ABSTRACT
To ensure the high reliability of aircraft structures, the Refill Friction Stir Spot Welding (RFSSW) process must be characterized by a high load capacity of the welds and a small standard deviation of the load capacity spread. This allows us to obtain uniform functional properties in the connections, ensuring the high quality of the process. This work aims to select the most favorable technological parameters for the welding process of EN AW-7075-T6 Alclad aluminum alloy sheets, which are used for the production of aircraft structures. The best networks were calculated using the Statistica 13.3 program. The obtained results were compared with the results of previous investigations. It has been shown that a model using neural networks allows for the determination of connection parameters with much greater accuracy than the classical model. The maximum error in estimating the load capacity of the connection for the mathematical model was 6.13%, and the standard deviation was 14.51%. In the case of neural networks, the maximum error value did not exceed 1.55%, and the standard deviation was 3.74%. It was shown that, based on the neural model, it is possible to determine the process parameters that ensure the required quality capacity of the process, ensuring a probability of obtaining the required load capacity of the connections amounting to P = 0.999935 with a defect rate of 0.0065%. This possibility is not provided by the classical model due to its large error in estimating the process spread and the high sensitivity of the process input parameters to the output parameters.
ABSTRACT
In this investigation, an AA2519-T62 FSW butt joint was subjected to shot peening with an air pressure of p = 0.6 MPa, a processing time of t = 10 min (per side), and a steel ball diameter of dk = 1.5 mm. In order to evaluate the impact of shot peening on the low-cycle behavior, the samples were tested with coefficient R = 0.1 at total strain amplitudes of 0.35%, 0.4%, and 0.5%. The shot-peened welds are characterized by a higher value of stress amplitude, a lower value of plastic strain amplitude, and their fatigue life increased slightly. The cyclic strength coefficient and the cyclic strain hardening exponent were reduced by 45% and 55%, respectively, as the result of the surface layer hardening. The shot peening process had no noticeable effect on the character of crack initiation and propagation. Almost in all cases, the cracking started in the area under the weld face, located close to the boundary between the thermo-mechanically affected zone and the stir zone at the advancing side. Only at the heaviest loadings (εac = 0.5%) were cracks initiated in the heat-affected zone at the retreating side. Despite the introduction of small cracks in the stir zone, their presence did not affect the decohesion character of the welded joint. Overall, it was observed that there is a minimal, positive impact of shot peening on the properties of the investigated joints.
ABSTRACT
This article presents the influence of the Shot Peening (SP) process on residual stress and surface roughness of AMS 5504 joints welded using the Tungsten Inert Gas (TIG) method. Thin-walled steel structures are widely used in the aviation and automotive industries, among others. Unfortunately, the fatigue properties become worse during the welding process. Samples of 1 mm-thick AMS 5504 steel plates were first prepared using TIG welding and then strengthened by the Shot Peening (SP) process. The technological parameters of the SP process were changed in the range of time t from 2 min to 4 min and of pressure p from 0.4 MPa to 0.6 Mpa. The residual stresses were measured by X-ray diffraction in three zones: fusion zone (FZ), heat-affected zone (HAZ) and base metal (BM). The results showed that SP introduced compressive residual stresses in all of the zones measured, especially in the FZ. The greatest value of compressive residual stresses σ = -609 MPa in the FZ was observed for the maximum parameters of SP (p = 0.6 MPa, t = 4 min). The increase in value of residual stress is about 580% when compared to welding specimens without treatment. As a result of shot peening in the FZ, the mean roughness value Ra decreased in range 63.07% to 77.67% in the FZ, while in the BM increased in range 236.87% to 352.78% in comparison to specimen without treatment. Selected surface roughness parameters in FZ and BM were analyzed using neural networks. In FZ, it was demonstrated that the most correlated parameters with residual stresses are Rt and Rsk. On the other hand, in the BM zone, the most correlated parameters were Rv, Rt and Rq. This enables the estimation of stresses in the welded joint after SP on the basis of selected roughness parameters.
ABSTRACT
This article presents the results of tests aimed at determining the effect of slide burnishing parameters on the surface roughness of shafts made of 42CrMo4 heat-treatable steel. The burnishing process was carried out using tools with polycrystalline diamond and cemented carbide tips. Before burnishing, the samples were turned on a turning lathe to produce samples with an average surface roughness Ra = 2.6 µm. The investigations were carried out according to three-leveled Hartley's poly selective quasi D (PS/DS-P: Ha3) plan, which enables a regression equation in the form of a second-order polynomial to be defined. Artificial neural network models were also used to predict the roughness of the surface of the shafts after slide burnishing. The input parameters of the process that were taken into account included the values of pressure, burnishing speed and feed rate. Overall, the burnishing process examined leads to a reduction in the value of the surface roughness described by the Ra parameter. The artificial neural networks with the best regression statistics predicted an average surface roughness of the shafts with R2 = 0.987. The lowest root-mean-square error and mean absolute error were obtained with all the network structures analysed that were trained with the quasi Newton algorithm.
