Abstract
Additive manufacturing has the potential to become a standard method for manufacturing products, and product information is indispensable for the item distribution system. While most products are given barcodes to the exterior surfaces, research on embedding barcodes inside products is underway. This is because additive manufacturing makes it possible to carry out manufacturing and information adding at the same time, and embedding information inside does not impair the exterior appearance of the product. However, products that have not been embedded information can not be identified, and embedded information can not be rewritten later. In this study, we have developed a product identification system that does not require embedding barcodes inside. This system uses a transmission image of the product which contains information of each product such as different inner support structures and manufacturing errors. We have shown through experiments that if datasets of transmission images are available, objects can be identified with an accuracy of over 90%. This result suggests that our approach can be useful for identifying objects without embedded information.
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References
Al, C.M., Yaman, U.: Improving the strength of additively manufactured objects via modified interior structure (2017)
Baich, L.J., Manogharan, G., Marie, H.: Study of infill print design on production cost-time of 3D printed ABS parts (2016)
Bakar, N.S.A., Alkahari, M.R., Boejang, H.: Analysis on fused deposition modelling performance. J. Zhejiang Univ. Sci. A 11(12), 972–977 (2010). https://doi.org/10.1631/jzus.A1001365
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). https://doi.org/10.1007/11744023_32
Cantrell, J., et al.: Experimental characterization of the mechanical properties of 3D-printed abs and polycarbonate parts. Rapid Prototyp. J. 23 (2017). https://doi.org/10.1108/RPJ-03-2016-0042
Chacón, J., Caminero, M., García-Plaza, E., López, P.: Additive manufacturing of PLA structures using fused deposition modelling: effect of process parameters on mechanical properties and their optimal selection. Mater. Des. 124, 143–157 (2017). https://doi.org/10.1016/j.matdes.2017.03.065
Dogan, M.D., et al.: G-ID: identifying 3D prints using slicing parameters. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–13. CHI 2020. Association for Computing Machinery, New York (2020). https://doi.org/10.1145/3313831.3376202
Fernandez-Vicente, M., Calle, W., Ferrandiz, S., Conejero, A.: Effect of infill parameters on tensile mechanical behavior in desktop 3D printing (2016)
Grimm, T.: Fused deposition modeling: a technology evaluation. Time-Compression Technol. 11(2), 1–6 (2003)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2016)
KennyLAlvarez, C., RodrigoFLagos, C., Aizpun, M.: Investigating the influence of infill percentage on the mechanical properties of fused deposition modelled ABS parts (2016)
Kumar, P.R., Ahuja, I.S., Singh, T.: Application of fusion deposition modelling for rapid investment casting – a review (2012)
Lanzotti, A., Grasso, M., Staiano, G., Martorelli, M.: The impact of process parameters on mechanical properties of parts fabricated in PLA with an open-source 3-D printer (2015)
Li, D., Nair, A.S., Nayar, S.K., Zheng, C.: Aircode: Unobtrusive physical tags for digital fabrication. arXiv abs/1707.05754 (2017)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004). https://doi.org/10.1023/B:VISI.0000029664.99615.94
Maekawa, S., Nitta, K., Matoba, O.: Transmissive mirror device with micro dihedral corner reflector array-the mirror forming a real image. ITE J. 30, 49–52 (2006)
Maia, H.T., Li, D., Yang, Y., Zheng, C.: LayerCode: optical barcodes for 3D printed shapes. ACM Trans. Graph. 38, 112:1–112:14 (2019)
Miura, N., Nagasaka, A., Miyatake, T.: Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification. Mach. Vis. Appl. 15(4), 194–203 (2004). https://doi.org/10.1007/s00138-004-0149-2
Miura, N., Nagasaka, A., Miyatake, T.: Extraction of finger-vein patterns using maximum curvature points in image profiles. IEICE Trans. Inf. Syst. 90(8), 1185–1194 (2007)
Novakova-Marcincinova, L., Kuric, I.: Basic and advanced materials for fused deposition modeling rapid prototyping technology (2012)
Okada, A., Silapasuphakornwong, P., Suzuki, M., Torii, H., Takashima, Y., Uehira, K.: Non-destructively reading out information embedded inside real objects by using far-infrared light. In: SPIE Optical Engineering + Applications (2015)
Popescu, D., Zapciu, A., Amza, C.G., Baciu, F., Marinescu, R.: FDM process parameters influence over the mechanical properties of polymer specimens: a review. Polym. Test. 69, 157–166 (2018). https://doi.org/10.1016/j.polymertesting.2018.05.020
Rangisetty, S., Peel, L.: The effect of infill patterns and annealing on mechanical properties of additively manufactured thermoplastic composites, p. V001T08A017 (Sept 2017). https://doi.org/10.1115/SMASIS2017-4011
Rankouhi, B., Javadpour, S., Delfanian, F., Letcher, T.: Failure analysis and mechanical characterization of 3D printed abs with respect to layer thickness and orientation. J. Fail. Anal. Prev. 16, 467–481 (2016)
Roberson, D.A., Espalin, D., Wicker, R.B.: 3D printer selection: a decision-making evaluation and ranking model (2013)
Schofield, J., Dawson, M.: Evaluation of dimensional accuracy and material properties of the makerbot 3D desktop printer. Rapid Prototyp. J. 21, 618–627 (2015). https://doi.org/10.1108/RPJ-09-2013-0093
Singh, S., Ramakrishna, S., Singh, R.: Material issues in additive manufacturing: a review. J. Manuf. Process. 25, 185–200 (2017). https://doi.org/10.1016/j.jmapro.2016.11.006
Song, W., Kim, T., Kim, H.C., Choi, J.H., Kong, H.J., Lee, S.R.: A finger-vein verification system using mean curvature. Pattern Recogn. Lett. 32, 1541–1547 (2011)
Suzuki, M., Matumoto, T., Takashima, Y., Torii, H., Uehira, K.: Information hiding inside 3-D printed objects by forming high reflectance projections. In: ICVIP (2017)
Tanaka, K., Mukaigawa, Y., Matsushita, Y., Yagi, Y.: Descattering of transmissive observation using parallel high-frequency illumination. In: IEEE International Conference on Computational Photography (ICCP), pp. 1–8 (2013)
Torrado, A.R., Roberson, D.A.: Failure analysis and anisotropy evaluation of 3D-printed tensile test specimens of different geometries and print raster patterns. J. Fail. Anal. Prev. 16, 154–164 (2016)
Torres, J., Cole, M., Owji, A., DeMastry, Z., Gordon, A.P.: An approach for mechanical property optimization of fused deposition modeling with polylactic acid via design of experiments (2016)
Ueno, R., Mitsugi, J.: Barcode fingerprinting: Unique identification of commercial products with their jan/ean/ucc barcode, pp. 416–420 (Feb 2018). https://doi.org/10.1109/WF-IoT.2018.8355122
Willis, K.D.D., Wilson, A.D.: Infrastructs: fabricating information inside physical objects for imaging in the terahertz region. ACM Trans. Graph. 32, 138:1–138:10 (2013)
Wong, K.V., Hernandez, A.: A review of additive manufacturing. ISRN Mech. Eng. 2012, 10 (2012)
Yang, J., Shi, Y.: Finger-vein ROI localization and vein ridge enhancement. Pattern Recogn. Lett. 33, 1569–1579 (2012)
Yang, W., Huang, X., Zhou, F., Liao, Q.: Comparative competitive coding for personal identification by using finger vein and finger dorsal texture fusion. Inf. Sci. 268, 20–32 (2014)
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Appendices
A A Layer Feature
The transmission image of the product is a photograph of the light intensity emitted from a certain point after passing through various paths. The smallest unit of a product that constitutes such a phenomenon can be defined as a “layer”. Regardless of the shape, infill pattern, infill density, and infill position, the transmitted light has only passed through many layers. In other words, the stacked feature of each layer becomes a transmission image. We have already defined the characteristics of each layer in Sect. 3.2. Therefore, here we verified how the transmission image changes according to the change in the characteristics by changing the parameters of each layer.
The elements that make up the layer are broadly divided into two types: those that affect the shape of the product and those that affect the state of the product. The elements that affect the shape are “Layer Thickness” and “Printing Width”. The elements that affect the state are “Printing Speed”, “Temperature of Heat-Bed”, “Temperature of Printing Head”, “Retraction Distance”, and “Environment”. The reason why “Inner Support Structure” is not included in these elements is that “Inner Support Strucure” is established by the positional relationship of multiple layers.
1.1 A.1 Experimental Setup
In order to confirm the effect on the transmission image of one layer, we installed a near infrared light source, a thin plate equivalent to one layer, and a near infrared camera as shown in Fig. 12(a). To make it easier to see how the light source changed before and after passing through the layer, the shape of the light source entering the thin plate was made circular and the light source was collimated. However, since coherent light is used as a light source, the acquired image is accompanied by speckles. Therefore, in order to give priority to the measurement of the rough change of the light source shape, the influence of speckle was ignored and the saturation was allowed. Also, in order to observe how the light diffuses after passing through one layer, it is necessary to take a transmission image after passing through multiple layers. In addition, we prepared a foundation with grooves formed at intervals of 5 mm, and obtained a transmission image by fitting multiple sheets into the foundation while changing the distance between the sheets.
Changes in transmission image due to layer characteristics. (a) a photograph showing the experimental setup for imaging. (b) the actually acquired transmission image. (c) an image of (b) displayed in pseudo color. (d)–(g) the change of the transmission image when “Layer Thickness” is changed. (h), (i) the change of the transmission image when “Printing Speed” is changed.
1.2 A.2 Effect of Parameters
Of the two types of elements that make up the layer, changes in shape are thought to have a large effect on the transmitted image. Therefore, we first conducted an experiment to observe changes in the transmission image when the parameters about the shape were changed. While “Printing Width” has the same value when using the same manufacturing equipment, “Layer Thickness” can be easily changed from the settings provided in the user interface. The transmission images were compared. Figure 12(b) shows what changes appear in the transmission image when the layer thickness is changed. In this experiment, four layer thicknesses of 0.1 mm, 0.2 mm, 0.3 mm, and 0.4 mm were prepared. We confirmed that the transmission image changed according to the thickness and positional relationship of the layer.
Next, an experiment was conducted to observe the change in the transmitted image accompanying the change in the parameters affecting the state. “Printing Speed” was selected as an element related to the state. The reason for verifying only one of the many state parameters is that the change in the state is considered to have a lower influence on the transmission image than the change in the shape. Figure 12(h) and (i) show the imaging results. Compared to when the shape parameters were changed (Fig. 12(d)–(g)), the effect on the transmitted image was small.
As described above, we have compared the transmission images of elements related to shape and elements related to state. Although there was a considerable change in both, the shape change is considered to be easy to use as a clue for identification.
B Robust Transmissive Images
When using a transmission image for object identification, it is desirable that the transmission image is not affected even if the orientation of the backlight or the position and orientation of the object changes. In order to achieve such robustness of the transmitted image, it is recommended that the transmitted light sufficiently passes through multipath. In general, reflected light, internal diffusion, and transmitted light are generated when light enters an object. If sufficient internal diffusion occurs, the transmission image obtained should be robust to the orientation and position of the target.
In order to verify the robustness of this system, we combined an XY scanning stage and a rotating stage to change the orientation and position of the target (Fig. 13(left)). The XY scanning stage is Thorlabs MLS203-1, and the rotary stage is Thorlabs PRMTZ8. In addition, for the object to be imaged, an object with a primitive shape was prepared: a cube with a size of 50\(\times \)50\(\times \)50 [mm], an infill density of 10%, and an infill pattern of diamond fill. Figure 13(right) show the imaging results when the XY scanning stage is moved, the target is rotated, or the camera position is changed. Looking at the region of interest in each imaging result, it was found that the orientation and position of the target had little effect on the transmitted image.
(left) a picture of an imaging system to verify robustness. (right top) the imaging results when the XY stage is moved in X and Y directions. (right middle) the imaging results when rotating the rotation stage. (right bottom) the result when the camera is moved in X direction and imaged toward the center of the object.
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Yamamoto, K., Kawamura, R., Takazawa, K., Osone, H., Ochiai, Y. (2021). A Preliminary Study for Identification of Additive Manufactured Objects with Transmitted Images. In: Degen, H., Ntoa, S. (eds) Artificial Intelligence in HCI. HCII 2021. Lecture Notes in Computer Science(), vol 12797. Springer, Cham. https://doi.org/10.1007/978-3-030-77772-2_29
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