In Situ Monitoring and Control of Laser-Directed Energy Deposition with Wire – Part 2: Geometry and Hardness Modeling and Closed-Loop Control

Laser-directed energy deposition with wire (L-DED/W) has gained attention due to its high deposition and utilization rates. However, components fabricated using this method often exhibit geometric inaccuracies and anisotropic mechanical properties, primarily stemming from unstable deposition dynamics and complex thermomechanical phenomena. To enable in situ monitoring and closed-loop control of final qualities such as geometry and hardness—which cannot be directly measured during deposition—this study employs the parameter–signature–quality (PSQ) framework.This paper, the second in a two-part series, builds upon the experimental foundation and insights developed in the first part to complete the steps toward real-time multi-input multi-output (MIMO) closed-loop control of the L-DED/W process, targeting simultaneous regulation of geometric accuracy and hardness. First, a high-fidelity MIMO system identification model is developed using a long-short-term-memory network, capturing the nonlinear and dynamic relationships between process parameters (power and speed) and melt pool signatures (width, size, and length) with high accuracy. Second, hardness is classified using a combination of process parameters and melt pool signatures, achieving strong predictive performance suitable for real-time application within the PSQ framework. Finally, a fuzzy logic controller is integrated with the PSQ models to achieve closed-loop MIMO control, demonstrating effective regulation of melt pool geometry while maximizing the likelihood of achieving high hardness, even under process uncertainties for real-world production.This study presents the first successful integration of fuzzy logic control and the PSQ framework for simultaneous control of both mechanical and geometric properties in a directed energy deposition process. It contributes to the broader field by demonstrating the feasibility of in situ regulation of otherwise unmeasurable final qualities and highlights fuzzy logic control as a flexible and computationally efficient alternative for multi-objective control in additive manufacturing.