Search Results (2)

Laser beam welding offers high productivity and relatively low heat input and is one key enabler for efficient manufacturing of sandwich constructions. However, the process is sensitive to how the laser beam is positioned with regards to the joint, and even a small deviation of the laser beam from the correct joint position (beam offset) can cause severe defects in the produced part. With tee joints, the joint is not visible from top side, therefore traditional seam tracking methods are not applicable since they rely on visual information of the joint. Hence, there is a need for a monitoring system that can give early detection of beam offsets and stop the process to avoid defects and reduce scrap. In this paper, a monitoring system using a spectrometer is suggested and the aim is to find correlations between the spectral emissions from the process and beam offsets. The spectrometer produces high dimensional data and it is not obvious how this is related to the beam offsets. A machine learning approach is therefore suggested to find these correlations. A multi-layer perceptron neural network (MLPNN), support vector machine (SVM), learning vector quantization (LVQ), logistic regression (LR), decision tree (DT) and random forest (RF) were evaluated as classifiers. Feature selection by using random forest and non-dominated sorting genetic algorithm II (NSGAII) was applied before feeding the data to the classifiers and the obtained results of the classifiers are compared subsequently. After testing different offsets, an accuracy of 94% was achieved for real-time detection of the laser beam deviations greater than 0.9 mm from the joint center-line. Full article

In the present study, aluminum alloy T-joints were welded using the laser stake-welding process. In order to improve the welding quality of the T-joints, an external electric current was used to aid the laser stake-welding process. The effects of the process parameters on the weld morphology, mechanical properties, and microstructure of the welded joints were analyzed and discussed in detail. The results indicate that the aided electric current should be no greater than a certain maximum value. Upon increasing the aided electric current, the weld width at the skin and stringer faying surface obviously increased, but there was an insignificant change in the penetration depth. Furthermore, the electric current and pressing force should be chosen to produce an expected weld width at the faying surface, whereas the laser power and welding speed should be primarily considered to obtain an optimal penetration depth. The tensile shear specimens failed across the faying surface or failed in the weld zone of the skin. The specimens that failed in the weld of the skin could resist a higher tensile shear load compared with specimens that failed across the faying surface. The microstructural observations and microhardness results demonstrated that the tensile shear load capacity of the aluminum alloy welded T-joint was mainly determined by the weld width at the faying surface. Full article