During laser fusion cutting, burr forms when the molten metal does not sufficiently exit the interaction zone. When it forms on the lower edge of the cut flank, burr becomes a factor limiting quality. Previous research has shown that a temporally regular and spatially localized melt flow can prevent the formation of burr. However, the high dynamics of the sub-processes involved can cause intrinsic instabilities that disrupt the flow and reduce the efficiency of the melt ejection. This paper presents a study on the correlation between process parameters, melt flow properties, and burr formation. It includes an experimental observation of the melt-flow dynamics using high-speed videography. In addition, a numerical CFD model was set up to examine fundamental flow properties, some of which are not observable experimentally. The dependency of the burr formation on the liquid Weber and Reynolds numbers is analyzed and it is demonstrated how the magnitude and allocation of vapor pressure gradients in the kerf decisively affect melt ejection and burr formation. Additionally, a previously unknown melt ejection regime is identified in the thick section range, which occurs at feed rates close to the maximum cutting speed under specific high-power process conditions. This regime is characterized by a significantly increased process efficiency that could open up a new high-speed process window.

Numerical and Experimental Investigation of the Melt Removal Mechanism and Burr Formation During Laser Cutting of Metals

Laser cutting process is widely used across the engineering sector for processing metals and alloys. Over the last few years there has been an increasing interest in exploiting ceramic matrix composite (CMC) based materials for both high and low-temperature applications in aerospace, power generation and nuclear applications, due to their superior thermal and structural properties. However, laser processing of anisotropic composites such as CMC is difficult due to its high melting temperature, and the differences in the physical and thermal properties of the resin/matrix and fibre. This research discusses about laser cutting of silica based CMC material using a continuous wave fibre laser operating at an average power of 2kW. 
The influence of various laser, kinematic and assist gas parameters on cut quality and speed was investigated, and the effect of various cutting strategies was studied in detail. The laser cut samples were analysed for surface and sub-surface cut characteristics using optical microscope, surface profiler, scanning electron microscope and X-Ray CT scan. Irrespective of the conventional wisdom (that high-power long pulse lasers are not suitable for composites) CW laser can be used to cut a 5 mm thick CMC material in line with the speed and quality requirements of the manufacturing industries. Multi-pass laser cutting shows better cutting performance, in terms of both speed and quality than the conventional single pass cutting technique. Assist gas composition plays a major role in controlling the material removal mechanism, and significantly influences the cut quality.

Laser Cutting of Ceramic Matrix Composites

High gas pressures (1.0-1.6 MPa) are employed in conventional inert laser cutting to achieve efficient material removal and high cut quality. However, this approach results in the emission of large quantities of by-products, which can pose a risk to human health and the environment. For applications such as nuclear decommissioning, where global extraction and containment can be challenging, hazardous by-product formation, rather than process efficiency, is the main priority. This paper demonstrates low-pressure (0.3 − 0.6 MPa) laser cutting techniques developed to reduce by-products. This study investigates the causal links between melt ejection and gas dynamic interactions in low-pressure laser cutting. Experiments were conducted using a 300 W Nd:Yb fibre laser to cut 304 stainless steel samples. Melt ejection and breakdown profiles were captured using a FASTCAM mini AX 200 camera. The lens combination fitted to the camera provided a spatial resolution of approximately 1 µm. The gas dynamic interactions were assessed through comparisons to existing studies of schlieren imaging in idealised environments. The results show that gas dynamics are crucial in melt ejection and breakdown mechanisms during laser cutting. The key findings of this study are images of breakdown mechanisms linked to low-pressure gas dynamics. The impact of this work is that breakdown mechanisms more favourable to reducing environmental risk have been demonstrated. A greater understanding of the risk is indispensable to developing new laser-cutting control methods for hazardous materials.

Reducing Environmental Risks in Laser Cutting: A Study of Low-Pressure Gas Dynamics

In laser cutting, the fundamental role of the gas flow for melt removal and kerf formation is generally accepted. Beyond this vague understanding, however, the underlying physical mechanisms are not yet fully understood. In particular, detailed data concerning the momentum and heat transfer between the gas and melt have seldom been reported. This study addresses the local interactions between the cutting gas and kerf surface (melt film surface) in a fundamental way based on a combined experimental, theoretical, and numerical approach. Typical solid-state laser cut edges are analyzed considering the characteristic surface structures and the basic influences of the gas flow on the global and local melt movement. Here, apparent structures in the micrometer range indicate the effect of vortical gas structures close to the wall. Theoretical investigation of the gas boundary layer is conducted by semi-empirical equations and the transfer of basic results from boundary layer theory. It is shown that the boundary layer is in transition between laminar and turbulent flow, and local flow separations and shock-boundary layer interactions primarily induce spatially periodic and quasi-stationary instability modes. An improved numerical model of the cutting gas flow confirms the theoretical results and exhibits good agreement with experimental cut edges, reproducing the relevant instability modes and quantifying the local momentum and heat transfer distributions between gas and melt. With the knowledge gained about the underlying physical mechanisms, promising approaches for improvements of the fusion cutting performance are proposed.

Laser Fusion Cutting: The Missing Link Between Gas Dynamics and Cut Edge Topography

Normally, the phrase "making the invisible visible" is used when sensors/cameras are used for monitoring purposes that operate in a different spectral range than the human eye. 
In this contribution to ICALEO 2023 we will interpret this phrase somewhat differently. 
There are several systems available in the market which capture or measure and analyze physical effects of the process zone and their properties during the laser processing, but none of these effects and properties stand for “seam quality” for themselves, which is typically defined by mechanical, geometrical and metallurgical properties of the solidified seam. The process properties like emitted visible and thermal radiation or geometrical values of the melt pool or the penetration depth of the laser forming the keyhole (penetration depth) only provide indications of how the desired quality might be achieved. It is also well known that the introduction of OCT in laser materials processing has significantly increased safety in both defect detection and process control. 
However, the focus of this presentation is on the use of artificial intelligence algorithms to "see new things." We will discuss how classified, physical properties can be derived from already reliable process information - "making the invisible visible," so to speak. Instead of defining complex rules for algorithms, the use of Data Science and Machine Learning methods uncovers hidden structures in noisy, unstructured data and makes it possible to find the relationships of the data to physical measurements.

Making the Virtually Invisible Visible - Sophisticated AI Models Based on Seamless In-Process Information Enable More Product Safety

In the attempt to produce lighter battery packs at lower cost, replacing common copper parts with aluminium components has been a popular approach in recent years. With regards to joining technologies, there is a growing interest in applying laser beam welding in battery manufacturing due to several advantages such as single-sided and non-contact access, whilst maintaining a narrow heat affected zone. Motivated by the need to control and reduce weld porosity in AA1060 battery busbar welding with the ultimate goal to enhance durability and reduce electrical resistance, this paper has been developed with the aim to study the mechanisms of porosity formation and implement a novel beam shaping approach to reduce the volumetric pores distribution. Porosity formation is attributed to two mechanisms: metallurgical porosity caused by chemical reactions and process porosity related to the instability of the keyhole and fast solidification rates. In this study, a multi-physics Computational Fluid Dynamics (CFD) model has been developed and calibrated to study the effect of a circular and a tailing laser beam shape on melt pool dynamics and evolution of porosity due to instability of the keyhole, and hence generate knowledge about the underlying physics of the welding process itself. The study will discuss roots of actions for optimisation of weld quality via integration of laser beam shaping lens in “off-the-shelf” welding heads.

Elucidating the Effect of Circular and Tailing Laser Beam Shapes on Keyhole Necking and Porosity Formation During Laser Beam Welding of Aluminium 1060 Using a Multi-Physics CFD Approach

Current trends in laser material processing towards higher efficiency, improved quality as well as increased processing of challenging materials and components lead to the objective of integrated process monitoring and control. For industrial laser welding it is thus desirable to obtain data on process stability and to generate information about processing quality or even to build up inline process control. 
The technical approach of a multi-sensor concept is presented, based on optical, acoustic and thermal sensors for the observation of laser welding processes for metallic materials.&nbsp;Basically, high-speed camera recordings and laser acoustic signals are used to classify laser welds of transmission components regarding typical defects. For this purpose, acquired process data were processed in a combined machine learning model, so that promising prediction accuracy can be achieved. 
In current work, a more complex sensor setup is being developed to further improve the prediction quality and enable transferability to a wide range of applications. 
The development of a cyber-physical system for AI-assisted data processing is presented, which will enable real-time response to dynamic process variations in the future. For this purpose, an optical system for 3D spatial dynamic beam shaping for laser welding and cutting is linked to the multi-sensor system. The data are processed via fast logic circuits (FPGA) into AI models using a cloud-based database and fed into a process control loop. 
The technical setup, AI model and database structures are presented as well as first results of the sensor data evaluation.

AI-Based Process Control for Laser Manufacturing Processes Using Multi-Sensor Conceptions

Laser-based production systems have become more and more popular in recent years due to their potential to achieve high precision and accuracy in a wide range of different applications. However, the kinematic systems used for laser material processing are often inherited from other production technologies such as milling. The use of mobile robots equipped with laser processing optics could disprove the current paradigm of adapted kinematic systems: scaling the size of the material processing system with the size of the components being processed, and thus the resources used. The trend of autonomous mobile robotic systems replacing classical kinematic systems in the field of material handling in industrial applications has been evident for years due to their higher flexibility, efficiency and lower operating costs. In this paper, the prototype of a corresponding mobile robot system is presented. In addition, the general design of the mobile robot is presented. One challenge is the accuracy of a mobile robot system, for a common LMP process such as laser cutting, the mobile robot must be able to follow a predefined trajectory as accurately as possible. For this purpose, two different measurement systems are presented and compared. To demonstrate the potential of the mobile robot, an exemplary laser material processing is also performed and evaluated. Finally, possibilities for improvement or further development, such as the integration of scanner optics or the use of several autonomous mobile robots to increase productivity, are shown.

An Approach Towards the Application of Mobile Robots in Laser Materials Processing

Exploiting the Potentials of Laser Beam Shaping for Welding Applications via Numerical Simulations

In this study, we present the development of an intelligent neural sensor to predict powder clogging during laser cladding by using machine learning techniques. A major factor contributing to these problems is the difficulty in accurately measuring powder mass flow rate, an essential open-loop variable that determines powder output from the nozzle. Accurate measurements of mass flow rate are essential for optimal process control and prediction of clogging events. To overcome these challenges, an experimental design was conducted manipulating factors such as laser power, travel speed, Z-step, N-layers, nozzle-to-substrate distance, and powder feed rate. The powder mass flow rate served as an independent variable to evaluate the neural sensor's predictive ability of clogging. A combination of three cameras and a microphone was used for real-time data acquisition during the experiments. The cameras captured the deposition process from different perspectives and generated thermal images, while the microphone recorded acoustic emissions. Customized cladding equipment enabled seamless data collection throughout the process. The results show that the smart neural sensor accurately monitors clogging during laser cladding, highlighting its importance to manufacturing. The developed models are able to effectively estimate the mass throughput under different parameters and temperature variations of the substrate material. Future studies may address the prediction of more complex coating properties such as porosity, crack formation, and metallurgical grain size distribution, thus expanding the potential of laser cladding and autonomous manufacturing systems.

Leveraging Machine Learning for Predicting and Monitoring Clogging in Laser Cladding Processes: An Exploration of Neural Sensors

Laser heat treatment is a process that changes the strength and hardness of the material. Unlike general heat treatment processes, laser heat treatment has the advantage of being able to apply heat locally and having a fast-cooling time. However, due to the high-intensity heat source, excessive heat can be applied to the material, resulting in surface melting, and softening. In this study, a deep learning model based on generative adversarial network was applied to predict the hardening and softening phenomena that occur during laser heat treatment of AH36 carbon steel. As input data, the temperature field of the cross-section, which is obtained by solving the 3-D heat conduction equation using COMSOL, was applied. The input data consist of two temperature distributions: one obtained when the surface temperature is at its maximum, and the other obtained after 0.5-second. The hardness and phase distributions of the cross-section used as ground truth were obtained from a 2 kW multi-mode fiber laser experiment. The model trained with only one-temperature field predicted the hardness distributions well, but the prediction of the softened region was inaccurate. On the other hand, in the case of the model trained with two-temperature fields, it showed high prediction accuracy for hardness and phase distributions in both the softened and hardened regions.

Prediction of Hardness and Phase Distributions in Laser Heat Treatment of AH36 Steel Using Deep Learning

On-the-fly evaluation and process control of laser surface microprocessing can be realized with the detection of infrared (IR) thermal radiation. In this study, a new multi-focus mirror was developed and tested for large-area detection of IR-thermal radiation during ultrashort pulsed laser surface micromachining. The IR-thermal detection in the laser micromachining was for the first time combined with a fast hardware signal analysis and laser power control using a field programmable gate array (FPGA) module. The new mirror has a long-line distribution of focused points on one side to collect the emitted thermal radiation along a laser scanning line and focuses the captured IR-thermal radiation to a point on the other side for signal analysis. The length of the detection line of the developed multi-focus mirror was 120 mm and it can be expanded up to tens of centimeters. This IR-thermal radiation signal is processed by the FPGA module with a fast evaluation of heat accumulation at a cycle time loop of 50 ns and on-the-fly control of the laser power during surface microprocessing. When the detected heat accumulation signal grows higher than the required value, then the average laser power is automatically decreased, and vice versa. This lR-thermal tracking system was applied for laser surface micromachining of metals and ceramics with polygon- and galvo-scanners. The presented lR-thermal tracking system with the multi-focus mirror can be used also in other applications, such as beam shaping, line scanning spectroscopy, linear surface irradiation, or non-destructive testing (IR-NDT).

Underline digital video library

Numerical and Experimental Investigation of the Melt Removal Mechanism and Burr Formation During Laser Cutting of Metals

Laser Cutting of Ceramic Matrix Composites

Reducing Environmental Risks in Laser Cutting: A Study of Low-Pressure Gas Dynamics

Laser Fusion Cutting: The Missing Link Between Gas Dynamics and Cut Edge Topography

Making the Virtually Invisible Visible - Sophisticated AI Models Based on Seamless In-Process Information Enable More Product Safety

Elucidating the Effect of Circular and Tailing Laser Beam Shapes on Keyhole Necking and Porosity Formation During Laser Beam Welding of Aluminium 1060 Using a Multi-Physics CFD Approach

AI-Based Process Control for Laser Manufacturing Processes Using Multi-Sensor Conceptions

An Approach Towards the Application of Mobile Robots in Laser Materials Processing

Exploiting the Potentials of Laser Beam Shaping for Welding Applications via Numerical Simulations

Leveraging Machine Learning for Predicting and Monitoring Clogging in Laser Cladding Processes: An Exploration of Neural Sensors

Prediction of Hardness and Phase Distributions in Laser Heat Treatment of AH36 Steel Using Deep Learning

Fast IR-Thermal Tracking of Laser Microprocessing with Multi-focus Mirror

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