Influence of drill speed and feed rate on bone damage

Implant failures and postoperative complications are often associated to the bone drilling. Estimation and control of drilling parameters are critical to prevent mechanical damage to the bone tissues. For better performance of the drilling procedures, it is essential to understand the mechanical behaviour of bones that leads to their failures and consequently to improve the cutting conditions. This paper investigates the effect of drill speed and feed-rate on mechanical damage during drilling of solid rigid foam materials, with similar mechanical properties to the human bone. Experimental tests were conducted on biomechanical blocks instrumented with strain gauges to assess the drill speed and feed-rate influence. A three-dimensional dynamic finite element model to predict the bone stresses, as a function of drilling conditions, drill geometry and bone model, was developed. These simulations incorporate the dynamic characteristics involved in the drilling process. The element removal scheme is taken into account and allows advanced simulations of tool penetration and material removal. Experimental and numerical results show that generated stresses in the material tend to increase with tool penetration. Higher drill speed leads to an increase of von-Mises stresses and strains in the solid rigid foams. However, when the feed-rate is higher, the stresses and strains are lower. The numerical normal stresses and strains are found to be in good agreement with experimental results. The models could be an accurate analysis tool to simulate the stresses distribution in the bone during the drilling process.

Occupational safety in grain elevators and feed mills.

Mode of access: Internet.

Annual feed bulletin.

Mode of access: Internet.

On-site staff evaluation of the forward operating locations identified to replace Howard Air Force Base due to ist [i.e. its] closure on 1 May, 1999 : a staff report /

"July 1999."

Annual Feed Bulletin

Title Varies: Annual R00eport; Annual Bulletin

Functionality, Robustness and Control of Nonlinear Network Dynamics: Modeling and Understanding the C. elegans Connectome

Thesis (Ph.D.)--University of Washington, 2016-06

Cleaner wrasse prefer client mucus: support for partner control mechanisms in cleaning interactions

Recent studies on cleaning behaviour suggest that there are conflicts between cleaners and their clients over what cleaners eat. The diet of cleaners usually contains ectoparasites and some client tissue. It is unclear, however, whether cleaners prefer client tissue over ectoparasites or whether they include client tissue in their diet only when searching for parasites alone is not profitable. To distinguish between these two hypotheses, we trained cleaner fish Labroides dimidiatus to feed from plates and offered them client mucus from the parrotfish Chlorurus sordidus, parasitic monogenean flat-worms, parasitic gnathiid isopods and boiled flour glue as a control. We found that cleaners ate more mucus and monogeneans than gnathiids, with gnathiids eaten slightly more often than the control substance. Because gnathiids are the most abundant ectoparasites, our results suggest a potential for conflict between cleaners and clients over what the cleaner should eat, and support studies emphasizing the importance of partner control in keeping cleaning interactions mutualistic.

Robust multivariable control of complex biological processes

This paper addresses advanced control of a biological nutrient removal (BNR) activated sludge process. Based on a previously validated distributed parameter model of the BNR activated sludge process, we present robust multivariable controller designs for the process, involving loop shaping of plant model, robust stability and performance analyses. Results from three design case studies showed that a multivariable controller with stability margins of 0.163, 0.492 and 1.062 measured by the normalised coprime factor, multiplicative and additive uncertainties respectively give the best results for meeting performance robustness specifications. The controller robustly stabilises effluent nutrients in the presence of uncertainties with the behaviour of phosphorus accumulating organisms as well as to effectively attenuate major disturbances introduced as step changes. This study also shows that, performance of the multivariable robust controller is superior to multi-loops SISO PI controllers for regulating the BNR activated sludge process in terms of robust stability and performance and controlling the process using inlet feed flowrate is infeasible. (C) 2003 Elsevier Ltd. All rights reserved.

Are the changes in postural control associated with low back pain caused by pain interference?

Background: Voluntary limb movements are associated with involuntary and automatic postural adjustments of the trunk muscles. These postural adjustments occur prior to movement and prevent unwanted perturbation of the trunk. In low back pain, postural adjustments of the trunk muscles are altered such that the deep trunk muscles are consistently delayed and the superficial trunk muscles are sometimes augmented. This alteration of postural adjustments may reflect disruption of normal postural control imparted by reduced central nervous system resources available during pain, so-called pain interference, or reflect adoption of an alternate postural adjustment strategy. Methods: We aimed to clarify this by recording electromyographic activity of the upper (obliquus extemus) and lower (transversus abdominis/obliquus internus) abdominal muscles during voluntary arm movements that were coupled with painful cutaneous stimulation at the low back. If the effect of pain on postural adjustments is caused by pain interference, it should be greatest at the onset of the stimulus, should habituate with repeated exposure, and be absent immediately when the threat of pain is removed. Sixteen patients performed 30 forward movements of the right arm in response to a visual cue (control). Seventy trials were then conducted in which arm movement was coupled with pain (pain trials) and then a further 70 trials were conducted without the pain stimulus (no pain trials). Results: There was a gradual and increasing delay of transversus abdominis/obliquus internus electromyograph and augmentation of obliquus externus during the pain trials, both of which gradually returned to control values during the no pain trials. Conclusion: The results suggest that altered postural adjustments of the trunk muscles during pain are not caused by pain interference but are likely to reflect development and adoption of an alternate postural adjustment strategy, which may serve to limit the amplitude and velocity of trunk excursion caused by arm movement.

Optimal control and operation of drum granulation processes

Process optimisation and optimal control of batch and continuous drum granulation processes are studied in this paper. The main focus of the current research has been: (i) construction of optimisation and control relevant, population balance models through the incorporation of moisture content, drum rotation rate and bed depth into the coalescence kernels; (ii) investigation of optimal operational conditions using constrained optimisation techniques; (iii) development of optimal control algorithms based on discretized population balance equations; and (iv) comprehensive simulation studies on optimal control of both batch and continuous granulation processes. The objective of steady state optimisation is to minimise the recycle rate with minimum cost for continuous processes. It has been identified that the drum rotation-rate, bed depth (material charge), and moisture content of solids are practical decision (design) parameters for system optimisation. The objective for the optimal control of batch granulation processes is to maximize the mass of product-sized particles with minimum time and binder consumption. The objective for the optimal control of the continuous process is to drive the process from one steady state to another in a minimum time with minimum binder consumption, which is also known as the state-driving problem. It has been known for some time that the binder spray-rate is the most effective control (manipulative) variable. Although other possible manipulative variables, such as feed flow-rate and additional powder flow-rate have been investigated in the complete research project, only the single input problem with the binder spray rate as the manipulative variable is addressed in the paper to demonstrate the methodology. It can be shown from simulation results that the proposed models are suitable for control and optimisation studies, and the optimisation algorithms connected with either steady state or dynamic models are successful for the determination of optimal operational conditions and dynamic trajectories with good convergence properties. (c) 2005 Elsevier Ltd. All rights reserved.

Improved robust control of nonlinear stochastic systems using uncertain models

We introduce a technique for quantifying and then exploiting uncertainty in nonlinear stochastic control systems. The approach is suboptimal though robust and relies upon the approximation of the forward and inverse plant models by neural networks, which also estimate the intrinsic uncertainty. Sampling from the resulting Gaussian distributions of the inversion based neurocontroller allows us to introduce a control law which is demonstrably more robust than traditional adaptive controllers.

Robust control of nonlinear stochastic systems by modelling conditional distributions of control signals

We introduce a novel inversion-based neuro-controller for solving control problems involving uncertain nonlinear systems that could also compensate for multi-valued systems. The approach uses recent developments in neural networks, especially in the context of modelling statistical distributions, which are applied to forward and inverse plant models. Provided that certain conditions are met, an estimate of the intrinsic uncertainty for the outputs of neural networks can be obtained using the statistical properties of networks. More generally, multicomponent distributions can be modelled by the mixture density network. In this work a novel robust inverse control approach is obtained based on importance sampling from these distributions. This importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The performance of the new algorithm is illustrated through simulations with example systems.

Conditional distribution modeling for estimating and exploiting uncertainty in control systems

This paper presents a general methodology for estimating and incorporating uncertainty in the controller and forward models for noisy nonlinear control problems. Conditional distribution modeling in a neural network context is used to estimate uncertainty around the prediction of neural network outputs. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localize the possible control solutions to consider. A nonlinear multivariable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non Gaussian distributions of control signal as well as processes with hysteresis.

Computer control of chemical process plants with special reference to distillation

A sieve plate distillation column has been constructed and interfaced to a minicomputer with the necessary instrumentation for dynamic, estimation and control studies with special bearing on low-cost and noise-free instrumentation. A dynamic simulation of the column with a binary liquid system has been compiled using deterministic models that include fluid dynamics via Brambilla's equation for tray liquid holdup calculations. The simulation predictions have been tested experimentally under steady-state and transient conditions. The simulator's predictions of the tray temperatures have shown reasonably close agreement with the measured values under steady-state conditions and in the face of a step change in the feed rate. A method of extending linear filtering theory to highly nonlinear systems with very nonlinear measurement functional relationships has been proposed and tested by simulation on binary distillation. The simulation results have proved that the proposed methodology can overcome the typical instability problems associated with the Kalman filters. Three extended Kalman filters have been formulated and tested by simulation. The filters have been used to refine a much simplified model sequentially and to estimate parameters such as the unmeasured feed composition using information from the column simulation. It is first assumed that corrupted tray composition measurements are made available to the filter and then corrupted tray temperature measurements are accessed instead. The simulation results have demonstrated the powerful capability of the Kalman filters to overcome the typical hardware problems associated with the operation of on-line analyzers in relation to distillation dynamics and control by, in effect, replacirig them. A method of implementing estimator-aided feedforward (EAFF) control schemes has been proposed and tested by simulation on binary distillation. The results have shown that the EAFF scheme provides much better control and energy conservation than the conventional feedback temperature control in the face of a sustained step change in the feed rate or multiple changes in the feed rate, composition and temperature. Further extensions of this work are recommended as regards simulation, estimation and EAFF control.

Mathematical modelling and control of agitated extraction columns

Liquid-liquid extraction has long been known as a unit operation that plays an important role in industry. This process is well known for its complexity and sensitivity to operation conditions. This thesis presents an attempt to explore the dynamics and control of this process using a systematic approach and state of the art control system design techniques. The process was studied first experimentally under carefully selected. operation conditions, which resembles the ranges employed practically under stable and efficient conditions. Data were collected at steady state conditions using adequate sampling techniques for the dispersed and continuous phases as well as during the transients of the column with the aid of a computer-based online data logging system and online concentration analysis. A stagewise single stage backflow model was improved to mimic the dynamic operation of the column. The developed model accounts for the variation in hydrodynamics, mass transfer, and physical properties throughout the length of the column. End effects were treated by addition of stages at the column entrances. Two parameters were incorporated in the model namely; mass transfer weight factor to correct for the assumption of no mass transfer in the. settling zones at each stage and the backmixing coefficients to handle the axial dispersion phenomena encountered in the course of column operation. The parameters were estimated by minimizing the differences between the experimental and the model predicted concentration profiles at steady state conditions using non-linear optimisation technique. The estimated values were then correlated as functions of operating parameters and were incorporated in·the model equations. The model equations comprise a stiff differential~algebraic system. This system was solved using the GEAR ODE solver. The calculated concentration profiles were compared to those experimentally measured. A very good agreement of the two profiles was achieved within a percent relative error of ±2.S%. The developed rigorous dynamic model of the extraction column was used to derive linear time-invariant reduced-order models that relate the input variables (agitator speed, solvent feed flowrate and concentration, feed concentration and flowrate) to the output variables (raffinate concentration and extract concentration) using the asymptotic method of system identification. The reduced-order models were shown to be accurate in capturing the dynamic behaviour of the process with a maximum modelling prediction error of I %. The simplicity and accuracy of the derived reduced-order models allow for control system design and analysis of such complicated processes. The extraction column is a typical multivariable process with agitator speed and solvent feed flowrate considered as manipulative variables; raffinate concentration and extract concentration as controlled variables and the feeds concentration and feed flowrate as disturbance variables. The control system design of the extraction process was tackled as multi-loop decentralised SISO (Single Input Single Output) as well as centralised MIMO (Multi-Input Multi-Output) system using both conventional and model-based control techniques such as IMC (Internal Model Control) and MPC (Model Predictive Control). Control performance of each control scheme was. studied in terms of stability, speed of response, sensitivity to modelling errors (robustness), setpoint tracking capabilities and load rejection. For decentralised control, multiple loops were assigned to pair.each manipulated variable with each controlled variable according to the interaction analysis and other pairing criteria such as relative gain array (RGA), singular value analysis (SVD). Loops namely Rotor speed-Raffinate concentration and Solvent flowrate Extract concentration showed weak interaction. Multivariable MPC has shown more effective performance compared to other conventional techniques since it accounts for loops interaction, time delays, and input-output variables constraints.