Preprint / Version 1

Defect Analysis by Computed Tomography in Metallic Materials: Optimisation, Uncertainty Quantification and Classification

Authors

  • Emanuele Avoledo Polytechnic Department of Engineering and Architecture, University of Udine, Via delle Scienze 206, Udine, 33100, Italy
  • Marco Petruzzi Enovis Corporation, Via Nazionale 52, Villanova, San Daniele del Friuli, 33038, Italy
  • Marco Pelegatti Polytechnic Department of Engineering and Architecture, University of Udine, Via delle Scienze 206, Udine, 33100, Italy
  • Alessandro Tognan Polytechnic Department of Engineering and Architecture, University of Udine, Via delle Scienze 206, Udine, 33100, Italy
  • Francesco De Bona Polytechnic Department of Engineering and Architecture, University of Udine, Via delle Scienze 206, Udine, 33100, Italy
  • MIchele Prassacco Enovis Corporation, Via Nazionale 52, Villanova, San Daniele del Friuli, 33038, Italy
  • Riccardo Toninato Enovis Corporation, Via Nazionale 52, Villanova, San Daniele del Friuli, 33038, Italy
  • Enrico Salvati Polytechnic Department of Engineering and Architecture, University of Udine, Via delle Scienze 206, Udine, 33100, Italy

DOI:

https://doi.org/10.62679/9eykx120

Keywords:

Computed Tomography, Additive Manufacturing, Titanium alloy, Uncertainty quantification, Defect classification

Abstract

This paper presents a novel methodology for optimising the X-ray Computed Tomography (CT) post-processing protocol for defect detection in metallic materials, assessing the associated uncertainties and performing a reliable defect classification. The approach aims to remove the systematic error that impacts defect reconstruction, thereby improving the accuracy of defect size and morphology assessment, which is essential for fatigue life prediction, particularly in materials produced through additive manufacturing (AM). The performance and robustness are evaluated by analysing eleven titanium alloy samples produced via electron beam melting (EBM). The developed methodology entails an iterative comparison of CT defect reconstructions with fractographic measurements to identify optimal parameters for CT post-processing. This aimed, in particular, to reduce the systematic error related to the renowned Murakami's parameter √area. The uncertainty of various defect features, such as equivalent diameter, sphericity and aspect ratio, is calculated by propagating the remaining stochastic uncertainty of √area. A K-means clustering algorithm is trained to cluster unlabelled defects into three major categories often encountered in AM, e.g. gas pore (GP), key hole (KH), and lack of fusion (LOF). Eventually, the labelled defects are processed through a support vector machine (SVM) to infer the analytical form of the decision boundaries.

References

[1] Uwe Zerbst, Mauro Madia, Christian Klinger, Dirk Bettge, and Y Murakami. Defects as a root cause of fatigue failure of metallic components. i: Basic aspects. Engineering Failure Analysis, 97:777–792, 2019. doi: https://doi.org/10.1016/j.engfailanal.2019.01.055.

[2] Enrico Salvati. Evaluating fatigue onset in metallic materials: Problem, current focus and future perspectives. International Journal of Fatigue, page 108487, 2024. doi: https://doi.org/10.1016/j.ijfatigue.2024.108487.

[3] Vaishnav Madhavadas, Divyansh Srivastava, Utkarsh Chadha, Sakthivel Aravind Raj, Mohamed Thariq Hameed Sultan, Farah Syazwani Shahar, and Ain Umaira Md Shah. A review on metal additive manufacturing for intricately shaped aerospace components. CIRP Journal of Manufacturing Science and Technology, 39:18–36, 2022. doi: https://doi.org/10.1016/j.cirpj.2022.07.005.

[4] Patricia Nyamekye, Saeed Rahimpour Golroudbary, Heidi Piili, Pasi Luukka, and Andrzej Kraslawski. Impact of additive manufacturing on titanium supply chain: Case of titanium alloys in automotive and aerospace industries. Advances in Industrial and Manufacturing Engineering, 6:100112, 2023. doi: https://doi.org/10.1016/j.aime.2023.100112.

[5] Shivank A Tyagi and M Manjaiah. Additive manufacturing of titanium-based lattice structures for medical applications–a review. Bioprinting, 30:e00267, 2023. doi: https://doi.org/10.1016/j.bprint.2023.e00267.

[6] János Plocher and Ajit Panesar. Review on design and structural optimisation in additive manufacturing: Towards next-generation lightweight structures. Materials & Design, 183:108164, 2019. doi: https://doi.org/10.1016/j.matdes.2019.108164.

[7] Jerard V Gordon, Sneha P Narra, Ross W Cunningham, He Liu, Hangman Chen, Robert M Suter, Jack L Beuth, and Anthony D Rollett. Defect structure process maps for laser powder bed fusion additive manufacturing. Additive Manufacturing, 36:101552, 2020. doi: https://doi.org/10.1016/j.addma.2020.101552.

[8] Jean-Pierre Kruth, Ludo Froyen, Jonas Van Vaerenbergh, Peter Mercelis, Marleen Rombouts, and Bert Lauwers. Selective laser melting of iron-based powder. Journal of materials processing technology, 149 (1-3):616–622, 2004. doi: https://doi.org/10.1016/j.jmatprotec.2003.11.051.

[9] Niloofar Sanaei and Ali Fatemi. Defects in additive manufactured metals and their effect on fatigue performance: A state-of-the-art review. Progress in Materials Science, 117:100724, 2021. doi: https://doi.org/10.1016/j.pmatsci.2020.100724.

[10] Bi Zhang, Yongtao Li, and Qian Bai. Defect formation mechanisms in selective laser melting: a review. Chinese Journal of Mechanical Engineering, 30:515–527, 2017. doi: https://doi.org/10.1007/s10033- 017-0121-5

[11] Aiden A Martin, Nicholas P Calta, Saad A Khairallah, Jenny Wang, Phillip J Depond, Anthony Y Fong, Vivek Thampy, Gabe M Guss, Andrew M Kiss, Kevin H Stone, et al. Dynamics of pore formation during laser powder bed fusion additive manufacturing. Nature communications, 10(1):1987, 2019. doi: https://doi.org/10.1038/s41467019-10009-2.

[12] Stefano Beretta and Simone Romano. A comparison of fatigue strength sensitivity to defects for materials manufactured by am or traditional processes. International Journal of Fatigue, 94:178–191, 2017. doi: https://doi.org/10.1016/j.ijfatigue.2016.06.020.

[13] Y. Murakami, M. Endo. Effects of defects, inclusions and inhomogeneities on fatigue strength. International journal of fatigue, 16(3):163–182, 1994. doi: https://doi.org/10.1016/0142-1123(94)90001-9.

[14] Enrico Salvati, Alessandro Tognan, Luca Laurenti, Marco Pelegatti, and Francesco De Bona. A defect-based physics-informed machine learning framework for fatigue finite life prediction in additive manu- facturing. Materials & Design, 222:111089, 2022. doi: https://doi.org/10.1016/j.matdes.2022.111089.

[15] Alessandro Tognan and Enrico Salvati. Probabilistic defect-based modelling of fatigue strength for incomplete datasets assisted by literature data. International Journal of Fatigue, 173:107665, 2023. doi: https://doi.org/10.1016/j.ijfatigue.2023.107665.

[16] Alessandro Tognan, Andrea Patanè, Luca Laurenti, and Enrico Salvati. A bayesian defect-based physics guided neural network model for probabilistic fatigue endurance limit evaluation. Computer Methods in Applied Mechanics and Engineering, 418:116521, 2024. doi: https://doi.org/10.1016/j.cma.2023.116521.

[17] Anyi Li, Shaharyar Baig, Jia Liu, Shuai Shao, and Nima Shamsaei. Defect criticality analysis on fatiguelife of l-pbf 17-4 ph stainless steel via machine learning. International Journal of Fatigue, 163:107018, 2022. doi: https://doi.org/10.1016/j.ijfatigue.2022.107018.

[18] Hongyixi Bao, Shengchuan Wu, Zhengkai Wu, Guozheng Kang, Xin Peng, and Philip J Withers. A machine learning fatigue life prediction approach of additively manufactured metals. Engineering Fracture Mechanics, 242:107508, 2021. doi: https://doi.org/10.1016/j.engfracmech.2020.107508.

[19] E. Avoledo, A. Tognan, and E. Salvati. Quantification of uncertainty in a defect-based physics-informed neural network for fatigue evaluation and insights on influencing factors. Engineering Fracture Mechanics, 292:109595, 2023. doi: https://doi.org/10.1016/j.engfracmech.2023.109595.

[20] Hossein Taheri, Mohammad Rashid Bin Mohammad Shoaib, Lucas W Koester, Timothy A Bigelow, Peter C Collins, and Leonard J Bond. Powder-based additive manufacturing-a review of types of defects, generation mechanisms, detection, property evaluation and metrology. International Journal of Additive and Subtractive Materials Manufacturing, 1(2):172–209, 2017. doi: https://doi.org/10.1504/IJASMM.2017.088204.

[21] Harry Shipley, Darren McDonnell, Mark Culleton, R Coull, R Lupoi, G O’Donnell, and D Trimble. Optimisation of process parameters to address fundamental challenges during selective laser melting of ti-6al-4v: A review. International Journal of Machine Tools and Manufacture, 128:1–20, 2018. doi: https://doi.org/10.1016/j.ijmachtools.2018.01.003.

[22] Galina Kasperovich, Jan Haubrich, Joachim Gussone, and Guillermo Requena. Correlation between porosity and processing parameters in tial6v4 produced by selective laser melting. Materials & Design, 105:160–170, 2016. doi: https://doi.org/10.1016/j.matdes.2016.05.070.

[23] Robert Snell, Sam Tammas-Williams, Lova Chechik, Alistair Lyle, Everth Hernández-Nava, Charlotte Boig, George Panoutsos, and Iain Todd. Methods for rapid pore classification in metal additive manufacturing. Jom, 72:101–109, 2020. doi: https://doi.org/10.1007/s11837-019-03761-9.

[24] Arun Poudel, Mohammad Salman Yasin, Jiafeng Ye, Jia Liu, Aleksandr Vinel, Shuai Shao, and Nima Shamsaei. Feature-based volumetric defect classification in metal additive manufacturing. Nature Communications, 13(1):6369, 2022. doi: https://doi.org/10.1038/s41467-022-34122-x.

[25] G Minerva, M Awd, J Tenkamp, F Walther, and S Beretta. Machine learning-assisted extreme value statistics of anomalies in alsi10mg manufactured by l-pbf for robust fatigue strength predictions. Materials & Design, 235:112392, 2023. doi: https://doi.org/10.1016/j.matdes.2023.112392.

[26] Anton Du Plessis, Philip Sperling, Andre Beerlink, Lerato Tshabalala, Shaik Hoosain, Ntombi Mathe, and Stephan G Le Roux. Standard method for microct-based additive manufacturing quality control 1: Porosity analysis. MethodsX, 5:1102–1110, 2018. doi: https://doi.org/10.1016/j.mex.2018.09.005.

[27] Anton Du Plessis, Igor Yadroitsev, Ina Yadroitsava, and Stephan G Le Roux. X-ray microcomputed tomography in additive manufacturing: a review of the current technology and applications. 3D Printing and Additive Manufacturing, 5(3):227–247, 2018. doi: https://doi.org/10.1089/3dp.2018.0060.

[28] Willi A Kalender. X-ray computed tomography. Physics in medicine & Biology, 51(13):R29, 2006. doi: http://dx.doi.org/10.1118/1.596063.

[29] Maureen Van Eijnatten, Roelof van Dijk, Johannes Dobbe, Geert Streekstra, Juha Koivisto, and Jan Wolff. Ct image segmentation methods for bone used in medical additive manufacturing. Medical engineering & physics, 51:6–16, 2018. doi: https://doi.org/10.1016/j.medengphy.2017.10.008.

[30] N Senthilkumaran and S Vaithegi. Image segmentation by using thresholding techniques for medical images. Computer Science & Engineering: An International Journal, 6(1):1–13, 2016. doi: https://doi.org/10.5121/cseij.2016.6101.

[31] K Bhargavi and S Jyothi. A survey on threshold based segmentation technique in image processing. International Journal of Innovative Research and Development, 3(12):234–239, 2014. doi: arXiv preprint arXiv:2306.06211.

[32] Joseph John Lifton. Evaluating the standard uncertainty due to the voxel size in dimensional computed tomography. Precision Engineering, 79:245–250, 2023.

[33] JJ Lifton, AA Malcolm, and JW McBride. On the uncertainty of surface determination in x-ray computed tomography for dimensional metrology. Measurement Science and Technology, 26(3):035003, 2015. doi: httpps://doi.org/10.1088/0957-0233/26/3/035003.

[34] Xiuyuan Yang, Wenjuan Sun, and Claudiu L Giusca. An automated surface determination approach for computed tomography. NDT & E International, 131:102697, 2022. doi: https://doi.org/10.1016/j.ndteint.2022.102697.

[35] Herminso Villarraga-Gómez, Jeffery D Thousand, and Stuart T Smith. Empirical approaches to uncertainty analysis of x-ray computed tomography measurements: A review with examples. Precision Engineering, 64:249 268, 2020. doi: https://doi.org/10.1016/j.precisioneng.2020.03.004.

[36] Filippo Zanini, Simone Carmignato, and Enrico Savio. Two different experimental approaches for the uncertainty determination of x-ray computed tomography dimensional measurements on lattice structures. CIRP Journal of Manufacturing Science and Technology, 47:205–214, 2023. doi: https://doi.org/10.1016/j.cirpj.2023.10.004.

[37] Pavel Müller, Jochen Hiller, Y Dai, JL Andreasen, H Nørgaard Hansen, and Leonardo De Chiffre. Estimation of measurement uncertainties in x-ray computed tomography metrology using the substitution method. CIRP Journal of Manufacturing Science and Technology, 7(3):222–232, 2014. doi: https://doi.org/10.1016/j.cirpj.2014.04.002.

[38] Tomáš Zikmund, Jakub Šalplachta, Aneta Zatočilová, Adam Břínek, Libor Pantělejev, Roman Štěpánek, Daniel Koutn`y, David Paloušek, and Jozef Kaiser. Computed tomography based procedure for reproducible porosity measurement of additive manufactured samples. NDT & E International, 103:111–118, 2019. doi: https://doi.org/10.1016/j.ndteint.2019.02.008.

[39] Leonard Schild, Lukas Weiser, Katja Höger, and Gisela Lanza. Analyzing the error of computed tomography based pore detection by using microscope images of matched cross-sections. Precision Engineering, 81:192–206, 2023. doi: https://doi.org/10.1016/j.precisioneng.2023.01.013.

[40] DC Montgomery. Applied statistics and probability for engineers, 2003.

[41] ISO/IEC Guide 98-3:2008-09. Uncertainty of measurement; part 3: Guide to the expression of uncertainty in measurement. Technical report, GUM, 1995.

[42] Yanzhou Fu, Austin RJ Downey, Lang Yuan, Tianyu Zhang, Avery Pratt, and Yunusa Balogun. Machine learning algorithms for defect detection in metal laser-based additive manufacturing: A review. Journal of Manufacturing Processes, 2022. doi: https://doi.org/10.1016/j.jmapro.2021.12.061.

[43] Zhenqiang Lin, Yiwen Lai, Taotao Pan, Wang Zhang, Jun Zheng, Xiaohong Ge, and Yuangang Liu. A new method for automatic detection of defects in selective laser melting based on machine vision. Materials, 2021. doi: https://doi.org/10.3390/ma14154175.

[44] ISO 5832-3:2021. Implants for surgery – metallic materials part 3: wrought titanium 6-aluminium 4-vanadium alloy. Technical report, ISO, 2021.

[45] ASTM E466-15. Standard practice for conducting force controlled constant amplitude axial fatigue tests of metallic materials. Technical report, ASTM Int., 2021.

[46] Grzegorz Pyka, Greet Kerckhofs, Jan Schrooten, and Martine Wevers. The effect of spatial micro-ct image resolution and surface complexity on the morphological 3d analysis of open porous structures. Materials Characterization, 87:104–115, 2014. doi: https://doi.org/10.1016/j.matchar.2013.11.004.

Downloads

Posted

06-12-2024

Categories

License

Copyright (c) 2024 Emanuele Avoledo, Marco Petruzzi, Marco Pelegatti, Alessandro Tognan, Francesco De Bona, MIchele Prassacco, Riccardo Toninato, Enrico Salvati (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

You are free to:

  1. Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
  2. Adapt — remix, transform, and build upon the material for any purpose, even commercially.
  3. The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms:

  1. Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  2. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.