FactoryMind is a Next.js web application that serves as an advanced interface for an Adaptive Hardware-in-the-Loop Digital Twin (AHIL-DT) platform. It provides a suite of AI-powered tools designed for the comprehensive monitoring, analysis, and optimization of factory production lines in real-time.
The core concept of the AHIL-DT is to create a living digital replica of a physical factory. This "digital twin" is not just a static model; it's continuously updated with real-time data from IoT sensors on the factory floor ("Hardware-in-the-Loop"). The "Adaptive" nature comes from the integrated AI engine, which analyzes this data to learn the factory's behavior, predict future states, and recommend optimal adjustments.
This application simulates the sophisticated user interface for such a system, allowing operators, engineers, and managers to interact with the digital twin to drive efficiency, safety, and productivity.
This section details the features that are currently implemented in the FactoryMind simulation.
The central hub of the application, providing a real-time, at-a-glance overview of the factory's operational status.
- Key Performance Indicators (KPIs): Displays critical metrics such as machine uptime, Hardware-in-the-Loop (HIL) synchronization latency, production rates, and the number of active faults.
- Live Production Line Visualization: Features a dynamic, animated 3D-style representation of the factory floor. It includes a moving conveyor belt and robotic arms with color-coded status indicators (e.g., green for nominal, yellow for warning, red for error/fault), providing an intuitive visual status of each workstation.
This module moves beyond reactive maintenance by forecasting equipment failures before they happen, a concept known as Remaining Useful Life (RUL) prediction.
- Real-time Analysis: Users can input simulated sensor data (e.g., temperature, pressure, vibration, voltage) for a specific piece of equipment using interactive sliders.
- AI-Powered Prediction: The Genkit-powered AI flow analyzes the data, detects anomalies, and predicts the equipment's RUL (e.g., "15 days," "2000 cycles").
- Actionable Insights: Based on the analysis, the system provides a confidence score for its prediction and suggests concrete maintenance actions (e.g., "Schedule lubrication for bearing assembly within 48 hours," "Replace coolant filter").
This powerful tool allows users to run "what-if" scenarios to find the most optimal factory configuration based on competing objectives.
- Goal Definition: Users can select multiple optimization goals from a checklist, such as maximizing production throughput, minimizing energy consumption, or improving operator ergonomics.
- Constraint-Based Analysis: Users define operational constraints in a text area (e.g., "Maintain quality score above 98%," "Robot arm speed not to exceed 1.5 m/s").
- AI-Generated Suggestions: The AI engine analyzes the goals and constraints to generate a new, optimized factory configuration (e.g., "Increase conveyor speed by 15%, re-allocate Task A to Station 3").
- Performance Comparison: The results are presented in a clear chart, comparing the predicted performance of the new configuration against the current setup across all target metrics.
A dedicated suite of tools to enhance operator safety and well-being.
- Live Ergonomics Monitoring:
- Utilizes a live webcam feed to perform real-time ergonomic analysis of an operator at their workstation.
- The AI provides continuous feedback on posture, identifying immediate risks like over-reaching or poor neck/back angles.
- The UI displays a live posture description and a boolean "Optimal/Not Optimal" status.
- Ergonomics Video Analysis:
- Allows users to upload pre-recorded videos of workstation tasks for a more in-depth assessment.
- The AI analyzes the entire video to produce an overall ergonomic score (0-100), identifies specific risk factors with timestamps, assesses operator fatigue, and provides a list of actionable suggestions for improvement.
This feature simulates the critical process of deploying a new, optimized configuration from the digital twin to the physical hardware on the factory floor.
- Safety Confirmation: When a user decides to deploy a new configuration from the Optimization page, a confirmation dialog appears.
- Process Simulation: The dialog explicitly states that a secure, atomic update protocol with safety checks is being initiated. This reinforces the concept of a safe deployment process, a critical requirement in real-world industrial environments. The UI then simulates the deployment process, providing feedback to the user.
A dedicated page that serves as a foundation for historical data analysis. It is currently a placeholder but is designed to house:
- Historical trend analysis for KPIs.
- Downloadable energy usage reports.
- Ergonomic scorecards to track improvements over time.
- Framework: Next.js (React App Router)
- Language: TypeScript
- Generative AI: Google Genkit
- Styling: Tailwind CSS
- UI Components: ShadCN UI
- Icons: Lucide React
- Charts: Recharts
This is a Next.js project bootstrapped with create-next-app.
- Node.js (v18 or later recommended)
- npm or yarn
First, install the project dependencies:
npm installTo run the application in development mode, execute the following command:
npm run devThis will start the Next.js development server, typically on port 9002. Open http://localhost:9002 with your browser to see the result.
The application pages will automatically reload as you make changes to the code.