AI-Powered CNC Machine Monitoring and Electronic Production Record Management System with Google Sheets Integration.

• 📊 CNC Machine Monitoring: Real-time tracking of CNC machine data (speed, temperature, coordinates, status)
• 📝 Electronic Production Records (ePR): Complete production tracking with quality metrics
• 🔄 Google Sheets Integration: Automatic synchronization of all data to Google Sheets
• 🤖 AI-Powered Analytics:
  • Intelligent data analysis and insights
  • Anomaly detection
  • Predictive maintenance recommendations
  • Quality assessment and optimization suggestions
• 🌐 Web Interface: Modern, responsive dashboard for data management
• 🔌 RESTful API: Complete API for integration with existing systems

• Backend: Node.js with TypeScript, Express.js
• Database: SQLite (better-sqlite3)
• AI: OpenAI GPT-4
• Integration: Google Sheets API
• Frontend: HTML5, CSS3, Vanilla JavaScript

• Node.js 18+
• Google Cloud Project with Sheets API enabled
• OpenAI API key (optional, for AI features)

1. Clone and install dependencies:

npm install

2. Set up environment variables:

cp .env.example .env

Edit .env and configure:

• GOOGLE_SPREADSHEET_ID: Your Google Sheets ID
• GOOGLE_SHEETS_CREDENTIALS_PATH: Path to Google service account credentials
• OPENAI_API_KEY: Your OpenAI API key (optional)

3. Set up Google Sheets:

   • Create a new Google Spreadsheet
   • Create a service account in Google Cloud Console
   • Download credentials JSON file to ./credentials/google-credentials.json
   • Share the spreadsheet with the service account email

4. Start the server:

# Development mode with auto-reload
npm run dev

# Production build
npm run build
npm start

5. Access the application:
   • Web Interface: http://localhost:3000
   • API Documentation: http://localhost:3000/
   • Health Check: http://localhost:3000/health

• GET /api/cnc?limit=100 - Get CNC machine data
• POST /api/cnc - Record new CNC data
• GET /api/cnc/analyze?limit=50&machine_id=xxx - AI analysis of CNC data
• GET /api/cnc/anomalies?limit=100 - Detect anomalies
• GET /api/cnc/maintenance/:machineId - Predictive maintenance

• GET /api/epr?limit=100 - Get EPR records
• POST /api/epr - Create new production record
• PUT /api/epr/:id - Update record
• POST /api/epr/:id/complete - Complete record with AI analysis
• GET /api/epr/:id/analyze - Get AI analysis for specific record

• POST /api/sync/cnc - Sync CNC data to Google Sheets
• POST /api/sync/epr - Sync EPR records to Google Sheets
• POST /api/sync/all - Sync all data
• GET /api/sync/status - Get sync status

curl -X POST http://localhost:3000/api/cnc \
  -H "Content-Type: application/json" \
  -d '{
    "machine_id": "CNC-001",
    "machine_name": "Haas VF-2",
    "status": "running",
    "operation_type": "Milling",
    "speed_rpm": 3000,
    "feed_rate": 100.5,
    "temperature": 45.2,
    "coordinates_x": 125.50,
    "coordinates_y": 200.25,
    "coordinates_z": 50.00,
    "program_name": "PART_001.nc"
  }'

curl -X POST http://localhost:3000/api/epr \
  -H "Content-Type: application/json" \
  -d '{
    "production_order": "PO-2024-001",
    "part_number": "PN-12345",
    "part_name": "Engine Block",
    "quantity": 100,
    "machine_id": "CNC-001",
    "operator_name": "John Doe",
    "notes": "First batch of Q1 2024"
  }'

# Analyze CNC performance
curl http://localhost:3000/api/cnc/analyze?limit=50

# Detect anomalies
curl http://localhost:3000/api/cnc/anomalies

# Predictive maintenance
curl http://localhost:3000/api/cnc/maintenance/CNC-001

curl -X POST http://localhost:3000/api/sync/all

• Machine identification and status
• Operation parameters (speed, feed rate, temperature)
• 3D coordinates (X, Y, Z)
• Program information
• Sync tracking

• Production order details
• Part information
• Machine and operator tracking
• Quality metrics
• AI analysis results
• Sync tracking

1. Performance Analysis: Comprehensive analysis of machine efficiency and performance trends
2. Anomaly Detection: Automatic detection of unusual patterns or potential issues
3. Predictive Maintenance: Forecasting maintenance needs based on historical data
4. Quality Assessment: AI-powered analysis of production quality and defect patterns
5. Optimization Recommendations: Suggestions for improving efficiency and reducing defects

The system automatically creates and manages two sheets:

• CNC_Data: All CNC machine data with timestamps
• EPR_Records: Complete production records with AI analysis

Data can be synced manually via API or automatically on a schedule.

# Install dependencies
npm install

# Run in development mode
npm run dev

# Build for production
npm run build

# Run tests
npm test

cnc-epr-ai-cloud/
├── src/
│   ├── database/
│   │   └── schema.ts          # Database models and operations
│   ├── services/
│   │   ├── googleSheets.ts    # Google Sheets integration
│   │   └── aiAnalyzer.ts      # AI analysis services
│   ├── routes/
│   │   ├── cncRoutes.ts       # CNC API endpoints
│   │   ├── eprRoutes.ts       # EPR API endpoints
│   │   └── syncRoutes.ts      # Sync API endpoints
│   └── server.ts              # Main application
├── public/
│   └── index.html             # Web interface
├── data/                      # SQLite database (auto-created)
├── credentials/               # Google credentials (create manually)
└── package.json

• Never commit .env or credentials files
• Use service accounts for Google Sheets access
• Implement authentication for production use
• Restrict API access with proper CORS settings
• Keep API keys secure

MIT

For issues and questions, please contact your system administrator.