PLC Application Case Study: Tire Vulcanization Control System Reduces Cycle Time by 20%!

📚 Reading Time: 8 minutes | Practical Value: ⭐⭐⭐⭐⭐

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Have you encountered the following issues?

– Long cycle time in the tire vulcanization process?

– Insufficient temperature control accuracy?

– Bottlenecks in improving production efficiency?

– Unclear direction for PLC program optimization?

⚠️ Industry Pain Points

1. 1. Traditional vulcanization control systems have slow response times, leading to capacity losses.

1. 2. Low precision in temperature curve control affects product quality.

1. 3. Complex program structure makes maintenance difficult.

🎯 Key Points of This Article

1. 1. Optimization plan for vulcanization machine control system

1. 2. Design of temperature closed-loop control program

1. 3. Production cycle optimization technology

▎ Step 1: System Architecture Optimization

Adopt a distributed control architecture to separately handle temperature control, pressure control, and timing control. The master station uses a high-performance CPU to ensure rapid response.

📋 Key Operations:

• Select Siemens S71500 series PLC

• Configure PROFINET fieldbus

• Use a multitasking interrupt program structure

💡 Expert Tip: Use a 4ms high-speed scanning cycle to handle temperature control tasks.

▎ Step 2: Temperature Control Algorithm Optimization

Implement an adaptive PID control algorithm that automatically adjusts control parameters based on different vulcanization stages.

📋 Key Operations:

• Write PID parameter self-tuning program

• Implement multi-segment temperature curve control

• Add feedforward compensation link

▎ Step 3: Production Cycle Optimization

Achieve a reduction in cycle time through parallel task processing and preheating optimization.

📋 Key Operations:

• Optimize mold preheating program

• Implement parallel temperature and pressure control

• Add intelligent early warning function

📊 Practical Application

After applying this solution, a tire manufacturing company reduced the vulcanization cycle from 45 minutes to 36 minutes, improved temperature control accuracy to ±1℃, and increased annual production capacity by 18%.

❓ Troubleshooting

Q1: How to handle temperature overshoot issues?

A1: Use a fuzzy PID control algorithm in conjunction with a gradient heating strategy.

Q2: How to protect production data after a power outage?

A2: Use power-fail retention registers and UPS power supply systems.

💻 Brand Compatibility Key Points

• Siemens: Supports SCL language programming, suitable for complex algorithm implementation.

• Mitsubishi: Built-in PID instructions, easy to configure.

• Omron: Supports mixed programming of ladder diagrams and ST.

📝 Summary

1. 1. A reasonable system architecture is the foundation for performance improvement.

1. 2. Algorithm optimization is key to control accuracy.

1. 3. Data-driven optimization is essential for continuous efficiency improvement.

⚠️ Note: During system optimization, ensure that the safety interlock program has the highest priority to avoid compromising safety due to performance optimization.

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