Efficient Surface Roughness Prediction in Laser Micromachining via Explainability‐Driven Feature Reduction

Ultra-short pulse (USP) laser micromachining is a key technology for sustainable manufacturing, offering high precision and minimal thermal damage across a wide range of materials. To enable its effective deployment in industrial environments, it is essential to develop monitoring systems capable of accurately predicting surface roughness at early processing stages, regardless of the initial workpiece condition. Given the high dimensionality of sensor data typically involved, real-time applicability requires lightweight, interpretable and computationally efficient machine learning (ML) models. This work presents an ML-based framework that addresses these requirements through explainability-driven feature reduction. By identifying and selecting the most relevant sensor features, the approach reduces input dimensionality while preserving model performance. Additionally, the computational cost of feature extraction is evaluated to assess the framework's feasibility in real-time scenarios. Overall, the proposed system is designed to adapt across multiple preprocessing techniques while minimizing processing latency, supporting the deployment of efficient monitoring solutions for industrial USP laser micromachining.