Color Independent Quality Assessment of 3D Printed Surfaces Based on Image Entropy

Okarma, Krzysztof; Fastowicz, Jarosław

doi:10.1007/978-3-319-59162-9_32

Cited by 6 publications

(7 citation statements)

References 18 publications

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“…As mentioned earlier, the direct use of entropy might be combined with the additional factor, namely the variance of local entropy, as proposed in the conference paper [26]. Since low entropy is typical for high-quality 3D prints and high variance of the local entropy indicates low quality, it has been assumed that their combination should provide a better classification, particularly when expressed in logarithmic scale.…”

Section: Discussion Of the Proposed Methodsmentioning

confidence: 99%

“…7. (4) MV AVGcol = MV combined − Fig. 6 Results obtained for the mean-variance (MV)-based metric [26] and division into 256 fragments The results obtained using the proposed approach are shown in Fig. 8, whereas their equivalents achieved assuming the division of images into 64 blocks are illustrated in Fig.…”

Section: Discussion Of the Proposed Methodsmentioning

confidence: 99%

“…Since a strong influence of the filament's colour on the entropy of the scanned image can be observed, a colour-independent quality evaluation should be based on the combination of the local entropy values and their variances calculated for the RGB and HSV colour models. The detailed results obtained for 18 high-and low-quality samples obtained for five colours of PLA filaments can be found in our earlier publication [26], which is the basic point for this paper.…”

Section: Quality Assessment Of 3d Printsmentioning

confidence: 98%

“…A further extension of this approach has been described in the paper [26]. Since a strong influence of the filament's colour on the entropy of the scanned image can be observed, a colour-independent quality evaluation should be based on the combination of the local entropy values and their variances calculated for the RGB and HSV colour models.…”

Section: Quality Assessment Of 3d Printsmentioning

confidence: 99%

“…Results obtained for the mean-variance (MV)-based metric proposed in[26] moderately high quality samples moderately low quality samples low quality samples high quality samples with cracks moderately high quality samples with cracks moderately low quality samples with cracks low quality samples with cracks…”

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Improved quality assessment of colour surfaces for additive manufacturing based on image entropy

Okarma

Fastowicz

2020

Pattern Anal Applic

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A reliable automatic visual quality assessment of 3D-printed surfaces is one of the key issues related to computer and machine vision in the Industry 4.0 era. The colour-independent method based on image entropy proposed in the paper makes it possible to detect and identify some typical problems visible on the surfaces of objects obtained by additive manufacturing. Depending on the quality factor, some of such 3D printing failures may be corrected during the printing process or the operation can be aborted to save time and filament. Since the surface quality of 3D-printed objects may be related to some mechanical or physical properties of obtained objects, its fast and reliable evaluation may also be helpful during the quality monitoring procedures. The method presented in the paper utilizes the assumption of the increase of image entropy for irregularly distorted 3D-printed surfaces. Nevertheless, because of the local nature of distortions, the direct application of the global entropy does not lead to satisfactory results of automatic surface quality assessment. Therefore, the extended method, based on the combination of the local image entropy and its variance with additional colour adjustment, is proposed in the paper, leading to the proper classification of 78 samples used during the experimental verification of the proposed approach.

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Section: Discussion Of the Proposed Methodsmentioning

confidence: 99%

Section: Discussion Of the Proposed Methodsmentioning

confidence: 99%

Section: Quality Assessment Of 3d Printsmentioning

confidence: 98%

Section: Quality Assessment Of 3d Printsmentioning

confidence: 99%

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confidence: 99%

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