This study explores the uncharted territory of beam shaping through a novel deformable mirror system in directed energy deposition laser wire, an emerging area in Additive Manufacturing. While beam shaping has shown substantial benefits in laser processes like welding and powder bed fusion, its potential in this specific domain remains unexploited. The research investigates the influence of three near-elliptical Gaussian beam shapes on melt pool and bead geometries during deposition with stainless-steel wire. The study reveals three distinct processing modes achievable at the same total power through beam shaping, with significant modifications observed in melt pool and bead structures. Reduced bead geometry variation and enhanced process stability were achieved with the beam shape with major axis along the wire feeding direction, and with highest average power density and intermediate peak power density. The findings underscore the potential of beam shaping to enhance robustness and increase energy utilization and productivity in this process.  

Laser Beam Welding (LBW) requires precise control to ensure high-quality welds. Accurate classification of joint gap widths and detection of tack welds are crucial for optimizing the process and enhancing product reliability.

This study investigates the application of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to classify instant joint gap widths and detect the presence of tack welds during welding. The goal is to facilitate adaptive joint gap bridging in robotized and autogenous butt joint welding. Sequences of images resembling a time series were captured during welding of prepared workpieces with varying joint gap widths along the joint line.

The results demonstrate that CNNs significantly outperform RNNs, achieving over 99 percent classification accuracy in both validation and test datasets, and 96 percent accuracy under conditions of substantial noise. These findings underscore the potential of CNNs in enhancing the precision and adaptability of welding automation. However, challenges remain in generalizing the CNN model to diverse and noisy operational environments.

In autogenous laser butt welding the variability of the joint gap can cause problems in terms of weld seam quality. A suitable strategy to alleviate this is to dynamically shape the laser beam instead of a circular-shaped beam with typical Gaussian or top hat distributions. Currently available systems cannot reach sufficient performance due to both the real time control system for the shape variation and the limited laser power currently manageable. In the present work, the possibility of bridging the joint gap during welding using a deformable mirror to elongate the focused laser beam from circular to transversal elliptical shape was investigated. The effect of the beam shaping on the geometry of the weld pool and of the weld cross sections was analysed, for different values of the gap in comparison with a circular Gaussian beam. It was demonstrated that the adoption of a transversal elliptical laser beam makes the welding process more stable, especially for large gaps (i.e. larger than the circular beam radius). Thanks to the beam shaping, the extension of the fused zone (in terms of the cross section area, height and width) resulted to be less sensitive to the gap's dimension; in addition, the extension of the heat affected zone and the presence of undercuts were evidently reduced.

To be a successful competitor among other technologies, metallic laser-directed energy depositionusing a laser beam would benefit from the support of intelligent automation making the processrobust, repeatable, and cost-efficient. This calls for technology leaps towards robust and accuratedetection and estimation of the conditions during processing and control schemes for robustperformance. This chapter discusses how developments in sensor technology and model-basedsignal processing can contribute to advancements in in-process monitoring of directed energydeposition using a laser beam and how developments in model-based feedforward- and feedbackcontrol can support automation. The focus is on how machine vision, optical emission spectroscopy,thermal sensing, and electrical process signals can support monitoring, control and better processunderstanding. These approaches are industrially relevant and have a high potential to support amore sustainable manufacturing. 

This study investigates the combination of three sensors to improve in-process monitoring of the liquid bridge between the feedstock wire and melt pool in hot-wire Directed Energy Deposition using Laser Beam. The stability of the deposition process relies on the transfer of metal between the molten feedstock wire and melt pool. Therefore, monitoring the condition of the liquid bridge and the interaction between the feedstock wire and melt pool is crucial. By utilizing a laser-optics-integrated visible range optical spectrometer and electrical sensors measuring voltage and current, relevant process changes and indications of instabilities were detected. Combined information from the current sensor and the spectrometer provided a better understanding of the process and helped to identify deviations leading to unstable deposition modes.

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.

This study explores the possibility of tailoring the fusion zone in conduction mode laser welding using a deformable mirror for beam shaping of multi-kilowatt continuous wave laser sources. Three power density distributions were shaped and used in bead on plate welding of Ti64 plates in conduction mode at three travel speeds. The effect on melt pool free surface geometry, cross section, microstructure and hardness profiles was measured and studied. It is shown that the geometry of the melt pool can be modified using a deformable mirror. A narrower and longer melt pool or a wider, shorter and shallower one were indeed obtained forming Gaussian-elliptical power density distributions oriented along and transverse to the travel direction, respectively. The latter distribution could be a favourable option for laser beam additive manufacturing as it could improve process efficiency while reducing remelting of the previous layer. This system has also a promising potential for adaptive process control since it could change fundamentally the beam shape at a rate faster than 10 ms. 

A computational fluid dynamics approach is used to analyse the influence of beam shaping in fusion welding on melt thermal flow. Three beam shapes are studied at several welding travel speeds: a reference Gaussian profile and its elliptic elongations along and transverse to the welding travel direction. It is found that these beam shapes change not only the intensity and direction of the melt thermocapillary flow but also the flow pattern. For instance, and contrary to the other profiles, the beam shape elongated along the welding travel direction generates melt front vortices that assist metal pre-heating. It can result in deeper penetration, larger melt volume, and lower amount of thermal energy diffused into the heat affected zone. The simple elongation of a beam profile has thus a non-linear effect on the melt flow and in turn on the seam geometry as well as the temperature gradients in the heat affected zone.

This study investigates the influence of resistive pre-heating of the feedstock wire (here called hot-wire) on the stability of laser-directed energy deposition of Duplex stainless steel. Data acquired online during depositions as well as metallographic investigations revealed the process characteristic and its stability window. The online data, such as electrical signals in the pre-heating circuit and images captured from side-view of the process interaction zone gave insight on the metal transfer between the molten wire and the melt pool. The results show that the characteristics of the process, like laser-wire and wire-melt pool interaction, vary depending on the level of the wire pre-heating. In addition, application of two independent energy sources, laser beam and electrical power, allows fine-tuning of the heat input and increases penetration depth, with little influence on the height and width of the beads. This allows for better process stability as well as elimination of lack of fusion defects. Electrical signals measured in the hot-wire circuit indicate the process stability such that the resistive pre-heating can be used for in-process monitoring. The conclusion is that the resistive pre-heating gives additional means for controlling the stability and the heat input of the laser-directed energy deposition.

In this work, a fast optical spectrometer was used to monitor the Directed Energy Deposition (DED) process, during the deposition of Alloy 718 samples with different laser power, thus different energy inputs into the material. Spectroscopic measurements revealed the presence of excited Cr I atoms in the plasma plume. The presence was more apparent for the samples characterized by higher energy input. The Cr depletion from these samples was confirmed by lower Cr content detected by Energy-Dispersive X-ray Spectroscopy (EDS) analysis. The samples were also characterized by higher oxidation and high-temperature corrosion rates in comparison to the samples produced with low energy input. These results prove the applicability of an optical emission spectroscopic system for monitoring DED to identify process conditions leading to compositional changes and variation in the quality of the built material.