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Production Engineering

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03-08-2022 | Computer Aided Engineering

Investigation of industrial die-cast Al-alloys using X-ray micro-computed tomography and machine learning approach for CT segmentation

Published in: Production Engineering | Issue 2/2023

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Abstract

The die-casting process creates the opportunity to design and manufacture complex parts in different industrial fields. However, the presence of micro-porosity and cracks can significantly impact the functionality and lifetime of the components. The impregnation technique improves the defect and upgrades the alloy's usability. Hence, identifying the impregnation technique's usefulness in sealing porosity is vital for enhancing the quality of the die casting products. It is hard to detect low atomic number impregnation resin located in the casting defects due to low X-ray attenuation. In this study, microfocus X-ray computed tomography (XCT) with advanced direct conversion detectors could effectively be employed to visualize casting defects in 3D. Also, to recognize the impregnated resin in Al-alloy both qualitatively and quantitatively. Dual-energy XCT recognized the resin material as P601 super sealant quantitatively. Casting defects could be identified in 2D CT images, and it is not easy to detect the impregnated resin with simple intensity-based image processing algorithms. Hence, an approach to improve resin detection through machine learning was studied. With a random forest classifier, trainable weka segmentation was used with three pre-defined classes. It precisely segmented the casting pore, Al-alloy, and resin material after well-trained the known data set.

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Stöhr J, Scherz A (2015) Creation of X-ray transparency of matter by stimulated elastic forward scattering. Phys Rev Lett 115(10):107402CrossRef

Chen XF, Song JB, Chen XY, Yang HH (2019) X-ray-activated nanosystems for theranostic applications. Chem Soc Rev 48(11):3073–3101CrossRef

Cao C, Toney MF, Sham T-K et al (2020) Emerging X-ray imaging technologies for energy materials. Mater Today 34:132–147CrossRef

Akman F, Durak R, Turhan MF, Kaçal MR (2015) Studies on effective atomic numbers, electron densities from mass attenuation coefficients near the K edge in some samarium compounds. Appl Radiat Isot 101:107–113CrossRef

Kamkaew A, Chen F, Zhan YH, Majewski RL, Cai WB (2016) Scintillating nanoparticles as energy mediators for enhanced photodynamic therapy. ACS Nano 10(4):3918–3935CrossRef

Zhou Y, Chen J, Bakr OM, Mohammed OF (2021) Metal halide perovskites for X-ray imaging scintillators and detectors. ACS Energy Lett 6(2):739–768CrossRef

Nikl M, Yoshikawa A (2015) Recent R&D trends in inorganic single-crystal scintillator materials for radiation detection. Adv Opt Mater 3(4):463–481CrossRef

Bossi RH, Cline JL, Georgeson GE (1991) X-ray computed tomographic inspection of castings. Rev Progress Quant Nondestr Eval 10B:1883–1790

Bossi R, Crews A, Georgeson G, Nelson J, Shrader J (1993) X-ray computed tomography for geometry acquisition. Rev Progress Quant Nondestr Eval 12:343–349CrossRef

10.

Georgeson G, Bossi R (1993) X-ray CT for quantitative casting material evaluation. Rev Progress Quant Nondestr Eval 12:1681–1688CrossRef

11.

Neel ST, Eliasen DS, Yancey RN (1995) Dimensional measurement of internal features in complex castings. Rev Progress Quant Nondestr Eval 14:689–694CrossRef

12.

Neel ST, Yancey RN (1996) X-ray computed tomography application in research. Rev Progress Quant Nondestr Eval 15:497–502CrossRef

13.

Neel ST, Gibson R, Daniels CR (1998) Dimensional accuracy in X-ray computed tomographic imaging. Rev Progress Quant Nondestr Eval 17:411–418CrossRef

14.

Bartscher M, Neuschaefer-Rube U, Wäldele F (2004) Computed tomography—a highly potential tool for industrial quality control and production near measurement. In: 8th International Symposium on Measurement and Quality Control in Production, Erlangen, Germany, pp 3–8

15.

Reinhart C, Poliwoda C, Guenther T, Roemer W, Maas S, Gosch C (2004) Modern voxel based data and geometry analysis software tools for industrial CT. In: 16th World Conference on NDT, Montreal, Canada, p 1–8

16.

Reinhart C (2008) Industrial computer tomography—a universal inspection tool. In: 17th world conference on nondestructive testing. 25–28 Oct 2008, Shanghai, China

17.

Thompson A, Leach R (2018) Introduction to industrial X-ray computed tomography. In: Carmignato S, Dewulf W, Leach R (eds) Industrial X-ray computed tomography. Springer, Cham, pp 1–23

18.

Ohno Y, Torikoshi M, Tsunoo T, Hyodo K (2005) Dual-energy X-ray CT with CdTe array and its extension. Nucl Instrum Methods Phys Res Sect A 548:72–77. https://doi.org/10.1016/j.nima.2005.03.069CrossRef

19.

Kastner J, Heinzl C (2015) X-ray computed tomography for non-destructive testing and materials characterization. In: Liu Z, Ukida H, Ramuhalli P, Niel K (eds) Integrated imaging and vision techniques for industrial inspection. Springer, London, pp 227–250CrossRef

20.

Buratti A, Bredemann J, Pavan M, Schmitt R, Carmignato S (2018) "Applications of CT for dimensional metrology. In: Carmignato S, Dewulf W, Leach R (eds) Industrial X-ray computed tomography. Springer, Cham, pp 333–369CrossRef

21.

Garcea SC, Wang Y, Withers PJ (2018) X-ray computed tomography of polymer composites. Compos Sci Technol 156:305–319CrossRef

22.

Hellier CJ (2013) Handbook of nondestructive evaluation, 2nd edn. McGraw-Hill, Columbus

23.

Kastner J, Harrer B, Degischer HP (2011) High resolution cone beam X-ray computed tomography of 3D-microstructures of cast Al-alloys. Mater Charact 62:99–107CrossRef

24.

Du Plessis A, Rossouw P (2015) X-ray computed tomography of a titanium aerospace investment casting. Case Stud Nondestruct Test Eval. https://doi.org/10.1016/j.csndt.2015.03.001CrossRef

25.

Shahani AJ, Xiao X, Lauridsen EM, Voorhees PW (2020) Characterization of metals in four dimensions. Mater Res Lett 8(12):462–476. https://doi.org/10.1080/21663831.2020.1809544CrossRef

26.

Kan K, Imura Y, Morii H, Kobayashi K, Minemura T, Aoki T (2013) Application of photon-counting X-ray computed tomography to aluminum-casting inspection. World J Nucl Sci Technol 3:106–108CrossRef

27.

Bandara A, Kan K, Morr H, Koike A, Aoki T (2019) X-ray computed tomography to investigate industrial cast Al-alloys. Prod Eng. https://doi.org/10.1007/s11740-019-00946-8.2019.12.20CrossRef

28.

Xiangjie Ma, Martin B, Ewald U, Peter H (2021) Classification of X-ray attenuation properties of additive manufacturing and 3D printing materials using computed tomography from 70 to 140 kVp. Front Bioeng Biotechnol 9:763960. https://doi.org/10.3389/fbioe.2021.763960CrossRef

29.

Martz HE, Logan CM, Schneberk DJ, Peter J (2016) X-ray imaging. CRC Press, Boca RatonCrossRef

30.

Arganda-Carreras I, Kaynig V, Rueden C, Eliceiri KW, Schindelin J, Cardona A, Sebastian Seung H (2017) Trainable Weka segmentation: a machine learning tool for microscopy pixel classification. Bioinformatics 33(15):2424–2426. https://doi.org/10.1093/bioinformatics/btx180CrossRef

31.

Furat O, Wang M, Neumann M et al (2019) Machine learning techniques for the segmentation of tomographic image data of functional materials. Front Mater 6:145CrossRef

32.

Dobens AC, Dobens LL (2013) FijiWings: an open source toolkit for semiautomated morphometric analysis of insect wings. G3 (Bethesda) 3:1443–1449CrossRef

33.

Krueger MA, Huke SS, Glenny RW (2013) Visualizing regional myocardial blood flow in the mouse. Circ Res 112:e88–e97CrossRef

34.

Hart N, Huang L (2012) Monitoring nests of solitary bees using image processing techniques. In: 19th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), Auckland, New Zealand, November 28–30, 2012. IEEE Explore, pp 1–4

35.

Kulinowski P, Dorozynski P, Mlynarczyk A, Weglarz WP (2011) Magnetic resonance imaging and image analysis for assessment of HPMC matrix tablets structural evolution in USP apparatus 4. Pharm Res 28:1065–1073CrossRef

36.

Mathew MD, Mathew ND, Ebert PR (2012) WormScan: a technique for high-throughput phenotypic analysis of Caenorhabditis elegans. PLoS ONE 7:e33483CrossRef

37.

Favazza TL, Tanimoto N, Munro RJ, Beck SC, Garrido MG, Seide C, Sothilingam V, Hansen RM, Fulton AB, Seeliger MW, Akula JD (2013) Alterations of the tunica vasculosa lentis in the rat model of retinopathy of prematurity. Doc Ophthalmol 127:3–11CrossRef

38.

Maiora J, Graña M (2012) Abdominal CTA image analysis through active learning and decision random forests: application to AAA segmentation. In: The 2012 International Joint Conference on Neural Networks (IJCNN), Brisbane, Australia, June 10–15, 2012. IEEE Explore, pp 1–7

39.

Macdonald W, Shefelbine S (2013) Characterising neovascularisation in fracture healing with laser Doppler and micro-CT scanning. Med Biol Eng Comp 51:1157–1165CrossRef

40.

Mutiargo B, Garbout A, Malcolm A (2019) Defect detection using trainable segmentation. 61. https://doi.org/10.1117/12.2521768

41.

Fuchs P, Kröger T, Garbe CS (2021) Defect detection in CT scans of cast aluminum parts: a machine vision perspective. Neurocomputing 453:85–96. https://doi.org/10.1016/j.neucom.2021.04.094 (ISSN 0925-2312)CrossRef

42.

Chen S, Kaufmann T (2022) Development of data-driven machine learning models for the prediction of casting surface defects. Metals 12(1):1. https://doi.org/10.3390/met12010001CrossRef

43.

Blondheim D (2022) Improving manufacturing applications of machine learning by understanding defect classification and the critical error threshold. Inter Metalcast 16:502–520. https://doi.org/10.1007/s40962-021-00637-0CrossRef

44.

Ho TK (1995) Random decision forests (PDF). In: Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, 14–16 August 1995. pp 278–282. Archived from the original (PDF) on 17 April 2016. Retrieved 5 June 2016

45.

Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20(8):832–844. https://doi.org/10.1109/34.709601CrossRef

46.

Hastie T, Tibshirani R, Friedman J (2008) The elements of statistical learning, 2nd edn. Springer (ISBN 0-387-95284-5)MATH

47.

Kyrieleis A, Titarenko V, Ibison M, Connolley T, Withers PJ (2010) Region-ofinterest tomography using filtered backprojection: assessing the practical limits. J Microsc 241:69–82CrossRef

48.

Muders J, Hesser J, Lachner A, Reinhart C (2011) Accuracy evaluation and exploration of measurement uncertainty for exact helical cone beam reconstruction using katsevich filtered back projection in comparison to circular Feldkamp reconstruction with respect to industrial CT metrology. In: International Symposium on Digital Industrial Radiology and Computed Tomography, Berlin, Germany

49.

Kikinis R, Pieper SD, Vosburgh K (2014) 3D slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Jolesz FA (ed) Intraoperative imaging image-guided therapy, 3(19):277–289 ISBN: 978-1-4614-7656-6 (Print) 978-1-4614-7657-3

50.

Schindelin J, Arganda-Carreras I, Frise E et al (2012) Fiji: an open-source platform for biological-image analysis. Nat Methods 9(7):676–682. https://doi.org/10.1038/nmeth.2019 (PMID22743772, on Google Scholar)CrossRef

51.

de Oliveira FB, Stolfi A, Bartscher M, Chiffre L, Neuschaefer-Rube U (2016) Experimental investigation of surface determina-tion process on multi-material components for dimensional computed tomography. Case Stud Nondestruct Test Eval. https://doi.org/10.1016/j.csndt.2016.04.003CrossRef

52.

Reiter M, Weiß D, Gusenbauer C, Erler M, Kuhn C, Kasperl S, Kastner J (2014) Evaluation of a histogram-based image quality measure for X-ray computed tomography. In: Proceeding’s 5th conference on industrial computed tomography (iCT 2014). Wels, Austria, NDT.net Issue, pp 273–282

53.

Nowotny R (1998) XMuDat: photon attenuation data on PC. IAEA-NDS-195 International Atomic Energy Agency, Vienna, Austria. http://www.mds.iaea.or.at/reports/mds-195.htm

54.

Jackson DF, Hawkes DJ (1981) X-ray attenuation coefficients of elements and mixtures. Phys Rep 70(3):169–233CrossRef

55.

Hawkes DJ, Jackson DF (1980) An accurate parametrisation of the X-ray attenuation coefficient. Phys Med Biol 25(6):1167–1171CrossRef

Title: Investigation of industrial die-cast Al-alloys using X-ray micro-computed tomography and machine learning approach for CT segmentation
Publication date: 03-08-2022
Published in: Production Engineering / Issue 2/2023
Print ISSN: 0944-6524
Electronic ISSN: 1863-7353
DOI: https://doi.org/10.1007/s11740-022-01147-6

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