Towards Sustainable Precision: Machine Learning for Laser Micromachining Optimization

TL;DR

This paper presents a machine learning framework that improves surface quality assessment in ultra-short pulse laser micromachining, enabling real-time monitoring with reduced computational costs and enhanced generalizability across preprocessing techniques.

Contribution

The paper introduces a novel machine learning approach that optimizes real-time surface quality monitoring in laser micromachining, addressing data complexity and computational efficiency.

Findings

Outperforms previous models in generalizability across preprocessing techniques

Reduces computational requirements for training and real-time analysis

Enhances surface quality assessment accuracy

Abstract

In the pursuit of sustainable manufacturing, ultra-short pulse laser micromachining stands out as a promising solution while also offering high-precision and qualitative laser processing. However, unlocking the full potential of ultra-short pulse lasers requires an optimized monitoring system capable of early detection of defective workpieces, regardless of the preprocessing technique employed. While advances in machine learning can help predict process quality features, the complexity of monitoring data necessitates reducing both model size and data dimensionality to enable real-time analysis. To address these challenges, this paper introduces a machine learning framework designed to enhance surface quality assessment across diverse preprocessing techniques. To facilitate real-time laser processing monitoring, our solution aims to optimize the computational requirements of the machine learning model. Experimental results show that the proposed model not only outperforms the generalizability achieved by previous works across diverse preprocessing techniques but also significantly reduces the computational requirements for training. Through these advancements, we aim to establish the baseline for a more sustainable manufacturing process.