Abstract
The article presents the development of a hybrid approach to quality control in additive manufacturing using the Fused Deposition Modeling (FDM) method, based on the integration of artificial intelligence techniques and statistical analysis. The study substantiates the need to shift from traditional quality control methods to intelligent monitoring systems capable of real-time defect detection and process analysis. The results of the analysis emphasize the effective application of techniques such as ANOVA, PCA, artificial intelligence algorithms, and image-based recognition systems in identifying irregularities and predicting potential issues in additive production. These technologies also play a vital role in fine-tuning critical FDM process parameters. The proposed integrated approach demonstrates considerable advantages in enhancing accuracy, process uniformity, and overall operational stability in 3D printing environments.