Machine Vision-Based Scanning Strategy for Defect Detection in Post-Additive Manufacturing

Abstract

The surge in 3D printer availability, and its applications over the past decade as an alternative to industry-standard subtractive manufacturing, has revealed a lack of post-manufacturing quality control. Developers have looked towards automated machine learning (ML) and machine-vision algorithms, which can be effective in developing such additive manufacturing (AM) technologies for industry-wide adoption. Currently, most research has explored in-situ monitoring methods, which aim to detect printing errors during manufacturing. A significant limitation is the single, fixed monitoring angle and low resolution, which fail to identify small or hidden defects due to part geometry. Therefore, we investigated a novel ex-situ scanning strategy that combines the advantages of robotics and machine vision to address the limitations; specifically, the viability of image-recognition algorithms in the context of post-fabrication defect detection, and how such algorithms can be integrated into current infrastructure by automatically classifying surface faults in printed parts. A state-of-the-art and widely accepted ML-based vision model, YOLO, was adapted and trained by scanning for prescribed defect categories in a sample of simple parts to identify the strengths of this method over in-situ monitoring. An automated scanning algorithm that uses a KUKA robotic arm and high-definition camera is proposed and its performance was assessed according to the percentage of accurate defect predictions, in comparison with a typical in-situ model.