In a dental implant system, the value of stress and its distribution plays a pivotal role on the strength, durability and life of the implant-bone system. A typical implant consists of a Titanium core and a thin layer of biocompatible material such as the hydroxyapatite. This coating has a wide range of clinical applications in orthopedics and dentistry due to its biocompatibility and bioactivity characteristics. Low bonding strength and sudden variation of mechanical properties between the coating and the metallic layers are the main disadvantages of such common implants. To overcome these problems, a radial distributed functionally graded biomaterial (FGBM) was proposed in this paper and the effect of material property on the stress distribution around the dental implant-bone interface was studied. A three-dimensional finite element simulation was used to illustrate how the use of radial FGBM dental implant can reduce the maximum von Mises stress and, also the stress shielding effect in both the cortical and cancellous bones. The results, of course, give anybody an idea about optimized behaviors that can be achieved using such materials. The finite element solver was validated by familiar methods and the results were compared to previous works in the literature.
One of the common issues that occur after total knee replacement surgery is the aseptic loosening. The problem usually occurs after about 15 years from the surgery. The destructive effects of residual particles due to wear, the stress shielding effect, and micro-movements are the causative factors for this type of loosening. In this research, using the advantages of functionally graded biomaterials (FGBM), it is tried to design a prosthetic system that can reduce the above-mentioned effects. For this purpose, the materials used in the most important part of the prosthesis system, i.e., the femoral part are redesigned so that the bioactivity between the prosthesis and bone, and the stress applied to the adjacent tissues increase simultaneously. In addition, to reduce the effect of wear at contact areas, wear-resistant biocompatible ceramics such as alumina and zirconia are used. The value of stress at the bone-prosthesis interface and adjacent tissues is the most important parameters. Two types of three-phase ceramic-based FGBMs are recommended. The prosthesis with three-phase hydroxyapatite-titanium-zirconia has increased the average stress in the bone tissues around high-risk areas up to 71.8% with respect to a commonly used Cr-Co prosthesis. The result for the prosthesis with three-phase hydroxyapatite-titanium-alumina is up to 65%, respectively. At bone-prosthesis interfaces, an increase of 92% in the stress for both zirconia-based and alumina-based is seen. Briefly, the recommended FGBMs can improve the bone-prosthesis performance in all desired indices.
In this paper, a new optimal adaptive controller for the active front steering control of a vehicle is proposed. Due to the availability and applicability of proportional-integrator-derivative (PID) controllers, this controller is picked up; but, to overcome its limitations, two optimization and adaptation schemes are employed. The reference transfer function between the yaw rate of a typical vehicle and its steering angle is derived. The actual dynamics is simulated using CarSim toolbox of MATLAB. Best vehicle handling was aimed to be reached for three famous driving manoeuvres by proposing an efficient but economic controller. An optimization is done on the PID coefficients for a specific road condition using the honey bees' algorithm, and then a two-layer artificial neural network is trained using the back propagation learning rule to adjust the controller coefficients for any arbitrary road conditions. The uncontrolled, desired, optimized controlled, and optimized plus trained controlled yaw rate of vehicles are drawn for three manoeuvers and three road conditions. The integral of squared error between the desired and actual trajectories for different manoeuvers and road conditions are evaluated and compared between each other. The performance of the proposed PID controller that was optimized by Bees Algorithm and trained by a neural network was proved. It is noted that the optimized PID controller is good for all road conditions but not excellent. The more we move away from the reference road (in which, the optimization is done), the error will be larger. Thus for other conditions, using the artificial neural network can help decrease the error significantly.
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