United States

Additive manufacturing of copper using laser powder bed fusion, enables the production of highly complex components, with excellent heat and electrical conductivity. However, the processing of copper by means of near-infrared laser radiation, which is commonly used, is challenging due to its high reflectivity. Nevertheless, it has been demonstrated that high densities and electrical conductivities can be achieved using high power laser systems. In order to process pure copper with reliable quality with different machines, it is essential to understand the conditions at which a continuous weld track is formed. For this purpose, weld tracks with varying laser power and scan speeds were welded on a copper substrate plate with an applied powder layer. The preheating temperature of the substrate plate and the beam size were varied to test different process conditions. The melt pool depths and widths were measured and a relationship was elaborated. Based on these results, cube samples with discrete weld tracks on top were manufactured. The melt pool depth was measured and compared with the predicted melt pool depth to investigate the transferability of the elaborated relationship from substrate to process conditions. It was found that with rising preheating temperature and for larger beam diameters at the same peak intensity, the weld width and weld depths increase. Furthermore, continuous weld tracks formed reliably in the keyhole welding regime. A good agreement between the weld depth of weld tracks on substrate and the elaborated relationship was revealed. However, the weld tracks were shallower than predicted.

ICALEO 2023

Influence of Temperature and Beam Size on Weld Track Shape in Laser Powder Bed Fusion of Pure Copper Using Near-Infrared Laser System

near-infrared laser system

pure copper

scaling laws

laser powder bed fusion

additive manufacturing

technical paper

**General Chair Welcome**

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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. 

The Business Session brought the business aspect and environment of the laser to the audience and the possibility to discuss with seasoned industry leaders from around the world. 

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.

**Who attended?**

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.

**Fields Represented:**

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Please register for the ICALEO 2023 Conference to join the event.

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.

The demands of the EV industry on the manufacturing processes are increasing rapidly in terms of productivity, strength, and electrical performance. Common laser tools are already reaching their performance limits in demanding fields of application, such as joining of copper and aluminum, or high-speed welding of thin metal foils. Innovative laser beam sources with new degrees of freedom in laser material processing are therefore needed to meet the increasing requirements. In this article, we examine selected laser applications in the EV industry, particularly in the areas of battery, e-motor and fuel cell production. In the case of electrical contacts, in addition to the requirements of high strength and low electrical resistance, there is also the difficulty that the welding depth may only be within a very narrow tolerance band, otherwise the parts would be destroyed if the component was shot through. In addition to the precise welding depth, the heat input is also a major factor in determining whether the component remains intact during the joining process. In this contribution results of fundamental process investigations when using two superimposed laser beams of high brilliance of the latest generation are presented. It is shown that both productivity and process stability can benefit from this new type of laser tool.

Latest Application Developments Across EV-Industries with Overlaying Highly Brilliant Laser Beams of the Next Generation

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%.

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

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.

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Influence of Temperature and Beam Size on Weld Track Shape in Laser Powder Bed Fusion of Pure Copper Using Near-Infrared Laser System

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