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5083 Al-Mg is the widely used material in food, chemistry, vehicle, machinery, and construction sectors, as well as in the aviation and space industries. The burnishing is normally used as the finishing operation for this material with the advantages such as surface roughness, reduced fracture formation, hardness, fatigue strength, and an increase of the wear resistance. These positive improvements are dependent on burnishing process parameters such as feed rate, burnishing force, ball diameter, and a number of revolutions. The study contains determination and optimization of the machining parameters and their effects on the surface roughness, microhardness, and the strength of 5083 Al-Mg material in the ball burnishing processes. Multiple regression and ANOVA analysis were performed to identify significant process parameters. A new Artificial Neural Networks (ANN) model with different neuron structures and algorithms has also been developed using experimental results to supplement the multiple regression model as the desired R 2 values could not be achieved with the latter. The ANOVA analysis indicated that both the burnishing force and the number of revolutions have a significant effect on the surface roughness and hardness with optimums 300 N and 200 rpm, respectively. Results from the two models were compared with each other. The developed ANN model is shown to estimate the surface roughness and the surface hardness with high reliability (R 2 = 0.999992) without costly experimental trials. K e y w o r d s : burnishing, surface roughness and hardness, microhardness, strength analysis, Artificial Neural Networks (ANN)

The stress concentration factor in a plate with a hole is very important for joints. There are many different types of joints and forms. Plates are usually connected with bolted, riveted and pin joints. Connection parts need to consist of hole/holes to make a joint using these machine elements. Machine parts are exposed to different stresses. In this study, the stress concentration factor in a plate with a circular hole under axial tension stresses was invegistated. The emprical (Peterson's) stress concentration factor (Kt) was compared with the results of analytical model, regression analysis (REGA), finite element analysis (FEA), artificial neural network (ANN) model. The stress concentration factor (Kt) was modeled using 5 different methods and the accuracy of Peterson's model was tested. The best results were obtained using ANN model. The emprical results and ANN predictions were compared by using statistical error analyzing the absolute fraction of variance (R2 = 0.999999788), root mean square error (RMSE = 0.000934125) and mean error percentage (MEP = 0.033902049) with the test data. ANN model can be used instead of Peterson's model. Kt was determined by the ANN with an acceptable accuracy.

Intersomatic fusion is a very popular treatment for spinal diseases associated with intervertebral disc degeneration. The effects of three different hybrid stabilization systems on both range of motion and intradiscal pressure were investigated, as there is no consensus in the literature about the efficiency of these systems. Finite element simulations were designed to predict the variations of range of motion and intradiscal pressure from intact to implanted situations. After hybrid stabilization system implantation, L4-L5 level did not lose its motion completely, while L5-S1 had no mobility as a consequence of disc removal and fusion process. BalanC hybrid stabilization system represented higher mobility at the index level, reduced intradiscal pressure of adjacent level, but caused to increment in range of motion by 20% under axial rotation. Higher tendency by 93% to the failure was also detected under axial rotation. Dynesys hybrid stabilization system represented more restricted motion than BalanC, and negligible effects to the adjacent level. B-DYN hybrid stabilization system was the most rigid one among all three systems. It reduced intradiscal pressure and range of motion at the adjacent level except from motion under axial rotation being increased by 13%. Fracture risk of B-DYN and Dynesys Transition Optima components was low when compared with BalanC. Mobility of the adjacent level around axial direction should be taken into account in case of implantation with BalanC and B-DYN systems, as well as on the development of new designs. Having these findings in mind, it is clear that hybrid systems need to be further tested, both clinically and numerically, before being considered for common use.

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