Hasil untuk "Manufactures"

G. Pisano, R. Hayes

Richard Y. Wang, V. Storey, Christopher P. Firth

Peter T. Ward, Rebecca Duray

J. Błażewicz, K. Ecker, E. Pesch et al.

Rachel Mason-Jones, B. Naylor, D. Towill

Pallock Halder, Satyajit Mojumder

AI agents are being increasingly deployed across a wide range of real-world applications. In this paper, we propose an agentic AI framework for in-situ process monitoring for defect detection in wire-arc additive manufacturing (WAAM). The autonomous agent leverages a WAAM process monitoring dataset and a trained classification tool to build AI agents and uses a large language model (LLM) for in-situ process monitoring decision-making for defect detection. A processing agent is developed based on welder process signals, such as current and voltage, and a monitoring agent is developed based on acoustic data collected during the process. Both agents are tasked with identifying porosity defects from processing and monitoring signals, respectively. Ground truth X-ray computed tomography (XCT) data are used to develop classification tools for both the processing and monitoring agents. Furthermore, a multi-agent framework is demonstrated in which the processing and monitoring agents are orchestrated together for parallel decision-making on the given task of defect classification. Evaluation metrics are proposed to determine the efficacy of both individual agents, the combined single-agent, and the coordinated multi-agent system. The multi-agent configuration outperforms all individual-agent counterparts, achieving a decision accuracy of 91.6% and an F1 score of 0.821 on decided runs, across 15 independent runs, and a reasoning quality score of 3.74 out of 5. These in-situ process monitoring agents hold significant potential for autonomous real-time process monitoring and control toward building qualified parts for WAAM and other additive manufacturing processes.

Xue Dong, Samuel Medina, Sai Pratyush Akula et al.

Metallic plating systems composed of titanium and its alloys remain the standard treatment for craniofacial bony fixation but may require secondary removal due to infection, implant migration, or discomfort. Thus, biodegradable metallic implants may eliminate complications and secondary procedures while maintaining structural integrity. Our previous work demonstrated the fabrication of immiscible Fe-AZ31 composites via additive manufacturing with improved degradation kinetics over pure Iron. This study aimed to evaluate the in vitro and in vivo biocompatibility of Fe-AZ31 composites for potential craniofacial fixation applications. Pure iron (Fe), Mg alloy (AZ31) and Fe-AZ31 samples were fabricated for extract-based cytotoxicity testing using HFF-1 fibroblasts, L929 fibroblasts and hFOB osteoblasts. Metal extracts were prepared at a 3 cm^2/mL surface-to-volume ratio in complete media at 37C and cell viability was measured by live/dead assay after 24 and 72h exposure. For in vivo evaluation, Fe-AZ31, Fe, and Ti plates were implanted subcutaneously in wild type mice for 6 weeks, 3 and 6 months. Implant degradation, histologic response, hematology, and serum biochemistry were assessed. Fe-AZ31 extracts demonstrated >70% cell viability across all cell types at both timepoints with normal cell morphology and adhesion, whereas AZ31 extracts caused marked cytotoxicity associated with pronounced alkalization (pH 10). In vivo, Fe-AZ31 implants exhibited gradual surface corrosion accompanied by mild, transient inflammation and minimal capsule formation over time. No systemic toxicity was observed. Hematology and serum biochemistry remained within physiological limits. Additively manufactured Fe-AZ31 composites demonstrate acceptable biocompatibility and favorable tissue responses, supporting their development as resorbable metallic fixation devices for craniofacial reconstruction.

Branko Kolarevic

Richard Disney, J. Haskel, Ylva Heden Westerdahl

K. Kim, M. Shahinpoor

J. Morales, J. McConville

Changxi Liu, Liqiang Wang, Miao Luo et al.

Owing to the cellular structure that limits dislocation motion upon stress loading, additively manufactured (AM) refractory high-entropy alloys (HEAs) exhibit an excellent strength-plasticity synergy. This work integrates micro/nano-mechanical experiments with statistical physics modeling to examine dislocation nucleation and slip in AM-fabricated TiNbTaZrMo HEA. Computational results indicated that the activation volume for initial dislocation nucleation is about one atomic volume, facilitating dislocation initiation. Nanoindentation and in-situ micro-pillar compression reveal no significant pop-in events, indicating that the cellular structure impedes dislocation slip and thus prevents plasticity reduction from dislocation slipping near grain boundaries. This work provides a thorough investigation into the interplay between incipient plasticity, dislocations, and cellular structure in AM-produced TiNbTaZrMo, offering new insights into the design and advancement of AM-fabricated refractory HEAs.

Yijun Ren, Lotje A Hoogervorst, Enrico G Caiani et al.

Background and purpose: The safety and performance of hip and knee prostheses can be assessed by analyzing peer-reviewed literature, registry reports, and safety notices published by national competent authorities/regulatory agencies, or manufacturers. The percentage of hip and knee prostheses with a safety signal published through any of these data sources is unknown. We aimed to assess the frequency of signals identified for a random sample of 10 hip stems, 10 hip cups, and 10 knee implants. Methods: 3 literature libraries were searched to find safety signals defined as information on patterns/occurrences that may alter the device’s benefit–risk profile, reported in peer-reviewed publications for the randomly selected implants. Annual registry reports from 5 national registries were examined to check whether any of the selected implants had outlier performance. The CORE-MD post-market surveillance (PMS) tool was used to collect all related safety notices from 13 competent authority/regulatory agency websites. Manufacturers’ websites were screened for any reported safety information. Results: Safety signals were identified for 21 of the 30 randomly selected implants: 18 identified by registries, 7 by the CORE-MD PMS tool, and 8 based on literature, with 10 implants identified by multiple sources. There was no systematic pattern in timing of publication with a particular source publishing safety signals earlier than other sources. Conclusion: 70% of the randomly selected hip and knee prostheses had ≥ 1 safety signals published, with registries as the source for the majority. No single source identified all 21 implants with signals, which highlights the need for a comprehensive surveillance strategy to aggregate safety signals from multiple sources.

Keerthana Chand, Tobias Fritsch, Bardia Hejazi et al.

Quality control in additive manufacturing (AM) is vital for industrial applications in areas such as the automotive, medical and aerospace sectors. Geometric inaccuracies caused by shrinkage and deformations can compromise the life and performance of additively manufactured components. Such deviations can be quantified using Digital Volume Correlation (DVC), which compares the computer-aided design (CAD) model with the X-ray Computed Tomography (XCT) geometry of the components produced. However, accurate registration between the two modalities is challenging due to the absence of a ground truth or reference deformation field. In addition, the extremely large data size of high-resolution XCT volumes makes computation difficult. In this work, we present a deep learning-based approach for estimating voxel-wise deformations between CAD and XCT volumes. Our method uses a dynamic patch-based processing strategy to handle high-resolution volumes. In addition to the Dice Score, we introduce a Binary Difference Map (BDM) that quantifies voxel-wise mismatches between binarized CAD and XCT volumes to evaluate the accuracy of the registration. Our approach shows a 9.2\% improvement in the Dice Score and a 9.9\% improvement in the voxel match rate compared to classic DVC methods, while reducing the interaction time from days to minutes. This work sets the foundation for deep learning-based DVC methods to generate compensation meshes that can then be used in closed-loop correlations during the AM production process. Such a system would be of great interest to industries since the manufacturing process will become more reliable and efficient, saving time and material.

Charles Wade, Devon Beck, Robert MacCurdy

This paper presents a novel gradient-informed slicing method for functionally graded additive manufacturing (FGM) that overcomes the limitations of conventional toolpath planning approaches, which struggle to produce truly continuous gradients. By integrating multi-material gradients into the toolpath generation process, our method enables the fabrication of FGMs with complex gradients that vary seamlessly in any direction. We leverage OpenVCAD's implicit representation of geometry and material fields to directly extract iso-contours, enabling accurate, controlled gradient toolpaths. Two novel strategies are introduced to integrate these gradients into the toolpath planning process. The first strategy maintains traditional perimeter, skin, and infill structures subdivided by mixture ratios, with automated 'zippering' to mitigate stress concentrations. The second strategy fills iso-contoured regions densely, printing directly against gradients to eliminate purging and reduce waste. Both strategies accommodate gradually changing printing parameters, such as mixed filament ratios, toolhead switching, and variable nozzle temperatures for foaming materials. This capability allows for controlled variation of composition, density, and other properties within a single build, expanding the design space for functionally graded parts. Experimental results demonstrate the fabrication of high-quality FGMs with complex, multi-axis gradients, highlighting the versatility of our method. We showcase the successful implementation of both strategies on a range of geometries and material combinations, demonstrating the potential of our approach to produce intricate and functional FGMs. This work provides a robust, open-source, and automated framework for designing and fabricating advanced FGMs, accelerating research in multi-material additive manufacturing.

Nathan Lane

I study the impact of industrial policies on industrial development by considering an important episode during the East Asian miracle: South Korea's heavy and chemical industry (HCI) drive, 1973--1979. Based on newly assembled data, I use the introduction and termination of industrial policies to study their impacts during and after the intervention period. (1) I reveal that heavy-chemical industrial policies promoted the expansion and dynamic comparative advantage of directly targeted industries. (2) Using variation in exposure to policies through the input-output network, I demonstrate that the policy indirectly benefited downstream users of targeted intermediates. (3) The benefits of HCI persisted even after the policy ended, as some results were slower to appear. The findings suggest that the temporary drive shifted Korean manufacturing into more advanced markets and supported durable change. This study helps clarify the lessons drawn from the East Asian growth miracle. JEL Codes: L5, O14, O25, N6. Keywords: industrial policy, East Asian miracle, economic history, industrial development, Heavy-Chemical Industry Drive, Heavy and Chemical Industry Drive.

Leoncio Jimenez

To conceptualize living systems based on the processes that create them, rather than their interactions with the environment, as in systems theory. Maturana and Varela (1969) at the University of Chile introduced the term autopoiesis (from Greek self and production). This concept emphasizes autonomy as the defining feature of living systems. It describes them as self-sustaining entities that preserve their identity through continuous self-renewal to preserve their unity. Furthermore, these systems can only be understood in reference to themselves, as all internal activities are inherently self-determined by self-production and self-referentiality. This thesis introduces the Fuzzy Autopoietic Knowledge Management (FAKM) model, which integrates the system theory of living systems, the cybernetic theory of viable systems, and the autopoiesis theory of autopoietic systems. The goal is to move beyond traditional knowledge management models that rely on Cartesian dualism (cognition/action) where knowledge is treated as symbolic information processing. Instead, the FAKM model adopts a dualism of organization/structure to define an autopoietic system within a sociotechnical approach. The model is experimentally applied to a manufacturing company in the Maule Region, south of Santiago, Chile.

M. Vázquez, J. Alonso, H. Domínguez et al.

M. Leary, L. Merli, F. Torti et al.

Y. Koren