Thermal Signature Research Articles

Residual Stress Optimisation for Manufacturing of a Nozzle Guide Vane in Mar-M-509 by Laser Powder Bed Fusion

Abstract This paper delves into the potential of Additive Manufacturing (AM) technologies, focusing on the utilization of Laser Powder Bed Fusion (LPBF) to optimize the manufacturing process for Mar-M-509, a Cobalt-based superalloy, in aeroderivative industrial gas turbine nozzle guide vanes. While Mar-M-509 offers exceptional properties for high-temperature applications, the rapid cooling rates inherent in LPBF introduce significant residual stresses, leading to poor success rates below 50%. The study systematically addresses this challenge by proposing a comprehensive methodology to optimize LPBF parameters. By fine-tuning processing conditions, including elevating the powder bed and build chamber temperatures, the research achieved notable reductions in residual stresses by up to 55%. Computational simulations played a pivotal role in predicting deformations and thermal signatures, enabling proactive adjustments to process conditions, ultimately enhancing part quality and process reliability. Validation through successful printing with a yield exceeding 75% underscores the effectiveness of this approach. Moreover, the study suggests the broader applicability of these optimization strategies beyond Mar-M-509, paving the way for advancements in AM techniques across diverse materials and industrial sectors. This research not only presents a robust solution for mitigating residual stresses in LPBF-produced components but also showcases a proactive methodology that promises significant implications for additive manufacturing's future advancements and applicability.

Methods for faster estimation of the entropy profile of a lithium-ion battery: A comparison of accelerated potentiometry and the estimation of entropy through thermal signatures

The operating temperature of a lithium-ion battery (LIB) has strong effects on its electrochemical performance, safety, and lifetime. Reliable predictions of cell temperature and heat generation rate are required for the design and thermal management of battery systems. The entropy variation of a LIB has a significant influence on the heat generation and the cell temperature variation, especially at a lower C-rate. Since the entropy is a function of State-of-Charge (SoC), and may change during ageing, the entropy profile may as well be used as an indicator for battery State-of-Health diagnostics. The present experimental methods, such as potentiometric or calorimetric methods, for determining the entropy variation throughout a battery's full SoC window are time-consuming and require specialized equipment with good thermal insulation and instrumentation. Hence, faster and more convenient methods are needed. In this paper, we compare an accelerated potentiometric method with a new entropy characterization method where the entropy profile is extracted from the thermal signature of a LIB during charge and discharge at low C-rates. The accelerated potentiometric method aims to obtain the entropy from thermal cycling while the cell is still not completely relaxed to its stable OCV. The thermal signature method monitors the variations of a cell's outer surface temperature during a low constant current charge or discharge, from which the continuous variation of entropy with the SoC can be extracted. The obtained entropy profile was validated through a modeling approach that combines a P2D-electrochemical model for battery operation and a 3D-finite-element model for the thermal behavior during battery operation. Good agreement between the model predictions and experiments was achieved on the temporal variation of the cell's outer surface temperature.

MultIHeaTS: A Fast and Stable Thermal Solver for Multilayered Planetary Surfaces

A fully implicit scheme is proposed for solving the heat equation in 1D heterogeneous media, available as a computationally efficient open-source Python code. The algorithm uses finite differences on an irregular grid and is unconditionally stable due to the implicit formulation. The thermal solver is validated against a stiff analytical solution, demonstrating its robustness in handling stiff initial conditions. Its general applicability for heterogeneous cases is demonstrated through its use in a planetary surface scenario with nonlinear boundary conditions induced by blackbody thermal emission. MultIHeaTS's advantageous stability allows for computation times up to 100 times faster than Spencer’s explicit solver, making it ideal for simulating processes on large timescales. This solver is used to compare the thermal signatures of homogeneous and bilayer profiles on Europa. Results show that homogeneous materials cannot reproduce the thermal signature observed in bilayer profiles, emphasizing the need for multilayer solvers. In order to optimize the scientific return of a space mission, we propose a strategy made of three local time observations that is enough to identify bilayer media, for instance, for the next missions to the Jovian system. A second application of the solver is the estimation of the temperature profile of Europa’s near surface (first 10s m) throughout a 1 million yr simulation with varying orbital parameters. The probability distribution of temperature through depth is obtained. Among its various applications, MultIHeaTS serves as the core thermal solver in a multiphysics simulation model detailed in the companion article by C. Mergny & F. Schmidt.

Digital twins for rapid in-situ qualification of part quality in laser powder bed fusion additive manufacturing

This work concerns the laser powder bed fusion (LPBF) additive manufacturing process. Currently, LPBF parts are inspected post-process using such techniques as X-ray computed tomography, optical and scanning electron microscopy, among others. This empirical build-and-test approach for qualification of part quality is prohibitively expensive and cumbersome. To enable rapid and accurate in-situ qualification of LPBF part quality, in this work, we developed a physics and data-integrated digital twin approach. To demonstrate the approach, Inconel 718 parts of various shapes were manufactured under differing LPBF processing conditions. The process was continuously monitored using in-situ thermal and optical tomography imaging cameras. The part-scale thermal history was predicted using an experimentally validated computational thermal simulation. The simulation-derived thermal history and sensor signatures were used as inputs to a k-nearest neighbor machine learning model. The machine learning model was trained with ground truth porosity and microstructure data obtained from post-process characterization. The approach predicted the onset of porosity, meltpool depth, grain size, and microhardness with an accuracy exceeding 90 % (R2). This work thus takes a critical step towards realizing an in-situ Born Qualified part quality assessment paradigm in LPBF.

Heating decoys to mimic thermal signatures of live animals for drones

Thermal sensors mounted on drones (unoccupied aircraft systems) are popular and effective tools for monitoring cryptic animal species, although few studies have quantified sampling error of animal counts from thermal images. Using decoys is one effective strategy to quantify bias and count accuracy; however, plastic decoys do not mimic thermal signatures of representative species. Our objective was to produce heat signatures in animal decoys to realistically match thermal images of live animals obtained from a drone-based sensor. We tested commercially available methods to heat plastic decoys of three different size classes, including chemical foot warmers, manually heated water, electric socks, pad, or blanket, and mini and small electric space heaters. We used criteria in two categories, 1) external temperature differences from ambient temperatures (ambient difference) and 2) color bins from a palette in thermal images obtained from a drone near the ground and in the air, to determine if heated decoys adequately matched respective live animals in four body regions. Three methods achieved similar thermal signatures to live animals for three to four body regions in external temperatures and predominantly matched the corresponding yellow color bins in thermal drone images from the ground and in the air. Pigeon decoys were best and most consistently heated with three-foot warmers. Goose and deer decoys were best heated by mini and small space heaters, respectively, in their body cavities, with a heated sock in the head of the goose decoy. The materials and equipment for our best heating methods were relatively inexpensive, commercially available items that provide sustained heat and could be adapted to various shapes and sizes for a wide range of avian and mammalian species. Our heating methods could be used in future studies to quantify bias and validate methodologies for drone surveys of animals with thermal sensors.•We determined optimal heating methods for plastic animal decoys with inexpensive and commercially available equipment to mimic thermal signatures of live animals.•Methods could be used to quantify bias and improve thermal surveys of animals with drones in future studies.

A study integrating in-process thermal signatures, microstructure, and corrosion behaviour of AISI 316L coatings on low carbon steel substrate deposited by laser-directed energy deposition (L-DED)

Surface coating through Laser Directed Energy Deposition (L-DED) of AISI 316L on low-carbon steel was carried out by varying laser fluence. The resulting melt pool characteristics, obtained through IR pyrometer, played a significant role in evolution of the microstructure. A higher heat input resulted in an equiaxed grain structure with finer grain sizes and improved deposition quality. The quantitative phase analysis revealed the presence of a dominant austenite phase and a secondary ferrite phase, along with traces of molybdenum carbide. Corrosion analysis reveals improved resistance at a higher laser fluence, attributed to increased content of corrosion-resistant alloying elements (Mo, Cr) and predominance of the austenite phase. Electrochemical Impedance Spectroscopy (EIS) confirms a higher corrosion resistance at a higher laser fluence, supported by a significantly higher polarization resistance and presence of a duplex-structured passive film. Results indicate that moderate laser fluence levels, ranging from 5 to 7.5 kJ/cm2, accompanied by medium to high thermal signatures and moderate cooling rates, yield coatings with refined microstructures, enhanced mechanical strength, and corrosion resistance. For superior corrosion resistance, a laser fluence of 10 kJ/cm2 is recommended, although careful consideration of its potential impact on mechanical properties is advised.

Thermal signature of a helical molecule: Beyond nearest-neighbor electron hopping

Thermal signature of a helical molecule: Beyond nearest-neighbor electron hopping

Ameliorating Ion-Transport Resistances in Membrane Capacitive Deionization Using Patterned Ion-Exchange Membranes and Ionomer Infiltrated Electrodes

Currently, 2 billion people live in water scarce regions and 4 billion people experience water scarcity for at least 1 month per year [1]. Besides implications to the broad masses, the U.S. Marine Corps also face enormous challenges of generating potable water during squad level field missions. Reverse osmosis is the most reliable and economical process for desalinating water, but it requires piping that can tolerate large pressures and is not conducive for portable missions. Membrane capacitive deionization (MCDI) is a modular and electrified water desalination platform that does not generate significant acoustic, thermal, or electromagnetic signatures nor does it require high pressure piping. Flow-by MCDI, which is commercialized, uses electrical energy to remove ions from water. The system architecture feature two porous electrodes covered by ion-exchange membranes – the latter material is used to prevent co-ion adsorption and greater Coulombic efficiency than the variant that does not utilize ion-exchange membranes.Our previous research improved the energy efficiency of MCDI by using IEMs with lower area-specific resistance (ASR) values and porous ionic conductors in the spacer channel that augmented solution conductivity and curtailed ohmic losses. Reducing these resistances enabled the MCDI to operate at 700 mV lower cell voltage at 2 mA cm-2 current density. This is important for shrinking the size of the MCDI unit and lowering the overall capital costs.This talk presents our recent work examining ionic charge transport resistance at patterned and non-patterned ion-exchange membrane interfaces in MCDI. Membrane surface patterning increased the interfacial area between the membrane and aqueous stream promoting greater salt removal fluxes. Soft-lithography methods were used to micropattern poly(phenylene alkylene) cation exchange membranes and anion exchange membranes with systematically varied topographies (e.g., cylindrical well and line structures that vary from 9 to 20 mm). We can reduce the MCDI cell voltage by 1 V when utilizing micropatterned ion-exchange membranes and ionomer infiltrated electrodes (operating at 2 mA cm-2 with a 2000 ppm NaCl feed). This translated to a 2.4x greater energy normalized adsorbed salt (ENAS) value - a metric used to assess the energy consumption for MCDI. Our future work examines electrode materials deposited at pattern membrane interfaces to reduce interfacial transport resistances from the membrane to the electrode. This strategy has been shown to be effective in reducing charge-transfer resistances in both proton exchange membrane (PEM) and AEM fuel cells and reducing the water dissociation kinetics resistance in bipolar membranes [2-5]. References Somini Sengupta, Weiyi Cai, A Quarter of Humanity Faces Looming Water Crises, in The New York Times. 2019: United States of America.Kole, S., et al., Bipolar membrane polarization behavior with systematically varied interfacial areas in the junction region. Journal of Materials Chemistry A, 2021. 9(4): p. 2223-2238.Breitwieser, M., et al., Tailoring the Membrane-Electrode Interface in PEM Fuel Cells: A Review and Perspective on Novel Engineering Approaches. Advanced Energy Materials, 2018. 8(4).Hee-Tak Kim, T.V.R., Ho-Jin Kweon, Microstructured Membrane Electrode Assembly for direct methanol fuel cell. Journal of The Electrochemical Society, 2007. 154(10).Tomizawa, M., et al., Heterogeneous pore-scale model analysis of micro-patterned PEMFC cathodes. Journal of Power Sources, 2023. 556. Figure 1

(Invited) Thermo-Electrochemical Stability of Solid-State Batteries

Solid-state batteries (SSBs) are promising due to higher energy density and improved safety. While interface instability due to electrochemical-mechanics interaction, transport and morphological dynamics at solid-solid interfaces has been identified as a critical determinant of failure modes, the role of thermo-electrochemical effect is still elusive. A wide range of factors, such as, high reactivity of lithium, propensity for hotspot formation (e.g., at solid/solid point contacts), and cathode instability may affect thermo-electrochemical signature of solid-state architectures. In addition, spatio-temporal evolution of thermal and reaction heterogeneities and the occurrence of failure mechanisms such as lithium filament growth can be critical. In this presentation, the important role of internal thermal signature and mechanistic origin of thermo-electrochemical instability in solid-state battery architectures will be discussed.

Thermal Image Based Occupant Count Measurement Model using Human Body Temperature for Smart Building

Objectives: The proposed Occupant Count Measurement (OCM) model aims to enhance sustainability, energy efficiency, comfort, and safety in smart buildings by accurately determining occupant count using thermal camera images and body temperature data. Methods: The model leverages real-time thermal camera images without the need for a pre-existing dataset. Key parameters include temperature threshold, occupant motion, size, and shape to ensure accurate occupancy estimation. The K-means algorithm identifies and clusters regions of interest (ROI) in thermal images corresponding to human body temperatures. The model also employs sensors like PIR, RGB cameras, and thermal image sensors. Manual counting serves as a benchmark for comparison. Findings: The K-means algorithm extracts regions with elevated temperatures related to human bodies from thermal images, partitioning them into K-clusters based on temperature ranges and assigning each pixel to one of the clusters. A temperature threshold differentiates human clusters in the thermal image, while connected component labeling refines human object segmentation by identifying blobs, which are then used for occupant counting. The model's precision is assessed using diverse image sensors and compared to the actual number of occupants. The proposed OCM model achieves an accuracy of about 90.2% compared to traditional methods. Novelty: This study introduces an OCM model that uses thermal images to estimate the number of occupants in a room based on their body temperature. The method focuses on detecting and counting occupants by their overall thermal body signature, providing a novel approach to occupant measurement in smart buildings. Keywords: Thermal Images, Segmentation, Occupant Estimation, Occupant Comfort, Smart Building

Thermoregulatory integration in hand prostheses and humanoid robots through blood vessel simulation

In this paper, we introduce an innovative approach for generating robotic faces with a thermal signature similar to that of humans and equipping prosthetic or robotic hands with a lifelike temperature distribution. This approach enhances their detection via infrared cameras and promotes more natural interactions between humans and robots. This method integrates a temperature regulation system into artificial skin, drawing inspiration from the human body’s natural temperature control via blood flow. Central to this technique is a fiber network simulating blood vessels within the artificial skin. Water flows through these fibers under specific temperature and flow conditions, forming a controlled heat release system. The heat emission can be adjusted by changing the dilation of these fibers, primarily by modulating the frequency of circulation. Our findings indicate that this approach can replicate the varied thermal characteristics of different human faces and hand areas. Consequently, the robotic faces appear more human-like in infrared images, aiding their identification by infrared cameras. At the same time, the prosthetic hands achieve a more natural temperature, reducing the discomfort typically felt in direct contact with synthetic limbs. The aim of this study was to address the challenges faced by the users of prosthetic hands. The results from this study show a promising direction in humanoid robotics, fostering improved tactile interactions and redefining human–robot relationships. This innovative technique facilitates further advancements, blurring the lines between artificial aids and natural biological systems.

Thermal imaging analysis of ballast fouling: Investigating the effects of parent rock and fouling materials through IRT passive camera

This study explores the use of infrared thermography (IRT) technology for the non-destructive evaluation of ballast fouling in railway tracks, focusing on the influence of parent rock types and fouling materials. Utilizing thermal imaging, the research investigates how variations in ballast conditions affect surface temperature, which serves as an indicator of structural integrity and health. The experimental setup involved ballast samples derived from three different rock types—basalt, limestone, and andesite—fouled with commonly encountered materials like sand and clay at varying percentages. Results demonstrate that fouling level and type significantly influence the thermal signatures captured by IRT passive camera. Notably, ballast derived from darker rocks exhibited higher temperatures, indicating greater emissivity, while fouled ballast showed distinct temperature patterns compared to clean samples, emphasizing the potential of thermal imaging in detecting and quantifying fouling in ballast layers. This research underscores the viability of IRT passive camera in the routine maintenance and monitoring of railway infrastructure, providing a foundation for further development of integrated diagnostic tools for railway management systems.

Using Thermal Signature to Evaluate Heat Stress Levels in Laying Hens with a Machine-Learning-Based Classifier.

Infrared thermography has been investigated in recent studies to monitor body surface temperature and correlate it with animal welfare and performance factors. In this context, this study proposes the use of the thermal signature method as a feature extractor from the temperature matrix obtained from regions of the body surface of laying hens (face, eye, wattle, comb, leg, and foot) to enable the construction of a computational model for heat stress level classification. In an experiment conducted in climate-controlled chambers, 192 laying hens, 34 weeks old, from two different strains (Dekalb White and Dekalb Brown) were divided into groups and housed under conditions of heat stress (35 °C and 60% humidity) and thermal comfort (26 °C and 60% humidity). Weekly, individual thermal images of the hens were collected using a thermographic camera, along with their respective rectal temperatures. Surface temperatures of the six featherless image areas of the hens' bodies were cut out. Rectal temperature was used to label each infrared thermography data as "Danger" or "Normal", and five different classifier models (Random Forest, Random Tree, Multilayer Perceptron, K-Nearest Neighbors, and Logistic Regression) for rectal temperature class were generated using the respective thermal signatures. No differences between the strains were observed in the thermal signature of surface temperature and rectal temperature. It was evidenced that the rectal temperature and the thermal signature express heat stress and comfort conditions. The Random Forest model for the face area of the laying hen achieved the highest performance (89.0%). For the wattle area, a Random Forest model also demonstrated high performance (88.3%), indicating the significance of this area in strains where it is more developed. These findings validate the method of extracting characteristics from infrared thermography. When combined with machine learning, this method has proven promising for generating classifier models of thermal stress levels in laying hen production environments.

Three-temperature radiation hydrodynamics with PLUTO: Thermal and kinematic signatures of accreting protoplanets

In circumstellar disks around young stars, the gravitational influence of nascent planets produces telltale patterns in density, temperature, and kinematics. To better understand these signatures, we first performed 3D hydrodynamical simulations of a 0.012 M⊙ disk with a Saturn-mass planet orbiting circularly in-plane at 40 au. We tested four different disk thermodynamic prescriptions (in increasing order of complexity: local isothermality, β cooling, two-temperature radiation hydrodynamics, and three-temperature radiation hydrodynamics), finding that β cooling offers a reasonable approximation for the three-temperature approach when the planet is not massive or luminous enough to substantially alter the background temperature and density structure. Thereafter, using the three-temperature scheme, we relaxed this assumption, simulating a range of different planet masses (Neptune-mass, Saturn-mass, and Jupiter-mass) and accretion luminosities (0 and 10−3 L⊙) in the same disk. Our investigation revealed that signatures of disk–planet interaction strengthen with increasing planet mass, with circumplanetary flows becoming prominent in the high-planet-mass regime. Accretion luminosity, which adds pressure support around the planet, was found to weaken the midplane Doppler flip, which is potentially visible in optically thin tracers such as C18O, while strengthening the spiral signature, particularly in upper disk layers sensitive to thicker lines, such as those of 12CO.

Study on Infrared (IR) Camouflage Using Mylar in the Defense Sector

Abstract: This project investigates the feasibility of utilizing Mylar, a highly reflective polyester film, for infrared (IR) camouflage in the defense sector. Through a comprehensive analysis of Mylar's optical properties, including its reflectivity and reflectance, this study aims to assess the material's potential for reducing thermal signatures and enhancing stealth capabilities of military assets. The research involves theoretical and numerical analysis to evaluate Mylar's performance under various environmental conditions, such as temperature extremes, humidity, and exposure to ultraviolet (UV) radiation. Furthermore, the project explores the integration of Mylar-based IR camouflage into existing defense systems, including vehicle coatings, personnel gear, and tactical equipment. Recommendations for the deployment and optimization of Mylar-based camouflage solutions are provided, addressing factors such as material selection, camouflage design, compatibility with sensor systems, and logistical support. The findings demonstrate Mylar's efficacy in decreasing IR detectability, providing a cost-effective and versatile solution in IR camouflage. This research contributes significantly to the field of defense technology, offering practical insights and paving the way for further advancements in stealth and counter-detection measures in military applications.

Detection of bean damage caused by Epilachna varivestis (Coleoptera: Coccinellidae) using drones, sensors, and image analysis.

The Mexican bean beetle, Epilachna varivestis Mulsant (Coleoptera: Coccinellidae), is a key pest of beans, and early detection of bean damage is crucial for the timely management of E. varivestis. This study was conducted to assess the feasibility of using drones and optical sensors to quantify the damage to field beans caused by E. varivestis. A total of 14 bean plots with various levels of defoliation were surveyed aerially with drones equipped with red-blue-green (RGB), multispectral, and thermal sensors at 2 to 20 m above the canopy of bean plots. Ground-validation sampling included harvesting entire bean plots and photographing individual leaves. Image analyses were used to quantify the amount of defoliation by E. varivestis feeding on both aerial images and ground-validation photos. Linear regression analysis was used to determine the relationship of bean defoliation by E. varivestis measured on aerial images with that found by the ground validation. The results of this study showed a significant positive relationship between bean damages assessed by ground validation and those by using RGB images and a significant negative relationship between the actual amount of bean defoliation and Normalized Difference Vegetation Index values. Thermal signatures associated with bean defoliation were not detected. Spatial analyses using geostatistics revealed the spatial dependency of bean defoliation by E. varivestis. These results suggest the potential use of RGB and multispectral sensors at flight altitudes of 2 to 6 m above the canopy for early detection and site-specific management of E. varivestis, thereby enhancing management efficiency.

Knowing the spectral directional emissivity of 316L and AlSi10Mg PBF-LB/M surfaces: gamechanger for quantitative in situ monitoring

For a deep process understanding of the laser powder bed fusion process (PBF-LB/M), recording of the occurring surface temperatures is of utmost interest and would help to pave the way for reliable process monitoring and quality assurance. A notable number of approaches for in-process monitoring of the PBF-LB/M process focus on the monitoring of thermal process signatures. However, due to the elaborate calibration effort and the lack of knowledge about the occurring spectral directional emissivity ε′λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\varepsilon }^{\\prime}}_{\\lambda }$$\\end{document}, only a few approaches attempt to measure real temperatures. In this study, to gain initial insights into ε′λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\varepsilon }^{\\prime}}_{\\lambda }$$\\end{document} occurring in the PBF-LB/M process, measurements on PBF-LB/M specimens and metal powder specimens were performed for higher temperatures up to T = 1290 °C by means of the emissivity measurement apparatus (EMMA) of the Center for Applied Energy Research (CAE, Wuerzburg, Germany). Also, measurements at ambient temperatures were performed with a suitable measurement setup. Two different materials—stainless steel 316L and aluminum AlSi10Mg—were examined. The investigated wavelength λ ranges from the visible range (λVIS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\lambda }_{VIS}$$\\end{document} = 0.40–0.75 µm) up to the infrared, λ = 20 µm. The influence of the following factors were investigated: azimuth angle φ, specimen temperature TS, surface texture as for PBF-LB/M surfaces with different scan angles α, and powder surfaces with different layer thicknesses t\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$t$$\\end{document}.

Multi-sensor-based process monitoring and quality assessment during laser forming of novel titanium foam to prevent cellular structure damage

Titanium foam finds its application in different fields like battery electrodes, heat exchangers, biological prosthetics, and lightweight structures for aeronautical parts. However, the inherent property of the foam to fail during mechanical loading conditions poses a challenge in providing final shape to these materials for practical applications. The present work investigates forming of Titanium foam (Ti Foam) using inline process monitoring for achieving controlled foam deformation while maintaining its quality without failure. Infrared (IR) pyrometer and laser-based displacement sensor were used to monitor the foam deformation behavior and thermal signature. The monitoring data were used to understand the process mechanism of laser forming. The bending mechanism within the foam was analysed with respect to solid sheet bending process. The correlation of the achieved bending angles, was attempted with the process parameters for surface, microstructural, elemental, and phase transformation. A critical analysis of Ti foam behavior, like surface melting, in situ oxidation, phase transformation, and formation of nano-structures on foam surface was addressed based on process parameter combinations. Micro-computed tomography (μCT) was carried out to investigate the pore deformation mechanism. Metallurgical characterisations for foam surface and phase analysis were carried out using X-ray photoelectron spectroscopy (XPS) and tunneling electron microscopy (TEM) to determine the chemical and crystallographic states of the laser-formed surface.

A Bayesian Approach for Forecasting the Probability of Large Earthquakes Using Thermal Anomalies from Satellite Observations

Studies have demonstrated the potential of satellite thermal infrared observations to detect anomalous signals preceding large earthquakes. However, the lack of well-defined precursory characteristics and inherent complexity and stochasticity of the seismicity continue to impede robust earthquake forecasts. This study investigates the potential of pre-seismic thermal anomalies, derived from five satellite-based geophysical parameters, i.e., skin temperature, air temperature, total integrated column water vapor burden, outgoing longwave radiation (OLR), and clear-sky OLR, as valuable indicators for global earthquake forecasts. We employed a spatially self-adaptive multiparametric anomaly identification scheme to refine these anomalies, and then estimated the posterior probability of an earthquake occurrence given observed anomalies within a Bayesian framework. Our findings reveal a promising link between thermal signatures and global seismicity, with elevated forecast probabilities exceeding 0.1 and significant probability gains in some strong earthquake-prone regions. A time series analysis indicates probability stabilization after approximately six years. While no single parameter consistently dominates, each contributes precursory information, suggesting a promising avenue for a multi-parametric approach. Furthermore, novel anomaly indices incorporating probabilistic information significantly reduce false alarms and improve anomaly recognition. Despite remaining challenges in developing dynamic short-term probabilities, rigorously testing detection algorithms, and improving ensemble forecast strategies, this study provides compelling evidence for the potential of thermal anomalies to play a key role in global earthquake forecasts. The ability to reliably estimate earthquake forecast probabilities, given the ever-present threat of destructive earthquakes, holds considerable societal and ecological importance for mitigating earthquake risk and improving preparedness strategies.

Geometrically-informed predictive modeling of melt pool depth in laser powder bed fusion using deep MLP-CNN and metadata integration

The laser powder bed fusion (LPBF) process has the ability to manufacture intricate geometries. However, the fabrication of complex geometries using the LPBF process for industrial applications remains challenging in terms of achieving consistent microstructure and mechanical properties. Experimental investigations show that in addition to process parameters, geometrical factors such as shape and size of parts impact the thermal history, microstructure, defect structure, and thus mechanical and fatigue properties. As a result, the standardization, qualification, and certification of additively manufactured (AM-ed) parts encounters substantial obstacles posed by the obscure impact of design parameters on component quality. In this paper, an artificial intelligence framework is proposed to predict the melt pool depth of the additively manufactured parts considering the geometrical information of the parts. More specifically, a deep Multilayer Perceptron and Convolutional Neural Network (MLP-CNN) framework is designed to predict melt pool depth for any part geometries when printing metallic parts via LPBF using metadata (i.e., numerical and image data). Quite often the melt pool depth generated during the AM processes is employed as a surrogate for thermal, defect- and micro-structural signatures. In melt pool depth prediction of complex geometries in LPBF, a digital twin environment using a finite element model (FEM) is developed and validated using experimental melt pool measurements for different geometries within multi-tracks and layers. The FEM was in agreement with experimentation with an error of less than 15%. Through the process simulation, a large dataset of melt pool depths was obtained for various part geometries and different process parameters. Next, the MLP-CNN framework was established to identify the parallel impact of part design and process parameters on melt pool depths. To enhance the performance and robustness of this model, data augmentation has been implemented by rotating and transferring geometries to artificially expand the dataset. Data augmentation helps to mitigate overfitting and promote better generalization, especially in the context of limited training data. After training, the proposed model is found to give accurate melt pool prediction even for new geometries not considered during training. The fusion of CNN and MLP has led the melt pool predictions for unseen geometries with an accuracy of 95%.