3D-Printed Object Identification Method using Inner Structure Patterns Configured by Slicer Software

Kubo, Yûki; Eguchi, Kana; Aoki, Ryôzô

doi:10.1145/3334480.3382847

Cited by 7 publications

(3 citation statements)

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“…Moreover, although not specifically addressed in this study, fill patterns, which vary among different slicer programs, serve as distinctive features for each program. Kubo et al [17] demonstrated the successful identification of 3D printed objects based on the differences in fill patterns. In addition, fill patterns are utilized as artistic patterns and can be used as evidence to distinguish genuine items from counterfeits [33].…”

Section: Software Domainmentioning

confidence: 99%

“…This type of research can be broadly categorized into two main areas. The first area involves tracing specific printers by leveraging the unique patterns or fingerprints of individual printers, which are based on subtle differences in the print quality [15][16][17]. The second area involves incorporating markers such as QR codes, watermarks, or tags into 3D digital files or objects during printing to identify the printing device used [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

“…They extended this identification method beyond printers to include filaments, devices, and layer thicknesses. Kubo et al [17] identified 3D objects by analyzing variations in auditory periodicity patterns resulting from differences in internal structural patterns. Separate studies were proposed for 3D printer identification based on hot-end temperature comparison [22].…”

Section: Introductionmentioning

confidence: 99%

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Improving Estimation of Layer Thickness and Identification of Slicer for 3D Printing Forensics

Shim,

Hou

2023

Sensors

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This study emphasizes the significance of estimating the layer thickness and identifying slicer programs in the realm of 3D printing forensics. With the progress in 3D printing technology, precise estimation of the layer thickness has become crucial. However, previous research on layer thickness estimation has mainly treated the problem as a classification task, which is inadequate for continuous layer thickness parameters. Furthermore, previous studies have concentrated on hardware-based printer identification, but the identification of slicer programs through 3D objects is a vital aspect of the software domain and can provide valuable clues for 3D printing forensics. In this study, a regression-based approach utilizing a vision transformer model was proposed. Experiments conducted on the SI3DP++ dataset demonstrated that the proposed model could handle a broad range of data and outperform the current classification models. Additionally, this study proposed a new research direction by introducing slicer program identification, which significantly contributes to the field of 3D printing forensics.

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Section: Software Domainmentioning

confidence: 99%