Enhancing dimensional accuracy in 3D printing: a novel software algorithm for real-time quality assessment

Notably, despite the widespread application of 3D printing technology across diverse industries, issues like dimensional variations continue to limit its full-scale production potential. In this research, the dimensional variation between the CAD model and a 3D printed specimen by extrusion technique is investigated by a developed software algorithm. In contrast to previously employed techniques such as coordinate measuring machines, laser scanning, optical profilometry, and CT scanning, which have been highlighted in the literature, the developed software algorithm is cheap and stands out by relying on computer vision for the assessment of dimensional deviations in the printed model. The proposed software algorithm assesses the dimensional quality of 3D printed components through a comprehensive three-step methodology: preparation, measurement, and analysis. The software scrutinizes both the CAD model and the G-code-sliced model, extracting crucial dimensional data that serves as a reference for monitoring deviations during the actual 3D printing process. The software is fully tested across a diverse 3D geometry, capable of predicting real-time dimensional variances that could otherwise result in printing failures. The solution not only ensures the preservation of economic and human resources in additive manufacturing but also enhances the overall efficiency of the process. The paper concludes that the choice of the appropriate method should be contingent on the specific part type and the level of accuracy required.