Search Results (7,507)

Laser Powder Bed Fusion for metals (PBF-LB/M) is a complex additive manufacturing process in which metal powder is selectively melted layer-by-layer to fabricate 3D parts. Process parameters critically influence the resulting microstructure in nickel alloys, with features such as melt pool marks, grain size and orientation, porosity, and cracks serving as key process signatures. These features are typically analyzed post-process to identify suboptimal conditions. This research aims to develop automated post-process measurement and analysis techniques using image processing, pattern recognition, and statistical learning to correlate process parameters with part quality. Optical microscopy images of build surfaces are analyzed using machine learning algorithms to evaluate porosity, grain size, and relative density in fabricated test coupons. Effect plots are generated to identify trends related to increasing energy density. A novel deep learning approach based on Mask R-CNN is used to detect and segment melt pool regions in optical microscopy images. From the segmented regions, melt pool dimensions—such as width, depth, and area—are extracted using bounding geometry coordinates. Manually labeled images (Type I and Type II) are used to train the model. A comparison between ResNet-50 and ResNet-101 backbones shows that the ResNet-50-based model (Model 2) achieves superior performance, with lower training loss (0.1781 vs. 0.1907) and validation loss (8.6140 vs. 9.4228). Quantitative evaluation using the Jaccard index, precision, and recall metrics shows that the ResNet-101 backbone outperforms ResNet-50, achieving about 4% higher mean Intersection-over-Union, with values of 0.85 for Type I and 0.82 for Type II melt pools, where Type I is detected more accurately due to its more regular morphology and clearer boundaries. By extending Faster R-CNNs with a mask prediction branch, the method allows for precise melt pool measurements, providing valuable insights into process quality and dimensional accuracy, and aiding in the detection of defects in PBF-LB-fabricated parts. Full article

Atypical joint spaces, such as those encountered in complex segmental bone loss and large structural defects, remain challenging to manage with conventional implants within divisions across orthopedics, including arthroplasty, tumor reconstruction, trauma, and spine. Additive manufacturing advances have made patient-specific implants a possibility, and this promising solution has enabled the creation of implants with customized geometry and controlled surface porosity to enhance osseointegration, reduce rejection rates, optimize biomechanics, and promote longevity. Despite its potential, patient-specific implants are still eclipsed in use by conventional, “off-the-shelf” implants due to their lower cost, documented long-term durability, insurance coverage, and the strength of available clinical evidence supporting their use. This narrative review summarizes current materials and manufacturing approaches for additively manufactured metal porous implants, including imaging and design workflows, lattice and pore architecture, and how the printing process influences implant stiffness, fatigue strength, surface roughness, and porosity. We also discuss the experimental and preclinical data on mechanical performance, fatigue resistance, and osseointegration for new developments in the field. Emerging trends such as material innovation, streamlined digital planning-to-implant workflows, 4D printing and other advanced additive manufacturing concepts, and cost-reduction efforts are examined in the context of clinical practicality. In this review, the integration of engineering principles with early clinical outcomes will provide orthopedic surgeons with a realistic understanding of the benefits and limitations of the future utilization of additive manufacturing in clinical practice. Full article

The successful application of 3D and 4D food printing is fundamentally governed by the rheology and microstructure of edible inks. These factors control every step, from extrusion and nozzle deposition to the final product functionality. This review systematically examines how formulation variables, including starch/protein composition, water content, and hydrocolloids, determine the network architecture and critical rheological properties, such as yield stress and viscoelasticity. These properties determine printing outcomes such as filament formation, stacking accuracy, and the stability of sensitive components. This review explores 4D printing as a “3D + 1D function,” where printed structures provide additional features over time, such as a controlled color change or bioactive release, while post-printing treatment often activates these features. Through case studies of novel inks, we show how interfacial chemistry and process parameters influence texture and stability. Finally, we discuss the application of rheological metrics for predicting printability and outline the critical need for developing multi-parameter, process-relevant printability indices to advance the field of digital food manufacturing. Full article

Metal additive manufacturing (MAM) enables the production of complex, high-value components for sectors such as aerospace, energy, and biomedical engineering. However, its large-scale industrial adoption remains constrained by internal defects, residual stresses, distortions, microstructural variability, and the complexity of the coupled process-parameter space. This review examines how artificial intelligence (AI)—including machine learning, deep learning, and optimization algorithms—is being applied to address these challenges across the MAM workflow. A structured literature review was conducted covering studies published between 2015 and 2025, identified through searches in Scopus, Web of Science, and IEEE Xplore. The selected literature is analyzed according to key functional domains of metal additive manufacturing: design for additive manufacturing (DfAM), process modeling and simulation, in situ monitoring and control, and microstructure and property prediction. AI approaches are further categorized by learning paradigm, including supervised learning, deep learning, reinforcement learning, and hybrid physics–machine learning models. The review highlights recent advances in AI-assisted parameter optimization, defect detection, and digital-twin frameworks for process supervision. At the same time, it identifies persistent challenges, particularly the scarcity and heterogeneity of datasets, limited transferability across machines and materials, and the need for uncertainty-aware models capable of supporting validation and certification. Overall, the analysis indicates that the integration of multi-sensor monitoring with hybrid physics-informed AI models represents the most promising near-term pathway to improve process reliability, reduce trial-and-error experimentation, and accelerate industrial qualification in metal additive manufacturing. Full article

Molten Metal Jetting (MMJ) is an emerging metal additive manufacturing process that produces components via on-demand jetting of discrete droplets. This paper reports properties of T6 heat-treated AlSi7Mg alloy produced in different build orientations via MMJ. A Xerox ElemX machine was used to print AlSi7Mg coupons in horizontal, tilted, and vertical orientations. The aluminum feedstock was melted at 825 °C and was printed onto a 475 °C heated print bed using a jetting frequency of 400 Hz and a drop spacing of 500 μm. Coupons were heat treated to a T6 temper. The average yield strengths of heat-treated coupons in vertical and horizontal orientations were 240.4 ± 7.3 MPa and 244.6 ± 7.1 MPa respectively. This indicates that the vertical build orientation had minimal adverse effect on strength. However, average strain (11.5% ± 1.2% versus 14.6% ± 3.5%) values for the vertical and horizontal orientations, respectively, showed more pronounced effects. X-ray CT analysis of vertically oriented coupons revealed increases in porosity in material deposited above heights of ~90 mm. Above this build height, the measured surface temperature dropped below ~455 °C. External heating methods are therefore advised in order to maintain a surface temperature ≥ 455 ° and avoid excess porosity. Full article

This systematic review examines how digital technologies enable circular economy (CE) transitions across sectors and value chains. Analysing 266 peer-reviewed publications (2016–2025), we develop a comprehensive taxonomy of digital enablers—including IoT, AI, blockchain, cloud computing, additive manufacturing, and digital platforms—and map their applications to circular strategies such as reuse, remanufacturing, and recycling. Our findings reveal that data-driven technologies dominate CE implementation, with 89% of studies involving data collection, storage, analysis, or sharing functions. IoT emerges as the foundational technology for real-time tracking and monitoring, while AI and big data analytics optimise circular processes and predict maintenance needs. Blockchain ensures traceability and trust in circular supply chains, and cloud computing provides scalable infrastructure for collaboration. Manufacturing (41%) and construction (15.5%) are the most studied sectors, with strong European research leadership reflecting policy drivers such as Digital Product Passports. We identify three impact types: enabling (process optimisation), disruptive (business model innovation), and facilitating (ecosystem collaboration). Key barriers include technical complexity, organisational resistance, high implementation costs, and regulatory gaps. The review concludes with recommendations for integrated, multi-stakeholder approaches to realise a digitally enabled circular economy. Full article

Selective laser melting (SLM) is a complex additive manufacturing process involving rapid laser–material interaction, steep thermal gradients, and phase change phenomena. In this work, a two-dimensional thermal model based on the lattice Boltzmann method (LBM) is developed to simulate the SLM process of AlSi10Mg and 316L stainless steel (316L SS) alloys. The model captures the laser–material interaction, layer-by-layer deposition, phase change behavior, and heat transfer mechanisms, including conduction and convection. Experimental observations of melt pool width and depth were also performed on the microstructures of the two SLM alloys in order to compare the results with the numerical predictions. For the AlSi10Mg alloy, good agreement is obtained, with relative errors of 19.13% in melt pool width and 7.58% in depth, accurately capturing melt pool penetration and remelting behavior. In contrast, moderate deviations are observed for 316L SS, indicating a higher sensitivity to thermophysical properties and suggesting that further model refinement is required. Overall, the results demonstrate the capability of the LBM framework as an efficient and robust tool for analyzing thermal behavior in SLM and for supporting process parameter optimization. Full article

High nitrogen austenitic stainless steels are an important engineering structural material. Under annealing conditions, the addition of interstitial solid solution element nitrogen can improve the yield strength and tensile strength of the alloy without reducing its plasticity. In addition, nitrogen can partly or completely replace the more expensive nickel element at a relatively cheap element cost to improve economic benefits, while maintaining or even enhancing the excellent corrosion resistance of stainless steels. However, the cracks and defects caused by high nitrogen austenitic stainless steels during hot working in high temperature ranges have always been the pain points in the engineering field. High nitrogen elements bring high temperature strength, but also narrow the hot working temperature range, the possibility of nitride precipitation and the tendency of heat induced cracking, which limit the further engineering application of high nitrogen austenitic stainless steels. It is urgent to analyze and study the hot deformation law of high nitrogen austenitic stainless steels in engineering. This article commences with an examination of the developmental trajectory of high nitrogen austenitic stainless steel, elucidates the role and strengthening mechanism of nitrogen, and delineates the factors influencing the mechanical behavior of high nitrogen austenitic stainless steel during hot working. These factors include the impact of nitrogen content and manufacturing processes, hot-working parameters, grain size distribution, and the presence of precipitated phases. This article synthesizes various studies, analyzes the causes of thermal cracking, and proposes potential solutions. Ultimately, it summarizes the practical applications and future prospects of high nitrogen austenitic stainless steel, highlighting its substantial potential. Full article

Additive-manufactured (AM) 316L stainless steel, produced via direct metal laser sintering (DMLS) and characterised by a surface topography of high irregularities and tensile residual stresses with specific anisotropy, was subjected to milling and shot peening. The milling process caused a reduction in surface topography parameters, but tensile residual stresses increased significantly. The shot peening process was carried out according to the full factorial design 3² and technological parameters such as a shot diameter in the range of 1-3 mm and an air supply pressure between 0.2 and 0.6 MPa. As a result of the experiments and the analysis, reduced surface topography was achieved, and a favourable residual stress state was formed with compressive stresses. The mechanism of the changes was demonstrated via microstructure observation and statistical models obtained by mathematical analysis. Full article

Three-dimensional (3D) printing, also known as additive manufacturing (AM), has become increasingly integrated into dentistry because of its high precision, efficiency, and ability to fabricate patient-specific devices. This review comprehensively discusses the historical development of 3D printing and outlines the fundamental principles of the most widely used technologies in dentistry, including stereolithography (SLA), digital light processing (DLP), and liquid crystal display (LCD). These technologies enable the accurate and efficient fabrication of dental models, crowns, bridges, dentures, surgical guides, orthodontic appliances, and tissue engineering scaffolds. Current clinical applications are systematically summarized across major dental disciplines, including prosthodontics, orthodontics, oral and maxillofacial surgery, endodontics, periodontics, and pediatric dentistry. Despite existing challenges, such as limited long-term clinical data for certain materials, high initial equipment costs, and post-processing requirements, 3D printing offers substantial advantages in terms of customization, workflow efficiency, and clinical predictability of the final product. Future developments in advanced biomaterials, artificial intelligence-assisted workflows, bioprinting, and four-dimensional (4D) printing are expected to further expand the role of additive manufacturing in personalized and regenerative dentistry. Full article

The objective of this study is to review the literature on the natural resources needed for biodegradable materials underscoring the importance of natural fiber-based composites as a feasible alternative. The review focuses on the pivotal role of natural fiber-based composites in the formulation of industry benchmarks, the challenges associated with application of natural fibers, the application areas, and the mechanical properties as well as the determinants influencing the properties of the composites. The manufacturing methods were discussed and compared. In addition, the study highlights the successful instances where enset fiber-based composites have been adeptly implemented. The study also observed potential areas of future research to improve the performance of enset fiber-reinforced composites including the fabrication techniques and treatments. Hand lay-up and compression molding are the conventionally used composite fabrication methods, while the recent advances in 3D printing for composite fabrication bring new opportunities to solve many of the existing limitations. In addition, most research is currently limited to alkali treatment, whereas other fiber treatment techniques could further improve the mechanical performance by modifying the surface properties and removing the impurities. Moreover, hybridization, orientation of fiber, and addition of nano-particles are observed to have direct impact on the composite properties. The review scrutinizes comprehensive examination of the prevailing landscape and prospective courses for enset fiber applications within the realm of sustainable material science, utilizing diverse processing techniques and applications while pinpointing inherent challenges. Full article

A proposed study based on an artificial neural network (ANN) model will be used to predict microhardness (VHN) and tensile strength (TS) of Friction Stir Additive Manufacturing (FSAM) of AA8090 alloy. The process parameters taken into consideration were rotational speed (1000, 1500, 2000 rpm), traverse speed (45, 65, 85 mm/min) and tilt angle (0°, 1°, 2°). We performed 90 physical experiments (74 + 7 + 6 + 3), in which 74 experiments were generated with the help of the Central Composite Design of ANN modeling, seven independent experiments were used to validate the results, six repeat experiments were taken, and three mid-level interpolation experiments were performed. Out of 74 modeling runs, 60 samples were trained, 14 were internally tested, and seven separate modeling runs were exclusively tested externally. An ANN model was created based on the Adam optimizer, where the loss was taken to be Mean Squared Error (MSE). The level of model robustness was assessed employing 5-fold cross-validation and grouped validation (LOPCO, LOFLO-RPM, and LOFLO-TA). Under 5-fold cross-validation, the ANN had mean R² values equal to 0.940 (VHN), 0.920 (TS). In normalized training, the model achieves MAE = 0.26 and R² = 0.97, whereas testing in physical units has developed MAE values of 1.0 and 2.0, respectively (VHN and TS). These results correspond with the high predictive ability and generalization of the ANN model, as indicated by the uniform performance of the ANN model on training, cross-validation, internal testing, and independent validation. The importance analysis of features revealed that rotational speed was the most significant factor that influenced the tensile strength and microhardness. The constructed ANN model is a credible and sound system for optimizing and replicating processes from other friction-stir processing methods on AA8090 alloy. Full article

Manufacturing is going through a significant shift propelled by Industry 4.0 and smart manufacturing infrastructures, requiring sophisticated production control techniques that can adaptively adjust to fluctuating operational situations. This paper presents a novel five-step hybrid simulation framework for adaptive real-time production speed control in smart manufacturing lines, integrating conceptual modelling, hybrid simulation, algorithm redefinition, design of experiments, optimisation, and real-system implementation. The framework transforms the speed management systems into online digital twins capable of optimising system performance and mitigating unforeseen fluctuations, faults, and congestion. A comprehensive case study from the beverage manufacturing sector demonstrates the framework’s effectiveness, utilising a universal simulation platform to model both continuous fluid flow and discrete event processes. The proposed stepwise, multi-threshold algorithm employs multiple distinct logical thresholds evaluated sequentially to optimise both upstream and downstream station speeds, with decision thresholds independently adjustable for each production line segment. The experimental results show significant improvements, including around an 18% increase in overall throughput and a 95.7% reduction in work-in-process inventory. A comprehensive resiliency analysis and statistical tests under various disruption scenarios further validated the approach, demonstrating its superiority. Beyond the studied case, the framework provides a transferable pathway for real-time adaptive control across a wide range of smart manufacturing environments, enabling enhancements to operational efficiency without requiring additional capital investment in new equipment or infrastructure. Full article

This research delves into the impact of varying printing angles in the range (0°, 15°, 30°, 45°) on the thermal and mechanical characteristics of carbon fibre–PLA/PHA composites fabricated via fused filament fabrication (FFF). The microstructural arrangement within the 3D-printed PLA/PHA is unveiled through the application of SEM, X-ray microtomography and optical imaging. Tensile loading conditions are employed to extract meaningful mechanical parameters such as Young’s modulus, tensile strength, elongation at break, and mechanical energy, all of which are associated with the printing angle settings. The results indicate that the filaments exhibit a porosity of approximately 3%, while the porosity of the printed structure ranges from 27% to 38%, depending on the printing angle. Tensile modulus in the range 840 to 890 MPa is found not to be highly sensitive to the printing angle. However, tensile strength reaches 37 MPa for a printing angle of 30°. The variations across conditions are limited to approximately 6% in tensile stiffness and 16% in tensile strength. Finite element simulations based on 3D imaging indicate that an effective modulus of the solid phase between 1.6 and 1.8 GPa provides the closest agreement between experimental measurements and numerical predictions. This study presents novel findings concerning the deformation mechanisms associated with different length scales, from filament composite to filament arrangement, in the carbon fibre–PLA/PHA composite. This study highlights that while printing angle has a moderate influence on mechanical response, the overall structural integrity and interlayer cohesion of carbon fibre–PLA/PHA composites remain robust across a wide range of processing parameters, demonstrating their potential for reliable structural applications in additive manufacturing. Full article

Additively manufactured stainless steels are gaining considerable attention in the production of complex components, especially in the aerospace, food production, energy, and biomedical industries. Machining and achieving the desired surface properties of such materials remains a challenge. Abrasive waterjet machining technology appears to be one of the options due to the advantages it brings. Removing support structures and separating individual parts is also one of the possible applications of this technology. This study investigates the effects of process parameters for individual cut qualities (Q1–Q5) of abrasive waterjet on the surface properties of additively manufactured stainless steel (SS 316L) specimens, considering the different mechanical properties of the material due to the direction of layering of the material during its production. Experimental specimens were prepared by selective laser melting technology with parameters ensuring the best possible quality of the resulting part. The results of the study showed changes in the topography of the machined surface, especially in the roughness parameters. Scanning Electron Microscopy and Energy Dispersive X-ray Spectroscopy analysis proved the presence of fragmented abrasive particles in the cut areas. Full article