Vision-Based Quality Assurance of Composites Printed by Extrusion-Based 3D-Printers

Abstract

Additive manufacturing (AM) has been emerging as a promising production approach due to its expanded design freedoms and cost-effective batch size. Yet, its commercial acceptance is hindered by technical challenges in terms of quality assurance and defects of printed parts. Conventionally, this results in failed parts, waste of time, labor, and material. Though there have been successful attempts for process monitoring and quality assurance for single-material additive manufacturing processes, limited work has been reported for multi-material printing applications. This paper proposes a vision-based method for characterizing the material deposition accuracy of multi-material printing. More specifically, layer-wise images of a printed part are acquired, segmented, and compared with its equivalent digital reference, and position accuracy is measured by pixel differences, which provides a feasible solution for evaluating the overall performance of the multi-material AM process. An exploratory image acquiring setup using an offline camera is presented to experimentally validate the feasibility of the proposed workflow. The study concludes that detecting material deposition errors for two contrasting materials offers promising feasibility by using image processing methods and using the histogram of the image can quantify the degree of inaccuracy. This research provides a preliminary base for further research in the direction of integrating image acquisition systems with the printer, automating the image processing stage and extending the image histogram reading techniques for higher precision.