Influence of ultrasound on pore and crack formation in laser beam welding of nickel-base alloy round bars

Welding by laser beam is a method for creating deep and narrow welds with low influence on the surrounding material. Nevertheless, the microstructure and mechanical properties change, and highly alloyed materials are prone to segregation. A new promising approach for minimizing segregation and its effects like hot cracks is introducing ultrasonic excitation into the specimen. The following investigations are about the effects of different ultrasonic amplitudes (2/4/6 µm) and different positions of the weld pool in the resonant vibration distribution (antinode, centered, and node position) for bead on plate welds on 2.4856 nickel alloy round bars (30 mm diameter) with a laser beam power of 6 kW. The weld is evaluated by visual inspection and metallographic cross sections. The experiments reveal specific mechanisms of interaction between melt and different positions regarding to the vibration shape, which influence weld shape, microstructure, segregation, cracks and pores. Welding with ultrasonic excitation in antinode position improves the welding results

Deep Learning-Based Weld Contour and Defect Detection from Micrographs of Laser Beam Welded Semi-Finished Products

Laser beam welding is used in many areas of industry and research. There are many strategies and approaches to further improve the weld seam properties in laser beam welding. Metallography is often needed to evaluate welded seams. Typically, the images are examined and evaluated by experts. The evaluation process qualitatively provides the properties of the welds. Particularly in times when artificial intelligence is being used more and more in processes, the quantization of properties that could previously only be determined qualitatively is gaining importance. In this contribution, we propose to use deep learning to perform semantic segmentation of micrographs of complex weld areas to achieve the automatic detection and quantization of weld seam properties. A semantic segmentation dataset is created containing 282 labeled images. The training process is performed with DeepLabv3+. The trained model achieves a value of around 95% for weld contour detection and 76.88% of mean intersection over union (mIoU). © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Feasibility study for the manufacturing of hybrid pinion shafts with the cross-wedge rolling process

The Collaborative Research Center 1153 is investigating an innovative process chain for the production of hybrid components. The hybrid workpieces are first joined and then formed by cross-wedge rolling. Pinion shafts were manufactured to investigate the behavior of the joining zone under increased complexity of the forming process. For this purpose, six types of workpieces produced by three types of joining processes were formed into pinion shafts. The reference process provides a shaft with a smooth bearing seat. It was found that the increased complexity did not present any challenges compared to the reference processes. A near-net shape geometry was achieved for the pinions made of steel

Identification of the Effect of Ultrasonic Friction Reduction in Metal-Elastomer Contacts Using a Two-Control-Loop Tribometer

Pneumatic cylinders are widely used in highly dynamic processes, such as handling and conveying tasks. They must work both reliably and accurately. The positioning accuracy suffers from the stick-slip effect due to strong adhesive forces during the seal contact and the associated high breakaway forces. To achieve smooth motion of the piston rod and increased position accuracy despite highly variable position dynamics, sliding friction and breakaway force must be reduced. This contribution presents a specially designed linear tribometer that has two types of control. Velocity control allows the investigation of sliding friction mechanisms. Friction force control allows investigation of the breakaway force. Due to its bearing type, the nearly disturbance-free detection of stick-slip transients and the dynamic contact behavior of the sliding friction force was possible. The reduction of the friction force was achieved by a superposition of the piston rod’s movement by longitudinal ultrasonic vibrations. This led to significant reductions in friction forces at the rubber/metal interface. In addition, the effects of ultrasonic frequency and vibration amplitude on the friction reduction were investigated. With regard to the breakaway force, significant success was achieved by the excitation. The force control made it possible to identify the characteristic movement of the sealing ring during a breakaway process

Deep Learning-Based Weld Contour and Defect Detection from Micrographs of Laser Beam Welded Semi-Finished Products

Laser beam welding is used in many areas of industry and research. There are many strategies and approaches to further improve the weld seam properties in laser beam welding. Metallography is often needed to evaluate welded seams. Typically, the images are examined and evaluated by experts. The evaluation process qualitatively provides the properties of the welds. Particularly in times when artificial intelligence is being used more and more in processes, the quantization of properties that could previously only be determined qualitatively is gaining importance. In this contribution, we propose to use deep learning to perform semantic segmentation of micrographs of complex weld areas to achieve the automatic detection and quantization of weld seam properties. A semantic segmentation dataset is created containing 282 labeled images. The training process is performed with DeepLabv3+. The trained model achieves a value of around 95% for weld contour detection and 76.88% of mean intersection over union (mIoU)