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Detecting microscale surface imperfections in powder bed fusion through light scattering and machine learning – validation of inspection principles
Get PDFMicroscale surface imperfections in laser beam powder bed fusion (PBF-LB) additively manufactured parts, such as balling, spattering, and surface pores, can substantially reduce component quality but are difficult to detect with current real-time measurement and monitoring methods. This paper introduces a novel, rapid, and cost-effective method for detecting microscale surface imperfections in PBF-LB, utilising light scattering combined with machine learning (ML) algorithms. In the proposed method, a laser beam illuminates the measured surface, and the scattered light is captured and analysed to detect surface imperfections. The scattering patterns, which are associated with the illuminated surface and the configuration of the setup, are used to train unsupervised ML algorithms, including autoencoders and anomaly detection models, to classify surfaces as either uniform, without any imperfections or non-uniform, with imperfections. The ML models were trained on simulated scattering patterns of synthetic surfaces generated by a generative adversarial network (GAN) and validated on experimental datasets. The use of unsupervised models eliminates the need for data labelling, whilst the use of simulated and synthetically generated data reduces the time required for actual experiments and data collection. Experimental validation demonstrates that the most effective trained ML model achieved a classification accuracy of over 97 %, highlighting the potential of this technique for detecting microscale surface imperfections. This paper demonstrates the capability of our method to detect such imperfections on PBF-LB surfaces as an ex-situ process. Nonetheless, with further development, this approach has the potential to be adapted as on-machine and real-time defect detection method, by integrating the illumination source into a commercial PBF-LB machine and capturing scattered light information for real-time quality monitoring during the manufacturing process
Modeling proximalisation in axolotl limb regeneration
Get PDFThe axolotl (Ambystoma mexicanum) possesses a remarkable ability to regenerate tissues. Following limb amputation, a blastema of progenitor cells forms, expands, and reconstructs all distal structures, implying that mature cells near the wound retain positional memory along the proximal–distal (PD) axis. Key regulators of positional identity, such as Prod1 and Tig1, promote proximalisation—a shift toward a more proximal identity—when overexpressed, but the mechanisms governing this process remain unclear. In this study, we tracked changes in cellular density along the PD axis of regenerating axolotl limbs after transfecting distal blastemas with Tig1 and Prod1, mapping the spatiotemporal distribution of transfected cells and their progeny throughout regeneration. Using a continuous mathematical modelling approach, we predict a proximalisation velocity induced by factors eliciting proximal identity as Prod1 and Tig1, which is consistent with a proximalisation force driven by a positional potential. Our findings provide a foundational framework for understanding how cells acquire positional identity to guide limb regeneration in axolotls
The mitochondrial genome and phylogenetic placement of the world's most beautiful snail, Polymita picta
Get PDFInteractions of Nitrogen‐Vacancy Centers in Diamond with Electron Beams: Implications for Quantum Sensing and Photoluminescence Stability
Get PDFNitrogen-vacancy (NV) photoluminescence (PL) in diamond is fundamental to its function as a color center, underpinning advances in sensing science and quantum technologies. The work herein provides critical insights into the atomistic mechanisms of electron beams interacting with NV centers, which are crucial for advancing robust quantum sensing at the nanoscale and controlling the functional properties of nanodiamonds. NV PL and sensing stability of NV-rich fluorescent nanodiamonds (FNDs) under electron beam irradiation is probed, across a range of energies (20, 80, 100, and 200 keV) and fluences (≈103 to 107 e−nm−2). PL intensity, NV charge-state ratios, and sensing contrast, as monitored via optically detected magnetic resonance (ODMR) and magnetic modulation (MM) of PL, are examined. Results reveal complex mechanisms governing interactions between NV-centers and fast electrons, dominated by ionization and direct knock-on (DKO) effects, which allow to establish optimum imaging conditions where FNDs can be imaged with sub-nanometer resolution while preserving their PL and sensing properties (200 keV, <105 e−nm−2). This methodology enables controlled, top-down spatial patterning of diamond PL by selectively deactivating NVs, providing novel routes to patterned NV creation
A-SSGC: Adaptive Graph Construction Capturing Physicochemical Commonalities for Industrial Fault Diagnosis
Get PDFAccurate identification of subtle faults in industrial manufacturing remains a critical challenge, driving increased adoption of machine learning (ML) techniques. However, classical ML models often overlook complex inter-sample relationships rooted in shared physicochemical properties, thereby compromising diagnostic accuracy. Addressing this, we propose Adaptive Synergistic Similarity Graph Construction (A-SSGC), a novel algorithm that adaptively fuses multiple graph construction methods. A-SSGC employs an adaptive sparsification strategy, guided by node degrees, to capture physicochemical commonalities among samples effectively. A-SSGC significantly outperforms traditional ML models, basic graph construction techniques, and both unsupervised and semi-supervised deep graph construction approaches. It consistently outperforms these baselines across representative graph neural networks on multiple industrial manufacturing datasets. Visualization of the constructed graphs confirms the ability of A-SSGC to reveal physicochemical commonalities, thereby enhancing interpretability and supporting deeper analytical insights. By effectively capturing these commonalities, A-SSGC improves diagnostic performance. It also shows strong potential as a versatile tool for industrial data analysis, contributing to improved automation and reliability in manufacturing processes
Self-Attenuating Real-Time Vibration Control of a Flexible Long-Reach Robot
Get PDFIn this article, we address the critical challenge of vibration control of flexible long-reach robot manipulators used in nuclear decommissioning. The research is motivated by the urgent need to ensure precision and safety during the deployment of robotic systems in confined and hazardous environments, such as the through-wall deployment (TWD) system for the Sellafield nuclear site. The TWD system, featuring a rigid manipulator on a flexible two-link boom, is designed to maneuver through small openings in containment vessels. While this design avoids the need for bulky structures, the slenderness of the boom makes it prone to significant vibrations, potentially compromising the system’s stability and accuracy. To address these challenges, we propose a novel real-time flexible control system that suppresses vibrations using only the robot manipulator’s own actuation, without requiring additional actuators. The control strategy is based on the mixed-sensitivity H∞ synthesis for a dedicated dynamics model via inertial sensing, enhancing the robustness and adaptability over the multimodal flexibilities. Experimental validation demonstrated the control system’s effectiveness in reducing vibrations, thereby improving operational efficiency and safety. These findings have broader implications for deploying flexible, intelligent control systems in other high-stakes environments, such as the Fukushima Daiichi site, where similar vibration-related challenges are encountered
Web-Based AI-Driven Virtual Patient Simulator Versus Actor-Based Simulation for Teaching Consultation Skills: Multicenter Randomized Crossover Study
Get PDFBackground: There is a need to increase health care professional training capacity to meet global needs by 2030. Effective communication is essential for delivering safe and effective patient care. Artificial intelligence (AI) technologies may provide a solution. However, evidence for high-fidelity virtual patient simulators using unrestricted 2-way verbal conversation for communication skills training is lacking. Objective: This study aims to compare a fully automated AI-driven voice recognition-based virtual patient simulator with traditional actor-based consultation skills simulated training in undergraduate medical students for differences in developing self-rated communication skills, student satisfaction scores, and direct cost comparison. Methods: Using an open-label randomized crossover design, a single web-based AI-driven communication skills training session (AI-CST) was compared with a single face-to-face actor-based consultation skills training session (AB-CST) in undergraduates at 2 UK medical schools. Offline total cohort recruitment was used, with an opt-out option. Pre-post intervention surveys using 10-point linear scales were used to derive outcomes. The primary outcome was the difference in self-reported attainment of communication skills between interventions. Secondary outcomes were differences in student satisfaction and the cost comparison of delivering both interventions. Results: Of 396 students, 378 (95%) completed at least 1 survey. Both modalities significantly increased self-reported communication skills attainment (AI-CST: mean difference 1.14, 95% CI 0.97‐1.32 points; AB-CST: mean difference 1.50, 95% CI 1.35‐1.66 points; both P<.001). Attainment increase was lower for AI-CST than AB-CST (by mean difference 0.36, 95% CI −0.60 to −0.13 points; P=.04). Overall satisfaction was lower for AI-CST than AB-CST (8.09 vs 9.21; mean difference −1.13, 95% CI −1.33 to −0.92 for AI-CST vs AB-CST; P<.001). The estimated costs of AI-CST and AB-CST were £33.48 (US 77.87) per student, respectively. Conclusions: AI-CST and AB-CST were both effective at improving self-reported communication skills attainment, but AI-CST was slightly inferior to AB-CST. Student satisfaction was significantly greater for AB-CST. Costs of AI-CST were substantially lower than AB-CST. AI-CST may provide a cost-effective opportunity to build training capacity for health care professionals
Microbiological profiles of infectious corneal ulcers in Derbyshire and North Nottinghamshire—a 10-year analysis
No full textPurpose: To assess the spectrum of organisms causing microbial keratitis and their in-vitro anti-microbial sensitivities out of 2 hospitals in the East Midlands Region of the United Kingdom. Methods: A retrospective review was undertaken of all patients who underwent corneal scrapes for infectious keratitis between 2011 and 2021 at Royal Derby Hospital (RDH) in Derby and between 2009 and 2021 at King’s Mill Hospital in Mansfield. Results: In total, the results of 645 corneal scrapes (from 622 patients) were analysed after exclusions. Of these, 307 (47.6%) yielded positive cultures. The mean patient age was 52.6 ± 22.1 years (Mean ± St Dev) across both sites and 332 (51.4%) were from female patients. At RDH, there were 195 positive corneal scrape cultures, from which 250 species of organisms were isolated. At RDH, 64% (160/250) were Gram-positive bacteria, 32% (81/250) were Gram-negative bacteria, 2.4% (6/250) were Acanthamoeba species and 1.2% were fungi (3/250). At KMH, there were 112 positive cultures, from which 128 species of organisms were isolated. 14 corneal scrapes from KMH were polymicrobial. At KMH, 96% (123/128) were bacterial (51% Gram positive, 45% Gram negative), 3/128 (2.3%) were fungi and 2/128 (1.6%) were Acanthamoeba. Sensitivity testing confirmed that the fluoroquinolone class of antibiotics appeared to be effective against the majority isolates across the two hospital sites. Conclusion: There are differences in microbiological profiles between these neighbouring hospitals covering neighbouring populations. Despite these differences, reassuringly, the current first-line fluoroquinolone monotherapy treatment is an appropriate first-line treatment for both hospital sites
Framework to Predict Asphalt Pavement Aging and Its Effect on Pavement Remaining Fatigue Life
No full textThis paper aims to develop a modeling-based framework to predict asphalt pavement field aging and its effect on the pavement remaining fatigue life. It is one of the few attempts to systematically model pavement aging and investigate the pavement’s mechanical response and fatigue life evolution with aging considerations. Two road sections in Europe were selected to develop and validate the models. First, the pavement aging model was developed based on three physical processes (heat transfer, oxygen diffusion, and oxidative reaction) of pavement field aging. The model was validated using the field measurements of pavement temperature and aging product. An energy-based viscoelastic-continuum damage material model was then developed and validated through laboratory cyclic fatigue tests. After that, the coupled field aging and viscoelastic-continuum damage pavement model were developed by coupling the two models with the time-temperature-aging shift model. The pavement model was then validated with the falling weight deflectometer measurements at different pavement service years. Finally, the remaining fatigue life predictions with and without aging considerations were conducted based on the integrated model. Results indicate that the proposed model can effectively obtain the temporal evolution and spatial distribution of pavement temperature and aging gradients. The aging-induced modulus gradient causes the localization of high von Mises stress near the road surface, which demonstrates that aging is highly associated with pavement top-down and surface cracking. Based on the case study, field aging would shorten the pavement’s remaining fatigue life by up to 1.6 years when considering only the effects of aging gradient
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