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.
The Fe-28 at.% Al alloy was studied in this article. The aim was to describe the influence of gas atomized powder pre-milling before SPS (Spark Plasma Sintering) sintering on the structure and properties of the bulk materials. The initial powder was milled for 0.5, 1, and 8 h. It was proven that 1 h milling leads to the change in size and morphology of the particles, B2→A2 phase transformation, and to the contamination with the material from a milling vessel. Powder materials were compacted by the SPS process at 900, 1000, and 1100 °C. The differences between the bulk materials were tested by LM, SEM, and TEM microscopy, XRD, and neutron diffraction methods. It was proven that, although the structures of initial powder (B2) and milled powder (A2) were different, both provide after-sintering material with the same structure (D03) with similar structural parameters. Higher hardness and improved ductility of the material sintered from the milled powder are likely caused by the change in chemical composition during the milling process.
This study presents experimental and ANN modelling work to determine machining parameters and achieve better surface roughness in turning operation using coated and uncoated cermet cutting inserts (CCCT and UCCT). 50CrV4 (SAE 6150) material (Brinell hardness (HB) 311) was machined on CNC lathe. Processing parameters were determined using experimental design techniques. Cutting speed, feed rate, depth of cut, tip radius and type of cutting inserts were defined as turning processes parameters. During the machining processes cutting forces and then surface roughness were measured. Multiple regression and ANOVA analysis were performed and significant process parameters defined. An ANN model was also developed on the basis of experimental study results. The model is used for prediction of surface roughness and cutting forces achieving a very close agreement with experimental results.
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