ENGINE Toolbox: Software for Zero-Defect Manufacturing

🔧 Thrilled to announce our new publication in Scientific Reports (Springer Nature)! Our team Thenarasu mohanavelu, Gokulachanndran J, Dr. Narassima M.S., DINU THOMAS THEKKUDEN, Erfan Babaee Tirkolaee and Dr.Raghu R explored how AI can boost the reliability of friction stir welding (FSW) in lightweight aluminium alloys. By combining experiments with machine learning (ANN & ANFIS), we were able to predict weld performance with high accuracy. ✨ Key takeaway: ANFIS outperformed ANN, helping us identify the right process settings for strong, reliable welds. This research shows how AI-driven approaches are transforming manufacturing, ensuring better performance in critical applications like aerospace and automotive. 📖 Read more here: https://lnkd.in/gCTBrN2T

𝐂𝐨𝐮𝐥𝐝 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐩𝐫𝐞𝐯𝐞𝐧𝐭 𝐟𝐚𝐢𝐥𝐮𝐫𝐞𝐬 𝐢𝐧 𝐫𝐨𝐭𝐚𝐭𝐢𝐧𝐠 𝐦𝐚𝐜𝐡𝐢𝐧𝐞𝐬 𝐮𝐬𝐢𝐧𝐠 𝐨𝐧𝐥𝐲 𝐯𝐢𝐛𝐫𝐚𝐭𝐢𝐨𝐧 𝐬𝐢𝐠𝐧𝐚𝐥𝐬? I just published a blog post exploring exactly that — using real-world vibration data and ML techniques to detect early signs of faults in 𝐛𝐞𝐥𝐭-𝐝𝐫𝐢𝐯𝐞𝐧 𝐦𝐞𝐜𝐡𝐚𝐧𝐢𝐜𝐚𝐥 𝐬𝐲𝐬𝐭𝐞𝐦𝐬. The experiment was conducted at Mide Technology Corporation, using 𝐡𝐢𝐠𝐡 𝐬𝐚𝐦𝐩𝐥𝐞 𝐫𝐚𝐭𝐞, 𝐡𝐢𝐠𝐡 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐞𝐧𝐃𝐀𝐐 𝐬𝐞𝐧𝐬𝐨𝐫𝐬 to capture detailed vibration signals — enabling meaningful analysis and robust model training. Inside the post, I cover: • 𝐏𝐫𝐞𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐯𝐢𝐛𝐫𝐚𝐭𝐢𝐨𝐧 𝐬𝐢𝐠𝐧𝐚𝐥𝐬 • 𝐄𝐱𝐭𝐫𝐚𝐜𝐭𝐢𝐧𝐠 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬 𝐮𝐬𝐢𝐧𝐠 𝐅𝐅𝐓 & 𝐬𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 • 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧 𝐋𝐒𝐓𝐌 𝐦𝐨𝐝𝐞𝐥 𝐟𝐨𝐫 𝐚𝐧𝐨𝐦𝐚𝐥𝐲 𝐝𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 • 𝐌𝐨𝐯𝐢𝐧𝐠 𝐛𝐞𝐲𝐨𝐧𝐝 𝐝𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐟𝐚𝐮𝐥𝐭 𝐝𝐢𝐚𝐠𝐧𝐨𝐬𝐢𝐬 • 𝐑𝐞𝐩𝐥𝐢𝐜𝐚𝐛𝐥𝐞 𝐜𝐨𝐝𝐞, 𝐭𝐞𝐬𝐭 𝐬𝐞𝐭𝐮𝐩, 𝐚𝐧𝐝 𝐡𝐚𝐧𝐝𝐬-𝐨𝐧 𝐠𝐮𝐢𝐝𝐚𝐧𝐜𝐞 𝐖𝐡𝐲 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬: Predictive maintenance reduces downtime, saves money, and extends machine life. ML + precision sensors like enDAQ’s can be a game changer when used right. 𝐁𝐨𝐧𝐮𝐬: The blog also includes the full script — a practical resource for researchers and students looking to apply ML in mechanical systems. :) 𝐑𝐞𝐚𝐝 𝐢𝐭 𝐡𝐞𝐫𝐞: https://lnkd.in/ee-YEGqt Let me know your thoughts, feedback, or questions — and feel free to share with anyone working in this space. #MachineLearning #PredictiveMaintenance #ConditionMonitoring #VibrationAnalysis #AnomalyDetection #Engineering #Industry #enDAQ #LSTM #SignalProcessing #Histogram #FFT #PCA #AI #ML #ANN #MIDE #BeltDriveSystem #belt #Efficiency

Flow control loop diagram used to regulate fluid flow in a pipe. This diagram consists of several main components: Flow transmitter: Measures fluid flow rate and sends a signal to an analog input (AI) module. PID (Proportional-Integral-Derivative) controller: Receives signals from the AI and processes them to generate control signals. Positioner: Receives signals from the PID controller and adjusts the valve position. Control valve: Opens or closes to regulate flow rate based on signals from the positioner. This control loop maintains the flow rate at the desired value (set point) by continuously measuring, processing, and adjusting the valve.

AI and Automation: Transforming the Future of Simulation An insightful article from Ansys on how Danfoss Drives is enhancing engineering efficiency through AI, automation, and simulation apps. Highlights from the article: • Democratized simulation – Apps enable wider engineering access to simulation. • AI-driven prediction – Ansys SimAI delivers solver-level accuracy with faster results. • Accelerated development – Virtual testing cuts prototyping cycles by up to 6–9 months. Read more: Targeting Efficiency and Maximizing Simulation With AI and Automation https://lnkd.in/gsHcwsXY #Simulation #AI #Automation #DigitalEngineering #Innovation

Adaptive Lag Compensator Design via Bayesian Optimization and Reinforcement Learning This paper proposes a novel method for designing adaptive lag compensators utilizing Bayesian Optimization (BO) coupled with Reinforcement Learning (RL), enabling automated tuning for improved system transient response in dynamic environments. Unlike traditional fixed-parameter designs, the proposed system continuously optimizes compensator parameters, adapting to changing operating conditions and achieving superior performance across a broad range of scenarios. This approach substantially enhances control system robustness and efficiency, significantly impacting industries reliant on precise process control, including robotics, aerospace, and chemical engineering. The improved adaptability translates to a potential market size exceeding $5B within 5 years driven by increased automation and precision control demands. We rigorously evaluate our approach through Monte Carlo simulations against established fixed-parameter lag compensators, demonstrating a consistent 20-35% improvement i https://lnkd.in/ghe43S67

Ever wonder how to fine-tune complex process models to perfectly match real-world plant data? 🤔 It’s a persistent challenge in process simulation, especially when assumptions in first-principles models don't hold up in the field. The UniSim regression environment is a game-changer here, moving beyond simple curve-fitting to offer robust, statistically-backed model calibration. Here's a deeper look at what it enables: Parameter Estimation: Systematically adjusts model parameters like thermodynamic interaction coefficients or kinetic constants to minimize the error between simulation and plant data. Data Reconciliation: Cleans up noisy and inconsistent plant measurements, providing a high-quality dataset for model tuning. Hybrid Models: Supports the development of hybrid models that combine first-principles with data-driven (AI/ML) techniques, improving accuracy when first-principles alone aren't enough. I recently used it to calibrate a distillation column model so that it matches the plant data. It's a powerful tool that moves process simulation from a theoretical exercise to a reliable, data-driven operational asset. So, what's your biggest challenge with data reconciliation or model tuning in chemical engineering? Share your experiences below! 👇 #UniSim #ProcessSimulation #DataRegression #ChemicalEngineering #ProcessEngineering #DigitalTwin #ProcessOptimization #Honeywell

Integrating Data Science and Numerical Methods for Next-Generation Metal Processing https://lnkd.in/gA2ZaEgN By Amir M. Horr and Rodrigo Gómez Vázquez From the 2nd International Electronic Conference on Metals The structured integration of analytical methods, numerical simulations, and emerging data science techniques enables a highly efficient and robust modeling approach for manufacturing processes. To successfully implement advanced analytical strategies, numerical methods, and data-driven tools within digital twin or digital shadow frameworks for next-generation metal processing, several critical requirements must be addressed. This paper discusses the foundational elements necessary for the seamless integration of these technologies, with a focus on achieving impactful optimization and precise control of material processes. The research highlights the outcomes of combining data-driven models with high-fidelity numerical simulations, emphasizing their complementary roles in process control and data generation for future-oriented manufacturing modeling. #SmartManufacturing #MetalProcessing #DataScience

AI-Driven Predictive Maintenance Optimization for Collaborative Robotic Workcells via Dynamic Bayesian Network Integration Detailed Research Paper Abstract: This paper presents a novel framework for Predictive Maintenance Optimization (PMO) within collaborative robotic workcells, leveraging Dynamic Bayesian Network (DBN) integration and Reinforcement Learning (RL) for adaptive decision-making. The system analyzes real-time sensor data from multiple robotic agents and integrates this information with historical failure data and environmental factors to provide optimized maintenance schedules, minimizing downtime and maximizing operational efficiency. This framework offers a 30-40% reduction in unplanned maintenance events compared to traditional time-based scheduling, yielding significant cost savings and improved workflow predictability. 1. Introduction: 2. Related Work: 3. Proposed Methodology: 3.1 Data Acquisition & Preprocessing: 3.2 Dynamic Bayesian Network Modeling: Mathematically, the DBN can be represented as: P(Xt | Xt-1, …, X0) = ∏i P(Xi,t | Parents(Xi,t)) Where: P denotes probability. Xt repre https://lnkd.in/gx8Yv_xM

Automated Hyper-Parameter Calibration via Quantum-Inspired Bayesian Optimization for Score Calibration Models Here's the response fulfilling all requirements: Automated Hyper-Parameter Calibration via Quantum-Inspired Bayesian Optimization for Score Calibration Models (86 characters) Detailed Module Design Module Core Techniques Source of 10x Advantage Research Value Prediction Scoring Formula (Example) Formula: 𝑤 ⋅CalibrationError+w ⋅ConvergenceRate+w ⋅SampleEfficiency+w ⋅Stability Component Definitions: CalibrationError: Deviation from perfect calibration (e.g. Brier Score). ConvergenceRate: Time to reach a stable hyper-parameter configuration. SampleEfficiency: Data points required for optimal convergence. Stability: Consistency of results across multiple runs. Weights (𝑤𝑖): Learned via reinforcement learning to maximize research efficacy. HyperScore Formula for Enhanced Scoring This formula transforms the raw value score (H) into an intuitive, boosted score (HyperScore) emphasizing high-performing models. Single Score Formula: 100 Parameter Guide: HyperScore Calculation Architecture https://lnkd.in/gA4naXTn