Neural Network Modeling and Particle Swarm Optimization (PSO) of Process Parameters in Pulsed Laser Micromachining of Hardened AISI H13 Steel

TLDR

Pulsed laser micromachining offers tool‑free, high‑precision removal of 3‑D features on difficult‑to‑cut metals, but its success depends on complex material absorption, reflectivity, and ablation characteristics, making careful selection of process parameters critical. The study aims to model and optimize process parameters for pulsed laser micromachining. Experiments were performed on AISI H13 hardened steel with a pulsed Nd:YAG laser, and the relationship between process parameters and quality metrics was modeled using artificial neural networks, followed by multi‑objective particle swarm optimization to minimize surface roughness and volume error. The ANN predictions matched experimental results, demonstrating that the models and PSO approach can accurately identify optimal settings for machining T‑shaped deep features with straight and tapered walls on hardened AISI H13 steel.

Abstract

This article focuses on modeling and optimizing process parameters in pulsed laser micromachining. Use of continuous wave or pulsed lasers to perform micromachining of 3-D geometrical features on difficult-to-cut metals is a feasible option due the advantages offered such as tool-free and high precision material removal over conventional machining processes. Despite these advantages, pulsed laser micromachining is complex, highly dependent upon material absorption reflectivity, and ablation characteristics. Selection of process operational parameters is highly critical for successful laser micromachining. A set of designed experiments is carried out in a pulsed Nd:YAG laser system using AISI H13 hardened tool steel as work material. Several T-shaped deep features with straight and tapered walls have been machining as representative mold cavities on the hardened tool steel. The relation between process parameters and quality characteristics has been modeled with artificial neural networks (ANN). Predictions with ANNs have been compared with experimental work. Multiobjective particle swarm optimization (PSO) of process parameters for minimum surface roughness and minimum volume error is carried out. This result shows that proposed models and swarm optimization approach are suitable to identify optimum process settings.