Abstract
Additive manufacturing has emerged as one of the revolutionary technologies of today, enabling quick prototyping, customized production, and reduced material waste. However, its reliability is often weakened due to faults arising during printing, which remain undetected and, thus, give rise to product defects, waste generation, and safety issues. Most of the existing fault detection methods suffer from limited accuracy, poor adaptability within different printing conditions, and a lack of real-time monitoring capability. These factors critically limit their effectiveness in practical deployment. To address these limitations, the current study proposes a novel process control approach for additive manufacturing with the integration of advanced segmentation, detection, and monitoring strategies. The implemented framework involves segmentation of layer regions using MaskLab-CRFNet, integrating Mask R-CNN, DeepLabv3, and Conditional Random Fields for precise defect location; detection is performed by MoShuResNet, hybridizing MobileNetV3, ShuffleNet, and Residual U-Net for lightweight yet robust fault classification; and monitoring is done by BLC-MonitorNet, which incorporates Bayesian deep networks, ConvAE-LSTM, and convolutional autoencoders together for reliable real-time anomaly detection. Experimental evaluation demonstrates superior performance, with the achievement of 99.31% accuracy and 97.73% sensitivity. This work presents a reliable and interpretable process control framework for additive manufacturing that will improve safety, efficiency, and sustainability.