiffness identification method can significantly improve the accuracy and safety of robot-assisted bone drilling.

Tool Wear Condition Monitoring Method Based on Deep Learning with Force Signals.

Tool wear condition monitoring is an important component of mechanical processing automation, and accurately identifying the wear status of tools can improve processing quality and production efficiency. This paper studied a new deep learning model, to identify the wear status of tools. The force signal was transformed into a two-dimensional image using continuous wavelet transform (CWT), short-time Fourier transform (STFT), and Gramian angular summation field (GASF) methods. The generated images were then fed into the proposed convolutional neural network (CNN) model for further analysis. The calculation results show that the accuracy of tool wear state recognition proposed in this paper was above 90%, which was higher than the accuracy of AlexNet, ResNet, and other models. The accuracy of the images generated using the CWT method and identified with the CNN model was the highest, which is attributed to the fact that the CWT method can extract local features of an image and is less affected by noise. Comparing the precision and recall values of the model, it was verified that the image obtained by the CWT method had the highest accuracy in identifying tool wear state. These results demonstrate the potential advantages of using a force signal transformed into a two-dimensional image for tool wear state recognition and of applying CNN models in this area. They also indicate the wide application prospects of this method in industrial production.

Spinal biomechanics modeling and finite element analysis of surgical instrument interaction.

When the spinal surgery robot assists the surgeon perform the surgery, the patient is prone on the operating table. However, due to the force of the surgical instruments on the spine, there is a corresponding deformation in the surgical field, which affects the accuracy of the operation. In order to improve the accuracy and safety of the operation, this paper reconstructs the three-dimensional model of the lumbar spine which includes the vertebral body and the intervertebral disc based on the CT scan data, and then the lumbar spine is analyzed by the finite element method. The mathematical model of the relationship between force and displacement is established by using response surface methodology based on the simulation results. After that, the position control system is constructed based on the mathematical model. Through the simulation of the control system, the trajectory curve of the end of the manipulator is compared and the validity of the mathematical model is verified.

Finite element analysis for fatigue behaviour of a self-expanding Nitinol peripheral stent under physiological biomechanical conditions.

Self-expanding Nitinol stents are increasingly used to treat femoropopliteal artery (FPA) occlusions, but the risk of stent fatigue failure exists due to complex artery deformation during daily activities. Finite element analysis (FEA) has been widely applied to study the stent fatigue behaviours, but physiological deformation and atherosclerotic plaque were not considered simultaneously in previous studies. In this work, to show the necessity and feasibility of considering both factors in evaluation of the stent fatigue behaviours, a comprehensive FEA framework considering both factors is established, and an easy loading method for the complex boundary condition is proposed. Four comparative simulations are successfully conducted, and the stent fatigue behaviours are analysed based on the distributions and maximum values of the self-defined mean and alternating strains. Results show that both the physiological deformation and atherosclerotic plaque significantly contribute to the stent fatigue life. The case with the complex boundary condition and atherosclerotic plaque is the most critical of the four cases, and the minimum safety factor is 0.62. In conclusion, it is necessary to consider both physiological deformation and atherosclerotic plaque in the evaluation of stent fatigue behaviours, and ignoring any of them would lead to overestimation of the stent fatigue life. The work in this paper offers a solid foundation for accurate evaluation of the stent fatigue lifetime in patient-specific surgery plans via FEA.

Multilevel Fuzzy Control Based on Force Information in Robot-Assisted Decompressive Laminectomy.

The lumbar spinal stenosis (LSS) is a kind of orthopedic disease which causes a series of neurological symptom. Vertebral lamina grinding operation is a key procedure in decompressive laminectomy for LSS treatment. With the help of image-guided navigation system, the robot-assisted technology is applied to reduce the burdens on surgeon and improve the accuracy of the operation. This paper proposes a multilevel fuzzy control based on force information in the robot-assisted decompressive laminectomy to improve the quality and the robotic dynamic performance in surgical operation. The controlled grinding path is planned in the medical images after 3D reconstruction, and the mapping between robot and images is realized by navigation registration. Multilevel fuzzy controller is used to adjust the feed rate to keep the grinding force stable. As the vertebral lamina contains different components according to the anatomy, it has different mechanical properties as the main reason causing the fluctuation of force. A feature extraction method for texture recognition of bone is introduced to improve the accuracy of component classification. When the inner cortical bone is reached, the feeding operation needs to stop to avoid penetration into spinal cord and damage to the spinal nerves. Experiments are conducted to evaluate the dynamic stabilities of the control system and state recognition.

State Recognition of Bone Drilling Based on Acoustic Emission in Pedicle Screw Operation.

Pedicle drilling is an important step in pedicle screw fixation and the most significant challenge in this operation is how to determine a key point in the transition region between cancellous and inner cortical bone. The purpose of this paper is to find a method to achieve the recognition for the key point. After acquiring acoustic emission (AE) signals during the drilling process, this paper proposed a novel frequency distribution-based algorithm (FDB) to analyze the AE signals in the frequency domain after certain processes. Then we select a specific frequency domain of the signal for standard operations and choose a fitting function to fit the obtained sequence. Characters of the fitting function are extracted as outputs for identification of different bone layers. The results, which are obtained by detecting force signal and direct measurement, are given in the paper. Compared with the results above, the results obtained by AE signals are distinguishable for different bone layers and are more accurate and precise. The results of the algorithm are trained and identified by a neural network and the recognition rate reaches 84.2%. The proposed method is proved to be efficient and can be used for bone layer identification in pedicle screw fixation.