A Theoretical Framework for Intelligent Laser Processing: Closing the Loop with In-Situ Diagnostics and Machine Learning

This study presents a comprehensive theoretical framework for the real-time control of laser-matter interactions by integrating in-situ diagnostics with machine learning (ML). Moving beyond empirical open-loop systems, we conceptualize laser processing as a cyber-physical system governed by nonlinear dynamics. The core of our framework is a state-space model where a ML-based observer continuously estimates the process state (e.g., melt pool depth, temperature) from high-speed diagnostic data (e.g., imaging, spectroscopy). This estimate drives a reinforcement learning controller that generates optimal laser parameters (e.g., power, speed) to maintain a desired outcome. Using Lyapunov stability theory, we derive a critical learning rate threshold ne that guarantees closed-loop stability. Our analysis identifies key patterns: control performance is heavily dependent on diagnostic data quality, with high-speed imaging (>۵۰ kHz) enabling over ۷۰% improvement in spatter suppression. The framework also predicts emergent, non-intuitive control policies, such as laser power "dithering" for keyhole stabilization. This work provides a foundational theoretical blueprint for developing self-optimizing laser systems in additive manufacturing and materials processing, outlining a clear path from data acquisition to intelligent actuation.

کلیدواژه ها:

Laser-Matter Interaction ، In-Situ Diagnostics ، Machine Learning ، Real-Time Control ، Cyber-Physical Systems ، Additive Manufacturing ، State-Space Control