Process Parameter Prediction and Modeling of Laser Percussion Drilling by Artificial Neural Networks

Finding process parameters for laser-drilled blind holes often relies on an engineer's experience and the trial-and-error method. However, determining such parameters should be possible using methodical calculations. Studies have already begun to examine the use of neural networks to improve the efficiency of this situation. This study extends the field of research by applying artificial neural networks (ANNs) to predict the required parameters for drilling stainless steel with a certain depth and diameter of blind holes, and it also pre-simulates the drilling result of these predicted parameters before actual laser processing. The pre-simulated drilling results were also compared with real-world observations after drilling the stainless steel. These experimental findings confirmed that the proposed method can be used to accurately select laser drilling parameters and predict results in advance. Being able to make these predictions successfully reduces time spent, manpower, and the number of trial-and-error shots required in the pre-processing phase. In addition to providing specific data for engineers to use, this method could also be used to develop a set of reference parameters, greatly simplifying the laser drilling process.

Automated summary

This study applies artificial neural networks to predict parameters for drilling blind holes in stainless steel, and pre-simulates the drilling results before actual processing. The experimental findings confirm the accuracy and practicality of this method, which contributes to improving the efficiency of the laser drilling process.