While extrusion-based Additive Manufacturing (AM) facilitates the production of intricately shaped parts especially for polymer processing with customized geometries, the process’s diverse parameters often lead to various defects that significantly impact the quality and hence the mechanical properties of the manufactured parts. One prominent defect in polymer-based AM is warping, which can significantly compromise the quality of 3D-printed parts. In this work, a deep learning (DL) approach based on convolutional neural networks (CNN) was developed to automatically detect warpage defects in 3D-printed parts, subsequently leading to quality control of the 3D-printed parts. Experiments were conducted using a customized Delta 3D printer with acrylonitrile butadiene styrene (ABS) and polylactic acid (PLA) materials, following the ASTM D638 tensile specimen geometry and employing design of experiments (DoE) methodology. The CNN dataset was generated by autonomously capturing high-quality (HQ) images at regular intervals using a Raspberry Pi (RPi) setup, storing the timestamped images on Google Drive, and categorizing them into ‘warped’and ‘unwarped’classes based on user-defined criteria. The novelty of this research lies in creating a setup for gathering image-based datasets and deploying a DL-based CNN for the real-time identification of warpage defects in 3D printed parts made of ABS and PLA materials, achieving an outstanding accuracy rate of 99.43%. This research furnishes engineers and manufacturers with a step to bolster quality control in polymer-based AM, offering automated defect correction through feedback control. By enhancing the reliability and efficiency of AM processes, it empowers practitioners to achieve higher standards of production.