Post by Udhayamoorthi arachalakumar

🚀 Project Showcase | Deep Koopman Model Predictive Control for Adaptive CNC Machining As part of my journey during my M.Sc. in Intelligent Manufacturing at Technische Universität Clausthal , I independently developed an end-to-end AI-driven adaptive control framework for CNC machining by integrating Physics-Based Digital Twins, Deep Koopman Learning, and Model Predictive Control (MPC). The proposed framework was evaluated against a conventional fixed-parameter CNC controller, demonstrating measurable improvements across multiple machining performance indicators. 📊 Experimental Results ✅ 44.46% reduction in Cutting Force ✅ 7.28% reduction in Tool Temperature ✅ 6.64% reduction in Power Consumption ✅ 5.06% reduction in Machine Vibration ✅ 3.99% improvement in Surface Roughness ✅ 2.09% reduction in Tool Wear 🔬 Project Highlights 🔹 Developed a physics-based Industrial Digital Twin modelling seven coupled machining states: Tool Wear, Cutting Force, Temperature, Surface Roughness, Vibration, Power Consumption, and Material Hardness. 🔹 Generated a 50,000-sample synthetic manufacturing dataset for nonlinear system identification and predictive control. 🔹 Implemented and benchmarked Linear System Identification, Dynamic Mode Decomposition with Control (DMDc), and Deep Koopman Learning for nonlinear process modelling. 🔹 Designed a Deep Koopman Model Predictive Controller (MPC) to optimise feed rate, spindle speed, and coolant flow under operational constraints in a closed-loop environment. This project demonstrates how latent-space system identification, data-driven control, and predictive optimisation can be combined to improve machining quality, process stability, and energy efficiency, highlighting the potential of AI-driven control strategies for Industry 4.0 and Smart Manufacturing. Working on this project significantly strengthened my understanding of Machine Learning, Deep Learning, Nonlinear Dynamical Systems, Model Predictive Control, Digital Twins, Intelligent Manufacturing, and Industrial AI, and I look forward to applying these concepts to real-world manufacturing systems. 📂 GitHub Repository Source Code & Technical Documentation 🔗 https://lnkd.in/eQh_G3n2 The repository includes: 📖 Complete technical documentation ⚙️ Physics-based CNC Digital Twin 🧠 Deep Koopman Neural Network 🎯 Model Predictive Control implementation 📊 Experimental results and performance evaluation 📈 Visualizations and comparative studies I'd be happy to hear your thoughts or feedback! #MachineLearning #DeepLearning #IndustrialAI #DigitalTwin #DeepKoopman #ModelPredictiveControl #SystemIdentification #ControlEngineering #Industry40 #SmartManufacturing #IntelligentManufacturing #Python #PyTorch #Engineering #Research #TUClausthal