Arc welding processes are increasingly being used in the additive manufacturing of metal components. Physics-based modeling of welding processes enables the study of welding parameter effects on the final weld shape, residual stress state, and distortion, helping to improve weld quality and reduce costs. However, the quality of the process simulation strongly depends on the mathematical description of the heat source. The parameters of the heat source model have a significant influence on the temperature field and, consequently, on the distortion and residual stress fields. This paper presents a trial-and-error method for determining the parameters for Goldak’s double-ellipsoidal heat source model. The transient temperature distribution and the size of the melt pool are determined through experimental studies. Numerical models are then set up in Simufact Welding 8.0 with a set of heat source parameters to reproduce the experimental trials. By comparing numerical finite element results with experimental results, the heat source parameters for a multi-pass additive manufacturing process are successfully calibrated and identified.

Metal additive manufacturing (AM) has attracted significant interest in high-value industries due to its ability to produce complex parts flexibly, but the reliance on costly manual monitoring remains a major burden for quality control. Artificial intelligence (AI)-driven models for automated defect detection are emerging as promising solutions. This study contributes a new annotated dataset for AI research in AM and evaluates the performance of four widely used convolutional neural network (CNN) models in detecting powder bed morphology defects, based on layer-wise images acquired by the EOSTATE PowderBed system during the metal laser-based powder bed fusion process. The models were trained through transfer learning methods with manually labeled and pre-processed data. Results demonstrated that ResNet50 and EfficientNetV2B0 achieved over 99% accuracy in defect classification, while YOLOv5 outperformed Faster region-based-CNN in defect detection and localization. However, lower average precision values in object detection tasks were attributed to variability in defect scales and annotation quality. This study confirms the potential of AI-based models for defect identification in AM, with YOLOv5 demonstrating clear advantages in managing complex, multi-scale defects. Future improvements will focus on expanding the dataset and refining annotation strategies to further enhance model robustness.

Melt track monitoring in the laser powder bed fusion (LPBF) process is crucial for preventing internal defects in as-printed parts. Uncontrollable melt pool dynamic behavior easily leads to melt track morphology defects. Existing monitoring methods face challenges in balancing modeling accuracy and physical interpretability. Specifically, traditional physics-based models typically require complex monitoring equipment, extensive simulation data, and empirical formulas, resulting in high costs and limited applicability. Meanwhile, conventional data-driven models lack physical constraints, leading to insufficient interpretability, process parameter sensitivity, and poor generalization. To address these challenges, this article proposes a deep Gaussian process-based method for LPBF melt track morphology prediction. The proposed model employs kernel functions in the first layer to learn melt pool evolution patterns and embeds the Rosenthal equation into the second-layer kernel function as a physical constraint, constructing a physically interpretable multilayer Gaussian process framework. Finally, a softmax classifier based on melt track geometric deviation achieves five-category melt track morphology recognition. Multi-condition experimental results demonstrated that the proposed method achieved root mean square errors of 0.069, 0.020, and 0.039 for melt track geometry, outperforming traditional data-driven models in prediction accuracy. The classification accuracy reached 90.76%. Furthermore, the influence of different features on melt track morphology is quantified through time-lagged mutual information analysis and other visualization methods. This study provides an effective solution for achieving quality monitoring and defect prediction in the LPBF process.

Vat photopolymerization (VPP) additive manufacturing has emerged as a transformative approach for fabricating high-performance ceramic components with intricate geometries. This review comprehensively examines VPP technologies, including stereolithography, digital light processing, and two-photon polymerization, highlighting their mechanisms, advantages, and limitations. Critical challenges faced by ceramic VPP include light scattering from particles, slurry viscosity control, sedimentation, and post-processing shrinkage. The required optimized characteristics suitable for VPP of ceramic slurries and pre-ceramic polymers are also discussed. The latter offers a promising alternative, enabling the shaping of complex architectures with reduced defects and enhanced thermal stability, supported by active/passive fillers that mitigate shrinkage and improve density. Ceramic VPP applications span biomedical implants, microreactors, aerospace components, and energy devices. Key advancements include the integration of multimaterial systems, hybrid precursors, and nanocomposites. However, challenges persist in achieving uniform curing depths, minimizing anisotropic shrinkage, and scaling production. Future research should focus on material innovation, process parameter optimization, and advanced characterization techniques to unlock the full potential of VPP for next-generation ceramic manufacturing. This technology offers an effective solution for high-value ceramic applications.

Hip osteoarthritis is a degenerative joint disease commonly associated with aging. One effective treatment to restore patients’ quality of life is total hip arthroplasty, in which the damaged hip joint is replaced with a prosthetic implant. Currently, there is a growing demand for customized artificial hip joints tailored to individual anatomical dimensions. However, the conventional casting method generally used to fabricate these implants is often considered ineffective. Additive manufacturing technology, also known as 3D printing, has emerged as a promising alternative. This technology enables the fabrication of complex designs with high accuracy and customizable geometries and sizes without altering the physical components of the 3D printing machine. This study aims to develop a 3D-printed artificial hip joint prosthesis using a dental photopolymer resin reinforced with titanium dioxide (TiO2) nanoparticles. The mechanical performance of the prostheses was evaluated through both experimental and simulated compression testing. Four concentrations of TiO2 nanoparticles were tested, namely 0%, 1%, 3%, and 5%. The results showed that the prosthesis reinforced with 3% TiO2 nanoparticles exhibited the highest strength (717.2 N), while the one with 5% TiO2 nanoparticles exhibited the lowest strength (241.8 N).

Conventional optimization of fused deposition modeling (FDM) often relies on trial-and-error or heuristic approaches, which lack scalability and precision, especially for complex geometries such as impellers. While prior studies have integrated artificial intelligence (AI) or multi-criteria decision-making (MCDM) techniques for process optimization, their combined application remains limited, particularly in scenarios that prioritize energy-efficient and sustainable manufacturing. This study introduces a novel hybrid AI-MCDM framework for the multi-objective optimization of FDM-printed composite impellers, integrating mechanical performance, energy consumption, and material utilization within a unified decision-making model. A key feature of the approach is the real-time tracking of energy usage, enabling dynamic evaluation of process efficiency. Experimental validation demonstrates a 7% enhancement in tensile strength, a 25% reduction in energy consumption, and a 30% decrease in material wastage compared to baseline configurations. These results underscore the potential of AI-driven simulation and optimization frameworks to support sustainable additive manufacturing, with significant implications for aerospace, biomedical, and energy sector applications.

The weld bead is the basic structural unit in metal additive manufacturing, yet the multiphysics coupling inherent to hybrid laser-arc processing greatly complicates the prediction of bead dimensions. Despite the exploration of numerous predictive methods, research on explainable prediction of weld-bead dimensions remains limited. In this work, we developed a particle swarm optimization (PSO)-based ensemble prediction model (PSO-EP) for laser-arc hybrid additive manufacturing, and through SHapley Additive exPlanations (SHAP) analysis, comprehensively uncovered the underlying links between process variables and bead geometry. Experimental evidence indicated that our PSO-EP outperformed individual models and alternative ensembles, delivering superior accuracy, reflected by an R-squared value of 0.9567 for bead width and an R-squared value of 0.9492 for bead height, and markedly lowering prediction errors. The SHAP findings indicated that weld speed is the dominant determinant of bead width, while laser power plays a pivotal role in bead height. Subsequent single-factor dependence analysis showed that different process variables had significantly different impacts on bead size across their respective value intervals. This study provides important theoretical support for the intelligent development of the laser-arc hybrid additive manufacturing process.

The trade-off between strength and plasticity has posed a challenge to the broader application of conventional metallic structural materials in high-speed, heavy-load, and extreme service environments. Heterogeneous structure designs could potentially overcome these limitations with their inherent superior combination of strength and plasticity. To harness this potential, this study employed a directed energy deposition additive manufacturing (AM) technology to fabricate a novel heterostructure in as-built (AB) A131 steel, consisting of alternating coarse and fine-grain layers along the building direction. In addition, a heat treatment process was applied to fabricate a near-homogeneous microstructure, allowing for the investigation of the role of crystal misorientation in tensile anisotropy. Compared to the performance of commercial hot-rolled ASTM A131 steel (yield strength [σYS]: 346.5 MPa; ultimate tensile strength [σUTS]: 545.0 MPa), the AB A131 steel achieved significant enhancements of 168.3% and 78.0% in σYS and σUTS, respectively, when maintaining a comparable elongation of 24.6% along the deposition direction similar to the ASTM A131 standard. Comprehensive experimental characterizations, combined with molecular dynamics simulations, were conducted to investigate the underlying formation mechanism of the heterostructure and the origins of mechanical anisotropy. It was found that single-pass deposition produced three distinct microstructure regions with different grain sizes owing to dendrite growth. With repeated thermal cycles, these evolved into a layered heterostructure consisting of alternating fine crystals and coarse-columnar grains. This heterostructure remarkably contributed to an exceptional improvement in strength, accompanied by only a minor reduction in plasticity. These findings present an efficacious avenue for substantially augmenting the mechanical properties of conventional iron-based alloys, offering useful references for overcoming the strength-plasticity trade-off in other alloys fabricated by AM.

Titanium alloys are gaining attention for their potential to improve implant performance in biomedical applications. This study investigates the Ti-10Ta-2Nb-2Zr alloy fabricated using laser-powder bed fusion (L-PBF) for potential biomedical applications. The research aims to examine the influence of processing parameters on material structure and properties, and to develop porous structures based on triply periodic minimal surfaces (TPMS) to reduce elastic modulus and improve mechanical compatibility with bone tissue. Spherical Ti-10Ta-2Nb-2Zr powder was processed using L-PBF with varying laser power (250 – 280 W), scanning speed (500 – 1000 mm/s), and hatch spacing (80 – 100 μm). Maximum relative density of 99.91% was achieved at volumetric energy density of 70 J/mm3. Differential scanning calorimetry revealed the β-transus temperature at 862°C. Microstructural analysis showed the formation of martensitic α’-phase in the as-built condition with acicular morphology. Heat treatment at 900°C resulted in the formation of a lamellar α + β structure. Mechanical properties in the as-built condition were characterized by yield strength of 551.8 MPa, ultimate tensile strength of 641.2 MPa, elongation of 19.0%, and elastic modulus of 89.0 GPa. After heat treatment, strength characteristics decreased by 15 – 18%, whereas elastic modulus reduced to 86.0 GPa. TPMS porous structures (gyroid, Schwarz, and split) with 50% porosity demonstrated an elastic modulus of 9.2 – 9.7 GPa, representing approximately 18% of the solid material value. These results demonstrate the potential of Ti-10Ta-2Nb-2Zr as a promising alternative to conventional Ti-6Al-4V for orthopedic applications, offering enhanced mechanical properties and reduced stress shielding due to its lower elastic modulus and tailored porous architectures.

Microwave-absorbing structures are increasingly vital for applications such as electromagnetic protection, stealth technology, and wireless communications. However, their broader adoption is often limited by drawbacks such as excessive thickness, narrow absorption bandwidth, and high manufacturing costs. This study presents the design, fabrication, and evaluation of a sunflower-inspired metastructure for broadband microwave absorption, achieved via fused deposition modeling three-dimensional printing. The metastructure, inspired by the spiral geometry of sunflower seed arrangements, integrates multi-layered, gradient spiral elements composed of carbon black-carbonyl iron powder/polylactic acid (CB-CIP/PLA) composites. Electromagnetic simulations were employed to systematically optimize key structural parameters, including the gradient impedance increment and individual layer thicknesses, to maximize absorption efficiency. Both simulated and experimental results demonstrate that the absorber achieves an effective absorption bandwidth of 12.13 GHz (5.87 – 18.00 GHz) with reflection loss below 10 dB, covering the C, X, and Ku frequency bands. The performance is attributed to the synergistic effects of interfacial polarization and natural magnetic resonance within the CB-CIP/PLA composite. The metastructure also exhibits stable, wide-angle absorption properties, maintaining bandwidths exceeding 10 GHz for incident angles up to 50° under both transverse electric and transverse magnetic polarizations. The proposed sunflower-inspired design demonstrates significant advantages in bandwidth-to-thickness ratio, fabrication efficiency, and polarization insensitivity compared to conventional biomimetic absorbers. These findings highlight the promise of bio-inspired design strategies for developing lightweight, efficient, broadband microwave absorbers, providing valuable reference for future advancements in the field.