Laser Cut Edges Research Articles

New approaches to edge passivation of laser cut PERC solar cells

Recently the development trend in the PV industry is towards much larger wafer formats. With increasing wafer area and the resulting increase in short-circuit current at the cell level, there is also a trend towards sub-cells (solar cell cut into smaller pieces) for module integration. Using sub-cells, the resistance losses through the connection can be reduced. Modules based on sub-cells achieve higher levels of fill factors and thus a higher nominal power. However, the energy yield of such sub-cells is reduced compared to full cells due to the non-passivated laser edge. The laser cut edge causes a high recombination of the charge carriers, which negatively affects the pseudo fill factor as well as open-circuit voltage of the cell. The current work introduces two different approaches for passivating the laser separated PERC solar cells. The experiments were performed on p-type PERC monofacial cells and laser scribe and mechanical cleavage (LSMC) technique was used to obtain sub-cells from the host cells. The method ‘laser scribing and simultaneous Al doping’ increases the pFF of the cleaved cells by +0.2–0.4%abs in comparison to the reference cleaved cells whereas the method ‘laser scribing and subsequent Al doping’ shows an improvement in efficiency of the cleaved cells by + 0.2% abs.

Effects of Sheared Edge and Overlap Length on Reduction in Tensile Fatigue Limit before and after Hydrogen Embrittlement of Resistance Spot-Welded Ultra-High-Strength Steel Sheets

The effects of a sheared edge and overlap length on the reduction in the tensile fatigue limit before and after hydrogen embrittlement of resistance spot-welded ultra-high-strength steel sheets were investigated. Ultra-high-strength steel sheets with sheared and laser-cut edges were subjected to resistance spot welding followed by hydrogen embrittlement via cathodic hydrogen charging and subjected to static tensile shear and fatigue tests. The distance between the resistance spot weld and the sheared and laser-cut edges was changed by changing the overlap length, and the influence of the weld position was investigated. In the tensile shear test, the maximum load decreased with decreasing overlap length and the maximum load decreased with hydrogen embrittlement, but the effect of hydrogen embrittlement was smaller than that in the fatigue test. In the fatigue test, the fatigue mode changed from the width direction to the sheared edge direction with the increase in the repeated load. Even if the overlap length was reduced, the fracture changed to the sheared edge direction. In the specimens with sheared edges, the effect of fatigue limit reduction due to hydrogen embrittlement was greater than in the specimens with laser surfaces. In particular, the effect was greatest when the fatigue mode was changed via hydrogen embrittlement.

Fatigue performance of fiber laser fusion cut edges on thick plates

Fatigue performance of cut edges is an important factor in structural design, especially when geometrical stress concentration factors (SCFs) are present. High power laser cutting systems have enabled efficient and fast fusion cutting with nitrogen as an assistant gas. The fusion cutting induces recast layer, which features crack-like defects close to cut edge. In this study, plates with thickness 16 ≤ t ≤ 30 mm have been fatigue-tested. A 10 kW fiber laser cutting system was used to cut plates with nitrogen as an assistant gas. High strength steel (HSS) S690 specimens included unnotched coupon specimens and welded T-profile with longitudinal gusset in post-weld treated conditions, where laser cut edge was found critical. Fatigue performance of S690 cut edges were found close to 170 MPa m = 3 when SCFs of welded specimens were included on analysis. Local properties at the cut edge including hardness measurements and scanning electron microscope (SEM) fractography. Fractography obtained local quality use with theory of critical distances -based fatigue strength assessment was demonstrated.

Automated quality evaluation for laser cutting in lithium metal battery production using an instance segmentation convolutional neural network

Separating lithium metal foil into individual anodes is a critical process step in all-solid-state battery production. With the use of nanosecond-pulsed laser cutting, a characteristic quality-decisive cut edge geometry is formed depending on the chosen parameter set. This cut edge can be characterized by micrometer-scale imaging techniques such as confocal laser scanning microscopy. Currently, experimental determination of suitable process parameters is time-consuming and biased by the human measurement approach, while no methods for automated quality assurance are known. This study presents a deep-learning computer vision approach for geometry characterization of lithium foil laser cut edges. The convolutional neural network architecture Mask R-CNN was implemented and applied for categorizing confocal laser scanning microscopy images showing defective and successful cuts, achieving a classification precision of more than 95%. The algorithm was trained for automatic pixel-wise segmentation of the quality-relevant melt superelevation along the cut edge, reaching segmentation accuracies of up to 88%. Influence of the training data set size on the classification and segmentation accuracies was assessed confirming the algorithm’s industrial application potential due to the low number of 246 or fewer original images required. The segmentation masks were combined with topography data of cut edges to obtain quantitative metrics for the quality evaluation of lithium metal electrodes. The presented computer vision pipeline enables the integration of an automated image evaluation for quality inspection of lithium foil laser cutting, promoting industrial production of all-solid-state batteries with lithium metal anode.

Laser fusion cutting: The missing link between gas dynamics and cut edge topography

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 semiempirical equations and the transfer of basic results from the boundary layer theory. It is shown that the boundary layer is in transition between the laminar and turbulent flow, and local flow separations and shock-boundary layer interactions primarily induce spatially periodic and quasistationary instability modes. An improved numerical model of the cutting gas flow confirms the theoretical results and exhibits good agreement with experimental cut edges, reproducing relevant instability modes and quantifying the local momentum and heat transfer distributions between the gas and melt. With the knowledge gained about the underlying physical mechanisms, promising approaches for improvements of the fusion cutting performance are proposed.

Laser machining of free-standing silicon nitride membranes

Patterning free-standing Silicon Nitride membranes has enabled the development of various SiN-based devices such as quantum resonators, biosensors, temperature detectors, and plasmonic devices. Patterning SiN membranes usually involves expensive and time-consuming cleanroom techniques that limit flexibility in the development of new SiN membrane designs. While ultrafast laser machining has been proposed to create micron to submicron features in these membranes, laser machining of large area features has not been reported as it often results in membrane failure due to stress concentration during laser machining. We show that continuous laser machining can be used to create a crack and to extract the fracture toughness of the membrane (1.9 ± 0.3 MPa.m1/2). To successfully patterned SiN membranes without failure, a low stress laser machining method is developed, where stress concentration can be minimized by adjusting the laser drilling sequence, a result confirmed by finite element simulations. Complex shapes can thus be machined over a large area with a very high success rate. Laser parameters are optimized to improve the quality of the laser-cut edges without the formation of laser-induced periodic surface structure. Transmission Electron Microscopy and Electron Energy Loss Spectroscopy indicate a modification of the Si/N ratio over a small region next to the laser machined features. These results demonstrate that low stress ultrafast laser machining can be used to create patterns in SiN membranes quickly and economically, enabling the rapid development of new membrane designs.

Experimental and statistical analysis of the effects of punching and laser cutting methods on induction motor efficiency and total magnetic losses in silicon lamination sheets

In this study, M400-50A electrical steel laminations (ESLs) used in electrical machines were cut with punching and laser methods and the effect of these cutting methods on the efficiency and total losses of induction motors was investigated. ESLs were prepared by laser and punching cutting methods concerning an induction motor with a power of 5.5 kW. After the stator and rotor packages were created, processes such as winding, varnishing, and mechanical assembly were performed on the motors. It is prepared for motor efficiency (MEfficiency) and total magnetic losses (TLosses) tests according to the direct measurement method specified in the IEC 60034-2-1-1A standard. Four different frequencies (f: 50, 75, 100 and 125 Hz) and five different motor loading rates (MLoad: 25, 50, 75, 100 and 125%) were defined as independent variables in the tests. MEfficiency and TLosses values were measured as dependent variables. Plastic deformation and edge rounding were observed on the edges cut with punching and edge rounding and angled edge formation on the edges cut with the laser. The average surface roughness of the laser-cut edges was determined to be higher than the ESLs cut by punching. It was observed that TLosses increased while MEfficiency decreased with increasing frequency and MLoad value in motor tests. It was determined that the training, testing, and experimental data are quite compatible with each other and the R2 values are close to 1 in the statistical analyzes performed with ANN (Artificial neural network). In addition, it was specified that RMSE (Root mean square error) and MAPE (Mean absolute percentage error) values vary between 0.007 and 13.310% for each cutting method and mathematical models can be used to predict MEfficiency and TLosses with ANN.

Minimizing Thermal Damage During Thulium Laser-Assisted Partial Arytenoidectomy: Pulsed Versus Continuous Cutting in an Ex-Vivo Calf Model.

The 2 µm-wavelength thulium laser is an effective cutter during partial arytenoidectomy, but thermal trauma can damage adjacent laryngeal tissue. Pulsing laser energy may reduce trauma when compared to continuous-wave cutting. This study measured temperature changes, thermal trauma, and time to complete partial arytenoidectomy, with and without pulsing, in an ex-vivo calf model. Tissue temperature and time to complete a trans-cartilaginous cut were measured during partial arytenoidectomy on ex-vivo calf vocal folds (N = 24) using a thulium laser in continuous-wave (CW, N = 12) and pulsed-wave (PW, N = 12) modes. Energy was 5 W for CW and PW cuts; pulse-widths were 250, 500, and 750 ms. Thermal damage was analyzed histologically by measuring the depth of lactate dehydrogenase (LDH) inactivation perpendicular to the laser-cut edge at the vocal process. Paired t-tests compared CW and PW modes. Change in temperature was lower using CW (6.5°C) compared to PW modes (250 ms = 18°; 500 ms = 16°; 750 = 19°; P < .05). Trans-cartilaginous cuts were completed faster using CW (37 seconds) compared to PW (250 ms = 136 seconds; 500 ms = 61 seconds; 750 = 44 seconds; P < .05), and both modes delivered the same total Joules. The average depth of LDH depletion (thermal damage) was similar for all cuts. 1. Thulium laser cuts in continuous-mode unexpectedly produced less tissue heating yet created similar thermal damage than pulsed-mode cuts during simulated partial arytenoidectomy. 2. Trans-cartilaginous cuts were completed significantly faster in continuous-mode as compared to pulsed-mode cutting. 3. Pulsing the thulium laser does not minimize thermal damage compared to continuous wave cutting during thulium laser-assisted partial arytenoidectomy.

Influence of a Pronounced Pre-Deformation on the Attachment of Melt Droplets and the Fatigue Behavior of Laser-Cut AISI 304

Laser cutting is a suitable manufacturing method for generating complex geometries for sheet metal components. However, their cyclic load capacity is reduced compared to, for example, milled components. This is due to the influence of the laser-cut edge, whose characteristic features act as crack initiation sites, especially resolidified material in the form of burr and melt droplets. Since sheet metal components are often formed into their final geometry after cutting, another important factor influencing fatigue behavior is the effect of the forming process on the laser-cut edge. In particular, the effect of high degrees of deformation has not yet been researched in detail. Accordingly, sheets of AISI 304 were processed by laser cutting and pre-deformed. In the process, α’-martensite content was set to be comparable despite different degrees of deformation. It was found that deformation to high elongations caused a large part of the melt adhesions to fall off, but those still attaching were partially detached and thus formed an initial notch for crack initiation. This significantly lowered the fatigue strength.

Optimization of Process Parameters for Laser Cutting Process of Stainless Steel 304: A Comparative Analysis and Estimation with Taguchi Method and Response Surface Methodology

The research presents an analysis and comparison of the Taguchi Design and Response Surface Methodology (RSM) in optimizing the laser cutting machine parameters on dimension accuracy for stainless steel products. The paper studies effects of input factors such as cutting speed, nitrogen pressure, power, and frequency on the quality cutting of stainless steel (304) specimens. In this paper, two objectives are examined: targeting laser-cut edge to perpendicular 90 degrees and maximizing the cutting accuracy. The paper proposes a simple formula to optimize both targets by minimizing one new function definition, i.e., dimensional error. An L9 orthogonal array of Taguchi Methodology is adopted to minimize the number of experiments and shorten analysis time to achieve the optimal parameters. These results are compared with RSM. Box–Behnken Design (BBD) type of RSM requires more experiments than the Taguchi approach. RSM regression models as the quadratic functions of the control factors are developed to minimize the dimensional error of cutting products. Then, Analysis of Variance (ANOVA) and graphs will be analyzed to determine the influences of variables on the responses. Both Taguchi’s method and RSM found that the most influential factor on dimension accuracy is cutting speed, followed by laser power. While Taguchi provides good graphic visualization for quickly predicting the optimum condition, it cannot examine the interaction effects as RSM due to the lack of data. Besides, RSM reveals the percentage contribution of factors on dimensional error. Cutting speed has a maximum contribution, i.e., 39% of the total. The interaction of cutting speed and power contributes 16% of the total. In this study, RSM can predict optimum conditions more accurately than Taguchi. There are misleading results from the Taguchi method compared with RSM. However, the difference between these objective values is insignificant. The validation experiments show that the Taguchi method can be a practical approach for optimization problems. It can help reduce cost and time and achieve the desired optimal outputs. With cutting problems requiring high precision, the RSM method is highly recommended for identifying optimal parameter settings and interaction effects. With problems that their experimental runs consume high cost and time, Taguchi can be a suitable method for screening the significant variables. Although Taguchi and RSM are used widely for optimization problems in many fields, choosing the right methodology for various objectives is still a concern with different arguments and needs further research. Therefore, this study could be an adequate reference for parameter optimization problems in various fields.

Investigation of the influence of a two-step process chain consisting of laser cutting and subsequent forming on the fatigue behavior of AISI 304

In this work, the impact of laser cutting and a subsequent pre-deformation on the fatigue behavior of AISI 304 was investigated. This represents a typical process chain in steel sheet manufacturing. Laser cutting trials with variating process parameters were evaluated with regard to resulting dross formation and surface roughness. A subsequent deformation can influence the appearance of the laser cut edge. Fatigue tests up to 108 load cycles were carried out on specimens which were processed with selected parameter sets. It can be shown that dross attachment is the failure relevant feature which sets up a shape dependent, crack inducing notch effect. Furthermore, pre-deformation can detach melt droplets and therefore increase the fatigue strength.

Experimental validation of a machine learning algorithm for roughness quantification in laser cutting

Automatic quality assessment of laser cut parts is an essential building block in the deployment of data-driven strategies for process parameter optimization, predictive maintenance and continuous improvement in sheet metal workshops. From an industrial perspective, the major quality features of a laser cut edge are roughness, perpendicularity deviation and dross occurrence on the lower edge. While conventional contact-based profilometry can be used for highly accurate roughness measurements, its broad adoption in the daily operations of sheet metal workshops cannot be realized without significant deterioration of the workshop performance. As a faster and less demanding alternative to contact measurements, image-based roughness quantification by means of artificial neural networks has been evaluated in recent publications for a single material-thickness combination. Although promising results have been reported, extensive testing of the system performance is required before the system can be deployed in an industrial setting. Therefore, as the next step towards industrial implementation, this contribution validates the performance of a multilayer perceptron algorithm for roughness prediction of materials with different reflectivity behavior, such as stainless steel, mild steel and aluminium. Additionally, the robustness of the method depending on the cut edge surface conditions and type of lighting is investigated.

A robust methodology for optimizing the topology and the learning parameters of an ANN for accurate predictions of laser-cut edges surface roughness

The Feed-Forward and Backpropagation Artificial Neural Networks (FFBP-ANN) are generally employed for cut surfaces quality characteristics predictions. However, the determination of the neurons on the hidden layer and the training parameters’ values are tasks requiring many trials according to the Full-Factorial Approach (FFA). Therefore, in this work, a methodology is presented for the optimization of an FFBP-NN and the application of the Taguchi Design of Experiments (TDE). Nine combinations of four variables were examined, having three levels each, according to the L9 (34) orthogonal array. The number of neurons in the hidden layer (N), the learning rate (mu), the increment factor (mu+) and the decrement factor (mu-) are employed as variables. In addition, Mean Squared Error (MSE) and overall regression index (Rall) was decided as the objective functions. Thus, TDE diminishes the FFBP-ANN arrangements to nine from eighty-one of FFA. The optimized FFBP-ANN predicts the surface roughness in various cut depths during laser cutting of thin thermoplastic plates.

Image-based modelling and visualisation of the relationship between laser-cut edge and process parameters

This article presents a new way of evaluating the laser cutting process. We show that it is possible to deduce the underlying process parameters directly from the laser-cut edge using a convolutional neural network (CNN).For this purpose, we developed a suitable CNN architecture and generated a broad database of 3336 stainless steel (1.4301) edges that were cut with different combinations of four process parameters. RGB images and 3D point clouds of the edges were used as input to the network, and the process parameters were the regression targets (output). We found that the CNN estimates the process parameters well and performs better on the RGB images. The mean error is 1.1m/min, or 7% of the range, for the feed rate and 0.2mm, or 4% of the range, for the focus position.The proposed method could be used to monitor the condition of a laser cutting machine by evaluating an image of a cut edge. Because defective machine components can cause the actual process parameters (in the sheet metal) to differ from the set values, they can be identified quickly by comparing the CNN output with the chosen settings.As our approach offers a new perspective on the process, the visualisation of the CNN might offer a better understanding of the process. We applied layer-wise relevance propagation to visualise the relationship between each individual pixel of the input image and the output of the CNN. We show the potential of this technique with some examples.

Models for emulating the human quality assessment of laser cut edges

With the increasing degree of automation in production, the ability to automatically assess and monitor the process quality is gaining in importance. In laser cutting, however, the quality of the cut edge is usually evaluated by humans. This paper aims to emulate their quality assessment based on objective criteria and to expand the understanding of which properties of the edge are relevant.For this purpose, we carried out an expert survey: five experts rated the quality of 100 laser cut edges. Additionally, numerous objective features were measured for each edge. Based on this data, two models were developed that mimic the human quality assessment very well.

Roughness prediction of laser cut edges by image processing and artificial neural networks

Quality assessment of parts produced by laser cutting is a time-consuming task which typically requires expert knowledge. One of the main characteristics determining the quality of a cut edge is the roughness of the striations profile generated during the cutting process. Usual practice is to measure roughness by means of contact-based profilometry. However, such measurement requires significant effort to be performed and is difficult to automate in industrial practice. In this paper, an algorithm based on the multilayer perceptron architecture is described for efficient image-based roughness prediction of laser cut samples. The simple architecture of the selected neural network allows performing the training of the network with a low number of data points. This offers a clear advantage for possible industrial implementations. Additionally, the influence of the lighting conditions on the roughness prediction quality is evaluated.

Fatigue strength characterization of high and ultra-high-strength steel cut edges

This paper investigates the fatigue strength characteristics of CO2 laser, fiber laser and plasma cut edges of S690 and S1100 steel grades. The effect on fatigue performance of surface quality, which was examined by contact measurements and 3D scanning, residual stresses and microhardness profile, is studied and edge surface characteristics are compared for different cutting methods and 8 mm and 12 mm plate thicknesses. Four main experimental fatigue test series were performed and analyzed to estimate fatigue performance (defined as FAT classes) of the cut surface. Additional tests were conducted to study critical defects and residual stresses influence on fatigue properties. Scanning electron microscopy (SEM) revealed multiple critical locations in the specimens. Fatigue properties were characterized based on critical defects and crack initiation and propagation origins. Geometrical discontinuity points in the cut edges were found to have a significant influence on fatigue performance. It is concluded that classification of thermally cut edges by cut edge characteristics could be used in fatigue strength assessment as an alternative to standard FAT classes.

Impact of machining-induced surface defects on the edge formability of commercially pure titanium sheet at room temperature

Despite the good properties of titanium, which have drawn the interest of various industries over the years, one of the major drawbacks of this material is its poor machinability. This has largely been attributed to its low thermal conductivity and elastic modulus. The ability to attain the optimum sheet edge performance during forming is dependent on the quality of the edges produced. Also, the demanding nature of aerospace part design has provoked the interest of both industry and academia to continually explore avenues tailored at enhancing part performance. The sort of edge surface integrity produced for aerospace part fabrication thus becomes a vital consideration in the quest to ensuring prime performance of components. This work seeks to study the influence of different machining-induced surface defects on the sheet edge performance of CP-Ti (grade 2) at room temperature. Hole expansion test was used to assess the edge surface formability of CP-Ti with different machining-induced edge defects. The research found that machining-induced surface defects act as stress concentration sites during the hole expansion test and have a major impact on the material flow. Electro-discharge machined edges were observed to exhibit high edge formability compared to laser and abrasive water jet cut edges due to the impact of machining-induced microstructural changes.

Effects of Laser Cutting on Microstructure and Magnetic Properties of Non-Orientation Electrical Steel Laminations

Non-oriented electrical steel (NOES) is commonly used as a core material in electric machines. These laminations are cut into the desired core shape that modifies the material properties near the cut edge, which consequently changes the magnetic performance of the core lamination. Magnetic property deterioration due to cutting has been reported in the literature but less attention has been given to the microstructural changes due to cutting and its relation to the magnetic properties. The present research focuses on the effects of laser cutting on microstructure and magnetic properties of NOES laminations. A number of lamination steel samples were laser-cut using an identical procedure to that expected on an industrial scale. Property measurements showed that the magnetic permeability of the samples decreased while the core losses increased. The core loss increase was significant, about 22% at 50 Hz and 1.5 T. This was attributed to the change in the micromagnetic characteristics of the sample due to cutting. The change in micromagnetics was investigated using magnetic domain imaging and distorted domain structure was observed near the laser-cut edge. Backscattered electron imaging and nanoindentation measurements were also used for further investigation of micromagnetic properties of the material.

Image-based roughness estimation of laser cut edges with a convolutional neural network

Laser cutting of metals is a complex process with many influencing factors. As some of them are subject to change, the cut quality needs to be checked regularly. This paper aims to estimate the roughness of cut edges based on RGB images instead of surface topography measurements.We trained a convolutional neural network (CNN) on a broad database of images and corresponding roughness values. The CNN estimates the roughness well with a mean error of 3.6 µm. Sometimes it is more reliable than the surface measuring device because the RGB images are less prone to reflectivity problems than the measurements.