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Fluid dynamics models for laser material processing with metal fusion in conduction mode generally assume a constant absorptivity. This parameter is known to govern the process. However, it used to be pre-set by extrapolating absorptance measurements made at different conditions or adjusted to reproduce experimental bead shapes. In this study a new approach is developed. It consists in predicting the absorptance as a function of local surface conditions, including the surface temperature. The proposed absorptance model is applied to the metal alloy Ti-6Al-4V. It is found that the absorptance of this alloy changes with surface temperature over a wide range of beam incidence angles. Thermo-fluid simulations with tracking of the free-surface deformation are performed for conduction mode beam welding test cases with a Yb fibre laser and different travel speeds. It is found that the absorptivity coefficient commonly used for this process clearly underestimates the absorptance and the melt pool geometry predicted for the process conditions of this study. The computational results are also compared against experimental results and good quantitative agreement of the melt pool depth, width, length, free surface contour geometry, and the curvature of the end depression left after re-solidification at the laser switch-off location is obtained. The results show that the absorptance field predicted depends on the melt pool development stage, on the spatial location within the beam spot, and on the process conditions.

The effect of different shapes of laser beam power density distribution was investigated numerically with respect to the thermo-hydrodynamics of the melt pool during welding. The process addressed is conduction mode bead on plate welding of the Titanium alloy Ti-6Al-4V. A new solver based on the volume of fluid method to track the deformation of the melt free surface was developed in the OpenFOAM software. Experiments were conducted for the purpose of validating the model. In addition to the traditional cross-cut images of the weld bead, top view images of the melt pool were analysed to perform the validation along the 3space dimensions. A good agreement between numerical predictions and experimental measurements was obtained, thus promising a confident utilization of this simulation model when investigating the influence of beam shapes on the resulting weld seam. The effect of three different beam shapes on the melt pool velocity flow, temperature fields, and melt geometry were studied. It was found that the melt pool size was largest for an elliptical power density distribution with the major axis along the welding direction. The results also showed that the laser beam with Gaussian power density distribution resulted in the deepest penetration.

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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.

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