Abedin Gagani, PhD’s Post

#AIcopilots help engineers improve thermal management, which ultimately results in extending the design boundaries for #AM. In a recent project with Brandenburgische Technische Universität Cottbus-Senftenberg, we studied the influence of overheating on the printability of AM components. 💥 Using #AMAIZE we pushed the design boundaries and achieved a successful production of distortion prone components. ⬇️ To learn more, check the link below ⬇️ https://lnkd.in/eQZD6khn #AI #ML #Additive #DigitalManufacturing #AIcopilot Felix Jensch, Omar Fergani, Dr. Katharina Eissing, Abedin Gagani

🚀 Game-changer for Aero-Engine Flow Diagnostics! Proud to share new advancements in Filtered Rayleigh Scattering (FRS) for aero-engine intakes. This non-intrusive technique is proving to be a breakthrough for measuring flow distortion, providing pressure, temperature, and 3D velocity data with 3.3% accuracy compared to traditional methods. With the integration of machine learning, FRS data processing is now 200x faster, moving us closer to real-time insights and making it a game-changer for future in-flight flow diagnostics. A special thanks to my co-authors for their invaluable contributions to this work. This achievement is a true team effort! Ulrich Doll, Nicholas Lawson FRAeS, Sergey Melnikov, Jonas Steinbock, Michael Dues, Pavlos Zachos, PhD, CEng MIMechE, FHEA, Ingo Roehle, David MacManus Article provided with open access. For learning more more about the SINATRA project, please visit: https://lnkd.in/eSjevDEp #FRS #FlowDistortion #AeroEngines #FlowDiagnostics #MachineLearning #AviationTech #CranfieldUni

Automatic heliostat learning for in situ #CSP plant metrology with differentiable ray tracing Heliostat fields, essential for energy yield, face challenges like surface deformations and alignment errors, affecting performance. Traditional calibration methods, like the camera-target approach, are widely used but have limitations. A 𝗻𝗲𝘄 𝗺𝗮𝗰𝗵𝗶𝗻𝗲-𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴-𝗯𝗮𝘀𝗲𝗱 method allows for precise in-situ heliostat surface measurement and irradiance prediction, improving power plant efficiency. 𝗧𝗵𝗶𝘀 𝘁𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲 𝗰𝗼𝘂𝗹𝗱 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲 𝗿𝗲𝘃𝗲𝗻𝘂𝗲 𝗯𝘆 𝘂𝗽 𝘁𝗼 𝟯𝟵% for smaller plants, offering a promising tool for #Concentrated #Solar Power optimization. https://lnkd.in/dRBbHbBP Dr. Ing. Max Pargmann Robert Pitz-Paal Stefan Kesselheim DLR Institut für Solarforschung German Aerospace Center (DLR) Helmholtz AI Stefan Kesselheim

📢 Thrilled to share that our paper is now available in the IEEE RO-MAN conference proceedings! 📢 In today’s manufacturing industry, the role of collaborative robots (cobots) is increasingly essential in supporting workers with challenging, repetitive tasks. However, while cobots enhance productivity and safety, they often lack the nuanced understanding needed to adapt to human needs and communicate naturally—leaving a gap in reducing worker stress and enhancing well-being. Our research introduces a multimodal approach to detect psychological stress during assembly tasks in industrial settings. By integrating physiological signals (ECG, EMG, EDA), facial action units, and voice features, and applying a self-attention-based late fusion technique. This advancement brings us closer to creating empathetic, human-aware robotic partners that can adjust their behavior to improve collaboration and reduce stress for operators. This research is part of an inspiring European project, Fluently: the essence of human-robot interaction, and I’m incredibly grateful for the guidance of my advisors and coauthors, Stefano Baraldo, Luca Maria Gambardella and Anna Valente, for their continuous guidance and support. Together, we’re paving the way for human-aware cobots that can adapt to workers’ needs and enhance their well-being in the workplace. https://lnkd.in/depRtmqD #HumanRobotCollaboration #IEEE #ROMAN #CollaborativeRobots #IndustrialAutomation #MachineLearning #MentalWellBeing

Unlock the secrets of carbide ceramic fibers, vital for advanced aircraft engines and aerospace vehicles! 🌌 This comprehensive review delves into their preparation methods, diverse applications like high-temp structural materials, catalyst carriers, sensors, and supercapacitors, and future development strategies. Explore the full article for groundbreaking insights https://lnkd.in/egpzaa-M. #AI #MaterialsScience #Ceramics #ResearchDevelopment #sciopen #TUP

Flights to Dublin are booked. We look forward attending the WindEurope Technology Workshop next week.   Michael Stamm, from the German Bundesanstalt für Materialforschung und –prüfung (BAM), will present the latest results of our joint study with LATODA and Romotioncam on How to revolutionize damage detection of wind turbines blades with thermographic inspection techniques and AI models.   Michael will start with a simple question: Do we really need thermography? Do we need this innovative approach to analyse, assess and measure the impact of leading edge erosion on a rotor blade?   What we know is that thermography helps to visualizes turbulent flow perturbations. Reduced aerodynamic performance diminished power generation, which results in #AEPloss.   What we know is that the combination of novel ground-based thermographic and visual inspections reveals the location and severity of blade damages.   With the unlimited help of AI models we can detect, localize, and match visual damages and thermographic turbulence patterns. It comes as no surprise that the quality of an automated detection tool is outstanding compared to conventional human visual inspections.   Come and see with you own eyes, how this new innovative, advanced, and cost-efficient inspection methodology increases wind turbine efficiency and at the same time reduces costs of rotor blade maintenance. Join us at the #WindEurope Technology Workshop in Dublin on June 10th-12th. LATODA | Romotioncam | BAM8.3 - Thermographic Methods #WindTech24 #rainerosion; #rotorblades #ArtificialIntelligence #Windenergy #AnnualEnergyProduction #Innovation

📣 I will be at AIAA this week! Check out the article below for key areas of discussion and market trends. Come stop by booth #14 to learn how our customers are benefiting from quicker time to market by using choosing Rescale as their HPC solution. 🚀 #hpc #ai #aiaa #engineering #aerospace

I'm delighted to announce that my research work, "Reinforcement Learning-Based Heuristic Planning for Optimized Energy Management in Power-Split Hybrid Electric Heavy-Duty Vehicles," has been published in the Journal of Energy (Elsevier; Impact Factor: 9). The paper is available at: https://lnkd.in/g-mNCEkk #DataDriven #EnergyAI #SciencePublication #elsevier #HEV , #EV, #Reinforcementlearning , #DRL, #vehicles #AI #machinelearning

Recommended Approach 1. Develop a Hybrid System: Integrate magnetic attraction with supplementary systems (e.g., electroadhesion for non-magnetic debris) to address a broader range of debris. 2. Enhanced Control Mechanisms: Utilize advanced AI and sensor technologies for precise maneuvering and targeting of debris fields. 3. Safety Protocols: Establish protocols to mitigate collision risks and manage the dynamics of the growing debris cluster. 4. Energy Management: Implement efficient energy storage and management systems to sustain laser propulsion and other operations. By addressing these considerations, the proposed system could significantly contribute to mitigating the space debris problem, enhancing the safety and sustainability of space activities. 3/3 Powered by OpenAI

This paper by Alex Ellery describes how lunar resources can be used to manufacture robotic sensors but they will have limited performance. Bio-inspiration from biological vision provides a solution to circumventing these performance limits. 🤖Read more at: https://lnkd.in/d7m-fDGM