This project focuses on developing a remote control device for the real-time detection and correction of errors in fused deposition modeling (FDM) 3D printing. It utilizes a Raspberry Pi computer and a webcam to capture images while a neural network trained with a dataset generated by the research team identifies errors such as warping, stringing, and spaghetti. Information is efficiently transmitted via MQTT, with instant notifications through Telegram and a user interface. The methodology spans from training the neural network to integrated control strategies with the remote device. Evaluation highlights high precision using confusion matrices and IoU, promising substantial improvements in industrial and critical 3D printing environments.