Optimal Laser Research Articles

Optimizing Femtosecond Texturing Process Parameters Through Advanced Machine Learning Models in Tribological Applications

Surface texturing plays a vital role in enhancing tribological performance, reducing friction and wear, and improving durability in industrial applications. This study introduces an innovative approach by employing machine learning models—specifically, decision trees, support vector machines, and artificial neural networks—to predict optimal femtosecond laser surface texturing parameters for tungsten carbide tested with WS2 and TiCN coatings. Traditionally, the selection of laser parameters has relied heavily on a trial-and-error method, which is both time-consuming and inefficient. By integrating machine learning, this study advances beyond conventional methods to accurately predict the depth and quality of textured features. The ANN demonstrated superior predictive accuracy among the models tested, outperforming SVM and Decision Trees. This machine learning-based approach not only optimizes the surface texturing process by reducing experimental effort but also enhances the resultant surface performance, making it well-suited for applications in sectors such as automotive and oil and gas.

Data-driven analysis of bonding strength in laser-structured metal-GFRP hybrid joints via groove morphology

Abstract This study aims to enhance predictions of the mechanical properties of mechanically interlocked hybrid joints by employing machine learning techniques coupled with feature engineering of cross-sectional groove morphology. Unlike mechanical fastening, which promotes localized stress, and adhesive bonding, which requires prolonged contaminant removal, mechanically interlocked joints offer a distinct advantage by eliminating the need for either. The mechanically interlocked joints in this study combine glass fiber reinforced composite fabricated via injection molding, with cold rolled steel structured by a nanosecond laser. Through optical microscopy, crucial groove dimensions such as depth and width are identified for feature extraction. Domain-specific feature engineering is employed to improve predictive accuracy, integrated with existing regression models. The concept of “structure density,” initially defined as groove width over hatch distance, is expanded during feature engineering to include additional relevant features over hatch distance. Experimental investigations identified optimal laser parameters for shear strength, yielding a maximum single lap shear strength of 33.3 MPa under specific conditions. The third polynomial regression model incorporating structure density features emerged as the most effective in predicting shear strength, demonstrating high accuracy in both interpolation and extrapolation scenarios. The study suggests potential cost savings by utilizing surface topography for shear strength prediction, with implications for industries amidst the increasing prevalence of composite materials.

An experimental study on laser cleaning of the soot deposition layer on a white marble surface

A coordinated application of the image binarization, area extrapolation, and laser-induced breakdown spectroscopy (LIBS) detection for monitoring and controlling the laser cleaning soot deposition layer process on a white marble surface was reported. The laser cleaning process used a fiber pulsed laser beam with a fixed wavelength of 1064 nm, a pulse repetition frequency of 100 kHz, and a focal spot diameter of 95 μm. Image binarization was conducted to determine 150 ns as the appropriate fixed pulse width for laser cleaning of the soot deposition layers from four discrete values of 50 ns, 100 ns, 150 ns, and 200 ns. The optimal laser energy density of 2.82 J·cm-2 was obtained through area extrapolation, while the optimal spot overlap rate of 20 % was determined to maximize the effective laser cleaning speed. The optimal number of cleanings for a soot deposition layer with a thickness of 77μm was determined to be 5 using LIBS detection and to monitor the laser cleaning process in real time for each small area of the soot deposition layer. Image binarization was used to evaluate the overall cleaning quality of large marble surfaces. By dynamically adjusting the optimal number of cleanings, the cleaning efficiency of the soot deposition layers on half of the surface area of a 20 cm diameter white marble artifact exceeded 95 %. The results indicate that the synergistic use of various online monitoring methods can optimize multiple laser parameters, enabling precise removal of the soot deposition layer on the white marble surfaces caused by anthropic factors, without damaging its surface.

Laser powder bed fusion of porous 304SS samples for use in developing oil impregnated ball bearings

The ball bearing market is mature where there is a massive range of products available with new ones being developed all the time due to technological advancements. Additive Manufacturing (AM) provides a promising approach for developing oil-impregnated ball bearings. Oil impregnated bearings are critical for applications requiring smooth and low-friction motion. This study explores the feasibility of utilizing laser powder bed fusion (LPBF) technology to fabricate 304 stainless steel (304SS) samples with open pores, which can then be impregnated with a lubricant. To achieve this, 304SS powder was used, and optimum selective laser melting (SLM) printing parameters were altered to induce intentional pores. Initial screening of samples involved Archimedes density measurements and computed tomography (CT) scanning was conducted on a selected samples to assess their porosity levels. CT scan foam analysis results indicated a correlation between hatch spacing and porosity. Results revealed trends in cell volume and solidified scanning tracks thickness, indicating greater connectedness with larger pores. Synthesis of these findings could help in the development of efficient and reliable open pores that may find use in oil-impregnated self-lubricating ball bearings.

Influence of crystal orientation and incident plane on n-type 4H-SiC wafer slicing by using picosecond laser

Laser slicing has been considered as the most efficient technique for slicing SiC wafers with the advantages of small kerf width (loss) and crack, low roughness on cleaved surface, high quality and efficiency, etc. Here, laser slicing of n-type 4H-SiC was demonstrated by using a homemade 1064 nm picosecond laser combined with mechanical stretch stripping. The influence of laser processing along [11 2¯ 0] and [1 1¯ 00] crystal orientations on Si-face and C-face of n-type 4H-SiC, specifically on the internal laser ablation lines and cracks, the peeling tensile strength, and the surface roughness of the peeled surfaces was investigated. The semi-insulating 4H-SiC wafer laser slicing was conducted for comparison. Under the same laser conditions, laser slicing of semi-insulating 4H-SiC was easier than that of n-type. The laser modification quality along [1 1¯ 00] orientation was better than that along [11 2¯ 0] orientation for both the semi-insulating and n-type 4H-SiC and the reasons were explained. The results indicated that the optimal laser slicing scheme detailed laser scanning along [1 1¯ 00] orientation and incidence from the C-face. Finally, a 6-inch, 420.36 µm-thick n-type 4H-SiC wafer was successfully sliced.

Research of laser width variability in line laser infrared thermal wave inspection of CFRP laminates and defect reconstruction method for mobile detection

The line laser scanning infrared thermography method is a rapid and comprehensive approach for defect detection. This study investigates the impact of line laser width on detecting Carbon fiber reinforced polymers (CFRP) defects, revealing that narrower line lasers are more effective for shallow subsurface defects, while wider line lasers provide greater stability for deeper subsurface defects. To address issues like target loss, misdetection, and leakage in moving detection, a quasi-static reconstruction method based on Discrete Fourier Transform and Principal Component Analysis (DFT-PCA) is proposed. This method tracks and localizes moving defects, converting the image sequence into a quasi-static format and applying frequency domain denoising to achieve clear defect boundaries and reduced background noise. The study further explores the optimal line laser width by analyzing the relationship between the defect diameter-to-depth ratio and the signal-to-noise ratio of the reconstructed images. The findings indicate that for defects with a diameter-to-depth ratio greater than 6, a line laser with a width smaller than 4 mm offers better detection results, whereas for a diameter-to-depth ratio smaller than 6, a line laser with a width larger than 4 mm is more effective. These conclusions confirm previous research on the varying effects of line laser width on detection performance.

Determination of the Optimum Laser Processing Parameters Required to Obtain a Stable Hydrophobic Surface on the Surface of AA1050 Aluminum Alloy

In this study, the surfaces of sheets made of AA1050 aluminum alloy, which has high aluminum purity and also high strength, were roughened with a fiber laser. The aim of the study is to obtain a surface with a highly stable contact angle. For this purpose, it was aimed to minimize the change in the contact angle with time. Three different laser processing parameters were used. The effects of the laser parameters on the stability of the contact angle were investigated. According to the data obtained as a result of the study, the texture type created on the surface was calculated as the most effective parameter to obtain a stable surface. The rate of influence of the texture type parameter on the result is 61.61 %. The parameter with the least effect on the result was calculated as laser scanned factor with 15.31%. The parameter that moderately affected the result was calculated as laser power with 23.08%. In addition, according to the ANOVA calculations, it was suggested that a more stable surface pattern could be obtained in the experiment with different parameters.

Optimizing laser-induced phase transformations in Sb2S3 thin films: Simulation framework and experiments

We present a novel simulation approach combined with pulsed laser experiments, spectroscopic ellipsometry, and Raman spectroscopy to comprehensively analyze phase transformation dynamics in thin films. The simulations apply to any thin film stack and incorporate critical factors, such as thin film interference, heat transfer, and temperature-dependent optical properties during heating and melting. As a case study, we investigate the picosecond laser-induced amorphization of antimony sulfide (Sb2S3) thin films, a promising alternative to traditional phase-change materials in photonic applications to validate the simulation model. The computational efficiency of our simulations enables not only the investigation of the laser-induced phase transformation but also the optimization of key process parameters and parameter fitting. The simulations identified optimal film thickness and laser fluence parameters that maximize energy efficiency, melting effectiveness, and quenching rate while ensuring high reflectivity contrast between the amorphous and crystalline states. By constructing a wide-ranging, high-resolution parameter map of the laser fluence and film thickness dependence of the melting process, we demonstrate how this model guides the understanding of phase transformation dynamics. Raman spectroscopy confirms the polycrystalline to amorphous transition of Sb2S3 and provides a semiquantitative estimate of the amorphous fraction as a function of laser fluence, which is qualitatively consistent with the simulation predictions of the model. The open-source simulation framework, experimentally validated, provides valuable insights into laser-induced amorphization dynamics in Sb2S3 and related phase-change material thin films, enabling rapid optimization of photonic devices.

AI-based spatially resolved parameter prediction in laser metal deposition for increased process stability

In the additive manufacturing of components using powder-based laser metal deposition (LMD), the heating of the volume during buildup is a decisive factor for process stability and contour accuracy. If the process parameters remain constant, this intrinsic heating leads to deviations in the deposited layer thickness during the process because of changes in the melt pool volume. This leads to contour deviations and potentially causes the process to fail if the process parameters are no longer within the suitable range. Particularly in the case of complex geometries, this previously required time-consuming process development for adapted process parameters and buildup strategies. This paper examines the potential of data-driven approaches to enhance the stability and precision of LMD processes. To this end, a machine learning (ML) model is employed to optimize laser power settings. The objective of the study is to reduce the thermally induced geometric deviations that often occur during the LMD process by utilizing experimental data. The methodology employs the use of the alloy Inconel 718, renowned for its high strength and temperature resistance, in conjunction with the utilization of computer numerical control machines equipped with laser and imaging systems. The ML model is trained to predict the optimal laser power required to obtain consistent melt pool properties. The results demonstrate that the ML approach is an effective means of reducing geometric deviations.

Accelerating wavepacket propagation with machine learning.

In this work, we discuss the use of a recently introduced machine learning (ML) technique known as Fourier neural operators (FNO) as an efficient alternative to the traditional solution of the time-dependent Schrödinger equation (TDSE). FNOs are ML models which are employed in the approximated solution of partial differential equations. For a wavepacket propagating in an anharmonic potential and for a tunneling system, we show that the FNO approach can accurately and faithfully model wavepacket propagation via the density. Additionally, we demonstrate that FNOs can be a suitable replacement for traditional TDSE solvers in cases where the results of the quantum dynamical simulation are required repeatedly such as in the case of parameter optimization problems (e.g., control). The speed-up from the FNO method allows for its combination with the Markov-chain Monte Carlo approach in applications that involve solving inverse problems such as optimal and coherent laser control of the outcome of dynamical processes.

A Machine Learning-Optimized System for Pulsatile, Photo- and Chemotherapeutic Treatment Using Near-Infrared Responsive MoS2-Based Microparticles in a Breast Cancer Model.

Multimodal cancer therapies are often required for progressive cancers due to the high persistence and mortality of the disease and the negative systemic side effects of traditional therapeutic methods. Thus, the development of less invasive modalities for recurring treatment cycles is of clinical significance. Herein, a light-activatable microparticle system was developed for localized, pulsatile delivery of anticancer drugs with simultaneous thermal ablation by applying controlled ON-OFF thermal cycles using near-infrared laser irradiation. The system is composed of poly(caprolactone) microparticles of 200 μm size containing molybdenum disulfide (MoS2) nanosheets as the photothermal agent and hydrophilic doxorubicin or hydrophobic violacein, as model drugs. Upon irradiation, the nanosheets heat up to ≥50 °C leading to polymer softening and release of the drug. MoS2 nanosheets exhibit high photothermal conversion efficiency and require low-power laser irradiation. A machine learning algorithm was applied to acquire the optimal laser operation conditions. In a mouse subcutaneous model of 4T1 triple-negative breast cancer, 25 microparticles were intratumorally administered, and after 3-cycle laser treatment, the system conferred synergistic phototherapeutic and chemotherapeutic effects. Our on-demand, pulsatile synergistic treatment resulted in increased median survival up to 39 days post start of treatment compared to untreated mice, with complete eradication of the tumors at the primary site. Such a system is therapeutically relevant for patients in need of recurring cycles of treatment on small tumors, since it provides precise localization and low invasiveness and is not cross-resistant with other treatments.

Physical Properties of HfO2 Nano Structures Deposited using PLD

On Quartz substrates, high purity transparent conductive the Di-Oxide of the (HfO2) Nano and micro-structure films were successfully coated. The method of (PLD) Deposition using Pulsed Laser, and the influence of laser energy was investigated. The XRD results revealed two distinct phases, cubic and monoclinic crystal shapes. Peaks in the Fourier-transform infrared (FTIR) spectra showed that HfO2 nanofilm was forming.The findings of the optical characteristics reveal an excellent transparency of nearly 80% (89 percent). As the laser wavelength decreases, the optical energy band gap values for Nanostructure of HfO2 were prepared, with values ranging from 5.24 to 5.53 eV. Also, the EDX results showed the appearance of hafnium peaks and oxygen peaks in varying proportions, which confirms that hafnium oxide is definitely obtained The results of the AFM reveal that when the pulsed laser intensity increases, the average grain diameter values increase from 52.96 to 74.54 nm. With regard to optimum pulsed laser energy, the based on I-V characterization, the generated factor ideality for created diodes was discovered to be decreasing, and the corresponding values of the barrier height grew. The ideality factor of the diode made the optimum pulsed laser energy (1800 mJ) was greater (n=3.1).

Effect of laser drilling process on combustor liner effusion hole quality

Abstract Effusion cooling is one of the significant cooling technologies in combustor liners in terms of cooling efficiency and weight reduction. However, effusion cooling technology is difficult to manufacture. In fact this technology requires laser-drilling of thousands of tiny holes with shallow angles on a sheet metal with a thickness generally varying between 0.5 to 1.5 mm. In addition, the use of thermal barrier coating is common in gas turbine engines and is one more challenge for the drilling process. In order to obtain more efficient gas turbine engines, the inlet temperature keeps increasing in the last decades, which induces the combustion chamber to operate in a hotter environment. Therefore, efficient cooling technology is needed, even if it is hard to manufacture. For laser drilling, several parameters have to be explored to obtain acceptable holes. This study includes the microstructure investigations of the holes produced with different laser parameters and the optimal laser parameters determined according to the microstructure of six different effusion cooling hole configurations. The results show that laser process differences affect the metal substrate microstructure and thermal barrier coating structure. Drilling method, peak power, number of pulses, gas type and pressure value have a significant effect on the hole geometry and its microstructure.

Superhydrophobicity induced antibacterial adhesion and durability of laser bidirectionally-textured zirconia surfaces with different inclination angles

Femtosecond laser bidirectional texturing with different laser energy densities and inclination angles was performed on zirconia (ZrO2) surfaces. The laser bidirectional texturing models under different inclination angles were constructed to predict the water contact angles (WCAs), and the superhydrophobicity induced antibacterial adhesion mechanism was analyzed. The results revealed that under the combined effect of the optimal laser energy density (7.0 J/cm2) and inclination angle (90°), the laser-textured ZrO2 surface could obtain a micro-nano dual-scale structure composed of periodic regular micro-cones and nanoparticles. This structure not only promoted the adsorption of carbon-containing hydrophobic groups from the stearic acid and the air to form a superhydrophobic surface with excellent chemical/mechanical durability and anti-fouling/self-cleaning abilities, but also minimized the contact area with the bacterial suspension (solid/liquid contact area ratio of 6 %) to enhance antibacterial ability. The optimal stearic acid-modified laser-textured ZrO2 surface exhibited an extremely low adhesive force (Fadhesive=6.69 μN) to the bacterial suspension, and thus achieved the highest antibacterial ratio of 85.3 %. This study is expected to enrich the application of ZrO2 ceramics in the medical field.

Effect of laser power during laser powder bed fusion on microstructure of joining interface between Tungsten and AISI 316L steel

This study investigates the deposition of tungsten (W) onto a 316L steel substrate by laser powder bed fusion (L-PBF), to optimize process parameters and analyze the interface between W and 316L. To obtain high-density W structure (98.89%), the optimal laser power was 350 W, and scan speed was 500 mm/s, but these parameters cause significant dilution of W in the W-316L interface; as a result, Fe7W6 intermetallics form, despite L-PBF being a non-equilibrium solidification process. These intermetallics which are brittle could degrade joint strength. By reducing laser power below 250 W, the dilution of W can be mitigated, and potentially minimize formation of intermetallics and increase joint stability for advanced manufacturing applications.

Micro-cracks generation and growth manipulation by all-laser processing for low kerf-loss and high surface quality SiC slicing.

Low kerf-loss and high surface quality silicon carbide (SiC) wafer slicing is key to reducing cost, improving productivity, and extending industrial applications. In this paper, a novel all-laser processing approach is proposed by combining laser micro-cracks generation and growth manipulation. The first high fluence pulsed laser is applied to generate micro-cracks inside SiC, which increases its laser energy absorption. The second low fluence pulsed laser achieves the manipulation of micro-cracks growth and interconnection to separate SiC wafer. The optimal laser processing parameters are obtained to separate a 4H-SiC substrate at a thickness of 500 µm. The sliced surface is clean with average surface roughness (Sa) of 186 nm, standard deviation of 0.037, and kerf-loss of 915 nm. This laser slicing approach can be applied for high-hardness transparent material separation.

Leveraging analog quantum computing with neutral atoms for solvent configuration prediction in drug discovery

We introduce an approach to sampling equilibrium solvent water molecule configurations within proteins that leverages analog quantum computing. We present a complete end-to-end study from the molecular biology application to the development of the quantum algorithm to the implementation on a neutral atom quantum processing unit (QPU). To do so, we combine a quantum placement strategy to the 3D Reference Interaction Site Model, an approach capable of predicting continuous solvent distributions. The intrinsic quantum nature of such coupling guarantees molecules not to be placed too close to each other, a constraint usually imposed by hand in classical approaches. We present first a full quantum adiabatic evolution model that uses a local Rydberg Hamiltonian to cast the general problem into an antiferromagnetic Ising model. Its solution is embodied into a Rydberg atom array QPU. Following a classical emulator implementation, a QPU portage allows to experimentally validate the algorithm performances on an actual quantum computer. As a perspective of use on next generation devices, we emulate a second hybrid quantum-classical version of the algorithm. Such a variational quantum approach uses a classical Bayesian minimization routine to find the optimal laser parameters. Overall, these Quantum-3D-RISM algorithms open a route towards the application of analog quantum computing in molecular modeling and drug design. Published by the American Physical Society 2024

Effect of Powder Spreading Parameters on Laser Absorption Behavior and Processability of High‐Strength Aluminum Alloy Fabricated by Laser Powder Bed Fusion

Laser powder bed fusion (LPBF) of rare‐earth‐modified high‐strength aluminum alloys presents a novel approach for manufacturing complex components with enhanced structural performance, particularly in aerospace applications. This study fabricates Al–Mg–Sc–Zr specimens using various powder spreading parameters to explore their impact on laser processability. The investigation reveals that varying the powder layer thickness from 30 to 70 μm yields the smallest irradiation diameter of 135 μm at an optimal thickness of 50 μm, attributable to effective multiple reflections, high laser absorption rates, and stability. With an optimal laser power of 400 W, a scanning speed of 600 mm s−1, and a hatching spacing of 60 μm, the sample produced at 50 μm layer thickness achieves a relative density of 99.23%, a top surface roughness of 15.42 μm, and a refined grain size of 1.67 μm. Following aging at 325 °C for 4 h, this sample exhibits a tensile strength of 518 MPa and an elongation of 15.6%. The findings establish a theoretical basis for controlling the morphology and properties of high‐strength aluminum alloys in laser additive manufacturing.

Effect of laser remelting on microstructure evolution and high-temperature fretting wear resistance in a laser-cladding self-lubricating composite coating on titanium alloy substrate

TC21 titanium alloy possesses the advantage including high strength, low density, and great resistance to damage. Combining it with surface modification technology, it is expected to improve the performance of aviation structural components. This work applied laser remelting technology to realize surface modification, and the hardness and fretting wear properties of the coating were investigated. This research has found that laser remelting technology can effectively refine grain size and improve the performance of coatings. By exploring different laser remelting powers (600 W, 900 W, 1200 W) and the different diameters of laser beam (3 mm, 6 mm), the optimal laser process was obtained with laser power and spot diameter of 600 W and 6 mm, respectively. The average grain size is 2.1 μm. The laser remelting coating has an average microhardness about 980 HV0.2, and it is 2.5 times and 1.3 times higher than the substrate and laser cladding hardness, respectively. A minimum coefficient of friction (COF) about 0.5 was obtained by the laser remelting coating, and the wear rate reaches the lowest of 1.87 × 10−6 mm3/(N·m) at fretting temperature with 500 °C. The abrasive wear and adhesive wear were the main wear mechanisms, and accompanied by oxidative wear. This study provides important reference for improving the performance of high-strength titanium alloy to obtain low defect, high-performance coatings with stable quality.

Thermal profile modeling and microstructural evolution in laser processing of Inconel 625 plates by comparison of analytical and numerical methods

Microstructural evolution of materials under specified process conditions and parameters can be predicted by thermal modeling of additive manufacturing laser processes. The objective of this study was to develop, analyze and compare two methods for prediction: an analytical method and a numerical method for laser processing of Inconel 625 material. These methods were compared with experimental results for thermal profiling, and the effect of thermal profiles on microstructure of the experimental samples was explored. Maximum temperature and cooling rate of the numerical method were shown in good agreement, while the analytical method proved more challenging when compared to the experimental results for three laser parameters. Cooling curves were correlated with microstructure in terms of grain size, morphology, and orientation, with findings trending with parameter adjustments. This research supports the numerical modeling approach as a method for examining optimal laser processing conditions for Inconel 625 that is ideally suited for complex fluid flow analyses.