As a key component in modern manufacturing, dispensing machines play a crucial role in precision coating, bonding, and encapsulation processes. They are widely used in industries such as consumer electronics, automotive electronics, medical devices, LEDs, and semiconductors. With increasing product complexity and higher precision requirements, dispensing control systems have undergone significant transformation—from manual operations to intelligent algorithm-based automation.

1. Manual Control: Human-Centered Beginnings
In the early stages, dispensing systems were mainly operated manually. Output was controlled via foot pedals or pressure adjustments, heavily relying on operator experience.

Advantages: Low cost and simple operation
Limitations: Poor repeatability, low precision, and high error rates

Example: In early mobile phone repairs or small-batch PCB assembly, technicians used manual syringes or semi-automatic tabletop dispensers. These methods were inefficient and prone to human error, making it difficult to ensure product consistency.

2. The Rise of Automation: Time-Pressure Control Systems
To improve production efficiency and consistency, time-pressure-based systems gradually replaced manual setups. These systems used set parameters for dispensing time and air pressure to automate glue output.

Core Technology:

Air pressure drives glue dispensing

Dispensing volume is controlled by time-pressure coordination

Suitable for low-to-medium viscosity adhesives

Advantages:

Improved productivity and consistency

Reduced operator involvement

Limitations:

Sensitive to environmental conditions (temperature, pressure)

Inconsistent glue volume when viscosity varies

Not suitable for high-precision or high-reliability tasks

3. Advanced Systems: Closed-Loop Control + Vision Assistance
As manufacturing demanded higher accuracy, dispensing systems evolved to integrate closed-loop control and machine vision technologies.

Key Technologies:

Closed-loop servo control: Uses sensors and encoders to monitor and adjust dispensing in real-time, improving accuracy and consistency

Machine vision: Industrial cameras and image processing algorithms identify product locations, correct alignment, and detect QR codes or part deviations

Example: In a project for an automotive ECU control board, the client required a 0.1mm dispensing accuracy and less than 5% deviation. By using servo control valves and vision alignment, we achieved highly consistent dispensing while reducing defect rates by 40%.

4. AI-Powered Dispensing: Intelligent Algorithms Take the Lead
With the rise of AI and Industrial IoT (IIoT), modern dispensing systems now incorporate smart algorithms that enable self-learning and real-time optimization.

Innovative Features:

Self-learning algorithms: Optimize paths and compensate for deviation using historical data

Viscosity compensation: Adjust parameters in real-time based on sensor feedback

Anomaly detection: AI models predict failures or maintenance needs

Adaptive parameter setting: Automatically match dispensing profiles to materials

Edge computing + cloud integration: Enable factory-wide data sharing and analytics

Example: A major EMS manufacturer deployed AI-enabled dispensers in their laptop motherboard production. The system automatically adapted to changes in temperature, humidity, and supply pressure, achieving stable 24/7 operations with maintenance cycles extended to over 30 days.

5. Future Outlook: From Intelligent to Autonomous & Collaborative
The journey toward smarter control systems is far from over. Future developments will focus on autonomous decision-making and inter-system collaboration.

Expected Trends:

Deep integration with MES/ERP for automated job scheduling and traceability

Natural language interfaces for human-machine interaction

AI-driven dispensing recipes with minimal human intervention

Collaboration with robots for integrated dispensing and assembly

Conclusion
The evolution of dispensing control systems mirrors the broader path of industrial automation. From manual handling to algorithmic intelligence, each technological leap has significantly improved productivity, quality, and flexibility. As the industry moves toward Industry 4.0, staying at the forefront of control technology is essential for competitiveness and innovation.