Millisecond-nanosecond-combined pulse laser drilling technology optimized by a BP neural network
Applied Optics
|September 22, 2025
Summary
This study introduces a BP neural network system with dual millisecond and nanosecond pulsed lasers for improved laser drilling. The system enhances drilling speed and accuracy while reducing energy consumption in aluminum alloys.
Area of Science:
- Materials Science
- Manufacturing Engineering
- Optical Engineering
Background:
- Laser drilling often suffers from irregular hole shapes, low accuracy, and high energy consumption.
- Existing single-laser systems struggle to balance drilling speed, quality, and efficiency.
Purpose of the Study:
- To develop an advanced laser perforation system addressing common challenges in laser drilling.
- To improve drilling speed, hole accuracy, and energy efficiency using a hybrid laser approach.
- To mitigate irregular hole shapes through intelligent parameter optimization.
Main Methods:
- Integration of millisecond and nanosecond pulsed lasers for enhanced perforation.
- Development of a backpropagation (BP) neural network-based predictive model for hole shape.
- Real-time optimization of laser energy density and pulse delay using the BP model based on cone angle.
Main Results:
- The combined laser system demonstrated increased perforation speed and reduced energy consumption.
- The BP neural network model accurately predicted hole shape characteristics, specifically the cone angle.
- Reliable prediction of aluminum alloy cone angle characteristics was achieved even with limited data.
Conclusions:
- The proposed BP neural network-guided dual-laser system effectively enhances laser drilling performance.
- The system achieves a balance between high drilling speed, superior hole quality, and lower energy consumption.
- This approach offers a viable solution for precise laser perforation in materials like aluminum alloys.
