Optimization of Drill Bits for Bone Drilling Procedure

2018 ◽  
Author(s):  
Jianbo Sui ◽  
Naohiko Sugita
Keyword(s):  
Genetic Algorithm ◽  
Mechanistic Model ◽  
Drill Bit ◽  
Bone Drilling ◽  
Drill Bits ◽  
Wide Range ◽  
Drilling Conditions

The paper presents a methodology to optimize drill bits to realize safe drilling of bone materials for many surgeries like orthopedics and neurosurgery. First, a mechanistic model is introduced to relate drilling forces to main drill bit geometry parameters. Then a genetic algorithm is developed to optimize drill bit geometry parameters by minimization of drilling forces based on the mechanistic model. Finally prototypes of drill bits with optimized geometry parameters are produced and drilling experiments are conducted to verify the advantages of these new drill bits. The results show that by comparison with normal drill bit, the average drilling forces are reduced to more than 50% by drill bits with optimized geometry parameters under a wide range of drilling conditions.

Mechanistic models to predict thrust force and torque in bone drilling: An in-vitro study validated with robot-assisted surgical drilling parameters

2021 ◽  
pp. 095440892110259
Author(s):  
Varatharajan Prasannavenkadesan ◽  
Ponnusamy Pandithevan
Keyword(s):  
Mechanistic Model ◽  
Preliminary Investigation ◽  
Cutting Parameters ◽  
Mechanistic Models ◽  
Drill Bit ◽  
Bone Drilling ◽  
Robot Assisted ◽  
Line Field ◽  
Thrust Forces

Compression plates are widely used in orthopaedic surgeries for internal fixation of fractured femurs. To fix the plate and thus to provide compression to a fracture, the self-tapping bone screws are tightened through predrilled pilot holes of smaller diameter. Preliminary investigation showed that the holes drilled with the inappropriate cutting parameters cause mechanical and thermal damages to the local host bone, which further lead to loosening of internal fixations. In this paper, the mechanistic models to predict the thrust forces and torques during bone drilling were developed, using a 3.20 mm diameter drill bit. As a procedure, the cutting action was investigated at three different regions of the drill point, namely cutting lips, secondary cutting edges and indentation zone. The models employed the analytical approach to account for the drill-bit geometry and cutting parameters, and an empirical approach to account for the material and friction properties. To complete the procedure, calibration experiments were conducted on bovine cortical femurs with two different spindle speeds (1000 and 3000 r/min) and feeds (0.03 and 0.06 mm/rev), and then the specific normal and friction coefficients were determined. The developed mechanistic models were validated with different ranges of parameters (500–3500 r/min speeds, and 0.02–0.07 mm/rev feeds) those commonly involved in manual and robot-assisted surgery. The validation study revealed that the thrust forces predicted using the mechanistic models showed a maximum error of only 5.80%. However, the torques predicted from the mechanistic model found with more error than the thrust forces. The predominant reasons for this under-prediction might because of the extrapolation used to determine the specific cutting pressures, slip-line field applied to the indentation zone instead of compressive fracture, and chip clogging involved during the bone drilling as demonstrated in earlier studies. Despite the deviations, the developed mechanistic models satisfactorily follow the trends of the thrust forces and torques experienced during bone drilling. The outcomes can be used to practice the bone drilling procedure and monitor the effect of process parameters on thrust forces and torques in the in-silico environment before performing actual surgery.

The Research of a Drill Bit for Underground Casing Drilling in Radial Water Jet Penetration Perforation

2011 ◽  
Vol 189-193 ◽  
pp. 3017-3021
Author(s):  
Lin Zhu ◽  
De Ming Xiao
Keyword(s):  
Cutting Parameters ◽  
Water Jet ◽  
Deep Penetration ◽  
Drill Bit ◽  
Test Results ◽  
Edge Angle ◽  
Drill Bits ◽  
Object Of Study ◽  
Drilling Conditions ◽  
Casing Drilling

The drill bit for underground casing drilling was taken as object of study, and systemic research have been done for N80 casing drilling in Radial water jet deep penetration perforation. A new drill bit was designed after researched the actual drilling conditions, the characteristic of current commonly used drill bits and the property of blades edge materials. The reasonable original-edge angle, cutting parameters have been obtained for the new drill bit. We choose some other commonly used drill bit, and then contrast experiments result have been done by the new drill bit. The test results show that the new drill bit have the advantages of centering reliable, drilling smooth and sub-chip completely, and it can meet the requirements of the down hole casing drilling.

Development of a Hybrid Artificial Neural Network and Genetic Algorithm Model for Regime Identification of Slurry Transport in Pipelines

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Sandip K Lahiri ◽  
Kartik Chandra Ghanta
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Operating Conditions ◽  
Misclassification Error ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Pipeline Design ◽  
Hybrid Artificial Neural Network

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN-GA method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters.

GPU-Based Haptic Simulator for Dental Bone Drilling

2011 ◽  
Author(s):  
Fei Zheng ◽  
WenFeng Lu ◽  
Yoke San Wong ◽  
Kelvin Weng Chiong Foong
Keyword(s):  
Real Time ◽  
Graphics Processing Units ◽  
Force Feedback ◽  
Mandibular Canal ◽  
Structure Design ◽  
Patient Specific ◽  
Force Model ◽  
Bone Drilling ◽  
The Real ◽  
Drill Bits

Dental bone drilling is an inexact and often a blind art. Dentist risks damaging the invisible tooth roots, nerves and critical dental structures like mandibular canal and maxillary sinus. This paper presents a haptics-based jawbone drilling simulator for novice surgeons. Through the real-time training of tactile sensations based on patient-specific data, improved outcomes and faster procedures can be provided. Previously developed drilling simulators usually adopt penalty-based contact force models and often consider only spherical-shaped drill bits for simplicity and computational efficiency. In contrast, our simulator is equipped with a more precise force model, adapted from the Voxmap-PointShell (VPS) method to capture the essential features of the drilling procedure. In addition, the proposed force model can accommodate various shapes of drill bits. To achieve better anatomical accuracy, our oral model has been reconstructed from Cone Beam CT, using voxel-based method. To enhance the real-time response, the parallel computing power of Graphics Processing Units is exploited through extra efforts for data structure design, algorithms parallelization, and graphic memory utilization. Preliminary results show that the developed system can produce appropriate force feedback at different tissue layers.

Closed-Form Correlation of Buildings Energy Use With Key Design Parameters Calibrated Using a Genetic Algorithm

2011 ◽  
Vol 133 (4) ◽  
Author(s):  
Raed I. Bourisli ◽  
Adnan A. AlAnzi
Keyword(s):  
Genetic Algorithm ◽  
Closed Form ◽  
Energy Use ◽  
Building Design ◽  
Office Buildings ◽  
Design Parameters ◽  
Wide Range ◽  
Variable Function ◽  
Real Coded Genetic Algorithm ◽  
The Difference

This work aims at developing a closed-form correlation between key building design variables and its energy use. The results can be utilized during the initial design stages to assess the different building shapes and designs according to their expected energy use. Prototypical, 20-floor office buildings were used. The relative compactness, footprint area, projection factor, and window-to-wall ratio were changed and the resulting buildings performances were simulated. In total, 729 different office buildings were developed and simulated in order to provide the training cases for optimizing the correlation’s coefficients. Simulations were done using the VisualDOE TM software with a Typical Meteorological Year data file, Kuwait City, Kuwait. A real-coded genetic algorithm (GA) was used to optimize the coefficients of a proposed function that relates the energy use of a building to its four key parameters. The figure of merit was the difference in the ratio of the annual energy use of a building normalized by that of a reference building. The objective was to minimize the difference between the simulated results and the four-variable function trying to predict them. Results show that the real-coded GA was able to come up with a function that estimates the thermal performance of a proposed design with an accuracy of around 96%, based on the number of buildings tested. The goodness of fit, roughly represented by R2, ranged from 0.950 to 0.994. In terms of the effects of the various parameters, the area was found to have the smallest role among the design parameters. It was also found that the accuracy of the function suffers the most when high window-to-wall ratios are combined with low projection factors. In such cases, the energy use develops a potential optimum compactness. The proposed function (and methodology) will be a great tool for designers to inexpensively explore a wide range of alternatives and assess them in terms of their energy use efficiency. It will also be of great use to municipality officials and building codes authors.

scholarly journals A Modified NM-PSO Method for Parameter Estimation Problems of Models

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
An Liu ◽  
Erwie Zahara ◽  
Ming-Ta Yang
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Wide Range ◽  
Estimation Problems

Ordinary differential equations usefully describe the behavior of a wide range of dynamic physical systems. The particle swarm optimization (PSO) method has been considered an effective tool for solving the engineering optimization problems for ordinary differential equations. This paper proposes a modified hybrid Nelder-Mead simplex search and particle swarm optimization (M-NM-PSO) method for solving parameter estimation problems. The M-NM-PSO method improves the efficiency of the PSO method and the conventional NM-PSO method by rapid convergence and better objective function value. Studies are made for three well-known cases, and the solutions of the M-NM-PSO method are compared with those by other methods published in the literature. The results demonstrate that the proposed M-NM-PSO method yields better estimation results than those obtained by the genetic algorithm, the modified genetic algorithm (real-coded GA (RCGA)), the conventional particle swarm optimization (PSO) method, and the conventional NM-PSO method.

Prediction of High Viscosity Liquid/Gas Two-Phase Slug Length in Horizontal and Slightly Inclined Pipelines

2021 ◽  
Author(s):  
Omar Shaaban ◽  
Eissa Al-Safran
Keyword(s):  
Numerical Optimization ◽  
Flow Behavior ◽  
Mechanistic Model ◽  
High Viscosity ◽  
Oil Well ◽  
Liquid Slug ◽  
Two Phase ◽  
Proposed Model ◽  
Wide Range ◽  
Flow Closure

Abstract The production and transportation of high viscosity liquid/gas two-phase along petroleum production system is a challenging operation due to the lack of understanding the flow behavior and characteristics. In particular, accurate prediction of two-phase slug length in pipes is crucial to efficiently operate and safely design oil well and separation facilities. The objective of this study is to develop a mechanistic model to predict high viscosity liquid slug length in pipelines and to optimize the proper set of closure relationships required to ensure high accuracy prediction. A large high viscosity liquid slug length database is collected and presented in this study, against which the proposed model is validated and compared with other models. A mechanistic slug length model is derived based on the first principles of mass and momentum balances over a two-phase slug unit, which requires a set of closure relationships of other slug characteristics. To select the proper set of closure relationships, a numerical optimization is carried out using a large slug length dataset to minimize the prediction error. Thousands of combinations of various slug flow closure relationships were evaluated to identify the most appropriate relationships for the proposed slug length model under high viscosity slug length condition. Results show that the proposed slug length mechanistic model is applicable for a wide range of liquid viscosities and is sensitive to the selected closure relationships. Results revealed that the optimum closure relationships combination is Archibong-Eso et al. (2018) for slug frequency, Malnes (1983) for slug liquid holdup, Jeyachandra et al. (2012) for drift velocity, and Nicklin et al. (1962) for the distribution coefficient. Using the above set of closure relationships, model validation yields 37.8% absolute average percent error, outperforming all existing slug length models.

Application of neural networks in membrane separation

2020 ◽  
Vol 36 (2) ◽  
pp. 265-310 ◽  
Author(s):  
Morteza Asghari ◽  
Amir Dashti ◽  
Mashallah Rezakazemi ◽  
Ebrahim Jokar ◽  
Hadi Halakoei
Keyword(s):  
Neural Networks ◽  
Chemical Engineering ◽  
Membrane Separation ◽  
Mechanistic Model ◽  
Computing Time ◽  
Permeate Flux ◽  
Practical Applications ◽  
Advantages And Disadvantages ◽  
Wide Range ◽  
Physical And Chemical

AbstractArtificial neural networks (ANNs) as a powerful technique for solving complicated problems in membrane separation processes have been employed in a wide range of chemical engineering applications. ANNs can be used in the modeling of different processes more easily than other modeling methods. Besides that, the computing time in the design of a membrane separation plant is shorter compared to many mass transfer models. The membrane separation field requires an alternative model that can work alone or in parallel with theoretical or numerical types, which can be quicker and, many a time, much more reliable. They are helpful in cases when scientists do not thoroughly know the physical and chemical rules that govern systems. In ANN modeling, there is no requirement for a deep knowledge of the processes and mathematical equations that govern them. Neural networks are commonly used for the estimation of membrane performance characteristics such as the permeate flux and rejection over the entire range of the process variables, such as pressure, solute concentration, temperature, superficial flow velocity, etc. This review investigates the important aspects of ANNs such as methods of development and training, and modeling strategies in correlation with different types of applications [microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), reverse osmosis (RO), electrodialysis (ED), etc.]. It also deals with particular types of ANNs that have been confirmed to be effective in practical applications and points out the advantages and disadvantages of using them. The combination of ANN with accurate model predictions and a mechanistic model with less accurate predictions that render physical and chemical laws can provide a thorough understanding of a process.

Estimation of CO2 Diffusivity in Brine by Use of the Genetic Algorithm and Mixed Kernels-Based Support Vector Machine Model

2018 ◽  
Vol 141 (4) ◽  
Author(s):  
Qihong Feng ◽  
Ronghao Cui ◽  
Sen Wang ◽  
Jin Zhang ◽  
Zhe Jiang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Diffusion Coefficient ◽  
Co2 Storage ◽  
Co2 Injection ◽  
Support Vector ◽  
Hybrid Technique ◽  
Machine Model ◽  
Proposed Model ◽  
Wide Range

Diffusion coefficient of carbon dioxide (CO2), a significant parameter describing the mass transfer process, exerts a profound influence on the safety of CO2 storage in depleted reservoirs, saline aquifers, and marine ecosystems. However, experimental determination of diffusion coefficient in CO2-brine system is time-consuming and complex because the procedure requires sophisticated laboratory equipment and reasonable interpretation methods. To facilitate the acquisition of more accurate values, an intelligent model, termed MKSVM-GA, is developed using a hybrid technique of support vector machine (SVM), mixed kernels (MK), and genetic algorithm (GA). Confirmed by the statistical evaluation indicators, our proposed model exhibits excellent performance with high accuracy and strong robustness in a wide range of temperatures (273–473.15 K), pressures (0.1–49.3 MPa), and viscosities (0.139–1.950 mPa·s). Our results show that the proposed model is more applicable than the artificial neural network (ANN) model at this sample size, which is superior to four commonly used traditional empirical correlations. The technique presented in this study can provide a fast and precise prediction of CO2 diffusivity in brine at reservoir conditions for the engineering design and the technical risk assessment during the process of CO2 injection.

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