Beam Analysis Dashboard Built with Python

I recently built a small Python tool that visualizes Miller indices crystallographic planes interactively and it turned out to be one of the most useful study aids I have made. The script takes (h k l) inputs and renders a 3D plot showing: → The Miller plane cutting through a unit cube → Axis intercepts with labels → The normal direction vector (h k l) → Shifted origin handling for negative indices Built with NumPy for geometry calculations and Plotly for interactive 3D rendering. As an engineering student currently studying crystallography, I found that actively building a visualization tool forced me to deeply understand the underlying math : intercept reciprocals, polygon sorting, coordinate transformations in a way that passive reading never does. The project also pushed me to grow practically with Python: handling edge cases, structuring clean functions and working with 3D data for the first time. I don't know yet where this kind of work will take me. But I do know that curiosity driven projects even small ones compound over time. #Python #MaterialsEngineering #Crystallography #MillerIndices #NumPy #Plotly #RUET #EngineeringStudent #LearningByDoing #ComputationalEngineering

Sensitivity analysis: knowing what really matters in your design When performance varies, the real challenge is often simple: 👉 Which parameters actually drive the behavior of my system? With SIMBA + Python, sensitivity analysis makes this explicit. In this example, a simple RLC circuit is analyzed by: -perturbing each component value by ±10% -running transient simulations automatically -extracting a single performance metric (overshoot) -computing normalized sensitivities for each parameter 📊 The result is a clear ranking of parameters, showing immediately where engineering effort should be focused. 💡 Why it matters: -reduce trial‑and‑error -improve design robustness -focus on the parameters that truly impact performance -increase confidence before hardware validation 👉 Sensitivity analysis is not about running more simulations, it’s about making better design decisions. 🔗 Python script and SIMBA model available in the technical resources (link in first comment). #PowerElectronics #Simulation #SensitivityAnalysis #Python #SIMBA #DesignRobustness Jérôme Cornau Emmanuel Rutovic Guillaume Fontes

For magnetic inductor modeling, μfem’s Python API provides flexibility and power - but setting up non-trivial geometries can still be time-consuming. To address this, we are developing a user interface focused on fast, intuitive setup and analysis. A first preview is shown below. This is an early stage prototype. Feedback is critical to shape the direction: - Which steps in your current workflow are the most time-consuming? - What capabilities are missing in existing tools? For questions and inquiries please reach out to us.

I built a web-based interactive simulation of transient heat conduction in cylindrical materials using Python and Streamlit. The model solves the heat equation in cylindrical coordinates: dT/dt = α ( d²T/dr² + (1/r) dT/dr ) The tool allows real-time control of material properties (ρ, Cp, k) and geometry, with immediate visualization of temperature evolution. link: https://lnkd.in/dDDQ_v_D Code: https://lnkd.in/dH7U7xE9 I would greatly value feedback from engineers and researchers working in heat transfer or numerical methods. #MechanicalEngineering #ProcessEngineering #ThermalEngineering #EngineeringSimulation #Python #Simulation #DigitalEngineering #EnergySystems

Stop waiting for results. JEOL’s streamlined SEM workflows reduce analysis time for battery materials, delivering high-resolution insights in a fraction of the time. ⏱️ #JEOLAsia #ElectronMicroscopy #SEM #RapidAnalysis

Say goodbye to slow spreadsheets. Switch to Python speed. ⏱️ Why wait 10 minutes for Excel to calculate your mechanical tests when Python does it in 0.02 seconds? In this article from my automation series, we're diving into Vector Computing. Say goodbye to for loops and line-by-line calculations. On the agenda: • ⚡ Raw performance: Process entire columns of data as a single block. • 🧮 Simplified physics: All about the 9550 factor for converting Torque to Power. • 🔍 Visual diagnostics: Transform your graph into a high-resolution oscilloscope to validate your results at a glance. Stop being a victim of your data files. Become the expert in control. Discover the method here: 👉 https://lnkd.in/ebgqzpPr #Engineering #Python #Industry40 #DataScience #DeFactoData

to illustrate why the world is held back by engineering software (CAD, etc.): what would the world look like today if python were paywalled like matlab is, and there were no alternatives? apply that to everything we do and see the issue

Built a pseudo-LiDAR scanner using just a webcam, Python, and computer vision. I made a real-time Pseudo-LiDAR Scan project that turns normal webcam input into a radar-style scanning interface. It combines edge detection and motion analysis to simulate a rotating scan effect and create a clean LiDAR-inspired HUD. It was a fun project to make. It took some time to make this because I had never really made something like this before, but nonetheless, it was fun to make; the physics behind it was mostly electromagnetic wave ranging. Using light pulses to measure distance was fun to learn about. What it does: • Captures live webcam input in real time • Detects edges and motion from the scene • Converts those features into a rotating radar / LiDAR-style sweep • Uses an echo buffer to leave behind fading scan trails • Highlights the closest beam hit and closest echo return Built with: • Python • OpenCV • NumPy #Python #ComputerVision #OpenCV #NumPy #SoftwareDevelopment #Engineering #RealTimeSystems #Simulation #SignalProcessing #MachineVision #TechProjects #Programming #Developer #Innovation #HUDDesign github: https://lnkd.in/eRtTh-2C

Part 2: Vectorizing the Math – High-Performance Image Processing in Julia The content discusses using Julia for efficient mathematical iteration and matrix manipulation, showcasing its advantages over Python. It explains the use of Images.jl and Luxor.jl for image processing and rendering, detailing a loop that transforms RGB pixels into grayscale and outputs smooth SVG graphics. Future applications include developing a micro-SaaS backend....

Excited to share my Computer Vision project: Smart Lane Detection System 🚗 This project focuses on real-time lane detection using Python, OpenCV, NumPy, Canny Edge Detection, Region of Interest (ROI) masking, and Hough Transform. The system is designed to detect road lane boundaries from video input and highlight lane lines for better road analysis and driving assistance. It demonstrates the practical implementation of Computer Vision and Image Processing techniques in real-time video analysis. 🔹 Key Features: • Real-time lane detection • Edge detection using Canny Algorithm • Lane line detection using Hough Transform • ROI (Region of Interest) masking • Video-based road lane analysis • Lane boundary highlighting 🔹 Technologies Used: Python | OpenCV | NumPy | Computer Vision | Image Processing 🔹 GitHub Repository: https://lnkd.in/dkbfaKvp I built this project as a self-learning initiative to strengthen my practical understanding of Computer Vision, image preprocessing, and real-time video processing. Looking forward to learning more and building advanced Computer Vision solutions. #ComputerVision #Python #OpenCV #NumPy #MachineLearning #ArtificialIntelligence #LaneDetection #ImageProcessing #GitHub #Projects #SoftwareEngineering #Developer