Additive manufacturing (AM) has transformed modern manufacturing by enabling the layer-by-layer fabrication of complex components directly from digital models. However, variations in process parameters, material behavior, thermal conditions, and machine dynamics can significantly affect product quality, dimensional accuracy, and process stability. This review examines advanced closed-loop control techniques for additive manufacturing, emphasizing real-time monitoring, feedback control, and intelligent process regulation. The AM process, major printing technologies, and conventional closed-loop control concepts are first discussed to establish the fundamental framework. In-situ monitoring techniques, including imaging and acoustic sensing, are examined for real-time defect detection and process validation. Advanced control strategies such as Proportional-Integral-Derivative (PID) control, Adaptive Control, Gain Scheduling, Model Reference Adaptive Systems (MRAS), and fuzzy logic-based control are then reviewed. These approaches enable dynamic adjustment of printing parameters in response to process deviations, thereby improving manufacturing consistency and reducing defects. The review also highlights an integration of AI with closed-loop systems to support predictive and autonomous control. Finally, current challenges and research opportunities are identified, emphasizing intelligent sensing, adaptive algorithms, and data-driven control for reliable and autonomous additive manufacturing.

Additive Manufacturing, Closed-Loop Control, In-Situ Monitoring

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