Open research questions in Additive Manufacturing and 3D Printing Technologies

What the literature leaves open

• Additive manufacturing, particularly Fused Filament Fabrication (FFF), enables the production of multi-material components with tailored mechanical properties; however, the compressive behavior of dual-material polymer systems remains insufficiently explored.

  Mechanical Response Under Compression of Dual-Material PLA/ABS Structures in Additive Manufacturing · 2026 · DOI

• Within the limitations of this study, the traditional lost-wax casting technique exhibited superior trueness, precision, and clinical tolerance compared with the other evaluated fabrica- tion methods.

  Evaluation of the Trueness and Precision of Cast, Milled-Cast, Milled, and 3D-Printed Post-and-Core Techniques Using Matching Software: An In Vitro Study · 2026 · DOI

• The conducted investigation demonstrates that a novel deposition method for adjacent tracks in the FLM- process can achieve increased tensile strength along the build direction (Z-direction). Low strength in the build direction, and consequently high degrees of anisotropy, are common limitations of the FLM- process. The design approach examined in this study directly addresses this weakness. The new deposition strategy reduces anisotropy by increasing the strength of printed PLA tensile specimens along the build direction by up to 40%, while decreasing it in the direction of deposition by 5%. This results in a reduced anisotropy of 12%. Since no additional hardware or special materials are required, this technique can be applied by any FLM-user with existing equipment. Future research should focus on optimizing the parameters of the spiralize approach to evaluate its full potential. Particularly valuable will be an analysis of defect distribution across different parameter sets, as it is plausible that variations in oscillation pitch and spiral radius influence the shapes and arrangements of the voids. Such investigations could, for example, be conducted using computed tomography. Due to the curved path deposition and increased thermal energy input, there is also potential to reduce process- related issues such as warping in more technical materials (e.g., ABS or PA12). 1948 DESIGN FOR ADDITIVE MANUFACTURING To achieve easy slicing of functional components with the presented strategy extensive software implementation – in regular slicing software – is required. Further improvements to the technique, followed by its transfer to realistic additive manufacturing parts, enable the production of higher-quality parts using FLM and supports its integration into new application scenarios like the lightweight construction or series production with increased strength and reduced anisotropy.

  Development and investigation of a new path-planning design for FLM-3D-printing to reduce anisotropy · 2026 · DOI

• Small startup, portfolio centralized in a specialty. Small company, not well known in the market. Small company, not well known in the market. Source: Market research carried out in 2022 available in the Project Book. The inclusion of competitive analysis at the beginning of the PDP, from the earliest stages, corroborates models such as Boone & Kurtz (2008), whose objective is to direct the development of the product according to its future positioning (Ueasangkomsate & Suksatean, 2024). In addition to the market view based on competitors, Kolomoyets & Dickinger (2023) emphasizes that customers are a source of market research. When it comes to the area of hospital and diagnostic 10 de 26 Revista de Gestão e Projetos – (GeP), 17(2), May/Aug., e31453, 2026 IMMERSIVE TECHNOLOGIES AND 3D PRINTING FOR HEALTHCARE: A NEW PRODUCT DEVELOPMENT PROJECT ARTICLE health – the core business of the company studied – in addition to the patient, the physician is a fundamental customer for the performance of a product (Faisal et al., 2020; Krunal et al., 2020b). In view of this, within the project under study, a design thinking event was held with doctors from a large hospital in the capital of São Paulo. The session engaged 30 professionals representing a wide range of specialties, from marketing and business to researchers and doctors. As for the target audience, seventeen doctors participated, ten men and seven women, specializing in radiology (4), surgery (4), and other specialties (9). Participants ranged in age from 40 to 80, creating a balanced and multidisciplinary environment conducive to creative collaboration. Synthesizing market and stakeholder inputs, the resulting portfolio prioritized surgical planning and case-based anatomical applications within the abdominal, thoracic, and orthopedic domains. Additionally, a 3D-printed fetal ultrasound souvenir was included as a strategic differentiator. This selection aligns with the literature framing 3D printing and immersive technologies as catalysts for medical visualization and personalization, provided that value is moderated by clinical evidence and implementation context (Alzoubi et al., 2023; Deng et al., 2023). Conversely, orthotics were excluded due to operational and staffing limitations, reinforcing the portfolio management principle that strategic selection must reconcile opportunity size with internal execution capacity (Knudsen et al., 2023). Moving forward in the market mapping phase, a brainstorming session was held to define the sales strategy, where a multifunctional format was adopted to evaluate the prescription logic, payer definitions, and technical restrictions. As detailed in Table 3, these sessions outlined the prescription models and specific technical restrictions. This collaborative approach follows stakeholder engagement principles, which emphasize interactive communication to align expectations and facilitate decision-making throughout the project lifecycle (PMI, 2021). Findings indicated that patients remained the primary payers in all scenarios, with market entry sequencing prioritizing premium brands in private payment contexts. Furthermore, the viability of the gestational product depended on hardware capable of exporting volumetric files.

  Immersive technologies and 3D printing for healthcare: a new product development project · 2026 · DOI

• </div><div class="htmlview paragraph">In this context, polymer-based composite Additive Manufacturing (AM) offers an underexplored yet highly promising pathway for sustainable production of load-bearing components.

  Design-Driven Sustainability of Automotive Components: A Comparative LCA from Conventional to Metal and Composite Additive Manufacturing · 2026 · DOI

• This study proposes a feature-based adaptive slicing and path planning algorithm for the robotic additive manufac- turing of complex continuous carbon fiber reinforced plastic (CCFRP) components. By automatically identifying explicit and implicit feature boundaries of STL models to build fea- ture surfaces, complex geometries are successfully decom- posed into simpler sub-volumes, enabling multi-directional adaptive slicing along the feature surface normals. This approach effectively addresses the manufacturing con- straints of overhanging and complex structures without the need for extensive support material. Utilizing geodesic dis- tance-based offset algorithms for curved surfaces, the pro- posed method generates smooth and uniform filling paths that ensure the uninterrupted deposition of continuous fibers, which is critical for maintaining structural integrity. The system’s effectiveness was validated through the fabrication of a complex rotor and two types of hollow pipes, as shown in Fig. 25. Experimental results demonstrate high dimen- sional fidelity, with a mean relative error of 1.94%. Further- more, the curved-layer slicing strategy and reinforced fiber paths significantly improved the interlaminar bonding and radial tensile strength compared to conventional three-axis planar slicing. Moreover, the algorithm preserves the flex- ibility of feature surface construction, providing an intrinsic interface and for the integration of localized reinforcement paths. This capability lays the groundwork for fully exploit- ing the load-bearing potential of continuous carbon fibers through performance-driven path planning. Despite the advancements achieved, further research is required to fully exploit the potential of robotic CCFRP manufacturing: (1) Utilizing identified feature edges to construct specific enhancement surfaces. This will allow the integration of specialized reinforcement path generation directly 1 3Progress in Additive Manufacturing into the developed slicing algorithm, achieving a uni- fied process and structural planning. (2) More sophisticated filling algorithms will be devel- oped to accommodate highly complex contours, such as multi-contour surfaces and complex enhancement lay- ers, to ensure optimal fiber orientation and packing den- sity in intricate geometries. Fermat spiral and subregion partitioning are among the candidate methods. (3) Investigation into the enhancement processes and tool- path strategies at the connection joints of partitioned sub-models is essential. Specifically, focus will be placed on critical stress regions, such as the blade root of the rotor model presented in this study, to improve the overall load-bearing capacity of segmented compo- nents. We plan to manually construct surfaces across sub-volumes using the feature boundaries extracted by the proposed algorithm. Continuous fibers will then be printed onto these surfaces to serve as reinforcement structures, addressing performance deficiencies at the connections. (4) To further enhance the reliability and autonomy of the additive manufacturing process, collision avoidance algorithms will be integrated into the system. This will involve real-time interference detection and path opti- mization between the robotic arm, the print head, and the complex geometry of the printed part, ensuring a safe and efficient manufacturing sequence. Acknowledgements This work was supported by Scientific Research Foundation of Hubei University of Education for Talent Introduction (No. ESRC20250011), Technological Talent Service Enterprise Pro- gram of Hubei Province (2023DJC204) and Advanced Manufacturing Digital Twin Innovation Practice Base (No. XGK04057), a project of the 2024 New Engineering Practice Base Construction Initiative of the Hubei Provincial Department of Education. Author contributions YW.T.: Conceptualization, methodology and writing- Original draft; F.Z.:Formal analysis, validation and writing- reviewing & editing; K.Y.:Experiment, data curation investigation and funding. YG.T.:Resources and supervision. Data availability No datasets were generated or analysed during the current study.

  Feature-based adaptive slicing for robotic additive manufacturing of complex continuous carbon fiber reinforced plastic components · 2026 · DOI

• The TTS-inspired model (Eq. 5) provides a compact, physically motivated description of the normalized per- manent strain as a function of both temperature and infill density. The exponential temperature dependence of εnorm is consistent with thermally activated viscoplastic flow in semicrystalline polymers above Tg, where the relaxation time decreases exponentially with temperature in a manner analogous to the Williams–Landel–Ferry (WLF) relation- ship [27]. The horizontal density shift ΔT = 132·(IDref − ID) captures the equivalence between a reduction in infill den- sity and an effective increase in thermal exposure: a lower- density structure reaches the same permanent deformation at a temperature ~ 13 °C lower for each 10% reduction in ID. The model was calibrated on a limited dataset (two den- sity levels, three temperatures) and rests on three simplifying assumptions: linear viscoplastic response, log-linear tempera- ture dependence within the tested range, and linear density shift. The first assumption introduces uncertainty at high permanent strains (> 15%), where progressive structural collapse may introduce non-linearities. The second limits reliable extrapo- lation below 140 °C (where creep is negligible) and above 200 °C (approaching the melting point of annealed PEEK). The third requires validation at additional density levels, par- ticularly at ID = 10%, which was excluded from sustained-load testing due to insufficient load-bearing capacity. Despite these limitations, the model provides a practical design tool for estimating permanent deformation under autoclave-repre- sentative conditions within the calibrated range. 4.5 Implications for autoclave tooling design The results of this study provide quantitative guidance for the design of lightweight PEEK Gyroid lattice structures 1) Post-printing annealing is essential. The increase in crystallinity from 12.5% to 31.7% raises the effective mechanical Tg (as measured by DMA) from ~ 145 °C to ~ 160 °C, extending the usable temperature window of the printed component and significantly improving dimensional stability under sustained load. 2) A relative density of at least 30% is recommended for tooling applications at temperatures of 160 °C or above. Below this threshold, permanent deformation under sus- tained autoclave pressure becomes substantial (Fig. 12), and residual contact pressure — critical for laminate consolida- tion quality — is significantly reduced (Fig. 13). 3) The bilinear constitutive laws (Table 6) and the predic- tive model (Eq. 6) can be directly integrated into structural analysis workflows for tooling design, enabling estimation of compressive stiffness, yield strength, and long-term deforma- tion as explicit functions of temperature and relative density.

  Thermo-mechanical performance of FFF-printed PEEK gyroid lattice structures across the glass transition: implications for lightweight autoclave tooling · 2026 · DOI

• FUTURE WORK Building upon the findings of this study, future research may explore the inclusion of additional process parameters such as nozzle temperature, layer thickness, and raster orientation to further refine predictive accuracy.

  Experimental and Predictive Study of Hardness in FDM-Printed PLA Using Artificial Neural Networks with Analysis of Infill Density and Printing Speed Effects · 2026 · DOI

• Building upon the findings of this study, future research may explore the inclusion of additional process parameters such as nozzle temperature, layer thickness, and raster orientation to further refine predictive accuracy. Expanding the dataset to cover a wider range of operating conditions would also enhance model generalization. Moreover, the application of advanced machine learning techniques, including deep neural networks and hybrid optimization methods, could improve the robustness of predictions. Extending this framework to other thermoplastic materials would further validate its applicability and support broader adoption in industrial environments. ACKNOWLEDGEMENTS The authors would like to express their sincere appreciation to the Faculty of Engineering at Misurata University and the Libyan Polymer Research Center (LPRC), Libya, for providing access to their laboratory facilities and technical support, which were essential for the successful completion of this work. DECLARATION OF CONFLICTING INTERESTS The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. FUNDING These authors received no financial support for the research, authorship, and/or publication of this article. DECLARATION OF GENERATIVE AI AND AI-ASSISTED TECHNOLOGIES IN THE WRITING PROCESS During the preparation of this work, the authors used ChatGPT to improve language and grammatical correctness. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the final content of the publication.

  Experimental and Predictive Study of Hardness in FDM-Printed PLA Using Artificial Neural Networks with Analysis of Infill Density and Printing Speed Effects · 2026 · DOI

• Negative-transfer mitigation, controlled factor studies, physics- driven learning, larger datasets. Faster correction, broader transfer learning, phys- ics–ML integra- tion, and more transparent robust models.

  Artificial intelligence-driven prediction and optimization in fused deposition modeling: a systematic review of process–structure–property relationships · 2026 · DOI

• should add more qualities/ parameters. Cube-only bench- mark; sustain- ability trade-off studied, but geom- etry generalization remains limited. Commercial mate- rial needs scaling validation. Ensemble surrogate outperformed the stand- alone KRG, SVR, and RBF models, reducing the MRE to 3.05%, 7.45%, and 6.53% for EO, IO, and density, respectively. NSGA- II-TOPSIS identified best settings near 195 °C, 50% infill, and 55–66 mm/min, yielding balanced high density with low ovality. BiLSTM + BO achieved R² 0.9965 for hard- ness and 0.9983 for roughness; validation errors 0.03–0.92% and 0.19–1.25%, outper- forming ANN and standalone BiLSTM. XGBoost + PSO slightly outperformed RF. Best compressive-strength setting: triangle, 81.57% infill, 0.162 mm, 0.577 mm cell size. Best SEA: grid, 9.65% infill, 0.213 mm, 0.610 mm. GA achieved the best yield-strength predic- tion with RMSE = 1.9526 MPa and R² = 0.9713. BO exhibited the highest modulus (R ² = 0.9776) with RMSE = 130.13 MPa. For toughness, GA achieved an RMSE = 102.86 MPa and R² = 0.7953. Overall, GA was the strongest across most properties. Only 18 samples; PLA cylindrical case limits broader generalization. Single TPU ortho- sis case; three parameters. PLA + and PSO settings limit transferability.

  Artificial intelligence-driven prediction and optimization in fused deposition modeling: a systematic review of process–structure–property relationships · 2026 · DOI

• The environmental impact and energy demand of polymer additive manufacturing have been assessed through life cycle assessment, but quantitative comparisons between the energy cost of virgin material production versus the energy required for closed-loop recycling of additive manufacturing polymers (PLA, PA12, thermoplastic polyurethane) at scale have not been established.

  Challenges and Frontiers in the Circularity of Polymer Additive Manufacturing: A Systematic Review of Recycling Pathways and Material Degradation · 2026 · DOI

• Binder jet 3D printing processes and their material properties have been reviewed, but specific recycling pathways for binder jet-printed parts and quantitative assessment of binder residue effects on mechanical properties during reprocessing cycles remain uncharacterized.

  Challenges and Frontiers in the Circularity of Polymer Additive Manufacturing: A Systematic Review of Recycling Pathways and Material Degradation · 2026 · DOI

• While near-infrared spectroscopy has been proposed for plastic solid waste identification in combination with support vector machine classification, its application to contaminated or mixed polymer streams from additive manufacturing waste has not been validated, limiting the practical implementation of automated sorting systems for 3D printed material recycling.

  Challenges and Frontiers in the Circularity of Polymer Additive Manufacturing: A Systematic Review of Recycling Pathways and Material Degradation · 2026 · DOI

• Multi-material additive manufacturing systems produce composite parts, but degradation pathways specific to interfacial bonding between dissimilar polymers during reprocessing have not been experimentally characterized, particularly regarding inter-molecular diffusion behavior during thermomechanical recycling of multi-material fused deposition modeling parts.

  Challenges and Frontiers in the Circularity of Polymer Additive Manufacturing: A Systematic Review of Recycling Pathways and Material Degradation · 2026 · DOI

• Vat photopolymerization resins have been reviewed for their processing parameters and curing behaviors, but specific quantitative data on the recyclability of photopolymer waste streams and the chemical composition changes after reprocessing cycles are lacking, preventing the establishment of closed-loop recycling protocols for this additive manufacturing process.

  Challenges and Frontiers in the Circularity of Polymer Additive Manufacturing: A Systematic Review of Recycling Pathways and Material Degradation · 2026 · DOI

• The degradation behavior of PA12 powder in selective laser sintering during reuse cycles has been characterized for material characteristics and dimensional accuracy, but the relationship between specific powder recycling cycles (e.g., 1st, 2nd, 3rd reuse) and the onset of irreversible degradation thresholds in mechanical strength remains incompletely mapped.

  Challenges and Frontiers in the Circularity of Polymer Additive Manufacturing: A Systematic Review of Recycling Pathways and Material Degradation · 2026 · DOI

• The mechanical degradation of recycled polylactic acid (PLA) and polyhydroxybutyrate (PHB) filaments after multiple thermomechanical processing cycles has not been systematically quantified across different reprocessing iterations, limiting understanding of the maximum number of reprocessing cycles these materials can tolerate before performance becomes unsuitable for additive manufacturing.

  Challenges and Frontiers in the Circularity of Polymer Additive Manufacturing: A Systematic Review of Recycling Pathways and Material Degradation · 2026 · DOI

• laboratories and machines. To counteract these limitations, researchers are exploring foundation models and self-supervised pretraining to learn invariant representations from large amounts of unlabeled text [311, 312], as well as physics-informed machine learning, which involves integrating thermal or melt-pool physics into the model to regularize out-of-training regime predictions [313, 314]. Together, these investigations show that, while ML has been highly promising in AM, true generalizability across environments, materials, and technologies remains an open research gap, which is further detailed in Table 5. 7.3 Data fusion from simulations, experiments, sensors, and applying them in making ML models in AM The previous section of this review provides a brief overview of the use of machine learning in AM. AM is moving toward a data-rich, digitally driven domain, and ML offers transformative potential by enabling process optimization, defect detection, property prediction, and material characterization. However, the effectiveness of ML in AM depends heavily on the availability and quality of input data. It has also been noted that data generation can be accomplished through experiments, simulations, and in situ sensors. To fully harness the predictive power of ML, there is an increasing need to combine diverse sources of information. This is when data fusion plays a critical role. It is increasingly recognized as an essential tool for developing robust and generalizable ML models for AM. Although data fusion in AM shows promise, it faces various technical and practical hurdles that impede its smooth integration into ML workflow. These challenges include data heterogeneity, synchronization, uncertainty, and scalability, all of which must be addressed to fully realize the benefits of integrated data-driven solutions. Heterogeneity and multimodality in data are the key challenges in ML data fusion for AM. The data for ML models in the AM process can originate from various sources, including simulations, sensors, and experiments. Each data type varied in structure, scale, and other characteristics. This complicates integrating these data sources into machine learning, as ML models typically require data to be clean, consistent, and formatted in a similar way. The literature indicates that researchers have employed a variety of approaches to predict the mechanical properties of lattice structures. However, because there is no standardized framework for aligning these methods, the resulting data remain fragmented and difficult to compare.

  Artificial intelligence in additive Manufacturing: advances in smart materials, lattice optimization, and process intelligence · 2026 · DOI

• Integration of DT and AI for real-time control Digital Twin (DT) technology, combined with AI and ML, is a key component of the next-generation AM. Over the next several years, experts have predicted that AM systems may change from static, pre-programmed operations to intelligent, self-adaptive platforms. On these platforms, every printing process is represented by a high-fidelity virtual model that is updated in real time. Future DTs will not only keep track of geometry but will also use data from multimodal sensors to dynamically record and anticipate heat history, microstructural evolution, residual stresses, and mechanical performance. Figure 29 shows that the Twin Learning (TL) model illustrates how DT and AI work together in a feedback loop. AI is an engine that makes decisions within a DT framework. Advanced predictive algorithms will detect problems before they occur, and reinforcement learning agents will automatically adjust the laser power, material flow, or scan patterns to keep objects in the best possible state. Physics-informed neural networks combine sensor data with basic rules of material science, making it possible to make accurate predictions even when there is not much experimental evidence. This closed-loop feature Fig. 29 The construction processes of the Twin Learning (TL) model The International Journal of Advanced Manufacturing Technology1 3 will let printers fix themselves in milliseconds, making sure that complicated, safety–critical parts in the aerospace, biomedical, and energy industries are always of high quality.These real-time, AI-enabled feedback loops form the foundation for next-generation digital-twin ecosystems that extend beyond individual machines toward distributed, virtual-first manufacturing systems. In the coming decades, hybrid modeling techniques that combine physics-based simulations with AI-driven learning are likely to significantly reduce the time required to develop these processes. This will make "virtual-first" manufacturing the norm. Edge computing moves real-time control closer to the machine, making it easier to make quick decisions for high-speed AM operations. Figure 30 illustrates the entire digital twin framework for additive manufacturing, which further elucidates this concept. Federated learning may link manufacturing networks worldwide, enabling different facilities to collaborate in creating AI models without sharing sensitive data. This would ensure that the quality is the same all over the world. Finally, the combination of DT and AI is expected to turn AM into a network of self-optimizing manufacturing cells that can change into new designs, materials, and environmental conditions without any help from people [349, 350].

  Artificial intelligence in additive Manufacturing: advances in smart materials, lattice optimization, and process intelligence · 2026 · DOI

• Advanced composite and metallic AM materials specifically optimized for wind turbine blade applications remain underdeveloped. Research is needed on material property characterization, process parameter optimization, and long-term performance validation for AM composites and metallic systems at blade-scale manufacturing.

  A review of additive manufacturing techniques for wind turbine blade production: capabilities, AI integration, and Scale-Up Potential · 2026 · DOI

• Hybrid manufacturing techniques combining subtractive processes with additive manufacturing for wind turbine blade production have not been comprehensively investigated. Development of integrated AM-subtractive hybrid workflows and their scalability to utility-scale blade manufacturing requires systematic research.

  A review of additive manufacturing techniques for wind turbine blade production: capabilities, AI integration, and Scale-Up Potential · 2026 · DOI

• Long-term field validation data for reinforcement learning-based adaptive control in AM manufacturing systems is limited to selected case studies. Industrial-scale testing of reinforcement learning approaches for reducing downtime and response times in wind turbine blade production environments has not been systematically conducted.

  A review of additive manufacturing techniques for wind turbine blade production: capabilities, AI integration, and Scale-Up Potential · 2026 · DOI

• Multimodal sensing systems for AM process monitoring show approximately 22% relative improvement over single-sensor approaches in specific experimental setups, but unified validation protocols comparing multimodal versus single-sensor configurations across different AM techniques and defect types remain undeveloped.

  A review of additive manufacturing techniques for wind turbine blade production: capabilities, AI integration, and Scale-Up Potential · 2026 · DOI

• Large-scale industrial datasets for AM quality assurance in wind turbine blade production do not exist. Most reported performance metrics derive from laboratory-scale experiments and synthetic datasets rather than real utility-scale manufacturing environments, preventing validation of AI models at production scale.

  A review of additive manufacturing techniques for wind turbine blade production: capabilities, AI integration, and Scale-Up Potential · 2026 · DOI

Most-cited papers in Additive Manufacturing and 3D Printing Technologies

• Predicting the future of additive manufacturing: A Delphi study on economic and societal implications of 3D printing for 2030 · Technological Forecasting and Social Change · 2017 · 422 citations
• Projection micro stereolithography based 3D printing and its applications · International Journal of Extreme Manufacturing · 2020 · 391 citations
• 3D printing parameters, supporting structures, slicing, and post-processing procedures of vat-polymerization additive manufacturing technologies: A narrative review · Journal of Dentistry · 2021 · 343 citations
• A critical review of 3D printing in construction: benefits, challenges, and risks · Archives of Civil and Mechanical Engineering · 2020 · 294 citations
• Additive manufacturing in polymer research: Advances, synthesis, and applications · Polymer Testing · 2024 · 178 citations
• Unlocking the future of precision manufacturing: A comprehensive exploration of 3D printing with fiber-reinforced composites in aerospace, automotive, medical, and consumer industries · Heliyon · 2024 · 172 citations
• 3D printable elastomers with exceptional strength and toughness · Nature · 2024 · 158 citations
• Experimental and numerical investigation of PLA based different lattice topologies and unit cell configurations for additive manufacturing · The International Journal of Advanced Manufacturing Technology · 2024 · 153 citations
• Additive manufacturing of highly entangled polymer networks · Science · 2024 · 148 citations
• 3D printing of ceramics: Advantages, challenges, applications, and perspectives · Journal of the American Ceramic Society · 2024 · 128 citations

Most recent work

• Smart additive manufacturing: An IoT-driven framework for predictive failure detection and sustainable operation · Additive Manufacturing Frontiers · 2026
• Modelling of tensile strength of 3D printed PETg, ABS and ASA based novel filament · Journal of Engineering and Applied Science · 2026
• Physics-Aligned Data Augmentation for Reliable Property Prediction in Direct Ink Writing Under Extreme Data Scarcity · Journal of Manufacturing and Materials Processing · 2026
• Intelligent composite 3D printing: the role of artificial intelligence, machine learning, and in-situ monitoring in next-generation additive manufacturing · Frontiers in Mechanical Engineering · 2026
• A review of additive manufacturing techniques for wind turbine blade production: capabilities, AI integration, and Scale-Up Potential · Frontiers in Mechanical Engineering · 2026
• Artificial intelligence in additive Manufacturing: advances in smart materials, lattice optimization, and process intelligence · The International Journal of Advanced Manufacturing Technology · 2026
• Machine learning-driven digital twin for real-time temperature prediction and adaptive process control in large-scale additive manufacturing · Journal of Manufacturing Systems · 2026
• Effect of build position on the accuracy and flexural properties of resin model materials for three-dimensional printing technologies · The Journal of Indian Prosthodontic Society · 2026
• Numerical analysis and experimental evaluation of temperature profile in a miniaturized screw-assisted 3D print head · The International Journal of Advanced Manufacturing Technology · 2026
• Effect of Natural Weathering Conditioning on the Mechanical and Surface Performance of Additively Manufactured Acrylonitrile Styrene Acrylate (ASA) Components · Arabian Journal for Science and Engineering · 2026

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