Laser Path Research Articles

Development and evaluation of laser acoustic measurement system for supersonic jet noise

Acoustic measurements in the near field are significant to suppress the noise generated by a supersonic jet. The microphone measurement is commonly used to measure noise, but it is not appropriate for the jet noise measurement in the near field, because it may be broken or disturb the sound field. In this study, we propose an optical acoustic measurement method using a laser and a 2-D position sensitive detector. This system can measure the propagating direction and the spectra of acoustic waves by detecting the angle and direction of laser path deflection by acoustic wave passing. To evaluate this laser measurement method, it was applied to various supersonic jet noise phenomena, such as Mach waves and screech tones, and then the results were compared with those of microphone measurements. The spectra measured by the laser measurement show good agreement with those by microphone measurements, and the difference in OASPL was less than 0.7 dB. As for the propagating direction, it is calculated using the principal component analysis in the laser measurement, and its results also show good agreement with those of acoustic intensity vector measurement.

Development of dual-path multi-pass Thomson scattering system in GAMMA 10/PDX

A dual-path Thomson scattering (DPTS) system was developed to measure electron temperature and density in a GAMMA 10/PDX central cell (CC) and an end cell (EC). The DPTS comprises CC Thomson scattering system (CC-TS) and an EC Thomson scattering system (EC-TS). In the CC-TS, we developed a multipass system to improve the signal intensities and time resolution. In the EC-TS, a double-pass system was developed as a preliminary step in developing a multipass system. In a previous EC-TS, the optical components for the double-pass configuration were placed in a vacuum vessel. To reduce the influence of stray light and facilitate alignment, a hole for the double-pass laser path was made on the partition wall in the vacuum chamber such that the probe laser could be extracted into the atmosphere and the double-path configuration could be easily adjusted. We applied DPTS to detached plasma experiments and successfully measured the core and edge electron temperatures and densities with the detached plasma experiments with the additional application of supersonic molecular beam injection and central electron cyclotron heating in the core plasma to simultaneously introduce intermittent particle flux.

Development of Additive Strategy Generator for Metal Additive Manufacturing Build Prediction Using Laser Path Generation Algorithm

Development of Additive Strategy Generator for Metal Additive Manufacturing Build Prediction Using Laser Path Generation Algorithm

Temperature determination of multiple gas slabs using a single absorption line

In this study, temperatures of multiple gas slabs are measured from a single absorption line using a tunable diode laser absorption spectroscopy technique. While typical laser absorption spectroscopy techniques require two or more absorption lines to determine the gas temperatures, however, this study presents the temperature determination method using an absorption line and its conditions for successful application. To generate two air slabs along with a single laser path, a gas heating apparatus composed of two chambers is used. The air is used as a test gas and a tunable diode laser absorption spectroscopy system is set up to detect an absorption line of the oxygen molecule in the air. Using the spectroscopic characteristics that the line shape and absorption strength are functions of gas properties and beam path length, temperature values of multiple gas slabs are experimentally determined by matching a measured spectral absorbance with a calculated one with known pressure and beam path length values. The determined temperatures are compared with temperature values set using thermocouples. It is observed that the temperatures of the gas slabs can be successfully measured when the pressures of the gas slabs are considerably different. The effect of gas properties and beam path length on the temperature determination is discussed. Measurement uncertainty due to random noise in the signal is investigated through a simulation approach.

Carbon Dioxide Concentration Estimation in Nonuniform Temperature Fields Based on Single-Pass Tunable Diode Laser Absorption Spectroscopy.

We propose a novel technique to accurately predict carbon dioxide (CO2) concentrations even in flow fields with temperature gradients based on a single laser path absorption spectrum measurement and machine learning. Concentration measurements in typical tunable diode laser absorption spectroscopy are based on a ratio of two integrated absorbances, each from a spectral line with different temperature dependence. However, the inferred concentrations can deviate significantly from the actual concentrations in the presence of temperature gradients. Furthermore, it is also difficult to find an analytical expression to compensate for the effect of nonuniform temperature profiles on concentration measurements. In this study, the entire absorption feature was considered since its shape and peak intensities vary with temperature and concentration. Specifically, a predictive model is obtained in a data-driven manner that can identify and compensate for the effect of a nonuniform temperature field on the spectrum. Despite a very detailed understanding of the CO2 absorption spectrum, it is nearly impossible to collect sufficient spectra for model acquisition by varying all temperature gradient conditions. Therefore, the model was obtained using only simulated data, much like the concept of a "digital twin". Finally, the predictive performance of the acquired model was verified using experimental data. In all test cases, the predictive performance of the model was superior to that of the two-line method. Additionally, a gradient-weighted regression activation mapping analysis confirmed that the model utilizes both the peak intensities as well as the change in the shape of absorption lines for prediction.

Study on the formation mechanism of functional surface microstructure by ultrasonic vibration assisted laser processing

In this paper, a novel ultrasonic vibration-assisted laser surface processing technology is proposed for preparing microstructures on metal surfaces. Firstly, the formation mechanism of single laser spot on metal surface is analyzed, and the mathematical model of three-dimensional morphology of single crater is derived, the influence of ultrasonic vibration on the crater morphology is studied. Secondly, the influence of ultrasonic vibration on the position distribution of heat affected zone in single laser path is studied. After, the influence of ultrasonic vibration and laser scanning speed on the shape of the laser path is expressed by pushing the overlap rate between the heat affected zones. The calculation results show that the larger ultrasonic amplitude, the greater effect on the curve shape. Finally, the correctness of the calculation and simulation results is verified by experiments. It is observed that ultrasonic vibration can promote the discharge of the melt and provide a new method for changing the surface microstructures.

A Fast Soft Robotic Laser Sweeping System Using Data-Driven Modeling Approach

Soft robots have great potential in surgical applications due to their compliance and adaptability to their environment. However, their flexibility and nonlinearity bring challenges for precise modeling, sensing, and control, especially in constrained cavities. In this article, a simple, compact two-segment soft robot for flexible laser ablation is proposed. The proximal hydraulic-driven segment can offer omnidirectional bending so as to navigate toward lesions. The distal segment driven by tendons enables precise, fast steering of laser collimator for laser sweeping on lesion targets. The dynamics of such mechanical steering motion can be enhanced with a metal spring backbone integrated along the collimator, thus facilitating the control with certain linearity and responsiveness. A soft robot modeling and control scheme based on Koopman operators is proposed. We also design a disturbance observer so as to incorporate the controller feedback with real-time fiber optic shape sensing. Experimental validation is conducted on simulated or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ex-vivo</i> laser ablation tasks, thus evaluating our control strategies in laser path following across various contours/patterns. As a result, such a simple compact laser manipulation can perform up to 6 Hz sweeping with precision of path following errors below 1 mm. Such modeling and control scheme could also be used on an endoscopic laser ablation robot with unsymmetric mechanism driven by two tendons.

Influence of microphone tilt angle on instability identification in micromilling of thin-walled Ti6Al4V

High speed micromilling is the most preferred manufacturing process to fabricate complex 3D featured thin-walled structures like stents, turbine blades, micro-fins, cabin parts etc., that are extensively used in bio-medical, energy, automobile and aerospace sectors. Fabricating thin-walled structures with high dimensional and geometrical accuracy requires chatter free machining parameters. Different sensors like accelerometer, current sensors, laser based sensors, vibration pick-ups etc. are available for capturing the vibration while machining thin-walled structures. Each sensor has its own advantages and disadvantages, like laser based sensors adds noise to response as chips fly past in the laser path and accelerometer captures deflection of both micro-cutting tool and thin-walled workpiece. Hence, In-process chatter identification is a challenging task in thin-wall micromilling. Also, the Filtering of signals to remove the noise from signals captured via contact type and laser based sensors results in loss of low-amplitude chatter data in high-speed micromilling. Consequently, there is a need of sensor, which functions similar to contact type with less or no loss in low-amplitude chatter data. The proposed work compares vibration signal captured using accelerometer and microphone at different tilt angles for radial milling of thin-walled Ti6Al4V at different radial immersion to analyse the effect of tilt-angle on chatter identification. The recorded signals from both accelerometer and microphone are analysed in time domain by estimation and comparison of statistical parameters, in frequency domain by analysing the distributed frequency and in time–frequency domain by analysing the energy of the signals. Complete characterization of the signals in time, frequency and time–frequency domain/methods indicated that microphone at 60° tilt angle can be used as an alternative sensor for chatter onset identification without loss in chatter data irrespective of cutting condition. Machined surface analysis of thin-walled structures complements the microphone as an effective chatter identification sensor.

Accelerating Thermal Simulations in Additive Manufacturing by Training Physics-Informed Neural Networks With Randomly Synthesized Data

Abstract The temperature history of an additively manufactured part plays a critical role in determining process–structure–property relationships in fusion-based additive manufacturing (AM) processes. Therefore, fast thermal simulation methods are needed for a variety of AM tasks, from temperature history prediction for part design and process planning to in situ temperature monitoring and control during manufacturing. However, conventional numerical simulation methods fall short in satisfying the strict requirements of time efficiency in these applications due to the large space and time scales of the required multiscale simulation. While data-driven surrogate models are of interest for their rapid computation capabilities, the performance of these models relies on the size and quality of the training data, which is often prohibitively expensive to create. Physics-informed neural networks (PINNs) mitigate the need for large datasets by imposing physical principles during the training process. This work investigates the use of a PINN to predict the time-varying temperature distribution in a part during manufacturing with laser powder bed fusion (L-PBF). Notably, the use of the PINN in this study enables the model to be trained solely on randomly synthesized data. These training data are both inexpensive to obtain, and the presence of stochasticity in the dataset improves the generalizability of the trained model. Results show that the PINN model achieves higher accuracy than a comparable artificial neural network trained on labeled data. Further, the PINN model trained in this work maintains high accuracy in predicting temperature for laser path scanning strategies unseen in the training data.

Mechanical Properties and Wear Susceptibility Determined by Nanoindentation Technique of Ti13Nb13Zr Titanium Alloy after "Direct Laser Writing".

Laser treatment has often been applied to rebuild the surface layer of titanium and its alloys destined for long-term implants. Such treatment has always been associated with forming melted and re-solidified thin surface layers. The process parameters of such laser treatment can be different, including the patterning of a surface by so-called direct writing. In this research, pulse laser treatment was performed on the Ti13Nb13Zr alloy surface, with the distance between adjacent laser paths ranging between 20 and 50 µm. The obtained periodic structures were tested to examine the effects of the scan distance on the microstructure using SEM, the roughness and chemical and phase composition using EDS and XRD, and the mechanical properties using the nanoindentation technique. After direct laser writing, the thickness of the melted layers was between 547 and 123 µm, and the surface roughness varied between 1.74 and 0.69 µm. An increase in hardness was observed after laser treatment. The highest hardness, 5.44 GPa, was obtained for the sample modified with a laser beam spacing of 50 µm. The value of the distance has been shown to be important for several properties and related to a complex microstructure of the thin surface layer close to and far from the laser path.

A close look at temperature profiles during laser powder bed fusion using operando X-ray diffraction and finite element simulations

In laser powder bed fusion (LPBF), complex components are manufactured layer-by-layer via scanning the cross-sections of a 3D CAD model using a high intensity laser. Throughout this process, the material is exposed to temperature profiles that significantly differ from conventional manufacturing methods, and result in development of a unique and inhomogeneous microstructure and high levels of residual stresses in additively fabricated parts. The large temperature gradients and rapid cooling rates around the moving laser spot, and the overall heterogeneity of the temperature field need to be better understood in order to optimize the process parameters for increased production quality. In this study, operando X-ray diffraction (XRD) was employed to measure and compare temperature histories on the laser path under various processing conditions for Hastelloy X. Finite element thermal simulations were validated based on the acquired XRD data and then used as a supplementary tool to discuss the cooling behaviour and thermal heterogeneities across the geometry. The increase in the deposited energy density was qualitatively linked with higher temperature levels and slower cooling rates during LPBF. The melt-pool lengths showed strong sensitivity to the laser power and little variation with the scanning speed. Furthermore, even for a single set of parameters, large variations in the temperature field within the build were observed such that the cross-section edges located at higher build layers were exposed to markedly higher temperature levels.

Local optimization of low power laser machining of MgO at the green state enables cost- and energy-efficient benchtop fabrication

Laser machining of ceramics at the green state offers a low-cost route to the near-net fabrication of ceramics. A matrix of laser machining (i.e., laser cutting and scanning) parameters were tested on 5 mm-thick green bodies of magnesium oxide (MgO) using a CO2 laser with a maximum power capacity of 130 W. The input parameters were feed rate and power; we scanned the feed rate of 3–14 mm/s and power of 23–31 W for laser cutting, and 10–60 mm/s and 16–27 W for laser scanning. The quality of the laser-cut and -scanned surfaces was tracked through SEM. Thermal damage along the laser path was assessed to optimize laser cutting. High laser power and/or long dwell time resulted in partial sintering. At both cutting and scanning operations, low feed rates induced thermal damage, while high feed rates provided better edge retention and surface smoothness. However, very high feed rates failed to generate complete cuts and through-holes due to insufficient transfer of the excitation energy. Various combinations of the feed rate and power produced completely cut 5 mm cubes with smooth surfaces via laser cutting in a single pass. The most energy-efficient combination of 12 mm/s and 28 W rendered the highest surface quality with minimum thermal damage. This parameterization set provides a practical roadmap for choosing process parameters for the laser machining of MgO at the green state.

Data-driven prediction of geometry- and toolpath sequence-dependent intra-layer process conditions variations in laser powder bed fusion

Geometrical features and toolpath sequence are two important factors that cause process condition variations, such as variations in the meltpool temperature or meltpool size, that might lead to undesired material properties in the laser powder bed fusion (LPBF) process. Due to the high dynamics and complex physics of the LPBF process, it is difficult to predict variations in process conditions with simulations alone. Advances in measurement technology and computational technologies open up new possibilities for smart manufacturing. In this paper, a data-driven method to predict intra-layer variations in the processing conditions that source from the toolpath sequence and part geometry is presented. The approach is demonstrated using two-color on-axis pyrometer measurements. Three demonstration cases are presented in which it is demonstrated (1) how the trained predictive model can be used as a filter to ease the interpretation of process variations and discover patterns related to toolpath and part geometry, and (2) how to generate predictions that can be used for feedforward control, i.e., for adjusting laser power or scanning speed along the toolpath using a meltpool temperature prediction model generated based on on-axis measurements. Results show that the developed prediction model is able to meaningfully predict process variations resulted from toolpath sequence and geometry. Predictions are aligned with the results from the related work of others and for the case of 180° laser path turnarounds in our high-speed X-ray imaging experiments. The potential issues related to the current maturity status of the process and measuring equipment that could in practice affect the performance of the proposed solutions are also discussed.

A neural network for evaluating the concentration of leaked gas clouds detected by TDLAS at oil and gas stations

Due to the significant advantages of methane sensitivity and area-type leakage detection, tunable diode laser absorption spectroscopy (TDLAS) gas detection has been promoted as an effective method for microleakage monitoring in oil and gas stations. However, the output of the TDLAS detector is the integral concentration (IC). Based on the relevant research, the alarm threshold and risk are assessed via the gas concentration rather than the IC. How to evaluate the concentration of leaked gas clouds based on IC from TDLAS detectors is still a challenge. To address this problem, the characteristics of IC and the influence of the laser path, wind speed and leakage rate were studied via computational fluid dynamics (CFD). A neural network classification model (NNCM) was proposed to obtain the probability distribution of the maximal concentration along the laser path (Cmax ). The results indicated that the IC is strongly correlated with the Cmax . Considering the accuracy and operability, the NNCM with input features of IC, wind speed and angle of laser path was selected. Field tests showed that the developed model achieved the concentration evaluation of leaked gas clouds. Additionally, the NNCM can also quantify the uncertainty of the results, which avoids misjudgments caused by deviations.

Remote Operation of an Open-Path, Laser-Based Instrument for Atmospheric CO2 and CH4 Monitoring

The technical specifications and the evaluation of the remote operation of the open-path, tunable diode laser absorption spectroscopic (TDLAS) instrument are presented. The instrument is equipped with two low optical power diode lasers in the near-infrared spectral range for the atmospheric detection of carbon dioxide, methane, and water vapors (CO2, CH4, and H2O). Additionally, the instrument eliminates the requirement of retroreflectors since it detects the back reflection of the laser beam from any topographic target. The instrument was operated remotely by measuring background concentrations of CO2 and CH4 in the atmosphere from 24 November 2022 to 4 January 2023. The accuracy of CO2 and CH4 measurement retrievals on a 200 m laser path was estimated at 20 ppm (4.8%) and 60 ppb (3.1%), respectively. The CH4 accuracy is comparable, but the CO2 accuracy is noticeably lower than the accuracy achieved in local operation. The accuracy issues raised are studied and discussed in terms of the laser driver’s cooling performance.

Development of the multi-directional ablation process using the femtosecond laser to create a pattern on the lateral side of a 3D microstructure

Two-photon stereolithography (TPS) is widely used for the fabrication of various three–dimensional (3D) structures with sub-micron fabrication resolution in a single fabrication process. However, TPS is unsuitable for microstructures with fine-hole patterns. The laser ablation process can be easily drilled, or made holes in various materials. However, in the case of laser ablation, the focal plane of the laser is fixed, which is limited to the processing plane. In this study, a multidirectional ablation process is studied to apply laser ablation to various processing planes of a 3D microstructure fabricated by the TPS process. A 3D hybrid fabrication process with the advantages of both TPS and laser ablation is expected to improve the fabrication efficiency. The 3D hybrid process is proposed based on a single laser source. The microstructure is fabricated using TPS, and the multi-directional ablation process creates a hole in the lateral side of the 3D microstructure. To develop the multidirectional ablation process, the reflecting mirror system should be designed to adaptably rotate the laser focal plane and guide the laser path for the target process plane. Through various examples, we demonstrate the ability of the multi-directional ablation process with various examples.

An Improved Method for Optimizing CNC Laser Cutting Paths for Ship Hull Components with Thicknesses up to 24 mm

In this paper, the essence and optimization objectives of the hull parts path optimization problem of CNC laser cutting are described, and the shortcomings of the existing optimization methods are pointed out. Based on the optimization problem of the hull parts CNC laser cutting path, a new part-cutting constraint rule based on partial cutting is proposed, which aims to overcome the drawbacks of the traditional algorithms with serial cutting constraint rules. This paper addresses the problem of optimizing the path for CNC laser cutting of hull parts, including an empty path and the order and directions used for the provided cut contours. Based on the discretization of the part contour segments, a novel toolpath model for hull parts called hull parts cutting path optimization problems based on partial cutting rules (HPCPO) is proposed in this paper. To solve the HPCPO problem, a segmented genetic algorithm based on reinforcement learning (RLSGA) is proposed. In RLSGA, the population is viewed as an intelligent agent, and the agent’s state is the population’s diversity coefficient. Three different segmented crossover operators are considered as the agent’s actions, and the agent’s reward is related to the changes in the population’s fitness and diversity coefficients. Two benchmark problems for HPCPO were constructed to evaluate the performance of RLSGA and compared with four other algorithms. The results showed that RLSGA outperformed the other algorithms and effectively solved the HPCPO problem.

Thin Films Fabricated by Pulsed Laser Deposition for Electrocatalysis

Thin Films Fabricated by Pulsed Laser Deposition for Electrocatalysis

Evaluation and compensation of geometrical errors of X-ray computed tomography system using a laser tracking interferometer

X-ray computed tomography (X-ray CT) is a new coordinate measuring technology that can measure both the external and internal features. However, its measuring accuracy is insufficient compared with coordinate measuring machines. To improve the measurement accuracy of the X-ray CT system, it is necessary to evaluate and compensate for each error component that influences the dimensional measurement results. In this paper, we propose a method for evaluating and compensating for the geometrical errors of the X-ray CT system, which is one of the dominant error factors, using a laser tracking interferometer. A laser tracking interferometer is not required the re-alignment of the laser path, and the data can be obtained using a common coordinate system. The proposed method was applied to a CT system designed for non-destructive testing. The result clarified that the geometrical errors of the X-ray CT system were over 100 μm. Additionally, the errors in measuring the distance between the spheres were demonstrated to be reduced by compensating for the geometrical errors of the CT system.

Noise Immune Absorption Profile Extraction for the TDLAS Thermometry Sensor by Using an FMCW Interferometer

Tunable diode laser absorption spectroscopy techniques usually utilize direct absorption spectroscopy (DAS) to measure absorbance profiles for temperature measurements. The DAS method is sensitive to thermal noises and fails to work in harsh application cases. A noise immune method is proposed for robust temperature measurement by using coherent detection and retroflected optical alignment. The absorption spectrum is shifted to a high-frequency band of MHz level away from ambient noises. The optical alignment suppresses thermal radiations along the laser path. Gas temperatures were measured in cases of both laboratory and porotype setup of aeroengine, i.e., the steady and the harsh states. Transition lines of H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O at 7182.94 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> , 7185.59 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> , and 7444.35 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> were selected for performance evaluations of target molecules, i.e., of water vapor. Performance comparisons were made with the DAS method for temperature detection. In a steady-state case, standard deviations of the proposed method were below 0.9 K at SNR of 25 dB, while those of the DAS method were above 3.9 K. In an onsite harsh application case, the fluctuations of path-averaged temperatures obtained by the proposed method were also smaller. The coherent detection and retroflected alignment enable better performance in hostile cases.