Intelligent Optimization of Cold Deburring Power Supply Parameters for Multi-Material Workpieces

Cold deburring systems rely on high-voltage discharge to remove fine burrs from metallic and composite components. The efficiency and quality of the process depend heavily on the energy parameters of the discharge—voltage, pulse width, and frequency—which vary with workpiece material and geometry.
To achieve adaptive performance, an intelligent optimization framework is embedded within the power supply control system. Through multi-sensor feedback—current sensing, surface temperature monitoring, and optical reflection analysis—the controller evaluates real-time discharge characteristics and surface response. Using a material-parameter mapping database, it automatically adjusts pulse energy and frequency to optimize burr removal without surface damage.
The dual-channel output structure separates main discharge energy from auxiliary cooling pulses, maintaining optimal thermal balance. The controller implements a self-learning algorithm that refines pulse timing and discharge intervals based on historical efficiency data, improving consistency across different workpiece types.
An integrated vision module assesses post-discharge surface reflectivity, signaling the controller to terminate discharge once burr removal reaches the desired level. This closed-loop control approach reduces over-processing, minimizes energy consumption, and extends electrode lifespan.