Laser powder bed fusion (LPBF) enables the fabrication of metallic components with complex geometries directly from raw powders. The process typically employs continuous-or pulsed-wave lasers, which significantly impact the thermal-fluid dynamics and subsequently affect the microstructure. However, the behaviour during pulsed-wave LPBF remains inadequately understood. This study developed a highfidelity multi-physics modelling framework to simulate the evolution of point-by-point laser exposure during pulsed-wave LPBF. The effects of laser power and exposure time on thermal-fluid behaviour in single-/multi-track and multi-layer pulsed-wave LPBF were investigated and validated against experiments. The results reveal that variations in either laser power or exposure time can result in similar molten pool morphology during a single exposure, though their dynamic behaviours exhibited marked differences. Increased laser power augmented the drilling rate of the molten pool, while exposure time exhibited a minimal effect on the depth growth rate, thereby enhancing the predictability of its behaviour. Additionally, the critical molten pool depth at which the drilling rate changes remained nearly constant, irrespective of laser power or exposure time. During point-by-point scanning of a single melt track, gaps formed between exposures due to mismatches in laser power, exposure time and point distance, resulting in track discontinuities. In subsequent scanning, deep gaps arose from poor bonding within intra-tracks and insufficient melting between inter-tracks and inter-layers. Keyhole pores primarily formed during the laser-off period of the pulse cycle at high laser powers or exposure times, as surface tension and gravity drove molten material forward, but solidification pinned the keyhole tip, leading to defects. These findings significantly advance the understanding of melt pool dynamics and defect formation in pulsed-wave LPBF. (c) 2025 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.

Industry 5.0 advances Human-Robot Collaboration (HRC) by integrating human adaptability with robot precision, speed and endurance. However, dynamic task planning in HRC remains a significant challenge due to the need to simultaneously balance productivity, ergonomics, and trust between human and robotic agents. This paper proposes a trust-driven, multi-objective framework for dynamic task allocation and scheduling in HRC environments. A fuzzy-logic trust model is developed to assign each agent-task pair a trust score that represents the likelihood of successful task execution. This trust score is incorporated as an explicit optimization objective alongside cycle time and ergonomic risk. To solve the resulting NP-hard tri-objective optimization problem, a hybrid optimizer is proposed, which minimizes cycle time and ergonomic risk, while maximizing agent trust. A digital twin is developed to estimate robot execution times and evaluate operator ergonomics. The proposed novel algorithm enhances the conventional NSGA-II by addressing loss of selection pressure, premature convergence, repeated evaluation of large populations, and the lack of dynamic adaptation through the integration of variable neighborhood local search, simulated annealing-based perturbation, and Analytic Hierarchy Process (AHP). The framework is evaluated using an industrial assembly case study involving three human-robot team configurations and is benchmarked against a Genetic Algorithm (GA) and the conventional NSGA-II. Results show that the proposed method achieves improved diversity and spacing metrics while converging to competitive Pareto-optimal solutions in fewer iterations. The proposed trust-driven framework provides an effective solution for dynamic HRC task planning by enabling balanced optimization of productivity, ergonomics, and operational trust in Industry 5.0 manufacturing environments.

The laser powder bed fusion (LPBF) of metal matrix composites (MMCs) involves distinctive rapid melting and nonequilibrium solidification dynamics. Elucidating the intricate evolution mechanisms of particles is critical for fabricating MMCs with superior strength-ductility synergy. In this study, both GH3536 Ni-based alloy and 5 wt% TiC-reinforced GH3536 composites (GH3536-5TiC) were fabricated via LPBF. The influence of volumetric laser energy density on the microstructure, mechanical properties, and multiscale evolution of TiC particles was systematically investigated. The experimental results revealed that a positive correlation existed between the energy density and both the TiC particle loss rate and average particle size, which was attributed to the coarsening and spattering behaviour of TiC particles, as demonstrated through multiscale evolution simulations. A dimensionless quantities framework based on kinetic calculations of the melt pool was established to determine the effect of energy density on TiC particle evolution. The growth mechanism of nanoscale TiC particles (<100 nm) is primarily governed by chemical transport, while microscale TiC particles (3–7 μm) mainly undergo impingement-driven coarsening. Low energy density was found to reduce the impingement-driven coarsening. In addition, this study demonstrated the hierarchical distribution of TiC particles after multiscale evolution. Compared to GH3536, the GH3536-5TiC fabricated under low energy density conditions demonstrated significantly enhanced tensile performance. At 1 173 K, its ultimate tensile strength and elongation values were found to be 304 MPa and 42%, respectively. Overall, this work provides a theoretical guideline for the performance optimisation of additively manufactured advanced composites via controlling the evolution of reinforcements.

NiTi alloys can be made to demonstrate multiple effects including the shape memory effect (SME), superelastic effect (SE), and elastocaloric effect (eCE), by finely tailoring the Ni content. Notably, laser 3D printing technology has shown great potential for manufacturing NiTi alloys with tunable Ni contents and different phase transformation temperatures (TTs) by changing the laser energy input. Hence, through processing-parameter design, laser 3D-printed NiTi alloys with various functional behaviors can be achieved. This, in turn, potentially enables the rapid prototype manufacturing of a compact multi-effect coupled heat-driven elastocaloric cooling device. However, the mechanisms governing functional differentiation using this technology remain unclear. This study evaluated the integration of laser 3D printing technology and a microstructure-derived functional differentiation strategy to verify the feasibility of heat-driven elastocaloric cooling. By strategically manipulating the laser power (P) and scanning speed (v) across 30 parameter sets, we achieved a precise functional differentiation of NiTi alloys from a single pre-alloyed powder. A comprehensive functional map in the P-v plane was established. It delineates the regions dominated by room-temperature SE/eCE or SME. NiTi alloys processed with a low-energy input excel as superelastic refrigerants, whereas those fabricated with a high-energy input are ideal thermal actuators. Furthermore, the physical mechanisms underlying this tunable functional behavior were revealed through detailed microstructural characterizations. This work has provided a fundamental and practical framework for laser 3D printing of functionally graded NiTi components, thereby paving the way for the development of compact, self-driving, and efficient elastocaloric cooling systems.

Additive manufacturing via laser powder bed fusion (LPBF) enables fabrication of complex high-silicon soft magnetic steels that are otherwise difficult to process. This study investigates the use of in-situ alloying of FeSi6.5 as a cost-effective alternative to pre-alloyed powder. As-built samples produced with commercial powder and as-blended samples using in-situ alloying were characterized for relative density, microstructure, and magnetic properties. The study showed that the commercial powder samples achieved higher densities (94.2–97.7

Efficiency is paramount in industry and can be vastly improved by driving improvements in procedure design. This study explores the feasibility of using electroencephalography (EEG) and gaze tracking for assessing the quality of procedural design. The study hypothesises that EEG and gaze information can be indicative of the difficulties workers face during procedural tasks and therefore used to identify areas for procedural design improvements. Fifteen participants completed a number of origami tasks, designed to contain problem points predicted to stimulate detectable emotional responses. The analysis of the fixation rate and pupil diameter revealed that participants fixated on steps either directly preceding or following the identified problem points, and pupil size increased during the execution of these steps. EEG analysis included power spectral densities (PSD) and event related potentials (ERP), though ERP was found not to be indicative enough for the purpose of this study. Participants provided feedback on challenging steps, which aligned with predictions. Brain activity patterns while undertaking problematic steps compared to base unstimulated brain activity showed that theta activity increased across the whole brain in 77% of recordings, most prominently in the left temporal region; delta activity increased in 65% of recordings, most prominently in the left temporal region; alpha activity decreased in in the occipital region in 65% of recordings but increased in the left temporal region in 70%; and beta activity increased in the left frontal region in 74% of recordings. These results validated the hypothesis, as they showed clear trends in the reactions to problem points. Finally, a framework is proposed for a procedure problem point identification using EEG and gaze tracking, and recommendations for further research have been outlined.

Additively manufactured (AM) parts typically possess high surface roughness (∼5–30 μm) and large surface features, resulting from balling, partial sintering/melting, and staircase effects. However, there are few widely accepted/adopted methodologies for measuring and characterising AM surfaces. This research proposes a practical, reliable, and repeatable methodology for the measurement, analysis and characterisation of surfaces produced via AM. Various line and surface roughness parameters are measured on the top and side faces of AM cubes, using both tactile and optical profilometers. The line-based tactile roughness measurement approach is considerably faster than surface measurements but offers a limited range of accuracy. The study has undertaken an exhaustive analysis to establish the applicability and degree of accuracy of line-measured roughness metrics, when benchmarked with full-surface measurements. An analysis of the limited range of applicability of line measurements is provided within a framework of probabilistic uncertainty analysis, performed on measured roughness data. This study also explores alternative methods for reducing the measurement time required without compromising the reliability of the results, by decreasing the resolution of the scanned data. It is demonstrated that deviations in surface parameters remained within 2% when the resolution was reduced by 50%; consequently, the measurement time was reduced by ∼75%.

The growing need for standardised and automated cardiac ultrasound (US) acquisition has driven the integration of deep learning into echocardiographic workflows. While existing deep learning (DL) models have shown promising results in tasks such as view classification and image quality assessment, most of these approaches focus either on differentiating among standard views or grading image quality within a standard view. However, these methods lack the capacity to model the sequential spatial transitions that occur during the acquisition process, limiting their applicability to real-time probe guidance and robotic control. To address this gap, we propose a classification framework designed for the process of acquiring the parasternal long-axis (PLAX) view under a fixed scanning protocol. Based on extensive probe movement experiments across multiple patients, we identified four representative echocardiographic views that appear during the search for the optimal PLAX position. These views correspond to distinct probe positions and orientations and reflect varying levels of image completeness. A dataset of 7,200 annotated images was used to train a ResNet50-based deep network for multi-class classification. The model achieved robust performance with accuracy, sensitivity, specificity, and F1 scores above 89%, and AUC exceeding 97% in patient-level cross-validation. It effectively captures spatially relevant features, distinguishes subtle view differences, and generalizes well to unseen data. The outputs provide interpretable feedback correlating image quality with probe position, enabling real-time scanning assessment. Furthermore, this work introduces a novel problem formulation and multi-class view classification under a fixed acquisition protocol. It provides a foundation for developing the next generation of intelligent US systems. By linking image classification to probe position and orientation, the proposed framework enables real-time feedback that can ultimately support autonomous scanning agents in locating diagnostically optimal cardiac views.

Selective laser melted (SLM) Al-8.3Fe-1.3V-1.8Si aluminum alloy presents superior mechanical properties. The objective of this study was to investigate the effects of unit cell design on the uniaxial compressive properties and fracture mechanism of SLM Al-8.3Fe-1.3V-1.8Si lattice. The results show that the compressive strengths of the body-centered cubic (BCC) and face-centered cubic with Z-axis strut (FCCZ) samples were 109 MPa and 298 MPa, respectively. In the FCCZ sample, the strain first concentrated at the interconnections between the Z-axis and diagonal struts and gradually extended into the Z-axis strut. The Z-axis struts could effectively endure the compressive stress and then buckle without cracking, resulting in the high and wide first maximum compressive peak up to 25 % strain. Due to the large first maximum compressive peak and the stable stress in the plateau and densification areas, the energy absorption at 50 % strain of the FCCZ sample was as high as 86 +/- 3 MJ/m3. Furthermore, the SLM Al-8.3Fe-1.3V-1.8Si lattice with the FCCZ unit cell exhibited better compressive strength and energy absorption than those of SLM Ti-based metallic lattices in the literature with comparable relative densities. Thus, SLM Al-8.3Fe-1.3V-1.8Si lattice is a promising candidate for high-strength and lightweight applications. Furthermore, this study is the first to propose that the digital image correction (DIC) strain maps be analyzed in more than just the loading direction alone, which has helped in the clear identification of the deformation/fracture mechanism and lattice design of SLM alloy lattices.

The influence of particle size on the processability and properties of iron-cobalt soft magnetic materials remains an area of limited exploration. In this study, two types of iron-cobalt powders with differing production methods and particle sizes, designated as ultra-fine and commercial powders, were selected to investigate these effects. For the ultra-fine powder, layer thicknesses of 0.01 mm and 0.03 mm were used to assess processability, while a 0.05 mm layer thickness was employed for the commercial powder. Samples with high relative densities and similar processing parameters were chosen for detailed analysis. The measured relative densities were 91.45%, 93.34%, and 96.25% for the 0.01 mm, 0.03 mm, and 0.05 mm samples, respectively. Distinct differences in microstructure and properties were observed. All samples exhibited nanoscale particle features, with the commercial sample showing the highest particle count and the finest diameters. The 0.01 mm sample was comparable in particle count, while the 0.03 mm sample showed similar particle diameters. In the ultra-fine powder samples, the melt track contours were not well-defined, whereas in the commercial powder sample, etched with Nital revealed clear melting tracks. Additionally, the nanoscale particles disappeared post-etching, and dendritic structure was observed in the transition areas of the melting tracks, highlighting the differences in material behavior based on powder properties and processing conditions.

Recognizing human intentions is a key challenge in human-robot interaction research. Much of the current work in this area centers on identifying human intentions within specific activities, often relying on a limited set of features. In contrast, this paper introduces a more versatile framework for intention recognition and introduces a novel model: the Spatial-Temporal Graph Attention Informer Neural Network (STGAIN). To recognize intentions, this model leverages spatial relationships between humans and objects in different scenes, along with their temporal evolution. In addition, to address an existing research gap this research developed a new dataset called Dynamic Scene Graph (DSG) with representative dynamic relationships, derived from 471 videos covering 20 categories of human intentions. This dataset represents people and objects in different scenes, and the relationships between them. The model was tested rigorously at different points in the videos to track how the scenes evolved and to assess prediction accuracy, comparing the results to a range of advanced algorithms. Our findings clearly demonstrate that STGAIN outperforms these models, showcasing its potential for advanced human intention recognition applications. This model represents a significant advance toward creating more human-centered robots, capable of understanding and adapting to human intentions in real-world situations.

The existence of solidification cracks caused by columnar grains in precipitation-hardened aluminium alloys limit the applicability of Al7075 components manufactured via laser powder bed fusion (LPBF) additive manufacturing. A novel approach was developed to co-incorporate submicron-sized B and micron-grade Ti6Al4V to eliminate hot cracks and to effectively transform coarse columnar grains into fine equiaxed grains, thus improving the mechanical performance of LPBF-fabricated modified Al7075 material. The grain refinement was mainly attributable to the heterogeneous nucleation promoted by the combination of in-situ-formed L12-Al3Ti and TiB2 nano-sized phases. After an optimised T6 heat treatment, excellent comprehensive mechanical properties were achieved, with a tensile strength of 460 MPa and an elongation of 13 %. This research provides an efficient and cost-effective path for addressing crack-sensitive metallic materials used for LPBF additive manufacturing processes.

Defects, particularly cracking defects, severely limit the application of Ni-based alloys fabricated via the laser powder bed fusion (LPBF) additive manufacturing process. To address the processability/strength trade-off, in this study a new Ni-based alloy (IN738M) was designed via thermodynamic calculations based on the traditional IN738 alloy. The processability, microstructure, phase precipitation behaviour and mechanical properties were systematically examined. The results demonstrate that the LPBF-fabricated IN738M exhibited excellent LPBF processability, achieving crack-free fabrication even with a high gamma ' phase mass fraction. The compositional modifications led to improvements in the microstructure, including the formation of a quasi-continuous carbide network at the interdendritic regions, altered grain orientation and grain refinement. This study also proposes a heat treatment strategy to achieve a bimodal distribution of the gamma ' phases for IN738M; the cellular structure was eliminated, with numerous MC-type carbides observed within grains and at the grain boundaries. The IN738M alloy exhibited a superior combination of ultimate tensile strength values (1462 +/- 23 MPa) and elongation values (10.2 +/- 0.4 %) at room temperature compared to the IN738 alloy (932 +/- 35 MPa and 2.6 +/- 0.3 %, respectively). These findings will provide valuable guidance for developing Ni-based alloys with enhanced LPBF processability and mechanical properties.

Soft magnetic materials are used in a wide range of devices, including mobile phones, computers, motors, and inductors. Among the soft magnets, the Fe-50 wt% Ni alloy exhibits superior properties such as high magnetic saturation and permeability with low coercivity, when manufactured by conventional methods such as injection moulding. However, until now, it is not clear if modern manufacturing methods such as those based on laser powder bed fusion can affect the magnetic response. This work aims to determine experimentally the influence of laser speed and power through additive manufacture and subsequent magnetic characterization of the manufactured samples. These measurements were compared with those of a commercial sample obtained via conventional fabrication methods. The results show that it is possible to achieve magnetic saturation similar to the commercial samples, with both values of similar to 1.7 T and acceptable permeability of 66 A.m(-1) compared to the commercial ones 159 A.m(-1) if samples were fabricated with 190 W laser power and 300 m.s(-1) laser speed.

The reincorporation of residues in the productive cycle helps reduce CO2 2 emissions and non-renewable resources extraction. Therefore, the present work studies the feasibility of reincorporating an aluminum residue in the productive cycle by means of an additive manufacturing technique. In this context, other issues considered are the importance of aluminum alloys, the improvement of their properties by adding alumina making composites materials, and issues that must be overcome when printing parts by laser powder bed fusion using a mix of Al and alumina powders. This work evaluates the suitability of Al alloy- alumina composite powders obtained by grinding Al alloy chips from sawing. Laser power and scanning speed were varied until the designed samples were obtained. The findings show that is possible to use powder obtained by grinding Al chips in the laser powder bed fusion technique to obtain Al-Al2O3 2 O 3 composites which exhibits hardness of 226 HV +/- 34.3.

Today, porous magnetic materials have been one of the focal points of studies in the field of treatment due to their absorbent properties. This study focuses on the production of porous magnetic FeSi6 alloy using laser powder bed fusion (LPBF) with in-situ alloying. Water atomized Fe and Si powders with high purity were used as the starting materials. Different hatch spacing and exposure time values were investigated to examine their impact on the porosity of the structure. Cylindrical specimens were produced, and their relative densities were measured. SEM analysis and magnetic property measurements were conducted to evaluate the resulting material. The study successfully achieved the production of high-porosity magnetic material, with some control over pore formation through the laser parameters. The magnetic properties of the material demonstrated results consistent with previous studies. Furthermore, the study found that in-situ alloying in the LPBF method yields magnetic properties comparable to those obtained using pre-alloyed powder, indicating the feasibility of this approach.

Human activity recognition, as a significant branch of artificial intelligence, requires increasingly generalized and precise methodologies due to growing demands. Therefore, this paper proposes a context-based method for recognising indoor human activities, which interlinks indoor human activities with interactions with objects, making the contextual relationship between humans and objects particularly crucial. In addition, this research has developed a new dynamic graph dataset based on publicly available video datasets and their associated descriptive scripts, instantiating the relationships between humans and objects. A novel architecture for human activity recognition is developed in this research. This architecture utilizes graph neural networks and self-attention mechanisms to learn the significance of the interactions between humans and objects and capture the relationships between video frames on a temporal level. The results demonstrate that the classification accuracy reaches 0.86 and it also performs better than other current advanced algorithms STGAT and STGCN. It is noteworthy that the approach also effectively reduces ambiguity in activity recognition.

Nickel aluminium bronze alloy specimens were produced using laser powder bed fusion (LPBF) and subjected to heat treatment to understand their corrosion behaviour when exposed to a 3.5 wt% NaCl solution. Electrochemical analysis, including impedance spectroscopy and polarization curves, was performed to characterize the samples after 30 days of immersion. The findings reveal that the as-built samples exhibit superior corrosion resistance compared to the heat-treated samples, primarily attributed to the lower presence of intermetallic phases, which hinder the alloy's passivation process.

Triply periodic minimal surface (TPMS) lattice structures with controllable mechanical properties and porous architecture are promising candidates for lightweight and energy-absorbing applications. In parametric structural design, the research on structural geometric characteristics, such as aspect ratio (R) and surface curvature, has mainly focused on fluid and heat transfer considerations. However, their impact on multi-directional mechanical properties has yet to be thoroughly investigated. This study, combining representative volume elements (RVE) based on periodic boundary conditions and theoretical surface morphological characteristics, investigates the mechanical properties and deformation behavior of lattice structures with different aspect ratios in multiple directions. The results indicate that the aspect ratio highly influences the deformation behavior of the lattice structure in different directions. The local curvature and force state of the lattice structure can be adjusted by aspect ratio to reduce local stresses and deformations in different loading directions. Compared with the simulation results of representative volume elements, the structural stress concentration and failure position can be predicted by the Gaussian curvature distribution. In addition, the elastic modulus of the structures in the [100] and [001] directions can also be adjusted by aspect ratio. Through experimental verification in the [001] direction, the structure with an aspect ratio of 2 can greatly enhance the elastic modulus (121%∼440%), maximum stress (10%∼183%), and energy absorption (33%∼85%). The significance of this work is to improve the understanding of the influence of geometric features on the mechanical properties and deformation behavior of lattice structures, which provides a new design approach for triply periodic minimal surface lattice structures in applications of impact protection and bone scaffold.

Cu/Ni heterogeneous materials integrate excellent thermal conductivity and high-temperature mechanical properties, enabling them to be widely used in the aerospace domain. Differences in the thermal and physical properties of the Cu and Ni materials, however, make them difficult to be processed using the laser powder bed fusion (LPBF) additive manufacturing process. This study systematically examines the effects of various LPBF process parameters on microstructure, element diffusion, bonding strength and microhardness at the Cu/Ni interface, as well as investigating the mechanisms of defect formation within Cu/Ni heterogeneous materials. The results indicate that a reasonable control of laser energy input (<100 J/mm3) facilitates the Cu/Ni components through strong interfacial metallurgical bonding without pore defect formation. Compared to single-material Cu alloy, the ultimate tensile strength (UTS) of the horizontally bonded Cu/Ni specimen increased by 55.25 %, without significant reductions in elongation. The vertically bonded Cu/Ni tensile specimen fractured in the middle of the Cu region rather than the interfacial region, indicating superb interfacial bonding strength. Another advantage lies in the enhancement of thermophysical properties, with a 109.5 % increase in thermal conductivity achieved in the LPBF-fabricated Cu/Ni heterogeneous materials compared to the single-material Ni alloy. Quasi-static compression experiments indicated that the Cu/Ni lattice structure could absorb more energy when compressed parallel to the build direction (BD), compared to being perpendicular to the BD. This study provides guidance for the design and manufacture of high-performance Cu/Ni heterogeneous components via LPBF.