The laser tube bending process (LTBP) is a new and powerful manufacturing method for bending tubes more accurately and economically by eliminating the bending die. The irradiated laser beam creates a local plastic deformation area, and the bending of the tube occurs depending on the magnitude of the heat absorbed by the tube and its material characteristics. The main bending angle and lateral bending angle are the output variables of the LTBP. In this study, the output variables are predicted by support vector regression (SVR) modeling, which is an effective methodology in machine learning. The SVR input data is provided by performing 92 experimental tests determined by the design of the experimental techniques. The measurement results are divided into two sub-datasets: 70% for the training dataset, and 30% for the testing dataset. The inputs of the SVR model are process parameters, which can be listed as the laser power, laser beam diameter, scanning speed, irradiation length, irradiation scheme, and the number of irradiations. Two SVR models are developed for the prediction of the output variables separately. The SVR predictor achieved a mean absolute error of 0.021/0.003, a mean absolute percentage error of 1.485/1.849, a root mean square error of 0.039/0.005, and a determination factor of 93.5/90.8% for the main/lateral bending angle. Accordingly, the SVR models prove the possibility of applying SVR to the prediction of the main bending angle and lateral bending angle in LTBP with quite an acceptable accuracy.

In this work, the process of dissimilar resistance spot welding (RSW) for AISI 304 and AISI 1060 steel sheets is experimentally investigated. The effects of the main process parameters such as welding current, electrode force, welding cycle, and cooling cycle on the tensile-shear strength (TSS) of dissimilar RSW joints are studied. To this aim, using a central composite experimental design based on response surface methodology (RSM), the experimental tests were performed. Furthermore, from the test results, an adaptive neuro-fuzzy inference system (ANFIS) was developed to model and estimate the TSS. The optimal parameters of the ANFIS system were obtained using a teaching-learning-based optimization (TLBO) algorithm. In order to model the process behavior, the results of experiments were used for the training (70% of the data) and testing (30% of the data) of the adaptive inference system. The accuracy of the obtained model was investigated via different plots and statistical criteria including root mean square error, correlation coefficient, and mean absolute percentage error. The findings show that the ANFIS network successfully predicts the TSS. In addition, the network error in estimating the TSS in the training and test section is equal to 0.08% and 5.87%, respectively. After modeling with TLBO-ANFIS, the effect of each input parameter on TSS of the dissimilar joints is quantitatively measured using the Sobol sensitivity analysis method. The results show that increasing in welding current and welding cycle leads to an increase in the TSS of joints. It is concluded that TSS decreases with increases in the electrode force and cooling cycle.

Purpose: This study aims to explore an active control strategy for attenuation of in-line and transverse flow-induced vibration (FIV) of two tandem-arranged circular cylinders. Design/methodology/approach: The control system is based on the rotary oscillation of cylinders around their axis, which acts according to the lift coefficient feedback signal. The fluid-solid interaction simulations are performed for two velocity ratios (V_r = 5.5 and 7.5), three spacing ratios (L/D = 3.5, 5.5 and 7.5) and three different control cases. Cases 1 and 2, respectively, deal with the effect of rotary oscillation of front and rear cylinders, while Case 3 considers the effect of applied rotary oscillation to both cylinders. Findings: The results show that in Case 3, the FIV of both cylinders is perfectly reduced, while in Case 2, only the vibration of rear cylinder is mitigated and no change is observed in the vortex-induced vibration of front cylinder. In Case 1, by rotary oscillation of the front cylinder, depending on the reduced velocity and the spacing ratio values, the transverse oscillation amplitude of the rear cylinder suppresses, remains unchanged and even increases under certain conditions. Hence, at every spacing ratio and reduced velocity, an independent controller system for each cylinder is necessary to guarantee a perfect vibration reduction of front and rear cylinders. Originality/value: The current manuscript seeks to deploy a type of active rotary oscillating (ARO) controller to attenuate the FIV of two tandem-arranged cylinders placed on elastic supports. Three different cases are considered so as to understand the interaction of these cylinders regarding the rotary oscillation.