United States

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.

ICALEO 2024

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

transmittance

optical properties

refraction index

laser sintering

nanoparticles

technical paper

  #### 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


#### About LIA

The Laser Institute of America (LIA) is the professional society for laser applications and safety. Our mission is to foster lasers, laser applications, and laser safety worldwide. Serving the industrial, medical, research and government communities for over 50 years, LIA offers technical information, training and networking opportunities to laser users
from around the globe.

#### About LIA Conferences

The conferences of the Laser Institute of America are the town square of the laser community. A large and diverse group, laser professionals often find themselves disconnected from each other. LIA conferences, however, bring the university researcher, the business executive and the production floor user together under one roof for a unique and
rewarding networking opportunity.

#### 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.
<br><br>


To access the site please register [**here**](https://icaleo.org/attend). 

Are you an LIA Member? Find a special discount code in your LIA account at https://www.lia.org/user to apply to your registration for additional savings!

Please register for the ICALEO 2024 Conference to join the event.

Attention laser enthusiasts, innovators, and industry leaders! Get ready to immerse yourself in the exciting world of lasers at the 43rd annual ICALEO conference. 

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.

Investigation of Ablation Efficiency in Alumina Ceramics Utilizing Femtosecond Laser With MHz/GHz Burst Regime

This study aims to reduce heat generation and enhance the strength of busbar welding in prismatic lithium-ion battery modules, crucial for electric vehicles. Effective heat management during welding is essential to maintain battery connections' integrity and prevent thermal runaway, a potentially catastrophic failure. High temperatures during welding can accelerate battery life degradation, damage sealing rubber and O-rings, and increase internal battery pressure. We conducted a comparative analysis between continuous wave (CW) laser welding and the pulsing welding method using Aluminum 1100 series with a thickness of 2mm on the top battery terminal. Temperature measurements were taken at the bottom of the terminal during tests to monitor heat distribution. CT scans assessed porosity and detected micro-cracks within the welding zone, critical for determining weld durability. Additionally, tensile tests evaluated the mechanical properties of the welds.

The results showed that the pulsing laser welding method produced significantly lower temperatures than CW welding, reducing the risk of thermal damage. The pulsing method also resulted in less porosity, indicating a more uniform weld structure. These findings suggest that pulsing laser welding offers superior performance in thermal management and weld integrity. The advancements identified in this study are expected to contribute to the widespread adoption of electric vehicles by ensuring that their battery packs are robust and resilient under various operational conditions.

Minimizing Heat Generation and Enhancing the Strength of Busbar Welding in Prismatic Lithium-ion Battery Modules and Packs for Electric Vehicles

Additive Manufacturing (AM) enables the production of functional and complex parts in a resource-efficient way, only applying the material to the desired location. Especially Laser-

based-Directed Energy Deposition (DED-LB) enables an excellent trade-off between production time and part complexity. However, this metal AM technology is currently lacking

in process stability, which limits a further industrialization. To overcome this, various industrial and academic efforts are focusing on investigating the application of digital process representations, often referred to as Digital Twins (DT), to improve the stability of DED-LB. This contribution presents a complete framework for a digital twin of the DED-LB process consisting of three major pillars. At first, a modular digitization framework for DED-LB is presented that utilizes an edge-cloud computing methodology to fuse data streams from multiple sensors monitoring the laser-

induced melt pool during production. The second pillar incorporates a thermal simulation model to predict the essential melt pool characteristics before the actual printing process. As third, a physics-informed neural network approach is applied to substantially reduce the computational time and efforts of the simulation’s melt pool predictions. In summary, the

presentation showcases a path towards data-based process control of an industrial-grade laser-based manufacturing system by utilizing digitization, physics-based simulation, and

artificial intelligence.

Increasing Process Stability Using a Digital Twin for DED-LB

Shape memory alloys exhibit unique properties that make them ideal for functionally integrated components, such as actuators. While commonly used Ni-Ti alloys are well-established, especially in biomedicine and aerospace, their high cost limits wider applications. Fe-based shape memory alloys present an affordable alternative, suitable for diverse applications, with a larger thermal hysteresis but lower recovery strain. Nonetheless, their functional properties can be enhanced through optimized processing methods like laser powder bed fusion and adjustments to their alloy composition. However, attributing the functional improvements to individual factors is complex due to interdependency and inseparability of some factors.

In order to address this ambiguous state of literature, laser powder bed fusion was used for modifying the composition and microstructure of a FeMnSiCr-alloy. For this purpose, in-situ modifications during the additive manufacturing process were carried out to alter the material. Subsequently, the impact of the resulting compositional changes on the functional properties was analyzed. Attendees will gain insights into the targeted use of the laser powder bed fusion process for in-situ customized iron-based shape memory alloys. In this regard, our research highlights the potential of laser-based additive manufacturing for modifying the alloy composition and microstructure of shape memory alloys and, thus, tailoring their functional properties.

Adapting the Mn Content Within a Fe-Mn-Si-Based Shape Memory Alloy by In Situ Parameter Variations in Laser-Based Additive Manufacturing

We have developed a fully automated laser processing and data acquisition machine, called Meister Data Generator (MDG), which can be used via the Internet 24 hours a day, 365 days a year. All the data taken by MDG is acquired in a database. MDG can perform grid search as well as autonomous optimization with artificial intelligence such as Bayesian optimization. Huge amount of data can be obtained by the automated processing setup, and we applied the data to build a deep neural network that works as a laser processing simulator. The input to the simulator is laser parameters, and the output is a shape of the material after processing. We have tried to make two types of simulators: shallow drilling of silicone and deep drilling of glass. The simulator created by deep learning was found to be applicable to drilling under conditions that were not included in the training data. We were able to predict drilling conditions that achieved more energy savings than the conditions used in the training data. In other words, we now have a predictive AI simulator. We are working with several companies to expand the scope of MDG technology. This simulator could be applied to microfabrication for semiconductor back-end processing.

Autonomous Micro Laser Processing with Artificial Intelligence

Residual stress is an important factor in manufacturing, including laser processing, and ignoring it can lead to product defects. This study presents a new deep learning approach to predict critical characteristics of laser-irradiated carbon steel, including thermal residual stress. The core of this study, a generative adversarial network (GAN), acts as a surrogate model, enabling more efficient utilization of computationally expensive simulations to generate the desired output. The GAN model is trained using data generated by numerical simulation. Three key laser processing parameters are taken as input: laser power, beam diameter, and irradiation time. Based on these inputs, the model predicts various properties of the laser-irradiated carbon steel throughout the process and after cooldown including residual stress. This deep learning model effectively captures the complex, non-linear relationships between laser parameters and process outputs. The model produces very fast results when compared to the traditional simulation, while the results are accurate. The model exhibits exceptional performance, achieving high R-squared values ranging from 0.975 to 0.999 across various predicted properties. The result demonstrates the effectiveness of the proposed deep learning approach for predicting laser processing outcomes in carbon steel. It facilitates efficient exploration of residual stress and melt pool evolution by enabling rapid evaluation of different laser parameter settings. Furthermore, this study emphasizes the benefits of utilizing a hybrid loss function for improved optimization of deep learning models.

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

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