United States

In order to achieve sustainable development goals, SDGs, decarbonization and low-carbonization are required. Electric and hybrid vehicles are indispensable for the conservation of natural environment, and the lightweight construction and the effective transfer of electricity become important. Thus, copper and aluminum have been becoming important materials because of their excellent electrical properties. However, in the welding of these materials, it is difficult to obtain strong joints, since there are problems in the brittle intermetallic compounds and the welding defects due to different melting points between copper and aluminum. Especially in joining of copper and aluminum by copper side irradiation, aluminum rich intermetallic compounds (IMC) shows brittle state, and mechanical strength decreases drastically. Therefore, mild heat input from copper to aluminum would be necessary to reduce the brittle IMC. Angled irradiation might result in the mild energy input to aluminum, because it can be expected that aluminum would be heated by the reflected light inside the keyhole generated in copper according to high light reflection of copper. In addition, stable welding can be expected by the superposed irradiation of blue and near-infrared lasers because of high light absorption rate of blue laser to copper. The angled and the superposed irradiation could achieve stable welding state, and the generation of aluminum rich IMC became smaller. Angled irradiation of near-infrared laser showed equivalent joining strength to the superposed irradiation of two wavelengths, and the combination of angled and superposed irradiation achieved the remarkable increase of joining strength in cross tensile test by 80%.

ICALEO 2023

Fundamental Study on High-Quality Welding of Copper and Aluminum by Angled and Superposed Irradiation of Blue and Near-Infrared Lasers

near-infrared laser

blue laser

laser welding

**General Chair Welcome**

![](https://assets.underline.io/markdown_image/1/image/d6e6dddc08bf2371b9136ea9c18dc5ab.png)              

Welcome to ICALEO® 2023!
	
We were excited to host the world’s leading international conference on Lasers and Electro-Optics in Chicago. The Laser has been shown to have the potential to revolutionize solutions for major global challenges like energy usage and supply, global warming, and CO2 footprint. Even the fusion energy breakthrough is based on laser technology. The conference, with peer-reviewed scientific and technical presentations, focused on the newest results in Micro Applications, Macro Applications, Additive Manufacturing, Beam Shaping and Frontiers in Laser Applications. Additionally, the Battery-Manufacturing Track gave an overview of the process combined with the newest advances in laser application results. 

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Whether this is the first time you are hearing about ICALEO or you are a longtime attendee, you will find something of interest, new ideas, and an excellent opportunity to network with other laser specialists worldwide. 

**Klaus Löffler **  
ICALEO General Chair   
Managing Director, Precitec GmbH

**What is ICALEO?**

ICALEO® brings together the brightest minds, seasoned professionals, and pioneering researchers from around the world. With a laser-focused agenda, ICALEO offers an exceptional platform to delve into the cutting-edge advancements, diverse applications, and transformative research that define lasers and electro-optics.

Participants will be immersed in a dynamic environment that fosters collaborative learning, networking, and idea exchange. Through engaging plenary presentations, technical sessions, an industry business panel, and networking events, attendees gain insights into the forefront of laser technology. From industrial applications to medical breakthroughs, additive manufacturing to materials processing, ICALEO covers the entire spectrum of laser-related disciplines. Moreover, the event serves as a launching pad for synergistic partnerships, paving the way for professionals to forge connections that shape the future of our industry.

Join us in Chicago and be part of an unparalleled gathering that challenges the boundaries of possibility within lasers and electro-optics. As we convene to share knowledge, explore innovations, and cultivate collaborations, ICALEO stands as a pivotal force driving the evolution of this transformative technology.

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ICALEO is tailor-made for professionals, researchers, academics, and enthusiasts who are deeply passionate about the world of lasers and electro-optics. If you're an industry trailblazer, an academic luminary, an aspiring expert, or a postgraduate student looking to dive into the latest developments and trends, this is an event you won't want to miss.

Industrial leaders seeking to stay ahead of the curve in their fields, engineers working on laser applications, and manufacturing experts interested in additive manufacturing and materials processing will find ICALEO to be an invaluable platform. Researchers and scientists exploring the forefront of laser technology, from fundamental principles to groundbreaking applications, will also discover a wealth of knowledge and networking opportunities.

Medical professionals investigating laser-based medical advancements, entrepreneurs seeking to innovate with lasers, educators aiming to stay updated with the latest in laser education, and policy makers shaping the industry's landscape are all essential attendees. Whether you're an established expert or a rising star, the 42nd annual ICALEO promises to offer a diverse ecosystem of expertise, connections, and knowledge sharing that caters to the laser and electro-optics communities.

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ICALEO® brings together the brightest minds, seasoned professionals, and pioneering researchers from around the world. With a laser-focused agenda, ICALEO offers an exceptional platform to delve into the cutting-edge advancements, diverse applications, and transformative research that define lasers and electro-optics.

technical paper

The mobility sector is considered as a major contributor to global greenhouse gas emissions and air pollution. As a result, many countries have initiated the transition from fossil fuel-powered to electrified powertrains. This transformation of the powertrain concept will lead to a rapid increase in the production of electric vehicles and, therefore, to a high demand for so-called traction bat­ter­ies. As a production step of the traction batteries, a connection between the cell connector and the terminal of the battery cell is required. For this purpose, laser beam welding is a reliable and efficient joining technique. In order to ensure continuous quality of the welding process during production and in order to detect defects in real time, reliable process monitoring is required. In this study, spectral and acoustic emissions during laser beam welding were recorded using a laser welding monitor and an optical microphone. For determining possible correlations between the signals and weld defects, various failure cases were generated by the systematic placement of disturbance elements. These elements included a contaminated surface, a gap between the cell connector and the battery cell, and a misalignment of the cell connector. Based on the recorded signals, statistical metrics were calculated. Finally, weld seams with and without defects were compared to assess the capability of both sensor systems for detecting the weld defects.

Inline Failure Detection in Laser Beam Welding of Battery Cells: Acoustic and Spectral Emission Analysis for Quality Monitoring

The shape of the laser beam significantly influences the geometry of both the cutting front and the cutting kerf. The angle of the cutting front influences the absorptivity while the width of the kerf defines the amount of material, which has to be molten. The geometry of the kerf therefore determines the maximum possible cutting speed. These relations between absorptivity, the width of the cutting kerf, and the maximum cutting speed are described by a simple model considering the conservation of energy and rough geometrical approximations. In order to verify the prediction of the model, the geometry of the interaction zone was observed by means of online high-speed X-ray imaging. The geometry of the cutting front and kerf was recorded with a framerate of 1000 Hz during fusion cutting of 10-mm-thick samples of stainless steel. The results show an increase of absorptivity in case of an enlargement of the beam in the direction of the feed, while the cross-sectional area of the cutting kerf increases in case of an enlargement of the width of the beam in the transversal direction. Finally, these findings show a good agreement with the simpel geometric model and allows to identify optimum beam shapes for a maximum cutting speed.

Influence of Beam Shaping on the Geometries of the Cutting Front and Kerf to Maximize the Speed of Laser Cutting

Cutting thick plates is affected not only by the laser power but also by the cut kerf width and the melt flow dynamics that determine the ejection of the molten material. Employing the same laser beam intensity distribution for various thicknesses is the limiting factor when cutting thicker plates. This paper investigates fiber laser fusion cutting of 25 mm thick aluminium with dynamic beam shaping (DBS). While both static and longitudinal dynamic intensity distributions fail to cut this thickness with a 4 kW laser power, a cut through is achieved using annular and elliptical intensity distributions. However, an improvement of 45&nbsp;% in cutting speed can be achieved using an elliptical intensity distribution compared to an annular one. In order to understand the effect of the beam shape, an infrared thermal camera is used to study lateral heat propagation when using different process parameters. The more efficient heat propagation in the cutting direction employing elliptical intensity distribution decreases the power losses compared to the annular intensity distribution. Moreover, to analyze the melt flow when changing the DBS frequency, high-speed imaging is utilized to observe the molten material inside the cut kerf. Finally, the cut edge quality is investigated for different cutting conditions.

Cutting Thick Aluminium Plates Using Laser Fusion Cutting Enhanced by Dynamic Beam Shaping

Fiber laser cutting has been widely used in many industrial applications, but the further improvement is required in the reduction of dross. Recently, the control of laser intensity distributions has been investigated in many laser applications, and some researchers have reported that intensity distribution of laser beam has a possibility to improve the cutting quality. Our previous study revealed that the uniform intensity distribution in the kerf width direction enabled to reduce the dross height, and twin spot beam was obtained by dividing a Gaussian beam with a roof axicon lens to control the power ratio in the kerf width direction. However, twin spot beam setting in the kerf width direction could improve the dross adhesion only in one side. Therefore, twin spot beam was set in the scanning direction, and the proper control of power ratio was investigated in laser cutting experiments for cold rolled steel sheet of 3.2 mm thickness by 3 kW fiber laser with a nitrogen assist gas. A ray tracing analysis and high-speed observation of molten metal ejection made it clear that high intensity of rear beam smoothly ejects molten metal at the lower part of workpiece, and a small ejecting width and the forward slanting ejection of molten metal were important to reduce the dross height. The dross height could be reduced to 16 µm by using the rear beam power of 90 % and the front beam power of 10 %.

Fiber Laser Cutting of Steel Plate by Twin Spot Beam Setting in Scanning Direction

Artificial intelligence is a trend that has been on innovation leader's radar for a long time. It is a technological enabler for a more sustainable future and will significantly increase business efficiency if used meaningfully. It affects our daily lives, and the way people and companies will work now and in the future. Insights into practical applications with various maturity degree will be presented from the perspective of a leading solution provider in the sheet metal industry. In times when there is a shortage of experienced operators, it is especially important to have a solution that enables machine operators to find the optimal machine settings and parameters to achieve the best performance and quality in shortest time. Artificial intelligence also supports the increase of the automatization degree of systems achieving a low-man production and ensures 24/7 production with assisting systems for monitoring the status of the laser cutting machines in operation and especially, during cutting. In addition to the monitoring, control procedures are presented to enhance the performance and quality demands of a laser cutting machine. A method for in-process quality control is presented prior to bending the sheet metals avoiding the processing of defect parts at an early stage.

AI in Laser Cutting - Insights Into Recent Achievements

Powder-based Laser Metal Deposition (LMD) offers a promising additive manufacturing process given the large number of available materials for cladding or generative applications. In laser cladding of dissimilar materials, it is necessary to control the mixing of substrate and additive in the interaction zone to ensure safe metallurgical bonding whilst avoiding critical chemical compositions that lead to undesired phase precipitation. However, the generation of empirical data for LMD process development is very challenging and time-consuming. In this context, different machine learning models are examined to identify whether they can converge with a small amount of empirical data. 
In this work, the prediction accuracy of Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM) and eXtreme Gradient Boosting (XGBoost) were compared using Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). A hyperparameter optimization was performed for each model. The materials used are 316L as substrate and VDM Alloy 780 as additive. The data set used consists of 40 empirically determined values. The input parameters are laser power, feed rate and powder mass flow rate. The quality characteristics height, width, dilution, Fe-amount and the seam contour are defined as output. 
As results, the predictions were compared with retained validation data and described as MSE and MAPE to determine the prediction accuracy for the models. BPNN achieved a prediction accuracy of 0.0072 MSE and 4.37% MAPE and XGBoost of 0.0084 MSE and 6.34% MAPE. The most accurate prediction was achieved by LSTM with 0.0053 MSE and 3.75% MAPE.

Prediction of Clad Quality in Laser Metal Deposition Using Dissimilar Materials: Performance Comparison of Machine Learning-Based Approaches

Lattice structures in additive manufacturing of 316L stainless steel has gained increasing attention due to their well suited mechanical properties and lightweight characteristics. Infill structures such as honeycomb, lattice, and gyroid have shown promise in achieving desirable mechanical properties for various applications. However, the design process of these structures is complex and time-consuming. In this study, we propose a machine learning-based approach to optimize the design of honeycomb, lattice, and gyroid infill structures in 316L stainless steel fabricated using Laser Powder Bed Fusion (L-PBF) technology under different loading conditions. 
A dataset of simulated lattice structures with varying geometries, wall thickness, distance and angle using a computational model that simulates the mechanical behavior of infill structures under different loading conditions was generated. The dataset was then used to train a machine learning model to predict the mechanical properties of infill structures based on their design parameters. 
Using the trained machine learning model, we then performed a design exploration to identify the optimal infill structure geometry for a given set of mechanical requirements and loading conditions. 
Finally, we fabricated the optimized infill structures using L-PBF technology and conducted a series of mechanical tests to validate their performance under different loading conditions. 
Overall, our study demonstrates the potential of machine learning-based approaches for the efficient and effective design of honeycomb, lattice, and gyroid infill structures in 316L stainless steel fabricated using L-PBF technology under different loading conditions. Furthermore, this approach can be used for dynamic loading studies of infill structures.

Predictive Modeling of Lattice Structure Design for 316L Stainless Steel Using Machine Learning in the L-PBF Process

Powder bed fusion of metals using laser beam (PBF‑LB/M) is a commonly used additive manufacturing process for the production of high-performance metal parts. AlSi10Mg is a widely used material in PBF-LB/M due to its excellent mechanical and thermal properties. However, the part quality of AlSi10Mg parts produced using PBF-LB/M can vary significantly depending on the process parameters. This study investigates the use of machine learning (ML) algorithms for the prediction of the resulting part density of AlSi10Mg parts produced using PBF-LB/M. An empirical dataset of PBF-LB/M process parameters and resulting part densities is used to train a ML models. Furthermore, a methodology is developed to allow density predictions based on simulated meltpool dimensions for different process parameters. This approach uses finite element simulations to calculate the meltpool dimensions, which are then used as input parameters for the ML models. The accuracy of this methodology is evaluated by comparing the predicted densities with experimental measurements. The results show that ML models can accurately predict the part density of AlSi10Mg parts produced using PBF-LB/M. Moreover, the methodology based on simulated meltpool dimensions can provide accurate predictions while significantly reducing the experimental effort needed in process development in PBF-LB/M. This study provides insights into the development of data-driven approaches for the optimization of PBF-LB/M process parameters and the prediction of part properties.

Data-Driven Density Prediction of AlSi10Mg Parts Produced by Laser Powder Bed Fusion Using Machine Learning and Finite Element Simulation

In aerospace, thermal applications demand compact, lightweight and efficient heat exchangers. Additive manufacturing processes offer the potential to create highly complex structures that are not achievable through traditional manufacturing methods. This work presents the development of an additively manufactured fluid-fluid heat exchanger that enhances performance, reduces weight and increases compactness compared to a conventional plate heat exchanger. A numerical model of the conventional plate heat exchanger was created, and fluid dynamics simulations with heat transfer were performed. Verification of the simulations was done by experiments. Then a novel heat exchanger was designed using a bottom-up approach and investigated at different levels of complexity using computational fluid dynamics. The internal structure of the final heat exchanger consists of a repeating triple-periodic Schwarz diamond minimum surface elongated in the direction of flow. The heat exchanger was manufactured in the laser powder bed fusion process in AlSi10Mg. It had a 108% higher compactness and 54% lower weight compared to the plate heat exchanger. Numerical analysis yielded the pressure loss was reduced by 50 to 59% while heat transfer was improved by 3 to 5%. Simulation results were validated by experimental assessment of the novel additively manufactured heat exchanger. Further investigations can be conducted to increase compactness and heat transfer by analyzing the minimum partition wall thickness and the impact of wall roughness and deposit formation.

Design and Assessment of an Additively Manufactured Schwarz Diamond Triply Periodic Minimal Surface Fluid-Fluid Heat Exchanger

Nowadays the technologies have to be more efficient, propose cutting edge processes, be more versatile while being environmental friendly. In this presentation, LASEA will introduce how the beam-shaping can improve the laser processes in particular in microfluidics, aeronautics, photovoltaic applications but also how the beam shaping is expected to increase the productivity of the machine in roller applications, roll-to-roll or batch-to-batch applications. 
Different technologies can be used to shape the laser beam as spatial light modulator (SLM), double SLM, diffractive optical element (DOE) or as well multi-plane light conversion (MPLC) to produce versatile shaped beam (square, round, triangular, specific shape…) or multiple beams (line of beams, matrix of beams,…). The direct laser interference patterning (DLIP) is also a technology that shapes the beam enabling the production of multi-scales processes; micrometric and sub-micrometric structures. Consequently, LASEA will present these different possibilities, their expected impacts and how to integrate them in a laser machine to achieve high throughputs processes. 
Moreover, the beam-shaping leading to multiple beams introduces many challenges in terms of laser machine design and laser development. Indeed, when the number of beams increases the laser has to be more powerful. During this presentation, LASEA will also expose the challenges to manage multiple beams and powerful laser source. Finally, the impacts and improvements expected by these types of technologies will also be present.

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Inline Failure Detection in Laser Beam Welding of Battery Cells: Acoustic and Spectral Emission Analysis for Quality Monitoring

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