The origin of residual stress in laser powder bed fusion lies in the physics of the process itself. A laser beam sweeps across a thin bed of metal powder, melting a tiny pool of material that solidifies within microseconds. Each layer is reheated and partially remelted by the layers deposited above it, creating steep thermal gradients and repeated cycles of expansion and contraction. When the transient thermal stress exceeds the temperature-dependent yield strength of the alloy, the material deforms plastically, and that irreversible strain is locked in as the part cools. The result is a stress field that can approach, or even exceed, the room-temperature yield strength of the printed alloy.

The consequences are far from academic. During a build, accumulated stress can crack delicate overhangs, warp the powder bed into the path of the recoater blade, and abort an entire print. After printing, high tensile stresses drive distortion and warpage that destroy dimensional tolerances, and they combine with surface defects to accelerate fatigue crack growth. In susceptible environments, they can even trigger stress corrosion cracking. The review emphasizes that these stresses exist at multiple scales: macroscopic Type I stresses govern global distortion, grain-scale Type II stresses arise from anisotropy and phase mismatch, and sub-grain Type III stresses are tied to the dense dislocation structures created by rapid solidification.

Because directly measuring stress evolution inside a working printer is essentially impossible, the authors place multiscale simulation at the heart of their proposed workflow. At the microscale, phase-field models and crystal plasticity finite element methods capture how grain structures nucleate, grow, and carry stress during solidification, explaining the formation of Type II and Type III stresses. Recent advances couple these frameworks with computational fluid dynamics melt-pool simulations and even machine learning surrogates that dramatically cut computational cost. At the mesoscale, thermal-fluid-mechanical models resolve individual scan tracks, linking melt-pool dynamics to residual stress development through mapped temperature fields and temperature-dependent material properties.

At the part scale, where full thermo-mechanical simulation can take weeks or months, the review highlights the inherent strain method as the pragmatic workhorse. This approach extracts the permanent plastic strain generated during printing from small calibration specimens or high-fidelity simulations, then applies it to a large finite element model in a single fast elastic analysis. Modified versions of the method account for residual elastic strain and scanning strategy effects, and commercial platforms such as ABAQUS, ANSYS, and Simufact Additive now embed these workflows. The authors stress that simulation is most valuable when used as a decision-support tool, calibrated against experiments and continuously refined, rather than as a post hoc explanation.

With predictive models in hand, the workflow turns to manufacturing optimization. Process parameters such as laser power, scan speed, and preheating temperature directly shape melt-pool geometry and thermal gradients, while scan strategy choices, including inter-layer rotation angles, island segmentation, and scan sequencing, redistribute shrinkage strains across the part. Notably, the review reports that the optimal rotation angle is material-dependent: 67-degree rotation outperformed 90-degree alternation in Inconel 718, while simple 90-degree strategies sufficed for other alloys. Artificial intelligence is increasingly entering this space, with frameworks like SmartScan using physics-informed optimization to sequence scan islands, and deep reinforcement learning agents that dynamically adjust laser power and velocity to stabilize melt-pool depth, cutting distortion by nearly half in some demonstrations.

Structural design offers another lever. Topology optimization frameworks now incorporate thermal stress constraints, build orientation selection, and support structure design, treating sacrificial anchors and heat dissipation pathways as design variables rather than afterthoughts. Feature-based surrogate models trained on geometric primitives can predict part-scale residual stress fields fast enough to embed in iterative design loops. The review argues that the future lies in co-optimizing topology, supports, and scan paths simultaneously, so that stress-aware design becomes an integral part of engineering workflow rather than a separate corrective step.

Material-level control closes the gap between idealized simulations and messy reality. Powder reuse changes particle size distributions, surface chemistry, and optical absorptivity, all of which inject run-to-run variability into the thermal history and therefore into the stress state. The authors recommend stricter reuse governance, including sieving, controlled refresh ratios, and traceability systems. Process atmosphere matters too: oxygen pickup in titanium alloys, spatter oxidation in nickel superalloys, and nitrogen uptake in stainless steels all couple atmospheric conditions to microstructure and stress. More exotic strategies exploit the material itself, such as low-transformation-temperature alloys whose martensitic transformations generate compressive strains that offset tensile stresses, and nanoparticle inoculants like LaB6 that refine grains in crack-prone aluminum alloys, broadening the printable process window.

Finally, post-treatment delivers the finishing blow to residual stress. Stress-relief heat treatment remains the baseline, but the review details how schedules must be tailored to each alloy’s metastable as-built microstructure: aging below 200 degrees Celsius preserves the strengthening silicon network in AlSi10Mg, while Ti-6Al-4V requires careful balancing of martensite decomposition against embrittlement, and heavily gamma-prime-strengthened nickel superalloys may need rapid heating above their precipitate dissolution temperatures to avoid treatment-induced cracking. Alternatives such as deep cryogenic treatment, which relieved over 70 percent of stress in AlSi10Mg without any strength loss, and thermal-vibration hybrid methods offer lower-temperature options. Surface techniques like shot peening and laser shock peening then implant deep compressive stress layers that multiply fatigue life, with hybrid peening combinations boosting compressive stress by more than two-thirds compared with laser peening alone.

The unifying message of the review is that no single knob controls residual stress. Instead, the authors propose a six-stage closed-loop workflow: define application-driven acceptance targets, run decision-oriented multiscale simulation, optimize the printing process, stabilize materials and atmosphere, apply tailored post-treatment, and validate the finished part against measurements that feed back into recalibrated models. By treating residual stress as a system-level challenge rather than an isolated defect, the framework aims to carry laser powder bed fusion from laboratory-scale optimization toward reliable, repeatable, and qualifiable industrial production, a transition that could finally unlock the technology’s full promise for safety-critical components.

Beyond the strategies themselves, the review draws attention to the practical challenge of verifying that residual stress has actually been reduced. Experimental characterization techniques such as hole-drilling, neutron diffraction, and X-ray diffraction each occupy a distinct niche. Hole-drilling is relatively inexpensive and can be performed in workshops, but it is destructive and provides only local information. X-ray diffraction offers surface-sensitive measurements that are well suited to assessing the compressive layers introduced by peening treatments, while neutron diffraction penetrates deep into thick sections, making it the method of choice for mapping internal stress fields in finished components. The cost and limited availability of these techniques explain why purely experimental, trial-and-error optimization of printing parameters remains impractical, and why the authors argue so strongly for simulation-guided workflows in which measurements are used sparingly, for calibration and validation rather than exhaustive mapping.

The fragmented nature of much of the existing literature emerges as a recurring theme. Studies that optimize scan strategies in isolation, for example, may reduce distortion while simultaneously degrading density or surface quality, creating trade-offs that only become apparent when the whole manufacturing chain is considered. Similarly, a heat treatment schedule developed for one powder lot may perform differently once powder reuse alters the starting microstructure. By organizing mitigation into a system-level workflow, the review makes these hidden interactions explicit and provides a structure in which each decision can be evaluated against application-driven acceptance targets rather than a single metric such as maximum stress magnitude.

The industrial significance of this framing is considerable. As laser powder bed fusion moves from prototyping into end-use production for aerospace, medical, and energy applications, qualification bodies increasingly demand demonstrated control of the internal stress state, not merely of geometry and density. A closed-loop workflow in which experimental measurements continuously feed back into recalibrated models offers a pathway to the repeatability that certification requires. It also supports the economic case for the technology: scrapped builds, post-print straightening, and unexpected failures during machining all carry substantial cost, and each of these traces back to unmanaged residual stress.

Looking forward, the review points toward several converging trends. Machine learning surrogates and reinforcement learning agents are making in-process and design-stage stress prediction fast enough for routine use, while in situ monitoring promises the data streams needed to close the loop during the build itself rather than after it. At the same time, material-level innovations such as transformation engineering and grain-refining inoculants are expanding the range of alloys that can be printed reliably. The authors acknowledge that open questions remain, including the transferability of calibrated models between machines and powder batches, but the overall trajectory is clear: residual stress is shifting from an unavoidable consequence of the process to a quantifiable, controllable, and designable feature of additive manufacturing.

Subject of Research: Residual stress mitigation and control in laser powder bed fusion metal additive manufacturing

Article Title: A workflow for residual stress control in laser powder bed fusion manufacturing

Article References: Zhou, S., Guo, Q., Li, M., Wang, Q., Xu, X., Chang, S., Yan, W., Li, L., & Ding, J. (2026). A workflow for residual stress control in laser powder bed fusion manufacturing. Advanced Materials Joining, 1(1), Article 10. https://doi.org/10.1007/s44500-026-00007-y

Image Credits: AI Generated

DOI: 10.1007/s44500-026-00007-y

Keywords: laser powder bed fusion, residual stress, metal additive manufacturing, multiscale simulation, scan strategy optimization, stress-relief heat treatment, inherent strain method, powder reuse, laser shock peening, topology optimization, phase transformation engineering, closed-loop workflow

Metal additive manufacturing involves highly nonlinear, multi-physics interactions that govern melt pool dynamics, solidification behavior, and microstructural evolution. Traditional approaches to quality control often rely on simplified assumptions or empirical process parameter tuning, which are insufficient to capture the full spectrum of physical mechanisms influencing the final product. In this context, machine learning excels by analyzing vast multimodal datasets collected from sensors and process monitoring systems, revealing hidden patterns and correlations that are difficult to model explicitly. By integrating ML algorithms, researchers can better characterize temperature fields, fluid dynamics within the melt pool, and stress/strain distributions throughout the build.

One pivotal domain where machine learning has shown remarkable potential is in real-time defect identification and suppression. Defects such as porosity, cracks, and keyholing significantly degrade mechanical performance and are traditionally hard to detect until post-process inspection. ML models trained on high-fidelity sensor data—such as infrared imaging, acoustic emission, and optical tomography—enable near-instantaneous detection of anomalies. This predictive capability can be harnessed to dynamically adjust process parameters such as laser power, scanning speed, or hatch spacing, thereby reducing defect formation mid-build through closed-loop control strategies.

Another major advantage of ML-powered quality control lies in its ability to optimize geometric fidelity. Additive manufacturing inherently suffers from distortions due to thermal gradients and residual stress accumulation, causing deviations from intended designs. Machine learning models facilitate precise prediction of deformation trends by correlating process inputs with build outcomes. Consequently, compensation strategies can be implemented to pre-emptively adjust machining paths or customize support structures, ensuring higher dimensional accuracy and consistency across batches.

Material property tailoring represents an additional frontier where ML algorithms are proving invaluable. Metal AM parts often exhibit anisotropic mechanical properties and microstructural heterogeneity resulting from complex thermal histories. Conventional modeling techniques struggle to predict these outcomes accurately. Through supervised and unsupervised learning on microstructural imaging and mechanical testing datasets, machine learning approaches enable finer control over attributes like hardness, tensile strength, and fatigue resistance. This capacity opens doors to bespoke manufacturing of components with site-specific properties tailored to functional requirements.

The future directions of machine learning in AM are grounded in their ability to fuse multi-physics simulation data, multi-modal sensor inputs, and real-world experimental observations into cohesive digital frameworks. Notably, the integration of digital twins—virtual replicas of physical processes—augments ML models’ predictive power, allowing practitioners to simulate process adjustments in silico before physical implementation. When combined with edge computing architectures, such real-time data processing enables rapid feedback loops and autonomous decision-making directly on the manufacturing floor, minimizing latency issues and maximizing production throughput.

However, significant challenges remain in fully harnessing ML for quality control in metal additive manufacturing. The complex, closed-loop nature of the process demands cross-scale coordination between microstructural phenomena and macro-scale mechanical behaviors that current models only partially address. Moreover, the collection of high-quality, labeled datasets suitable for training robust ML models remains a bottleneck due to experimental costs and variability in experimental setups. Addressing these gaps requires collaborative multidisciplinary efforts involving materials scientists, mechanical engineers, data scientists, and control systems experts.

Fundamental to advancing this technology is the further exploration of multi-physics coupling—how temperature, fluid flow, and mechanical stresses interact in a temporally evolving manner during the build. Machine learning, particularly physics-informed neural networks, promises to bring new insights into these tightly coupled phenomena by embedding physical laws directly into learning architectures. Such hybrid models can outperform purely data-driven or physics-only approaches, offering improved interpretability and generalizability.

The promise of real-time closed-loop control in metal AM could revolutionize the production of critical components for aerospace, biomedical, and automotive industries, where quality assurance is paramount. By reducing trial-and-error, enhancing reproducibility, and enabling adaptive manufacturing practices, ML-assisted systems can dramatically reduce costs and lead times. As research progresses, the combination of ML, digital twins, and edge computing stands poised to transform additive manufacturing from an artisanal practice into a highly automated, intelligent production platform.

In conclusion, the fusion of machine learning with metal additive manufacturing quality control represents a paradigm shift towards smarter, more reliable production processes. By elucidating the fundamental physical mechanisms and enabling active process regulation, these advanced computational tools open new frontiers for manufacturing complex metal parts with unprecedented precision and tailored properties. The ongoing development of robust ML models, integrated multi-physics simulations, and comprehensive sensor networks will be key drivers in this transformative journey, steering the future of metal AM quality assurance towards fully autonomous, data-driven manufacturing ecosystems.

Subject of Research: Not applicable

Article Title: Machine learning assisted quality control in metal additive manufacturing: a review

News Publication Date: 16-Sep-2025

Web References:
https://doi.org/10.1016/j.apmate.2025.100342
https://www.sciencedirect.com/journal/advanced-powder-materials

Image Credits: Zeqi Hu, Changlin Huang, Lechun Xie, Lin Hua, Yujie Yuan, Lai-Chang Zhang

Keywords

Materials

MetalMind represents a paradigm shift in how knowledge related to metal additive manufacturing is structured, accessed, and utilized. At its core, MetalMind leverages the power of knowledge graphs to interconnect diverse data modalities, machine parameters, material characteristics, and process outcomes within a unified, semantically rich framework. By encoding complex relationships and dependencies inherent in metal additive processes, this system empowers users not only to retrieve information efficiently but also to gain deeper insights through reasoning and inference mechanisms.

Traditional approaches to managing manufacturing knowledge often rely on siloed databases or simplistic repositories that fail to capture the multifaceted nature of the production ecosystem. MetalMind transcends these limitations by incorporating a human-centric design philosophy that prioritizes usability and interpretability. The knowledge graph structure enables intuitive exploration, allowing engineers and researchers to visualize and navigate the intricate web of process variables, microstructural evolutions, and performance metrics seamlessly.

The uniqueness of MetalMind lies in its ability to harmonize machine-generated data with expert knowledge and published scientific literature. This integration facilitates comprehensive understanding and fosters innovation by bridging the gap between theoretical research and real-world manufacturing constraints. As a living system, MetalMind continuously evolves, assimilating new findings and experimental results, thereby maintaining relevance in a fast-paced technological environment.

One of the pivotal technical innovations underpinning MetalMind is its sophisticated ontology design, which captures the essential concepts and relationships specific to metal additive manufacturing. Ontologies form the backbone of the knowledge graph, providing rigorous semantic definitions that enable automated reasoning. For instance, the system can infer potential causes of defects by analyzing linked parameter settings and observed material properties, offering actionable insights that traditional statistical analyses might overlook.

Moreover, MetalMind supports multi-scale data integration, encompassing information from powder characteristics at the microscopic level to macroscopic mechanical performance. This comprehensive data assimilation facilitates holistic process optimization, a crucial attribute given the sensitivity of metal additive manufacturing outcomes to subtle variations in input parameters. By delivering a contextualized knowledge environment, the system aids in reducing trial-and-error cycles that typically prolong development timelines and escalate costs.

The human-centric aspect of MetalMind emphasizes collaboration and knowledge sharing among diverse stakeholders, including material scientists, process engineers, and quality control specialists. User interfaces designed with cognitive ergonomics in mind ensure accessibility for individuals with varying expertise levels, fostering cross-disciplinary dialogue. This feature is particularly valuable in complex manufacturing settings where communication barriers often hinder innovation and impede problem-solving.

Another remarkable feature of MetalMind is its capability to support predictive analytics and decision-making processes through machine learning integration within the knowledge graph framework. By training models on the interconnected datasets, the system can forecast process outcomes under varying conditions, enabling proactive adjustments and enhancing reliability. This proactive approach aligns with the Industry 4.0 vision of smart factories driven by data-informed intelligence.

Furthermore, MetalMind facilitates traceability and provenance tracking by maintaining detailed records of data origins and transformations. This attribute not only bolsters confidence in the analysis results but also meets stringent regulatory and certification requirements that are increasingly pertinent in aerospace and biomedical manufacturing sectors. Such transparency ensures that stakeholders can audit the decision pathways underpinning process modifications.

The scalability of MetalMind is another critical advantage. Designed to accommodate expanding datasets and emerging technological developments, the system is adaptable to various metal additive techniques, including powder bed fusion, directed energy deposition, and binder jetting. This versatility positions MetalMind as a foundational infrastructure capable of supporting the broader additive manufacturing community.

Real-world applications of MetalMind already demonstrate its transformative potential. Case studies reveal reductions in defect rates and improvements in material consistency when engineers employ the system’s insights to fine-tune process parameters. Additionally, academic researchers benefit from accelerated hypothesis generation and validation cycles, streamlining experimental workloads and enhancing the pace of discovery.

Looking ahead, the integration of MetalMind with Internet of Things (IoT) devices and sensor networks promises to enable real-time knowledge updates, further narrowing the feedback loop between production and analysis. This convergence will catalyze the emergence of fully autonomous manufacturing systems capable of self-optimization, heralding a new era of efficiency and precision.

Despite its promising capabilities, the development and deployment of MetalMind are not without challenges. Issues surrounding data standardization, interoperability, and privacy must be carefully navigated to ensure widespread adoption. However, the modular design and compliance with open standards embedded within MetalMind’s architecture provide a robust foundation for overcoming these hurdles.

In conclusion, MetalMind exemplifies the fusion of artificial intelligence, semantic technologies, and human expertise tailored to the nuanced demands of metal additive manufacturing. By harnessing the strengths of knowledge graphs within a human-centric framework, it addresses critical bottlenecks in process understanding and control. This advancement not only enhances manufacturing outcomes but also sets a precedent for similar knowledge systems across diverse industrial domains.

As the manufacturing sector continues to embrace digital transformation, the advent of systems like MetalMind underscores the critical role of intelligent knowledge management in fostering innovation and competitiveness. The collaborative, adaptable, and insightful nature of MetalMind ensures that it will remain a vital tool for researchers and practitioners endeavoring to unlock the full potential of metal additive technologies.

The journey of MetalMind from concept to application reflects the broader trend towards integrating AI-driven solutions with domain-specific expertise. Its success is a testament to interdisciplinary collaboration and the strategic application of emerging technologies to address complex industrial challenges. The future of metal additive manufacturing—and indeed manufacturing at large—will be shaped by such intelligent systems that marry human intuition with the power of machine-augmented cognition.

Subject of Research: Metal additive manufacturing; knowledge graph-driven knowledge systems; human-centric manufacturing knowledge management.

Article Title: MetalMind: A knowledge graph-driven human-centric knowledge system for metal additive manufacturing.

Article References:
Fan, H., Fan, Z., Liu, C. et al. MetalMind: A knowledge graph-driven human-centric knowledge system for metal additive manufacturing. npj Adv. Manuf. 2, 25 (2025). https://doi.org/10.1038/s44334-025-00038-9

Image Credits: AI Generated

The core innovation here lies in the meticulous control of the extrusion distance during the direct ink writing process. Traditional metal 3D printing methods, such as selective laser melting or electron beam melting, often struggle with balancing shape complexity and internal porosity, resulting in either limited geometries or inadequate mechanical properties. By contrast, the distance-controlled DIW technique allows the printed titanium alloy ink to be extruded and deposited with variable spacing, enabling the creation of intricate shapes with predetermined porous networks. This dual capability addresses longtime challenges related to weight reduction, mechanical performance, and customization.

At the heart of this method is a specially formulated titanium alloy ink engineered for rheological properties compatible with DIW. The ink exhibits optimal viscosity and shear-thinning behavior, enabling smooth flow through the nozzle while maintaining shape fidelity upon deposition. Researchers achieved precise tuning of the spacing between printed filaments, effectively manipulating the microarchitecture within the bulk. By adjusting the print head’s travel speed and nozzle-substrate distance, they controlled not only the macroscopic shape but also the microscopic porosity distribution — a feat unattainable in conventional metal printing techniques.

One of the most compelling advantages of this technology is its enhanced shape diversity. Unlike standard metal additive manufacturing processes, frequently constrained by support structures and thermal residual stresses, distance-controlled DIW enables the fabrication of complex overhangs, lattice frameworks, and organic forms without supplemental supports. This capability stems from the viscoelastic properties of the titanium ink, combined with precise control over filament placement. As a result, designers can explore geometries previously deemed impractical or impossible, opening avenues for innovation in component design.

Controllable porosity is equally significant in this context. Porous metal structures are invaluable in fields such as biomedical engineering, where implants require osseointegration — the direct structural and functional connection between living bone and the implant surface. The ability to engineer porosity at specific scales and distributions allows for tailoring mechanical stiffness to match bone and facilitating nutrient flow for tissue regeneration. Beyond medicine, porous metallic architectures also offer opportunities in lightweight structural components, thermal management, and acoustic damping, making this advancement broadly applicable.

Central to the research is a series of detailed characterization studies that evaluate the mechanical properties of the printed titanium alloy. Through tensile testing, hardness measurements, and microstructural analysis, the authors confirmed that the novel DIW components exhibit strength and ductility comparable to conventionally manufactured titanium parts. Importantly, the engineered porosity does not come at the cost of structural integrity; by fine-tuning the filament distance, the material’s load-bearing capabilities can be optimized to meet application-specific requirements.

The synthesis of the titanium alloy ink involved sophisticated powder processing and binder selection to achieve the desired rheology and sintering behavior. Post-print processing includes a sintering step under controlled atmosphere to achieve full densification while preserving the designed porosity. This approach bridges the gap between soft material extrusion and hard, fully metallic final products — a complex challenge in metal additive manufacturing. The team’s multidisciplinary expertise in materials science, mechanical engineering, and manufacturing technology is apparent throughout this integrated development route.

Additionally, the digital control algorithms developed for this DIW process enable real-time modulation of deposition parameters, incorporating feedback loops that adjust filament spacing on-the-fly. This dynamic control offers a level of customization ideal for rapid prototyping and personalized manufacturing. By marrying digital precision with material innovation, this technique exemplifies the future of smart manufacturing, where digital content seamlessly drives functional physical outcomes.

The implications of this work extend into industrial sustainability as well. Titanium production and machining are notoriously resource-intensive and costly. By enabling near-net-shape fabrication combined with sparse, porosity-driven weight reduction, this direct ink writing method significantly reduces material waste and energy consumption. Such efficiency gains are crucial as heavy industries seek to minimize environmental impact while maintaining high-performance standards.

One compelling application highlighted by the research team is in aerospace structural components. Lightweight yet robust titanium parts with engineered porosity could reduce aircraft weight and improve fuel efficiency without compromising safety or durability. The ability to fabricate complex shapes allows for integration of multi-functional features such as internal cooling channels or vibration-damping lattice structures, boosting overall system performance.

In the biomedical domain, patient-specific implants manufactured through distance-controlled DIW can achieve perfect anatomical conformity and optimized mechanical compatibility. Porous layers tailored for biological integration promote faster healing and reduce implant rejection risks, benefiting outcomes in joint replacements, dental implants, and bone scaffolds. The adaptability of this technique inherently supports mass customization, a paradigm shift in medical device fabrication.

Moreover, the researchers envision future iterations of the technology incorporating multiple material inks, enabling gradient structures and compositional variations within a single printed part. Such multi-material capability would enable functionally graded materials with site-specific properties, further expanding the design space and application scope. This could be transformative for hybrid aerospace components, advanced prosthetics, and energy devices.

To broaden accessibility, the team is also developing open-source control software and modular hardware add-ons for existing DIW platforms. Democratizing this technology empowers smaller research labs and startups to experiment with distance-controlled metal printing without prohibitive investment, accelerating innovation cycles across various disciplines.

Critically, this work represents a vital step toward bridging fundamental additive manufacturing research and industrial-scale production. By addressing both material formulation and process control challenges, it offers a practical blueprint for upscaling distance-controlled direct ink writing techniques. The study’s comprehensive approach, rigorous validation, and strong performance data suggest this innovation is on the cusp of commercial viability.

As the manufacturing sectors seek agility, precision, and sustainability, the advent of distance-controlled DIW of titanium alloys marks a veritable breakthrough. It ushers in a new era where complex, lightweight, and functional metal parts are realized through a smart fusion of digital design and advanced material engineering. The ripple effects across aerospace, healthcare, automotive, and energy industries will likely be profound, heralding smarter, greener, and more personalized manufacturing solutions.

In conclusion, Bandala et al.’s pioneering work in distance-controlled direct ink writing of titanium alloy achieves a rare synthesis of enhanced shape diversity and controllable porosity. This advance circumvents many limitations of traditional metal additive manufacturing and unlocks unprecedented freedom in component design and function. With promising applications across multiple high-value sectors, this technique is poised to reshape the metal manufacturing paradigm, embodying the future of advanced manufacturing.

Subject of Research: Distance-controlled direct ink writing process applied to titanium alloy for enhanced shape diversity and controllable porosity in metal additive manufacturing.

Article Title: Distance-controlled direct ink writing of titanium alloy with enhanced shape diversity and controllable porosity.

Article References:
Bandala, E., Raymond, L., Mitchell, K. et al. Distance-controlled direct ink writing of titanium alloy with enhanced shape diversity and controllable porosity. npj Adv. Manuf. 2, 4 (2025). https://doi.org/10.1038/s44334-025-00016-1

Image Credits: AI Generated