Autogenous laser beam fillet welding of stainless-steel lap joints has a narrow process window because small changes in joint fit-up and beam placement at the top-sheet edge can cause large variations in seam geometry and fusion to the lower sheet. This work evaluated the influence of deformable mirror-based beam shaping on the weld formation. Five beam shapes were compared to a conventional top-hat reference case under otherwise identical welding conditions. Weld surface geometry was evaluated using laser line scanning along the seam for qualitative assessment of seam geometry and apparent weld width. Metallographic cross-sections were used to assess fusion zone geometry. Weld surface scans and transverse cross-sections show that beam shaping modifies seam geometry and fusion-zone formation. Compared to the top-hat reference, the shaped beams produced smoother weld surfaces and wider, shallower fusion regions to the lower sheet. The asymmetric beam shapes, oriented with the wider side of the intensity distribution toward the upper sheet, slightly increased (or maintained) throat thickness relative to the reference. Overall, deformable mirror- based beam shaping enables targeted redistribution of energy to tailor fusion geometry and shows potential to improve consistency in autogenous laser lap-fillet welding. 

Metal additive manufacturing (AM) facilitates the production of geometrically complex components, yet its broader industrial use remains limited by the risk of defect formation and uncertainties in their detection, originating from the highly dynamic and high-temperature process environment. To make additive manufacturing more reliable and establish high-quality parts, it is important to understand how these defects form and how their characteristics appear during the process. This review explains the main causes of common defects, such as cracking, porosity, lack of fusion, and inclusions in metal AM processes, including Powder Bed Fusion and Directed Energy Deposition. It also connects main defect formation mechanisms to the optical, thermal, acoustic, and spectroscopic signals that can be measured during the process. Moreover, it is described how commonly used in situ monitoring systems work and how their signals correspond to melt pool dynamics, vapor plume, particle movement, and the solidification process for each kind of defect. An overview is provided of how data from these systems are analyzed, including the extraction of features from images, the evaluation of temperature fields, and the use of time and frequency domain techniques for various signals. By linking the physics of defect formation to measurable process signals, the interpretation of sensor data is enabled, and potential strategies for monitoring specific problems are outlined. Finally, recent developments are examined, including the integration of multiple sensors, advanced feature-representation approaches, and real-time data interpretation coupled with adaptive control. Together, these directions represent promising advances towards more intelligent and reliable monitoring systems for the future of metal AM.

One challenge in laser beam welding is the variability in joint gaps encountered during autogenous welding of butt joints. This study therefore develops and evaluates an adaptive laser beam shaping technique that utilizes a deformable mirror. By integrating image processing and an adaptive Kalman filter, the proposed method provides real-time estimation of the gap width which varies from point to point along the joint. This capability allows for dynamic adjustments of the beam shape during processing through feed-forward control. Proof of concept was performed in welding sheet metal plates with stepwise changes in gap widths, up to 0.55 mm. The distortion of the welded parts and the topology of the weld seam were assessed both qualitatively and quantitatively using data from a laser line scanner. The data analysis revealed a reduction above 80% in workpiece residual distortion compared to conventional welding with a static circular beam shape. Additionally, welding quality at a 0.55 mm gap width met the laser welding standard (ISO 13919-1) level D. In contrast, approximately 10% of the measured points using a static circular beam did not meet the level D of this standard. The seamlessly integration of real-time gap measurement with adaptive beam shaping thus offers a promising solution to mitigate gap variability in laser beam welding of butt joints. This approach also highlights the broader potential of adaptive beam shaping in high-power metal processing. 

Laser welding processes are highly sensitive to the alignment of the laser beam with respect to the joint location. Even minor deviations—referred to as beam offsets can induce critical defects in the final weld. This issue is particularly pronounced in stake welding of hidden T-joints, where the joint interface is not accessible from the top surface, rendering conventional vision-based seam tracking methods ineffective. To address this challenge, this study proposes a computer vision-based system employing LED illumination to acquire real-time images of the melt pool for early detection of beam offsets. The underlying hypothesis is that variations in melt pool geometry are indicative of beam misalignment; however, this relationship is complex and non-linear, necessitating a data-driven approach. A convolutional neural network is developed and trained to classify melt pool images into categories representing correct alignment and beam offset conditions. Experimental validation is conducted using image datasets obtained from controlled laser welding trials. The proposed method demonstrates high classification accuracy and holds significant potential for in-process quality assurance and defect prevention in laser stake welding of T-joints.

Robotized laser beam welding demands precise control, with accurate joint gap estimation essential for adaptive gap-bridging. Challenges such as smoke, surface irregularities, and spectral emissions hinder classification performance. This study applies deep learning models to enable real-time gap classification during autogenous butt joint welding using beam-shaping. Images captured from varying joint gaps allowed evaluation under dynamic conditions. Four models were trained and assessed, with the best model surpassing application accuracy requirements. The findings support the development of adaptive welding systems for consistent weld quality in noisy, fluctuating environments.

This work presents a novel application of deformable mirror (DM)-enabled laser beam shaping to enhance gap bridging capability in autogenous butt joint welding of stainless steel sheets. Three distinct near-Gaussian beam geometries — circular (C), transverse elliptical (TE), and longitudinal elliptical (LE) – were experimentally evaluated for their influence on weld quality. The tests were conducted across constant joint gaps up to 0.6 mm in 2.0 mm-thick AISI 316 stainless steel. The investigation demonstrates that elliptical beam shapes, particularly those with the major axis aligned in the welding direction (LE), significantly improve gap-bridging performance and weld seam consistency. Metallographic analyses, in-process top-view weld pool imaging, and microhardness profiling collectively indicate that elliptical beam shapes reduce the sensitivity of the fusion zone geometry to gap variation, enabling bridging gaps up to 30% of sheet thickness, three times the typical 10% limit. The results further reveal that effective gap bridging is governed not solely by beam radius, but critically by the spatial distribution and gradients of power density across the gap, which modulate Marangoni-driven melt flow. These findings underscore the fundamental role of adaptive beam shaping in controlling weld pool dynamics and advancing process robustness without filler material. This study represents the first systematic exploration of DM-based elliptical beam shaping in autogenous butt welding and establishes a foundational methodology for real-time adaptive beam control in laser processing. 

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.

Laser metal fusion is widely used in production technology to manufacture parts, as in welding, cladding, and additive manufacturing. One development of interest to laser metal fusion is the recent advancements in deformable mirror technologies that allow them to withstand multi-kilowatt laser power. This enables the generation of various beam shapes and rapid switching between these within 10 milliseconds.

This thesis investigates the application of deformable mirror-based beamshaping and computer vision techniques to improve laser beam welding and directed energy deposition using laser and wire. Additionally, methods including analysis of melt pool images, cross-cut micrographs, surface topology measurements has been used to evaluate and characterize the results.

An industrially relevant challenge addressed is variation in the joint gap width during autogenous butt welding, which often results in defects such as incomplete groove filling and root concavities. Two near-elliptical beam shapes, with major axes aligned either along or transverse to the welding direction, were studied for gap bridging. Both beam shapes were capable of bridging gaps up to 0.6 mm wide during welding of 2.0 mm thick steel plates. It was shown that the beam shaping made it possible to meet higher quality levels in the ISO13919-1 standard compared to a ordinary circular static beam. The beam with the major axis along the welding direction performed even better in reducing defects compared to the one aligned transversely.

When adaptive beam shaping was used for gap bridging in butt joints it resulted in an 80% reduction in workpiece distortion, demonstrating its effectiveness in accommodating varying process conditions. Additionally, computer vision algorithms, including adaptive Kalman filters and convolutional neural networks, proved successful in gap width estimation, classification, and tack weld detection under varying conditions during welding.

In directed energy deposition using laser and wire, three near-elliptical beamshapes with different orientations relative to the deposition and wire feeding directions were examined. The beam with the major axis transverse to the deposition direction produced a narrower, higher bead, while the beam aligned with the feedstock wire direction (about 35◦ relative to deposition direction) yielded a wider, lower bead with better melting efficiency.

These findings presented in this thesis underscore the potential of dynamic beam shaping, especially when integrated with robust computer vision techniques, to enhance process control, making high power laser processing more reliable and efficient.

Lasersmältning av metall är en produktionsteknik som används for att tillverka komponenter, till exempel genom svetsning, påläggssvetsning eller additiv tillverkning. En utveckling som är intressant för lasersmältning är de senaste framstegen inom formbar spegelteknik gör att denna typ av spegeloptik kan klara av en lasereffekt på flera kilowatt, samt frambringa olika strålformer (beam shapes) och snabbt växla mellan dessa inom 10 millisekunder.

Denna avhandling undersöker ett system med en deformerbar spegel för laserstrålformning i kombination med datorseendeteknik (computer vision) för att effektivisera lasersvetsning och additiv tillverkning. Dessutom har metoder som analyserar bilder på smältan i processen, bilder på den resulterande mikrostrukturen i tvärsnitt samt topologimätningar använts för att utvärdera och karakterisera resultaten.

En industriellt relevant utmaning som undersökts är variationen i fogspaltbredd under stumfogsvetsning utan tillsatstråd, vilket ofta resulterar i defekter såsom svetsdiken. Två elliptiska laserstrålformer, med huvudaxlar riktade antingen längs med eller tvärs mot svetsriktningen, studerades med avseende

på förmågan att överbrygga fogspalten.Båda strålformerna kunde överbrygga en fogspalt på upp till 0,6 mm vidsvetsning av 2,0 mm tjocka stålplåtar. Det visade sig att strålformningen gjorde det möjligt att uppfylla högre kvalitetsnivåer enligt ISO13919-1-standarden jämfört med en vanlig cirkulär statisk laserstråle. Laserstrålen med huvudaxelnl ängs svetsriktningen presterade ännu bättre när det gällde att minska defekterj ämfört med den som var på tvären.

Den adaptiva strålformningen som används för överbryggning av spalter i stumfogar resulterade i en 80-procentig minskning av arbetsstyckets deformation, vilket demonstrerar dess effektivitet när det gäller att hantera varierande processförhållanden. Dessutom visade algoritmer för datorseende bland annat med ett adaptivt Kalman-filter eller ett neuralt nätverk, vara framgångsrik när det gäller att mäta, klassificera och detektera häftsvetsar och varierationer under svetsning.

I samband med laserbaserad additiv tillverkning undersöktes tre elliptiskal aserstrålformer med olika orienteringar i förhållande till deponerings- och trådmatningsriktningarna. Strålen med huvudaxeln tvärs mot deponeringsriktningen gav en smalare, högre deponering, medan laserstrålen i linje med tillsatstrådriktningen (cirka 35◦ i förhållande till deponeringsriktningen) gav en bredare, lägre deponering med bättre smälteffektivitet.

Resultat påvisar tydligt potentialen hos dynamisk laserstrålformning moten bättre processtyrningen, särskilt när den integreras med robust datorseende i ett återkopplat system, något som bidrar till att göra högeffekt-laserprocesser mera tillförlitliga och effektiva.

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.