Plastics : Artificial Intelligence and quality monitoring of advanced smart molding

Smart Molding is a manufacturing system that combines traditional injection molding machines with artificial intelligence to create a self-optimizing production process.

Key components of smart molding:

Sensor network:

• Temperature sensors throughout the mold and barrel
• Pressure sensors in the cavity and the hydraulic system
• Position sensors for the screw and mold movement
• Quality inspection cameras and sensors

A barrel is the heated chamber in an injection molding machine where plastic material is melted and transported to the mold. It contains the rotating screw that melts, mixes, and pushes the plastic forward.

Artificial intelligence control system does:

• Real-time data collection and analysis
• Machine learning algorithms for process optimization
• Predictive modeling for quality control
• Automated parameter adjustment

Predictive modeling is a process that uses data and statistical algorithms to forecast future outcomes or behaviors, helping to anticipate and prevent problems before they occur in manufacturing processes.

Key functions of smart molding:

Process control:

• Automatically adjusts injection speed
• Controls packing pressure and the packing time
• Manages cooling time and temperature of the plastic
• Optimizes cycle time for efficiency

Cycle time is the total time taken to complete one full production cycle (to make one part), including all steps like loading, processing, cooling, and ejection.

More effective quality management:

• Detects and prevents defects in real-time
• Maintains a consistent quality for all the parts
• Reduces scrap rates (wastage)
• Automatically adjusts for changes in material 

Scrap rate is the percentage of produced items that don't meet quality standards and must be discarded or recycled, typically calculated as (number of defective items ÷ total items produced) × 100.

Predictive capabilities:

• Forecasts maintenance needs
• Predicts potential defects
• Estimates tool wear to have efficient maintenace schedule
• Suggests optimal parameter settings for each part

Production analytics:

• Tracks overall equipment effectiveness
• Generates production reports
• Analyzes energy consumption to help save cost
• Monitors material usage

Benefits of smart molding:

Production Improvements:

• Reduced cycle times
• Lower scrap rates
• Consistent quality in all parts
• Decreased downtime

Cost Savings:

• Lower labor costs
• Reduces material waste
• Energy efficiency
• Lower maintenance costs by monitoring wear and tear

Quality enhancement:

• Fewer defects in the final goods
• More consistent parts throughout all batches
• Better process control
• Improved traceability of defects, errors, ...

Operational efficiency:

• Less operator (human) intervention
• Faster setup times
• Automated documentation

This technology represents a significant step toward fully automated, highly efficient plastic injection molding production.

Example of advanced smart molding (High-precision pen parts):

System Setup and Operation:

Initial Process:

• Machine loads the plastic material
• Sensors detect material properties (moisture, temperature)
• The AI system checks historical data for optimal parameters

Real-time Adjustments:

• The pressure sensor detects slight material viscosity change
• AI automatically adjusts the Injection speed from 85mm/s to 82mm/s, adjusts the barrel temperature from 230°C to 233°C and the back pressure from 65 bar to 68 bar

Back pressure is the resistance applied against the screw while it rotates backward during plasticizing, which affects how well the plastic material is mixed and melted in the injection molding machine.

Quality monitoring:

The vision system inspects each part and detects a slight dimensional shift of 0.02mm

AI system:

• Compares the shift to the quality thresholds
• Adjusts the cooling time from 12s to 13s
• Modifies the packing pressure

Production optimization:

• The system notices the energy usage spike
• AI reduces the cycle time by 1.2 seconds
• The system maintains the quality while improving efficiency
• It automatically documents all changes

Actions performed by the AI assisted system:

• Defect rate reduced from 2.5% to 0.3%
• Cycle time optimized by 8%
• Energy consumption reduced by 12%
• No interventions needed to perform the adjustments

This example shows how AI integration constantly optimizes the process while maintaining strict device quality standards.