The paper presents the results of an experimental investigation, done on the friction stir welding (FSW) plunging stage. Previous research works showed that the axial force and torque generated during this stage were characteristic for a static qualification of a FSW machine. Therefore, the investigation objectives are to better understand the relation between the processing parameters and the forces and torque generated. One of the goals is to find a way to reduce the maximum axial force and torque occurring at the end of the plunging stage in order to allow the use of a flexible FSW machine. Thus, the influence of the main plunge processing parameters on the maximum axial force and torque are analysed. In fact, forces and torque responses can be influenced by the processing parameter. At the end, a diagram presenting the maximum axial force and torque according to the processing parameters is presented. It is an interesting way to present the experimental results. This kind of representation can be useful for the processing parameters choice. They can be chosen according to the force and torque responses and consequently to the FSW machine capacities.

Retroperitoneal schwannomas are a rare entity. They originate from the Schwann cells of the nerve sheaths and may be of renal or pararenal origin. We report on two patients with retroperitoneal schwannomas, who received surgery under the suspicion of renal cell carcinoma.

Wire-arc additive manufacturing has become an alternative way to produce industrial parts. In this work 15 kg walls are built with an effective building rate of 4.85 kg/h using an ER100 wire providing good tensile properties and toughness under welding conditions. The thermal evolution of the walls during manufacturing is measured by thermocouples and an IR camera: it depends on process parameters, deposit strategy and the size of the part. The walls are then characterised as deposit and after heat treatment through hardness, tensile and Charpy-V notch tests. The results show a fine microstructure with unexpected retained austenite and coarse allotriomorphic ferrite in the as deposited walls. The final hardness values vary from about 220 to 280 HV2; the yield stress and tensile strength are 520 and 790 MPa, respectively, and a toughness of about 50 J is obtained at room temperature. The heat treatment transforms the retained austenite, leading to an improvement of the yield stress to 600 MPa.

Cold metal transfer (CMT) based wire-arc additive manufacturing (WAAM) is a promising method for the production of large-scale and complex metallic parts because of its high efficiency, less heat input and low cost. However, a critical and common problem with the arc welding processes is the irregular geometry at the beginning and end parts of the bead due to the ignition and extinction of the arc. Based on experimental investigations of the irregularities and different possible optimization methods, an improvement strategy consisting of configurations with a varying travel speed and an extra return path is presented in this paper. Experimental results show that this strategy can effectively enhance the geometric accuracy at the beginning and end parts of different single beads. In the manufacturing of a thin-wall part and a multi-pass cladding, the improvement of geometric accuracy has also been achieved by this strategy.

Cold metal transfer (CMT) based wire-arc additive manufacturing (WAAM) is increasingly popular for the production of large and complex metallic components due to its high deposition rate, low heat input and excellent material efficiency. The accurate prediction of the bead geometry is of great importance to enhance the stability of the process and its dimensional accuracy. Besides the wire feed speed (WFS) and travel speed (TS), the interlayer temperature is another key factor in determining the bead geometry because of the heat accumulation in the multilayer deposition. In this paper, considering the varying interlayer temperature, WFS and TS as inputs, an artificial neural network (ANN) is developed to predict the bead width, height and contact angle; then, by connecting the ANN model with a bead geometric model, a combined model is established to improve the ANN model. Based on experimental test data, with random combinations of input parameters, the combined model is demonstrated to be able to accurately predict the bead geometry (mean error < 5.1%). The general effect of interlayer temperature on the bead geometry was also investigated by experiment.

During Friction Stir Welding process, FSW, a tool / workpiece mechanical interaction is generated leading to forces and torques applied on the tool. These forces and torques are transmitted to the welding equipment impacting its technical requirements. This paper presents a forces and torques analysis according to the processing parameters on the welding at constant speed stage. The analysis was performed on the whole welding process windows, by varying one process parameter after the other. The goal of this work is to determine if and how the forces and torque could be reduced by working on the processing parameters. So, with lower forces and torque applied on the tool, the use of a standard and flexible welding equipment, allowing the welding of complex geometries, could be enabled.

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Friction Stir Welding (FSW) is an innovative welding process increasingly used by industry for the welding of aluminum alloys. In order to reduce the high investment costs of a dedicated FSW’s machine and in order to offer more flexibility to weld complex geometry, high payload robots may be used. A serial kinematics robot meets these specifications but under the stresses generated during welding, its structure readily deforms. The consequences are deviations of the tool nominal position with respect to the seam. The work presented here proposes to study the process tolerances with tool positioning defect. An experimental study enables to evaluate the influence of the tool position disorientation on weld quality, the travel force and torque generated. The objective is to estimate the impact of the disorientation on the tool mechanical interactions when welding using a serial kinematics robot.