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

Area of Science:

• Manufacturing Engineering
• Materials Science
• Computational Intelligence

Background:

• Laser drilling of blind holes traditionally relies on empirical methods and trial-and-error.
• Optimizing laser drilling parameters is crucial for efficiency and precision.
• Previous research explored neural networks for improving laser processing efficiency.

Purpose of the Study:

• To apply artificial neural networks (ANNs) for predicting laser drilling parameters for stainless steel blind holes.
• To pre-simulate drilling outcomes using predicted parameters before actual laser processing.
• To validate the accuracy of ANNs by comparing simulated results with experimental observations.

Main Methods:

• Development and application of artificial neural networks (ANNs) for parameter prediction.
• Simulation of laser drilling processes based on predicted parameters.
• Experimental validation of predicted parameters and simulated results through laser drilling of stainless steel.

Main Results:

• ANNs accurately predicted the required laser drilling parameters for stainless steel blind holes.
• Pre-simulated drilling results closely matched real-world experimental observations.
• The proposed method demonstrated high accuracy in parameter selection and result prediction.

Conclusions:

• The artificial neural network approach effectively reduces time, manpower, and trial-and-error in laser drilling.
• This method provides engineers with accurate data and can establish reference parameters for simplified laser drilling.
• The study validates the capability of ANNs for precise laser drilling parameter prediction and pre-simulation.