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Image-based porosity classification in Al-alloys by laser metal deposition using random forests

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Abstract

Additive manufacturing (AM) technologies enable complex, high-value components to be printed, with potential applications in the automotive, aerospace, and biomedical sectors. Porosity in AM processes for metals is a recurrent problem which can lead to adverse effects such as crack initiation and ultimately to parts’ early-life failure. There are several pore classifications described in the literature, which are focused on traditional manufacturing processes. The current lack of information makes it difficult to accurately identify and classify pores in AM-made parts. The present work describes a proposal based on image processing and machine learning, specifically random forests, to classify porosity automatically in metallographic images. The proposed method is divided into 3 stages. (1) Preprocessing stage: image denoising, smoothing, and unblurring to highlight the areas with pores. (2) Feature extraction stage: segmentation of pores and the morphological/geometrical features that describe the porosity. (3) Intelligent classifier stage: definition, training, testing, and validation of the random forest classifier. Our proposal has an accurate balance between the calculation of the feature importance as well as the number to use, the adequate number of trees to grow per forest, and the correct selection of the size of the database. The proposed method achieves an accuracy of 94.41% and out-of-bag error less than 5%. These results guarantee high precision in the porosity classification task. Our approach has the potential to be used in the porosity analysis of any metallic additively manufactured component.

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References

  1. A. C. F. on Additive Manufacturing Technologies, A. C. F. on Additive Manufacturing Technologies (2012) Subcommittee F42. 91 on Terminology Standard terminology for additive manufacturing technologies Astm International

  2. Jiang J, Xu X, Stringer J (2018) Support structures for additive manufacturing: a review. J Manufact Mater Process 2(4):64

    Google Scholar 

  3. Mahamood RM (2018) Laser metal deposition process of metals, alloys, and composite materials, Springer, New York

  4. Dass A, Moridi A (2019) State of the art in directed energy deposition: from additive manufacturing to materials design. Coatings 9(7):418

    Article  Google Scholar 

  5. Yasa E, Kruth J-P, Deckers J (2011) Manufacturing by combining selective laser melting and selective laser erosion/laser re-melting. CIRP annals 60(1):263–266

    Article  Google Scholar 

  6. Liu Y, Wang W, Xie J, Sun S, Wang L, Qian Y, Meng Y, Wei Y (2012) Microstructure and mechanical properties of aluminum 5083 weldments by gas tungsten arc and gas metal arc welding. Mater Sci Eng A 549:7–13

    Article  Google Scholar 

  7. Tabernero I, Lamikiz A, Martínez S, Ukar E, Figueras J (2011) Evaluation of the mechanical properties of inconel 718 components built by laser cladding. Int J Mach Tools Manuf 51(6):465–470

    Article  Google Scholar 

  8. Lowell S, Shields JE, Thomas MA, Thommes M (2012) Characterization of porous solids and powders: surface area, pore size and density, vol 16. Springer Science & Business Media, Berlin

    Google Scholar 

  9. Zdravkov BD, Čermák JJ, Šefara M, Jankŭ J (2007) Pore classification in the characterization of porous materials: A perspective. Central European J Chem 5(2):385–395. https://doi.org/10.2478/s11532-007-0039-3

    Google Scholar 

  10. B. C. o. M. o. I. American Welding Society (AWS) (2015) Guide for the visual examination of welds, Tech. rep. American National Standards Institute

  11. A. R. 577 (2013) Welding inspection and metallurgy, Tech. rep. American Petroleum Institute (API)

  12. Welding and allied processes - classification of geometric imperfections in metallic materials - part 1: Fusion welding (2020) Tech. rep., International Organization for Standardization

  13. Mays T (2007) A new classification of pore sizes, studies in surface science and catalysis 160 (Characterization of) 57–62

  14. Khanzadeh M, Chowdhury S, Marufuzzaman M, Tschopp MA, Bian L (2018) Porosity prediction: supervised-learning of thermal history for direct laser deposition. J Manuf Syst 47:69–82

    Article  Google Scholar 

  15. Zhang B, Liu S, Shin YC (2019) In-process monitoring of porosity during laser additive manufacturing process. Addit Manuf 28:497–505

    Google Scholar 

  16. Ye D, Wang W, Zhou H, Huang J, Wu W, Gong H, Li Z (2019) In-situ evaluation of porosity in thermal barrier coatings based on the broadening of terahertz time-domain pulses: simulation and experimental investigations. Opt Express 27(20):28150–28165

    Article  Google Scholar 

  17. Dilip J, Zhang S, Teng C, Zeng K, Robinson C, Pal D, Stucker B (2017) Influence of processing parameters on the evolution of melt pool, porosity, and microstructures in ti-6al-4v alloy parts fabricated by selective laser melting. Prog Addit Manuf 2(3):157–167

    Article  Google Scholar 

  18. Zhan X, Qi C, Gao Z, Tian D, Wang Z (2019) The influence of heat input on microstructure and porosity during laser cladding of invar alloy. Opt Laser Technol 113:453–461

    Article  Google Scholar 

  19. Schlüter S, Sheppard A, Brown K, Wildenschild D (2014) Image processing of multiphase images obtained via x-ray microtomography: a review. Water Resour Res 50(4):3615–3639

    Article  Google Scholar 

  20. Schwerdtfeger J, Singer RF, Körner C (2012) In situ flaw detection by ir-imaging during electron beam melting. Rapid Prototyp J 18(4):259–263

    Article  Google Scholar 

  21. Cai X, Malcolm AA, Wong BS, Fan Z (2015) Measurement and characterization of porosity in aluminium selective laser melting parts using x-ray ct. Virt Phys Prototyp 10(4):195–206

    Article  Google Scholar 

  22. Cunningham R, Narra SP, Montgomery C, Beuth J, Rollett A (2017) Synchrotron-based x-ray microtomography characterization of the effect of processing variables on porosity formation in laser power-bed additive manufacturing of ti-6al-4v. Jom 69(3):479–484

    Article  Google Scholar 

  23. Farzadi A, Solati-Hashjin M, Asadi-Eydivand M, Osman NAA (2014) Effect of layer thickness and printing orientation on mechanical properties and dimensional accuracy of 3d printed porous samples for bone tissue engineering. PloS one 9(9):e108252

    Article  Google Scholar 

  24. Deshpande S, Kulkarni A, Sampath S, Herman H (2004) Application of image analysis for characterization of porosity in thermal spray coatings and correlation with small angle neutron scattering. Surface and coatings technology 187(1):6–16

    Article  Google Scholar 

  25. Brooks AJ, Ge J, Kirka MM, Dehoff RR, Bilheux HZ, Kardjilov N, Manke I, Butler LG (2017) Porosity detection in electron beam-melted ti-6al-4v using high-resolution neutron imaging and grating-based interferometry. Progress Addit Manufact 2(3):125–132

    Article  Google Scholar 

  26. Serdeczny MP, Comminal R, Pedersen DB, Spangenberg J (2018) Numerical prediction of the porosity of parts fabricated with fused deposition modeling. In: 29th annual international solid freeform fabrication symposium (SFF Symp 2018), laboratory for freeform fabrication, pp 1849–1854

  27. García-Moreno A-I (2019) Automatic quantification of porosity using an intelligent classifier. Int J Adv Manuf Technol 105(5-6):1883–1899

    Article  Google Scholar 

  28. Zhang B, Allebach JP (2008) Adaptive bilateral filter for sharpness enhancement and noise removal. IEEE Trans Image Process 17(5):664–678

    Article  MathSciNet  Google Scholar 

  29. Vankawala F, Ganatra A, Patel A (2015) A survey on different image deblurring techniques, International Journal of Computer Applications 116 (13)

  30. Yang H-L, Huang P-H, Lai S-H (2014) A novel gradient attenuation richardson–lucy algorithm for image motion deblurring. Signal Process 103:399–414

    Article  Google Scholar 

  31. Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  32. Degenhardt F, Seifert S, Szymczak S (2019) Evaluation of variable selection methods for random forests and omics data sets. Brief Bioinform 20(2):492–503

    Article  Google Scholar 

  33. Breiman L (2001) . Machine Learning 45(1):5–32. https://doi.org/10.1023/a:1010933404324

    Article  Google Scholar 

  34. Deng H, Runger G (2013) Gene selection with guided regularized random forest. Patt Recog 46(12):3483–3489. https://doi.org/10.1016/j.patcog.2013.05.018

    Article  Google Scholar 

  35. Hapfelmeier A, Ulm K (2013) A new variable selection approach using random forests. Comput Stat Data Anal 60:50–69. https://doi.org/10.1016/j.csda.2012.09.020

    Article  MathSciNet  MATH  Google Scholar 

  36. Ishwaran H, Kogalur U (2014) Randomforestsrc: Random forests for survival, regression and classification (rf-src), R package version 1 (0)

  37. Genuer R, Poggi J-M, Tuleau-Malot C (2015) Vsurf: an r package for variable selection using random forests

  38. Janitza S, Celik E, Boulesteix A-L (2018) A computationally fast variable importance test for random forests for high-dimensional data. ADAC 12(4):885–915

    Article  MathSciNet  Google Scholar 

  39. Kuhn M, et al. (2008) Building predictive models in r using the caret package. J Stat Softw 28 (5):1–26

    Article  Google Scholar 

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Acknowledgments

Acknowledgments are due to program Cátedras-CONACYT (National Council for Science and Technology of Mexico) for the support provided by generating research opportunities through the project num. 730. Additionally, thanks are due to the CONACYT Consortium in Additive Manufacturing (CONMAD) for the use of experimental facilities for this work.

Funding

The authors received the financial support provided by CONACYT through the Programs Fordecyt (projects 297265 and 296384).

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Authors and Affiliations

  1. Center for Engineering and Industrial Development (CIDESI), Av. Playa Pie de la Cuesta No. 702. Desarrollo San Pablo, Querétaro, Qro., Mexico

    Angel-Iván García-Moreno, Juan-Manuel Alvarado-Orozco, Juansethi Ibarra-Medina & Enrique Martínez-Franco

  2. Dirección de Cátedras-CONACYT, Consejo Nacional de Ciencia y Tecnología (CONACYT), México City, Mexico

    Angel-Iván García-Moreno & Juansethi Ibarra-Medina

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  1. Angel-Iván García-Moreno
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  2. Juan-Manuel Alvarado-Orozco
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  3. Juansethi Ibarra-Medina
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  4. Enrique Martínez-Franco
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Correspondence to Angel-Iván García-Moreno.

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García-Moreno, AI., Alvarado-Orozco, JM., Ibarra-Medina, J. et al. Image-based porosity classification in Al-alloys by laser metal deposition using random forests. Int J Adv Manuf Technol 110, 2827–2845 (2020). https://doi.org/10.1007/s00170-020-05887-6

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  • DOI: https://doi.org/10.1007/s00170-020-05887-6

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