研究目的
To study the influence of process parameters of femtosecond laser helical machining on the formation of micro-holes and to establish a relationship model between five factors and the material ablation depth using BP neural network.
研究成果
The evolution of the micro-holes in the drilling process can be described as the center point of the micro-hole being opened up first, forming ring structures on both sides. The order of the effect of the five main parameters on the ablation rate is: repetition rate > single pulse energy > air-blowing pressure > rotation rate > rate of focus downward movement. The BP neural network can predict the ablation depth with a relative error within 3%, which is significant for exploring femtosecond laser helical drilling and improving production efficiency.
研究不足
The study focuses on the influence of process parameters on the formation of micro-holes in 304 stainless steel using femtosecond laser helical drilling. The generalization of the findings to other materials or laser drilling methods may require further investigation.
1：Experimental Design and Method Selection:
Designed a 5-factor and 5-level orthogonal experiment based on the orthogonal table to analyze the impacts of single pulse energy, repetition rate, rotation rate, rate of focus downward movement, and air-blowing pressure on the ablation depth. Used BP neural network to establish the relationship model between these factors and the ablation depth.
2：Sample Selection and Data Sources:
Used 304 stainless steel as the target material. Conducted additional drilling experiments to test the generalization ability of the established network.
3：List of Experimental Equipment and Materials:
KH7040A-1 five-axis ultra-fast laser micro-hole processing machine from Fujian Kehan Laser Company, 304 stainless steel specimen with thickness of 1 mm and all side lengths of 50 mm.
4：Experimental Procedures and Operational Workflow:
Carried out the micro-hole processing experiments with different processing parameters. Measured the depth and the access aperture of each hole using a VHX-1000 ultra-depth-of-field 3D microscope and SUPRA 55 field emission and scanning electron microscope (SEM).
5：Data Analysis Methods:
Used range analysis and variance analysis to determine the impact of various factors on the ablation depth. Trained the BP neural network with the acquired experimental data and tested its generalization ability.
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