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3D Sensing System for Laser-Induced Breakdown Spectroscopy-Based Metal Scrap Identification

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Abstract

Laser-induced breakdown spectroscopy (LIBS) is an analysis technique that determines the elemental composition of a target material. Metal scraps have a range of shapes and are contaminated with other substances such as paint or dirt. This makes it difficult to recognize each piece of metal scrap accurately and to obtain clear LIBS emission spectra of the target metals. In this study, two image processing algorithms are proposed to measure the three-dimensional shapes of metal scraps and to calculate the optimized (i.e., relatively clean and flat) surface areas of metal scraps. It was confirmed that 25% higher maximum classification accuracy was achieved when LIBS spectra were acquired from optimized rather than non-optimized (i.e., contaminated) surfaces.

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References

  1. Habib, K., Hansdóttir, S. T., & Habib, H. (2020). Critical metals for electromobility: Global demand scenarios for passenger vehicles, 2015–2050. Resources, Conservation and Recycling, 154, 104603. https://doi.org/10.1016/j.resconrec.2019.104603

    Article  Google Scholar 

  2. Sakai, S., Yoshida, H., Hiratsuka, J., Vandecasteele, C., Kohlmeyer, R., Rotter, V. S., Yano, J., et al. (2014). An international comparative study of end-of-life vehicle (ELV) recycling systems. Journal of Material Cycles and Waste Management, 16(1), 1–20. https://doi.org/10.1007/s10163-013-0173-2

    Article  Google Scholar 

  3. Santore, R. C., Ryan, A. C., Kroglund, F., Rodriguez, P. H., Stubblefield, W. A., Cardwell, A. S., Nordheim, E., et al. (2018). Development and application of a biotic ligand model for predicting the chronic toxicity of dissolved and precipitated aluminum to aquatic organisms. Environmental Toxicology and Chemistry, 37(1), 70–79. https://doi.org/10.1002/etc.4020

    Article  Google Scholar 

  4. Rahman, M. A., Lee, S.-H., Ji, H. C., Kabir, A. H., Jones, C. S., & Lee, K.-W. (2018). Importance of mineral nutrition for mitigating aluminum toxicity in plants on acidic soils: Current status and opportunities. International Journal of Molecular Sciences, 19(10), 3073. https://doi.org/10.3390/ijms19103073

    Article  Google Scholar 

  5. Park, J. W., Yi, H.-C., Park, M. W., & Sohn, Y. T. (2014). A monitoring system architecture and calculation of practical recycling rate for end-of-life vehicle recycling in Korea. International Journal of Precision Engineering and Manufacturing-Green Technology, 1(1), 49–57. https://doi.org/10.1007/s40684-014-0008-1

    Article  Google Scholar 

  6. Buffa, G., Baffari, D., Ingarao, G., & Fratini, L. (2020). Uncovering technological and environmental potentials of aluminum alloy scraps recycling through friction stir consolidation. International Journal of Precision Engineering and Manufacturing-Green Technology, 7(5), 955–964. https://doi.org/10.1007/s40684-019-00159-5

    Article  Google Scholar 

  7. Kim, D.-H., Kim, J.-H., Kim, Y.-G., Lim, J.-H., Park, H.-J., & Ye, B.-J. (2018). Evaluation of microstructure and mechanical properties on solution heat treatment of recycled A319 cutting chip. International Journal of Precision Engineering and Manufacturing-Green Technology, 5(3), 427–433. https://doi.org/10.1007/s40684-018-0018-5

    Article  Google Scholar 

  8. Lee, C.-M., Choi, Y.-H., Ha, J.-H., & Woo, W.-S. (2017). Eco-friendly technology for recycling of cutting fluids and metal chips: A review. International Journal of Precision Engineering and Manufacturing-Green Technology, 4(4), 457–468. https://doi.org/10.1007/s40684-017-0051-9

    Article  Google Scholar 

  9. Jantzi, S. C., Motto-Ros, V., Trichard, F., Markushin, Y., Melikechi, N., & De Giacomo, A. (2016). Sample treatment and preparation for laser-induced breakdown spectroscopy. Spectrochimica Acta Part B: Atomic Spectroscopy, 115, 52–63. https://doi.org/10.1016/j.sab.2015.11.002

    Article  Google Scholar 

  10. Peng, J., Liu, F., Zhou, F., Song, K., Zhang, C., Ye, L., & He, Y. (2016). Challenging applications for multi-element analysis by laser-induced breakdown spectroscopy in agriculture: A review. TrAC Trends in Analytical Chemistry, 85, 260–272. https://doi.org/10.1016/j.trac.2016.08.015

    Article  Google Scholar 

  11. Li, Y., Tian, D., Ding, Y., Yang, G., Liu, K., Wang, C., & Han, X. (2018). A review of laser-induced breakdown spectroscopy signal enhancement. Applied Spectroscopy Reviews, 53(1), 1–35. https://doi.org/10.1080/05704928.2017.1352509

    Article  Google Scholar 

  12. Hernández-García, R., Villanueva-Tagle, M. E., Calderón-Piñar, F., Durruthy-Rodríguez, M. D., Aquino, F. W. B., Pereira-Filho, E. R., & Pomares-Alfonso, M. S. (2017). Quantitative analysis of lead zirconate titanate (PZT) ceramics by laser-induced breakdown spectroscopy (LIBS) in combination with multivariate calibration. Microchemical Journal, 130, 21–26. https://doi.org/10.1016/j.microc.2016.07.024

    Article  Google Scholar 

  13. Guo, G., Niu, G., Shi, Q., Lin, Q., Tian, D., & Duan, Y. (2019). Multi-element quantitative analysis of soils by laser induced breakdown spectroscopy (LIBS) coupled with univariate and multivariate regression methods. Analytical Methods, 11(23), 3006–3013. https://doi.org/10.1039/C9AY00890J

    Article  Google Scholar 

  14. Wang, T., He, M., Shen, T., Liu, F., He, Y., Liu, X., & Qiu, Z. (2018). Multi-element analysis of heavy metal content in soils using laser-induced breakdown spectroscopy: A case study in eastern China. Spectrochimica Acta Part B: Atomic Spectroscopy, 149, 300–312. https://doi.org/10.1016/j.sab.2018.09.008

    Article  Google Scholar 

  15. Matsumoto, A., Tamura, A., Koda, R., Fukami, K., Ogata, Y. H., Nishi, N., Sakka, T., et al. (2016). A calibration-free approach for on-site multi-element analysis of metal ions in aqueous solutions by electrodeposition-assisted underwater laser-induced breakdown spectroscopy. Spectrochimica Acta Part B: Atomic Spectroscopy, 118, 45–55. https://doi.org/10.1016/j.sab.2016.02.005

    Article  Google Scholar 

  16. Torrione, P., Collins, L. M., & Morton, K. D. (2014). 5—Multivariate analysis, chemometrics, and machine learning in laser spectroscopy. In M. Baudelet (Ed.), Laser Spectroscopy for Sensing (pp. 125–164). Woodhead Publishing. https://doi.org/10.1533/9780857098733.1.125

    Chapter  Google Scholar 

  17. Pricylla Castro, J., & Rodrigues Pereira-Filho, E. (2016). Twelve different types of data normalization for the proposition of classification, univariate and multivariate regression models for the direct analyses of alloys by laser-induced breakdown spectroscopy (LIBS). Journal of Analytical Atomic Spectrometry, 31(10), 2005–2014. https://doi.org/10.1039/C6JA00224B

    Article  Google Scholar 

  18. Nardecchia, A., Fabre, C., Cauzid, J., Pelascini, F., Motto-Ros, V., & Duponchel, L. (2020). Detection of minor compounds in complex mineral samples from millions of spectra: A new data analysis strategy in LIBS imaging. Analytica Chimica Acta, 1114, 66–73. https://doi.org/10.1016/j.aca.2020.04.005

    Article  Google Scholar 

  19. Awasthi, S., Kumar, R., Devanathan, A., Acharya, R., & Rai, A. K. (2017). Multivariate methods for analysis of environmental reference materials using laser-induced breakdown spectroscopy. Analytical Chemistry Research, 12, 10–16. https://doi.org/10.1016/j.ancr.2017.01.001

    Article  Google Scholar 

  20. Lee, J. J., Moon, Y., Han, J. H., & Jeong, S. (2017). Analysis of major elements in pigmented melanocytic chicken skin using laser-induced breakdown spectroscopy. Journal of Biophotonics, 10(4), 523–531. https://doi.org/10.1002/jbio.201500343

    Article  Google Scholar 

  21. Awasthi, S., Kumar, R., Rai, G. K., & Rai, A. K. (2016). Study of archaeological coins of different dynasties using libs coupled with multivariate analysis. Optics and Lasers in Engineering, 79, 29–38. https://doi.org/10.1016/j.optlaseng.2015.11.005

    Article  Google Scholar 

  22. Dequaire, T., Meslin, P.-Y., Beck, P., Jaber, M., Cousin, A., Rapin, W., Coll, P., et al. (2017). Analysis of carbon and nitrogen signatures with laser-induced breakdown spectroscopy; the quest for organics under Mars-like conditions. Spectrochimica Acta Part B: Atomic Spectroscopy, 131, 8–17. https://doi.org/10.1016/j.sab.2017.02.015

    Article  Google Scholar 

  23. Werheit, P., Fricke-Begemann, C., Gesing, M., & Noll, R. (2011). Fast single piece identification with a 3D scanning LIBS for aluminium cast and wrought alloys recycling. Journal of Analytical Atomic Spectrometry, 26(11), 2166–2174. https://doi.org/10.1039/C1JA10096C

    Article  Google Scholar 

  24. Brooks, L., & Gaustad, G. (2019). Positive Material Identification (PMI) Capabilities in the Metals Secondary Industry: An Analysis of XRF and LIBS Handheld Analyzers. In C. Chesonis (Ed.), Light Metals 2019 (pp. 1375–1380). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-05864-7_170.

    Chapter  Google Scholar 

  25. Campanella, B., Grifoni, E., Legnaioli, S., Lorenzetti, G., Pagnotta, S., Sorrentino, F., & Palleschi, V. (2017). Classification of wrought aluminum alloys by Artificial Neural Networks evaluation of Laser Induced Breakdown Spectroscopy spectra from aluminum scrap samples. Spectrochimica Acta Part B: Atomic Spectroscopy, 134, 52–57. https://doi.org/10.1016/j.sab.2017.06.003

    Article  Google Scholar 

  26. Noll, R., Fricke-Begemann, C., Connemann, S., Meinhardt, C., & Sturm, V. (2018). LIBS analyses for industrial applications—an overview of developments from 2014 to 2018. Journal of Analytical Atomic Spectrometry, 33(6), 945–956. https://doi.org/10.1039/C8JA00076J

    Article  Google Scholar 

  27. Cabalín, L. M., González, A., Ruiz, J., & Laserna, J. J. (2010). Assessment of statistical uncertainty in the quantitative analysis of solid samples in motion using laser-induced breakdown spectroscopy. Spectrochimica Acta Part B: Atomic Spectroscopy, 65(8), 680–687. https://doi.org/10.1016/j.sab.2010.04.012

    Article  Google Scholar 

  28. Tognoni, E., & Cristoforetti, G. (2016). [INVITED] Signal and noise in laser induced breakdown spectroscopy: An introductory review. Optics & Laser Technology, 79, 164–172. https://doi.org/10.1016/j.optlastec.2015.12.010

    Article  Google Scholar 

  29. Hudson, S. W., Craparo, J., De Saro, R., & Apelian, D. (2017). Applications of laser-induced breakdown spectroscopy (LIBS) in molten metal processing. Metallurgical and Materials Transactions B, 48(5), 2731–2742. https://doi.org/10.1007/s11663-017-1032-7

    Article  Google Scholar 

  30. Palanco, S., Baena, J. M., & Laserna, J. J. (2002). Open-path laser-induced plasma spectrometry for remote analytical measurements on solid surfaces. Spectrochimica Acta Part B: Atomic Spectroscopy, 57(3), 591–599. https://doi.org/10.1016/S0584-8547(01)00388-3

    Article  Google Scholar 

  31. Sato, T., Kawaguchi, Y., Akiyama, H., & Ohmura, H. (2018). Detection of contaminants on pre-bond surface by LIBS. The Journal of Adhesion, 94(9), 689–700. https://doi.org/10.1080/00218464.2017.1388169

    Article  Google Scholar 

  32. Weiss, J., Cabalín, L. M., & Laserna, J. J. (2017). Angle of observation influence on emission signal from spatially confined laser-induced plasmas. Applied Spectroscopy, 71(1), 87–96. https://doi.org/10.1177/0003702816666285

    Article  Google Scholar 

  33. Nicolas, G., Mateo, M. P., & Piñon, V. (2007). 3D chemical maps of non-flat surfaces by laser-induced breakdown spectroscopy. Journal of Analytical Atomic Spectrometry, 22(10), 1244–1249. https://doi.org/10.1039/B704682K

    Article  Google Scholar 

  34. Vukašinović, N., & Duhovnik, J. (2019). Optical 3D Geometry Measurments Based on Laser Triangulation. In N. Vukašinović & J. Duhovnik (Eds.), Advanced CAD Modeling: Explicit, Parametric, Free-Form CAD and Re-engineering (pp. 191–216). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-02399-7_9.

    Chapter  Google Scholar 

  35. Lee, M., Baek, S., & Park, S. (2017). 3D foot scanner based on 360 degree rotating-type laser triangulation sensor. In 2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE) (pp. 1065–1070). Presented at the 2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE). https://doi.org/10.23919/SICE.2017.8105700.

    Chapter  Google Scholar 

  36. Chavolla, E., Zaldivar, D., Cuevas, E., & Perez, M. A. (2018). Color Spaces Advantages and Disadvantages in Image Color Clustering Segmentation. In A. E. Hassanien & D. A. Oliva (Eds.), Advances in Soft Computing and Machine Learning in Image Processing (pp. 3–22). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-63754-9_1.

    Chapter  Google Scholar 

  37. Felzenszwalb, P. F., & Huttenlocher, D. P. (2004). Efficient graph-based image segmentation. International Journal of Computer Vision, 59(2), 167–181. https://doi.org/10.1023/B:VISI.0000022288.19776.77

    Article  MATH  Google Scholar 

  38. van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., Yu, T., et al. (2014). scikit-image: Image processing in Python. PeerJ, 2, e453. https://doi.org/10.7717/peerj.453

    Article  Google Scholar 

  39. Kurita, T., Otsu, N., & Abdelmalek, N. (1992). Maximum likelihood thresholding based on population mixture models. Pattern Recognition, 25(10), 1231–1240. https://doi.org/10.1016/0031-3203(92)90024-D

    Article  Google Scholar 

  40. Liu, L., Hua, Y., Zhao, Q., Huang, H., & Bovik, A. C. (2016). Blind image quality assessment by relative gradient statistics and adaboosting neural network. Signal Processing: Image Communication, 40, 1–15. https://doi.org/10.1016/j.image.2015.10.005

    Article  Google Scholar 

  41. Zhou, W., Yu, L., Qiu, W., Zhou, Y., & Wu, M. (2017). Local gradient patterns (LGP): An effective local-statistical-feature extraction scheme for no-reference image quality assessment. Information Sciences, 397–398, 1–14. https://doi.org/10.1016/j.ins.2017.02.049

    Article  Google Scholar 

  42. NIST: Atomic Spectra Database Lines Form. (2021). https://physics.nist.gov/PhysRefData/ASD/lines_form.html. Accessed 17 Mar 2021.

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Acknowledgements

This study was supported by the R&D Center for Valuable Recycling (Global-Top R&D Program) of the Ministry of Environment (Project No. 2016002250003).

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  1. Seongyun Park, Jaepil Lee, and Eunsung Kwon contributed equally to this work as co-first authors.

Authors and Affiliations

  1. School of Mechanical Engineering, Gwangju Institute of Science and Technology, Gwangju, 61005, Republic of Korea

    Seongyun Park, Jaepil Lee, Eunsung Kwon, Daewon Kim, Sungho Shin, Sungho Jeong & Kyihwan Park

  2. Defense Agency for Technology and Quality, Jinju, 52851, Republic of Korea

    Daewon Kim

  3. Department of Basic Medical Sciences, Purdue University, West Lafayette, IN, 47907, USA

    Sungho Shin

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  1. Seongyun Park
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Park, S., Lee, J., Kwon, E. et al. 3D Sensing System for Laser-Induced Breakdown Spectroscopy-Based Metal Scrap Identification. Int. J. of Precis. Eng. and Manuf.-Green Tech. 9, 695–707 (2022). https://doi.org/10.1007/s40684-021-00364-1

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