Additive manufacturing最新文献

{"title":"Toward real-time feedback control for powder bed fusion additive manufacturing","authors":"H. Yeung","doi":"10.1016/j.addma.2025.105041","DOIUrl":"10.1016/j.addma.2025.105041","url":null,"abstract":"<div><div>Laser-Based Powder Bed Fusion (LB-PBF) is a widely used additive manufacturing technique known for its ability to produce intricate geometries, optimized structures, and lightweight designs. However, its broader industrial adoption is hindered by persistent quality issues arising from inadequate process control. Conventional LB-PBF systems rely on predefined laser power settings for each scan vector, which do not account for dynamic thermal variations caused by scan sequence, part geometry, and localized heat accumulation. This limitation often leads to defects in resulting parts such as lack-of-fusion in underheated regions or keyhole porosity in overheated areas. This study explores the feasibility of real-time, in-line laser power control using in-situ monitoring of the melt pool area as feedback. The proposed method continuously adjusts laser power within approximately 118 μs of measurement acquisition, ensuring corrections are made fast enough to potentially alleviate disturbances or instabilities in the melt pool. Experiments conducted on nickel superalloy 625 plates under various laser process conditions validate the effectiveness of this technique. The results demonstrate that real-time feedback control significantly improves process stability, laying the foundation for future advancements in adaptive additive manufacturing and defect mitigation strategies.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"114 ","pages":"Article 105041"},"PeriodicalIF":11.1,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-material top-down vat photopolymerization 3D printing based on liquid surface supported printing","authors":"Jinsi Yuan ,&nbsp;Bowen Hu ,&nbsp;Peng Cai ,&nbsp;Jinxing Sun ,&nbsp;Xiaoteng Chen ,&nbsp;Haijiang Wang ,&nbsp;Xiewen Wen ,&nbsp;Jiaming Bai","doi":"10.1016/j.addma.2025.105008","DOIUrl":"10.1016/j.addma.2025.105008","url":null,"abstract":"<div><div>Multi-material 3D printing holds transformative potential for fabricating complex functional components, yet current vat photopolymerization (VPP) techniques remain limited in material compatibility. Resin tank switching-based VPP methods are restricted to low-viscosity resins, while hybrid strategies integrating Direct Ink Writing with VPP enable the printing of high-viscosity pastes. Nevertheless, these strategies remain insufficient to accommodate the broad spectrum of material viscosities required for diverse multi-material printing applications. Here, we introduce a novel top-down multi-material VPP technique based on the liquid surface supported printing (LSSP) method, which exhibits broad slurry compatibility. The LSSP system accommodates a wide range of materials, including low-viscosity hydrogels, high-viscosity resins, and ceramic slurries. Moreover, it enables continuous gradient material printing—a capability unattainable with conventional VPP. By overcoming limitations in material adaptability and gradient structure fabrication, the LSSP system opens new avenues for manufacturing high-performance, multi-material, and functionally gradient structures.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"114 ","pages":"Article 105008"},"PeriodicalIF":11.1,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145570886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian optimization for laser powder bed fusion of defect-free AA2024","authors":"Dmitry Chernyavsky ,&nbsp;Denys Y. Kononenko ,&nbsp;Julia Kristin Hufenbach ,&nbsp;Jeroen van den Brink ,&nbsp;Konrad Kosiba","doi":"10.1016/j.addma.2025.105022","DOIUrl":"10.1016/j.addma.2025.105022","url":null,"abstract":"<div><div>Identifying optimal processing parameters remains a major challenge in additive manufacturing (AM), limiting its potential and broader industrial adoption. In this work, we present a Bayesian machine learning (ML) framework designed to efficiently determine optimal parameters for AM processes. We demonstrate its effectiveness through the successful processing of the AA2024 alloy into high-density components, known for its difficulty in processing, using laser powder bed fusion (PBF-LB/M). Our approach begins with Bayesian Optimization (BO) applied to an initial dataset containing only five processing parameter sets. Despite the limited data, the method accurately predicts conditions for producing crack-free components with a remarkably high density resulting in tensile properties similar to cast counterparts. We further extend the framework to perform bi-objective optimization, targeting both maximum build-up rate (BUR) and density. Experimental validation confirms that the framework can identify new parameter sets that significantly enhance BUR while maintaining high part quality. This work underscores the potential of BO strategies for accelerating optimal processing conditions discovery, especially for challenging materials and multi-objective scenarios.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"114 ","pages":"Article 105022"},"PeriodicalIF":11.1,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145570884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microstructural factors in melt-pool structure for mechanical behavior of Al-Fe alloy manufactured by laser-beam powder bed fusion: Single-crystal micropillar compression test approach","authors":"Dasom Kim ,&nbsp;Akihiro Choshi ,&nbsp;Yuhki Tsukada ,&nbsp;Naoki Takata","doi":"10.1016/j.addma.2025.105035","DOIUrl":"10.1016/j.addma.2025.105035","url":null,"abstract":"<div><div>This study was undertaken to address dominant microstructural factors in the melt-pool structure formed in powder bed fusion using laser beam (PBF-LB) for high strength and the mechanical behavior of Al-2.5 %Fe binary alloy samples additive-manufactured via PBF-LB. Single-crystal micropillars with a mean diameter of approximately 2 μm were fabricated at different regions of the melt-pool structure (melt-pool inside: MPI, melt-pool boundary: MPB), and compression tests were performed at various initial strain rates controlled by loading rate. The PBF-LB process produced numerous Al₆Fe metastable phases (with a few tens of nanometers in size) distributed in the α-Al supersaturated solid solutions containing highly concentrated Fe in a large part of the melt-pool inside (MPI). A relatively coarsened microstructure with a thickness of approximately 3 µm (composed of many granular α-Al phase with a few hundred nanometer size surrounded by Al₆Fe-phase particles) was localized at the melt-pool boundary (MPB) region. The MPI micropillars exhibited a higher 0.2 % proof stress than the MPB micropillars, whereas the following strain hardening appeared similar due to the activation of multiple slip systems. The MPI micropillars exhibit almost the same mechanical behavior after the 300 ℃ annealing for the formation of nanoscale precipitates consuming solute Fe, indicating that the refined Al₆Fe phase is a dominant contributor to the strengthening by the PBF-LB process. Intriguingly, MPI micropillars exhibited a negative strain rate sensitivity of flow stress at an early stage of deformation, whereas the negative strain rate sensitivity transitioned to positive after annealing at 300 ℃. TEM characterization revealed the dynamic precipitation of nanoscale Fe-rich precipitates inside the α-Al(fcc) matrix with high solute Fe contents even at ambient temperature. The nanoscale precipitates interact with introduced dislocations, resulting in the enhanced flow stress at an early stage of plastic deformation. The present study provided new insights into a local variation in strain-rate dependent strength depending on the location of the melt pool structure in Al alloys processed via PBF-LB, in terms of solute alloy-element contents (driving force for the dynamic precipitation) controlled by laser conditions of the PBF-LB process.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"114 ","pages":"Article 105035"},"PeriodicalIF":11.1,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145616193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Laser direct writing of single-crystal silicon nanostructures from liquid cyclohexasilane","authors":"Xingjie Yang,&nbsp;Ping Chen,&nbsp;Zhikun Liu","doi":"10.1016/j.addma.2025.105029","DOIUrl":"10.1016/j.addma.2025.105029","url":null,"abstract":"<div><div>Laser direct writing (LDW) of fully single-crystalline silicon nanostructures has been successfully demonstrated for the first time, using liquid-phase cyclohexasilane (CHS) as the precursor. The fully epitaxial, single-crystal nature of these nanostructures - including vertical pillars and horizontal wires - is confirmed by transmission electron microscopy (TEM), which reveals seamless lattice continuity with the substrate and identical diffraction patterns across various substrate orientations. This maskless, additive process yields subwavelength features - pillar and wire widths smaller than the laser wavelength - and supports vertical growth of pillar at the rate of 2900 nm/s. CHS proves to be a highly effective silicon precursor for LDW due to its low thermal decomposition temperature (∼200–300 °C) and higher silicon content (Si:H = 1:2 vs 1:4 for SiH₄), which together enable rapid deposition at relatively low temperatures. Crucially, the use of tightly focused, pulsed laser irradiation provides localized, transient heating that confines the reaction to the liquid-substrate interface. This local energy delivery promotes surface-limited precursor decomposition and epitaxial growth while suppressing bulk nucleation, thereby yielding continuous single-crystal silicon structures. This work establishes a foundational methodology for the direct, on-demand additive manufacturing of high-quality single-crystal silicon nanostructures, opening new pathways for creating complex, high-performance semiconductor devices.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"114 ","pages":"Article 105029"},"PeriodicalIF":11.1,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145536895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-informed hybrid deep learning-driven sintering simulation for next-generation metal additive manufacturing","authors":"Ali Kassab ,&nbsp;Meriam Chelbi ,&nbsp;Sajad Shirzad ,&nbsp;Christopher Pannier ,&nbsp;Pravansu Mohanty ,&nbsp;Georges Ayoub","doi":"10.1016/j.addma.2025.105028","DOIUrl":"10.1016/j.addma.2025.105028","url":null,"abstract":"<div><div>Predicting shrinkage and deformation during sintering in metal additive manufacturing (AM) is challenging due to complex interactions among thermal gradients, material properties, and part geometry. Finite element methods (FEM) provide high-fidelity simulations but are computationally intensive, limiting their use for real-time control and design iteration. To address this, we present a hybrid deep learning framework that couples a physics-encoded recurrent neural network (RNN) with a physics-informed graph neural network (GNN) for efficient and accurate sintering simulation. The RNN component, a Physics-Gated Autoregressive LSTM, models the global, time-dependent density evolution. Its architecture explicitly embeds physical principles, such as thermal activation, diffusion state memory, and density saturation, through custom gating mechanisms, achieving an R² score of 0.9954 on the test set by accurately resolving all three distinct phases of densification. The GNN component takes the predicted density evolution to extend this framework by predicting localized, three-dimensional deformation, incorporating spatial relative positions, directional vectors, and boundary condition information from mesh-based graph representations. To ensure physically consistent predictions, the GNN uses a physics-informed loss function that enforces mass conservation. Trained on a large FEM-generated dataset containing 175 geometries simulated using a hybrid, experimentally calibrated viscoplastic model based on the Olevsky-Skorohod framework, the proposed machine learning framework significantly reduces computation time. While FEM simulations require approximately 52 min per geometry, our hybrid model completes predictions in just 24.64 s, achieving a speed-up of nearly 127 times. By leveraging high-fidelity, physics-informed data, this scalable, data-driven method enables rapid sintering prediction, supporting real-time process optimization, quality control, and the design of metal additive manufacturing components.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"114 ","pages":"Article 105028"},"PeriodicalIF":11.1,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145570836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LLM-3D print: Large Language Models to monitor and control 3D printing","authors":"Yayati Jadhav ,&nbsp;Peter Pak ,&nbsp;Amir Barati Farimani","doi":"10.1016/j.addma.2025.105027","DOIUrl":"10.1016/j.addma.2025.105027","url":null,"abstract":"<div><div>Industry 4.0 has revolutionized manufacturing by driving digitization and shifting the paradigm toward additive manufacturing (AM). Material extrusion (MEX), a core AM method, produces customized and cost-effective products with minimal waste, challenging traditional subtractive manufacturing. Despite its advantages, MEX remains susceptible to defects that can compromise part quality and function, often requiring expert intervention. Existing rule-based and machine learning approaches struggle to generalize across different printers and sensors, while deep learning methods depend on large labeled datasets, limiting their scalability and adaptability. To address these challenges, we introduce a process monitoring and control framework that employs Large Language Models (LLMs) as autonomous controllers for additive manufacturing. Unlike rule-based or heavily data-dependent approaches, our method requires no domain-specific fine-tuning or training. Instead, the LLM leverages in-context learning, self-prompting, and iterative prompt-reason refinement to evaluate print quality from sequential image captures, detect and classify emerging failure modes, and query and modify the printer for relevant operating parameters. Through this adaptive reasoning process, the LLM not only interprets defects but also improves its own decision-making logic, autonomously formulating and executing corrective actions. This demonstrates a rule-free, self-improving approach to process control that extends beyond traditional quality assurance methods. We validated the effectiveness of the proposed framework by comparing it with a control group of engineers with different levels of AM expertise. The evaluation showed that LLM-based agents not only reliably identified common 3D printing errors such as inconsistent extrusion, stringing, warping, and poor layer adhesion, but also determined their causes and corrected them without human intervention. In addition to matching expert-level accuracy, the LLM was able to recognize emerging print errors earlier than human experts, highlighting its value as a proactive controller. To further demonstrate generalizability, we deployed and tested the framework on two different 3D printers with distinct sensor setups, confirming its adaptability across hardware. We also performed compression tests on baseline prints and on prints optimized by the LLM, with the optimized parts showing clear improvements in mechanical performance.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"114 ","pages":"Article 105027"},"PeriodicalIF":11.1,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CT-scan based mechanical finite element analysis of inter-filament voids in fused deposition modelling","authors":"Louis Remes ,&nbsp;Thierry J. Massart ,&nbsp;Caroline Fréderix ,&nbsp;Philippe Hendrickx ,&nbsp;Péter Berke","doi":"10.1016/j.addma.2025.105032","DOIUrl":"10.1016/j.addma.2025.105032","url":null,"abstract":"<div><div>Inter-filament voids are critical defects in fused deposition modelling, leading to stress concentrations with undesired effects on the strength of the printed part. This work presents a novel methodology for the computational analysis of the mechanical behaviour of 3D printed materials incorporating these voids by exploiting computed tomography scans to automatically generate conformal finite element (FE) meshes with user control on the geometrical details accounted for in the model. This is achieved through an advanced curvature-based local smoothing algorithm, which preserves mechanically relevant morphological characteristics while reducing computational complexity and filtering out irrelevant geometrical features. The proposed approach is applied to 3D printed short carbon fibre reinforced polyether ether ketone (SCFR PEEK) samples subjected to realistic loading conditions. Results show inter-filament void induced stress concentrations and the occurrence of plastic events in the microstructural volume, leading to guidelines for choosing the appropriate level of the geometrical details and features to embark in the FE model.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"114 ","pages":"Article 105032"},"PeriodicalIF":11.1,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Part consolidation and decomposition in redesign for additive manufacturing (RfAM): A taxonomy and review","authors":"Soonjo Kwon ,&nbsp;Yosep Oh ,&nbsp;David W. Rosen ,&nbsp;Samyeon Kim","doi":"10.1016/j.addma.2025.105044","DOIUrl":"10.1016/j.addma.2025.105044","url":null,"abstract":"<div><div>This paper defines <em>Redesign for Additive Manufacturing (RfAM)</em> as a subset of <em>Design for AM (DfAM)</em>, specifically focusing on altering an original design into a different form for production using AM technology. The authors categorize RfAM approaches based on changes in part count after redesign: <em>Part Consolidation (PC)</em> reduces the number of parts, <em>Part Decomposition (PD)</em> increases the number of parts, and <em>Part Modification (PM)</em> maintains the original part count. While PM has been extensively reviewed in existing literature, this study specifically focuses on the relatively less explored areas of PC and PD, analyzing 126 papers (56 for PC, 70 for PD). This research classifies these approaches across four common objectives: cost, quality, manufacturability, and sustainability. The study identifies seven primary objectives for PC (including lightweighting and modularization) and six for PD (including printability improvement and support structure reduction). Furthermore, this paper proposes a novel taxonomy for combinations of the three RfAM approaches (PC, PD, and PM), categorizing them as either sequential or integrated strategies. Finally, future research directions and opportunities are presented from multiple perspectives, including the development of integrated RfAM frameworks, automated shape optimization, adaptive process parameter consideration, and sustainability-oriented methodologies. This comprehensive taxonomy provides valuable guidance for leveraging AM’s capabilities through strategic redesign approaches.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"114 ","pages":"Article 105044"},"PeriodicalIF":11.1,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145733050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rapid rheology control and stiffening of 3D-printed cement mortar via CO2 flash mixing in a 2K printing system","authors":"Junli Liu ,&nbsp;Shipeng Zhang ,&nbsp;Lucen Hao ,&nbsp;Bo Wu ,&nbsp;Kaiyin Zhao ,&nbsp;Chi Sun Poon","doi":"10.1016/j.addma.2025.105003","DOIUrl":"10.1016/j.addma.2025.105003","url":null,"abstract":"<div><div>Carbon dioxide (CO₂) has been increasingly applied to modify the fresh and rheological properties of cement mortars and concrete, enhancing the mortars’ mechanical properties through CO₂ capture. For 3D printing, most research has adopted the 1 K (one-component) printing system for CO₂-integrated cement mortar prepared by batch mixing. In contrast, limited work has been conducted on mortars subjected to CO₂ flash mixing in the 2 K (two-component) system, where pumped fresh mortar was mixed with continuously injected CO₂ within a timeframe of seconds during secondary mixing. In this paper, we report the development of a novel low-carbon cement mortar mixture <img> consisting of ordinary Portland cement (OPC), ground granulated blast furnace slag (GGBS) and calcium hydroxide (CH) in binders <img> that exhibited instant change in rheological properties and rapid stiffening when subjected to CO₂ flash mixing. The rheological properties of the CO₂-mixed mortar improved with increasing proportions of GGBS and CH in the mortar mix. In-situ chord length measurements suggested that the improved rheological properties of the mortar after CO₂ flash mixing were related to the rapid growth of fine CaCO₃ crystals, driving subsequent particle flocculation. The instant flocculation was primarily attributed to electrostatic attraction between particles with opposite surface charges in the OPC-GGBS-CH system induced by CO₂ flash mixing.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"113 ","pages":"Article 105003"},"PeriodicalIF":11.1,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145428822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}