United States

The artificial intelligence approach in the monitoring of laser welding is widely studied to ensure the good weld quality. In this study, a deep neural network (DNN)-based monitoring framework for weld shape prediction was established. This weld shape prediction model takes an optical signal as its input and generates a weld shape image as its output. Optical signals were acquired using the optical emission spectroscopy (OES). For the training of prediction model, this acquired optical signal was preprocessed using three methods: principal component analysis (PCA), spectral feature extraction (SFE), and relative intensity calculation (RIC). The weld shape image used for model training was generated by obtaining an eigen-image using the singular value decomposition (SVD) method. Supervised learning was used, and the generated images were labeled with optical signals according to the process conditions. Model evaluation was performed by comparing the weld area in the eigen-images with the one in predicted images. The two weld images were completely overlapped using feature points (e.g., top surface or tip of welds), and then, image accuracy was quantitatively determined by dividing the intersection area of these images by their union area. The weld shape predicted by the DNN model showed the accuracy of ~90% for all input data types.

ICALEO 2024

DNN-Based Real-Time Monitoring of Weld Shape during the Laser Welding Process

weld shape prediction

dnn-based monitoring

singular value decomposition

laser welding

optical emission spectroscopy

  #### Welcome to the 43rd annual ICALEO!

Attention laser enthusiasts, innovators, and industry leaders! Get ready to immerse yourself in the exciting world of lasers at the 43rd annual ICALEO conference. This year, we gathered in the iconic city of Hollywood, California, for cutting-edge technology, groundbreaking presentations, and unparalleled networking opportunities.
Whether you're a seasoned professional or new to the field of photonics, the 43rd annual ICALEO had something for everyone. 

#### General Chair Welcome Message

It is with immense joy and anticipation that I extend a warm welcome to each and every one of you as the General Chair of this year's ICALEO conference.

This year, we find ourselves in the iconic setting of Hollywood, serving as a fitting backdrop for the extraordinary developments in laser technology and applications unfolding in our community.

Our main sessions cover a diverse range of topics, from the intricacies of micro and nano processing to the transformative power of laser additive manufacturing. We will explore the frontiers of battery technology, delve into the intricate world of artificial intelligence, and witness the seamless integration of lasers into these realms.

For those joining us for the first time, we extend an especially heartfelt welcome. ICALEO is not just a conference; it's a convergence of minds, ideas, and innovations. Beyond the formal sessions, I encourage you to engage in informal exchanges. The spirit of discovery and camaraderie is as much a part of the ICALEO experience as the scheduled sessions.

Thank you for being a part of ICALEO 2024. Whether you are a returning participant or a first-time attendee, your presence adds immeasurable value to our vibrant community. May this conference be, this year again, a catalyst for inspiration, collaboration, and breakthroughs that will resonate in our community and beyond.

Eric Mottay<br>
Congress General Chair, ICALEO 2024


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#### Program Tracks
Below is the list of all tracks that were on ICALEO 2024 Conference.

##### Laser Additive Manufacturing
* The Laser Additive Manufacturing (LAM) track is a forum to explore the latest innovations and research in laser-based additive manufacturing (LAM). The Laser Additive Manufacturing track focuses on diverse topics, including laser-based powder bed fusion (PBF-LB), directed energy deposition (DED), and other emerging techniques revolutionizing modern manufacturing processes. From materials science to process optimization, from design innovation to industrial implementation, this track connects researchers, engineers, and industry leaders to exchange insights and share breakthroughs in laser additive manufacturing.

##### Laser Materials Macroprocessing
* The Macroprocessing track comprises some of the most productive laser processing applications. Although these applications have been broadly adopted, researchers and industry are persistently innovating to provide new techniques and novel technologies to advance macro materials processing. The attendees will share and discuss those exciting achievements of their research with peers in these macroscopic specialized sessions.

##### Laser Materials Microprocessing
* The Microprocessing track will consist of papers and talks featuring a wide spectrum of applications and innovative equipment for materials processing in microscales with a strong focus on processes with Short and Ultrashort Pulsed Lasers. The presentations will introduce the latest and greatest advancements in their respective fields, presented by world-leading companies and research organizations. This year we will have talks around the headlines of “Beam shaping”, “Glass”, “Surface processing” and “Drilling” between other topics.

##### Frontiers in Laser Applications
* The Frontier in Laser Applications (FLA) track explores recent, cutting-edge applications of lasers, highlighting innovation at the transition of research and industry. The track has included presentations from a variety of fields: Novel 3-D Printing, Advanced Manufacturing, Laser Manufactured Devices, and Frontiers in Laser Processing. The sessions under the Frontier in Laser Application track promise to bring diverse and unique perspectives from real-world applications across laser manufacturing.

##### Laser in Battery Manufacturing
* Electrification is crucial to address global environmental challenges. The laser plays a key role in most manufacturing steps in battery production with all possible laser applications from ablation, structuring, welding, cutting, and marking. Further improvements in the batteries’ power densities, fast charging properties, and yield in battery production are related to photonics and, thus, lasers. We will hear and discuss the latest developments in research, development, and industrial applications. Join us for this year’s Battery Track.

##### Artifical Intelligence in Laser Processing
* The track ‘Artificial Intelligence in Laser Processing’ at ICALEO 2024 explores the fusion of AI and laser manufacturing. Discover cutting-edge research on optimizing laser processes and enhancing manufacturing efficiency. Explore AI’s diverse applications in parameter optimization, quality prediction, real-time process monitoring, and control. Join us to unlock the potential of AI-driven solutions revolutionizing laser materials processing, delivering unprecedented precision, productivity, and innovation.
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Attention laser enthusiasts, innovators, and industry leaders! Get ready to immerse yourself in the exciting world of lasers at the 43rd annual ICALEO conference. 

technical paper

Event cameras differ from conventional frame cameras: Rather than capturing images at a fixed rate, they asynchronously measure pixelwise brightness changes, and stream an event sequence that includes time, location and sign of the brightness changes. Event cameras provide attractive properties like high temporal resolution and latency (both in the order of ms), very high dynamic range (120 dB) and low power consumptions because no routine operation needs to be recorded. Hence, event cameras have been successfully used for instance for object tracking, pose estimation, or image deblurring. Due to the described advantages of event cameras compared to these sensors, this paper for the first time, investigates the potential of event cameras in laser welding. One of the key questions of event cameras is how to extract meaningful information from the event data for in-process monitoring. Therefore, we present different promising ways to process the unconventional asynchronous output of event cameras for process monitoring, including time-series representations such as event rates, as well as image representations such as projections on the xy-plane of the event camera view field or projections along the scanner path.

Our laser welding experiments show clear differences for different production scenarios in those event representations. Additionally, we propose a machine learning pipeline for the automatic detection of production errors with event camera.

Event-Based Vision in Laser Welding: An Approach for Process Monitoring

In laser welding, precision and reproducibility are fundamentally dependent on temporal and spatial processes of energy input. Induced by the dynamics of the melt pool, pressure equilibria in the vapor capillary, and solidification behavior, different weld seam qualities are achieved. To obtain the lowest possible defect frequency, new tailored joining strategies need to be investigated using multibeam and multiwavelength approaches. To improve the quality by influencing the process dynamics, a dual-beam approach is investigated that superimposes a stationary laser beam with a wavelength of 445 nm with a spatially modulated laser beam with a wavelength of 1070 nm. The aim is to utilize ∼10 times higher absorption of a 445 nm diode laser on copper with the high focusability of a 1070 nm fiber laser. In this context, the influence of the relative positions of the two beams to each other on the weld seam quality is investigated, while one of the beams moves either in front, behind, or coaxial to the other beam following the path of a line weld. The main objective is to observe how the laser beams influence each other and how the capillary depth and porosity vary for different parameters. To visualize the process dynamics, the welding experiments on copper are performed at the Deutsches Elektronen-Synchrotron DESY by means of in situ phase contrast videography. Quality-determining weld properties like the distribution of pores or process fluctuations are then extracted automatically from the image sequences by means of a trained neuronal network.

Analysis of Laser Beam Welding with Superimposed 445 nm and 1070 nm Wavelength Lasers on Copper by In situ Synchrotron Diagnostics

Laser beam shaping is an important tool for laser welding applications, particularly in battery welding where many combinations of materials, requirements and conditions can be present for production of a single unit. The ability to change intensity profile “on-the-fly”, without equipment changes, widens the processing window available to a single welding tool. Here we present a straightforward method for variable beam shaping, using freeform refractive optical elements in combination with conventional optical elements and systems.



PowerPhotonic’s manufacturing process is truly freeform, with no restrictions on rotational, translational or mirror symmetry. With this freedom, beam shaping elements can be designed to produce different output intensity profiles depending on the variation of another system parameter, such as input beam size, optic rotation or alignment. This allows optical power to be moved between different parts of an output profile with simple mechanical motion in the optical train. For example, translation of a single optic can result in adjustment of power between a central spot and a surrounding annulus.



In addition, freeform surfaces allow a wide variety of output profiles to be considered in the design, beyond the conventional Gaussians, flat-tops and rings. The flexibility in target intensity profiles combined with in-situ adjustability gives significant additional degrees of freedom for laser welding applications.

Variable Beam Shaping for Battery Welding Enabled by Freeform Optics

The advent of laser metal additive manufacturing (LMAM) unlocked a new phase in materials development and fabrication, where physical phenomenon in the melt-pool and Marangoni convection enables finer control of the material microstructure and physical properties. While these advantages make LMAM very attractive, there are various drawbacks to using metallic 'loose' powder  such as health hazards, difficulty in switching materials and recyclability. An approach to mitigate some of these disadvantages is the innovation of a novel polymer-metal composite, where metal powder is tightly packed with a polymer binder. This novel feedstock is produced via solvent casting, and sintered layer-by-layer similar to traditional LMAM process. Recent research has proved great potential in this approach, delivering promising efficiencies in LMAM and enhanced material characteristics in materials such as 304SS and high-entropy-alloys. This work will show how the selection of polymer binder can produce materials with a range of mechanical properties. Work will also be presented on a novel laser debinding approach using the polymer-metal composite. This work will cover a thorough investigation utilizing in-situ melt pool analysis combined with microstructural EBSD and XRD. In addition to increasing manufacturing efficiencies, this innovation has the potential to produce functionally gradient parts and enhanced coatings without the requirement of 'loose powder'.

Metal Additive Manufacturing Using Innovative Polymer-Metal Composite: process analysis and in-situ polymer ablation

Multi-layer TiO2/SiO2 films are frequently used as anti-reflection coatings (ARCs) due to their excellent optical properties. We report CO2 laser-assisted deposition, i.e., spin-coating of wet-films followed by laser processing, of multi-layer transparent films using a mixture of TiO2 and SiO2 nanoparticles. A simple heat transfer model is presented to select proper laser processing parameters. To minimize the reflectance, and enhance the transmittance, an ARC model is utilized to select the film material and the thickness of each layer. UV/Vis spectrophotometry was carried out to measure the optical reflectance. UV/Vis/NIR spectrophotometry shows that the sintered homogenous films are highly transparent, with an average transmittance of above 90% in a certain wavelength range. Due to the films porosity, an effective medium model was utilized to determine the optical constants, i.e., the refraction index (𝑛𝑐) and the absorption index (𝑘𝑐), of just the solid phase which is formed by the sintered nanoparticles in the nanocomposite TiO2/SiO2 films. The results demonstrate that the effective refraction or absorption index can be smaller than that of the solid phase. This trend indicates that nanocomposite thin films of arbitrary optical properties can be deposited using nanoparticles by varying the porosity, since the film consists of both solid phase and pores. The effect of TiO2 and SiO2 nanoparticles concentrations on the optical properties of multi-layer films is also investigated. The film roughness was examined by measuring the thickness with an optical profilometer. The effect of laser processing on the microstructural properties was investigated using SEM and XRD techniques.

CO2 Laser-Assisted Deposition of Multi-Layer Homogeneous TiO2/SiO2 Nanocomposite Films with Varying Refraction and Absorption Indices

Conventional roller- or blade-based powder application methods limit the versatility of powder-based additive manufacturing technologies such as Laser Powder Bed Fusion (PBF-LB). In particular, the restricted multi-material capabilities, the strong dependence on powder flowability and the need to fill the entire build platform with powder are major drawbacks of conventional powder application. Electrophotographic powder application (EPA) has emerged as a promising new technology to overcome these limitations. It utilizes electrostatic interaction forces to selectively attract and repel charged powder particles. This publication demonstrates the potential of EPA-PBF-LB for pharmaceutical 3D printing and applications in medical engineering. Using EPA, tablets containing different active pharmaceutical ingredients (APIs) have for the first time been successfully printed with a CO2 laser without thermal degradation of the APIs. This is an important step towards personalized medicine. It has also been shown that EPA-PBF-LB can be used to precisely functionalize surfaces to provide bactericidal properties. For this purpose, silver-nanocoated polyamide 12 (Ag-PA12) was selectively deposited and consolidated using EPA-PBF-LB. In this way, it was possible to locally achieve bactericidal properties. This can be of great interest for applications in the field of medical engineering or even in everyday life, e.g. on surfaces in public transport or hospitals.

Electrophotographic 3D Printing in Pharmacy and Medical Engineering

The demand for safe, power- and energy-dense, low-cost batteries is constantly increasing due to the global shift towards renewable energy sources and the associated need for battery electric vehicles and grid-level energy storage. However, current-generation lithium-ion batteries are struggling to meet the requirements and are reaching their physicochemical limits regarding energy density. Solid-state battery technology promises to improve the current state of the art of electrochemical energy storage and has proven itself in research, although not yet in commercial applications. One of the main challenges in bringing the technology to the market is the limited knowledge and research on the production of solid-state batteries with commercially viable technologies. This work presents a study on the application of laser cutting technology to components of sulfide-based solid-state batteries. Challenges such as the production atmosphere, handling of the components, and differences compared to conventional battery components are discussed. Process studies are utilized to identify appropriate parameters for cutting sulfide-based components based on both qualitative and quantitative assessments. The improvement of the resulting edge quality compared to mechanical punching is highlighted. The process requirements, the analysis of the behavior of sulfide-based components, and the proposed process parameters presented in this work pose a significant step toward the successful commercialization of sulfide-based solid-state batteries.

Establishing Laser Cutting of Components for Sulfide-Based Solid-State Batteries

In the additive manufacturing of components using powder-based laser metal deposition (LMD) / directed energy deposition (DED), controlling the heating of the volume during build-up is crucial for ensuring process stability and contour accuracy. When process parameters remain constant, inherent heating leads to variations in deposited layer thickness due to changes in the melt pool volume. These variations result in contour deviations and may even halt the process if parameters deviate from their optimal range. Especially with complex geometries, this often necessitates labor-intensive process development to adjust parameters and build-up strategies accordingly.

The melt pool volume serves as a key indicator of process stability in LMD. Measurement of the melt pool area can be achieved using a coaxial camera in the beam path. By adjusting the laser power during the process, the goal is to minimize melt pool variance. To accomplish this, an AI model is trained using data from LMD processes with consistent parameters during geometry build-up. The AI model learns the correlations between laser power, geometry, and other component-specific factors, and the size of the melt pool area. Subsequently, the trained AI model can predict the local laser power required for a stable process. This eliminates the need for extensive experimentation and evaluation to determine suitable process parameters, particularly when component geometry changes.

AI-Based Spatially Resolved Parameter Prediction in Laser Metal Deposition for Increased Process Stability

Incomplete cuts during laser fusion cutting result in a closed kerf, preventing the workpiece from detaching from the metal sheet, in turn necessitating rework or rejection. We demonstrate the approach of a vision transformer, used for image classification, to detect cut interruption during laser fusion cutting of steel and aluminum sheets. With events impending an incomplete cut in steel, we attempt to predict cut interruption before they even occur. To build a data set for training, cutting experiments are carried out with a 4 kW fiber laser, forcing incomplete cuts by varying the process parameters laser power and feed rate. The thermal radiation from the process zone during the cutting process is captured by a camera with a size of 256 px ×256 px at sample rates of 20∙103 frames per seconds. The kerf is recorded with a spectral sensitivity between 400 and 700 nm, without external illumination, allowing the melt to be observed in the range of the visual spectrum. The vision transformer model, which is used for image classification, splits the image into patches, linear embedded with an added position embedding and feed to a standard transformer encoder. For training the model, a set of images is labeled for the respective classes of a complete and incomplete cut for steel and aluminum and impending incomplete cut for steel. With the trained model, incomplete cuts in steel and aluminum can then be recognized and impending incomplete cuts in steel can be predicted in advance.

Vision Transformer Based Cut Interruption Detection and Prediction of Laser Fusion Cutting from Monitored Melt Pool Images

Femtosecond lasers are recognized for their precision and high-quality performance in the material micromachining field, experiencing increased integration into various industrial applications. Their ultra-short pulse duration nature enables precise and high-quality material processing, particularly beneficial for materials where conventional machining methods are lacking. Alumina ceramics, known for their excellent electrical insulation, corrosion resistance, wear resistance, and hardness, stand out as one of the materials significantly benefiting from femtosecond laser machining. However, the efficiency of these lasers in industrial applications has room for improvement. One way to cope with this is burst mode, featuring closely packed femtosecond pulses at MHz/GHz intra-burst repetition rates. This regime allows the division of the energy from a high-energy single-pulse into multiple smaller pulses, effectively making use of the full potential of the laser source.



This study employed the FemtoLux 30 laser to machine alumina ceramics, focusing on the impact of MHz and GHz burst mode on ablation efficiency. Our findings reveal that utilizing MHz/GHz burst mode reduces the ablation threshold (minimum fluence required to cause catastrophic damage), proportional to the increase in pulse number of the burst. Furthermore, the experiment involved milling of squares into the alumina substrate to investigate the ablation efficiency (removed volume per energy unit). As a result, machining in GHz burst mode achieved an ablation efficiency 50 times greater than that of the single-pulse regime. These results highlight the benefits of employing MHz/GHz burst mode for femtosecond laser machining of alumina ceramics, suggesting its further integration into industrial applications.

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Event-Based Vision in Laser Welding: An Approach for Process Monitoring

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