Advancing Digital Twin-Based Virtual Part Inspection in Additive Manufacturing: Systematic Literature Review of Virtual Inspection Methods in FFF

This paper presents a systematic literature review that analyzes virtual inspection methods (VIMs) in the context of additive manufacturing (AM), with a specific focus on fused filament fabrication (FFF) technology. Virtual part inspection, enabled by digital twin-based approaches, offers a promising way to improve quality assurance in AM processes. A structured methodology following the PRISMA 2020 statement was used to identify and analyze 39 relevant publications. The investigation aims to systematically address research questions related to quality characteristics, input parameters, and types of virtual inspection methods proposed in the literature. The results indicate that VIMs predominantly employ artificial intelligence methods compared to analytical and numerical methods. ISO 17296-3 outlines 16 quality characteristics for AM. However, the literature only addresses eight, with dimensional accuracy being the most frequently targeted aspect. VIMs utilize various input parameters, such as camera images, temperature data, and printing parameters, to deduce the quality characteristics of additively manufactured parts. The discussion focuses on the implications of these findings for improving quality assurance practices and identifies future research directions for VIMs. The overarching goal is to enhance the overall quality assurance process in AM.