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Application of the Hough Transform for Automated Analysis of Kolsky Bar Data

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Abstract

The Kolsky Bar, also known as the split-Hopkinson pressure bar, has become one of the most commonly used apparatuses when studying the dynamic behavior of materials. Despite its popularity, limited standards exists with respect to the design, data collection, and data analysis approach used. A lack of standardization can lead to lab-to-lab variation in reported dynamic behavior for nominally identical materials. A key step during data reduction is the appropriate selection of the signal windows used in the one-dimensional wave propagation analysis of recorded strain gauge signals. The presented work provides an automated analysis approach for selecting signal windows based on the Hough transform. The approach is agnostic to loading mode (e.g., tension vs. compression), applicable to both pulse-shaped and non-pulse shaped experiments, robust in the presence of naturally occurring signal oscillations and noise, and has rapid computation time. Two cases are selected to demonstrate the viability of applying the Hough transform to recorded Kolsky bar signals. In the first case, the bar wave speeds of maraging steel tension and compression Kolsky bars are determined. The second case demonstrates the application of the Hough transform technique in the study of the dynamic compression behavior of additively manufactured Inconel 718. A stress-strain curve generated using the automated HT-based technique is compared to those determined manually showing the automated approach provides a closely matching result. Window selection automation provides an important step toward improving consistency of results reported, data processing throughput, and traceability of dynamic mechanical property data generation.

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References

  1. Hough PVC (1962) Method and means for recognizing complex patterns, December 18 US Patent 3,069

  2. Bazin MJ, Benoit JW (1965) Off-line global approach to pattern recognition for bubble chamber pictures. IEEE Trans Nucl Sci 12(4):291–293

    Article  Google Scholar 

  3. Duda RO, Hart PE (1972) Use of the hough transformation to detect lines and curves in pictures. Commun ACM 15(1):11–15

    Article  Google Scholar 

  4. Maurice C, Fortunier R (2008) A 3d hough transform for indexing ebsd and kossel patterns. J Micro 230(3):520–529

    Article  CAS  Google Scholar 

  5. Zaefferer S (2007) On the formation mechanisms, spatial resolution and intensity of backscatter kikuchi patterns. Ultramicroscopy 107(2-3):254–266

    Article  CAS  Google Scholar 

  6. Pourdeyhimi B, Kim HS (2002) Measuring fiber orientation in nonwovens: The hough transform. Text Res J 72(9):803–809

    Article  CAS  Google Scholar 

  7. Xu B, Yu L (1997) Determining fiber orientation distribution in nonwovens with hough transform techniques. Textile Res J 67(8):563–571

    Article  CAS  Google Scholar 

  8. Illingworth J, Kittler J (1988) A survey of the hough transform. Comput Vision Graph Image Process 44(1):87–116

    Article  Google Scholar 

  9. Leavers VF (1993) Which hough transform? CVGIP: Image Understand 58(2):250–264

    Article  Google Scholar 

  10. Mukhopadhyay P, Chaudhuri BB (2015) A survey of hough transform. Pattern Recogn 48 (3):993–1010

    Article  Google Scholar 

  11. Kimme C, Ballard D, Sklansky J (1975) Finding circles by an array of accumulators. Commun ACM 18(2):120–122

    Article  Google Scholar 

  12. Wechsler H, Sklansky J (1977) Automatic detection of ribs in chest radiographs. Pattern Recogn 9(21):30

    Google Scholar 

  13. Tsuji S, Matsumoto F (1978) Detection of ellipses by a modified hough transformation. IEEE Comput Archit Lett 27(08):777–781

    Google Scholar 

  14. Tsukune H (1983) Extracting elliptical figures from an edge vector field. In: Proceedings conference computer vision and pattern recognition, pp 138–141

  15. Merlin PM, Farber DJ (1975) A parallel mechanism for detecting curves in pictures. IEEE Trans Comput 100(1):96–98

    Article  Google Scholar 

  16. Ballard DH (1981) Generalizing the hough transform to detect arbitrary shapes. Pattern Recognit 13(2):111–122

    Article  Google Scholar 

  17. Kierkegaard P (1992) A method for detection of circular arcs based on the hough transform. Mach Vis Appl 5(4):249–263

    Article  Google Scholar 

  18. Pei S-C, Horng J-H (1995) Circular arc detection based on hough transform. Pattern Recognit Lett 16(6):615–625

    Article  Google Scholar 

  19. Kolsky H (1949) An investigation of the mechanical properties of materials at very high rates of loading. Proc Phys Soc Sect B 62(11):676

    Article  Google Scholar 

  20. Lindholm US, Yeakley LM (1968) High strain-rate testing: Tension and compression. Exp Mech 8(1):1–9

    Article  Google Scholar 

  21. Nicholas T (1981) Tensile testing of materials at high rates of strain. Experiment Mechan 21 (5):177–185

    Article  Google Scholar 

  22. Harding J, Huddart J (1980) The use of the double-notch shear test in determining the mechanical properties of uranium at very high rates of strain. In: Mechanical properties at high rates of strain, 1979

  23. Gilat A (2000) Torsional kolsky bar testing. ASM Handbook 8:505–515

    Google Scholar 

  24. Gama BA, Lopatnikov SL, Gillespie JW Jr (2004) Hopkinson bar experimental technique: A critical review. Appl Mech Rev 57(4):223–250

    Article  Google Scholar 

  25. Gray GT III (2000) Classic split hopkinson pressure bar testing. ASM Handbook 8:462–476

    Google Scholar 

  26. Davies EDH, Hunter SC (1963) The dynamic compression testing of solids by the method of the split hopkinson pressure bar. J Mechan Phys Sol 11(3):155–179

    Article  Google Scholar 

  27. Chen WW, Bo S (2010) Split Hopkinson (Kolsky) bar: Design, testing and applications. Springer Science & Business Media, New York

    Google Scholar 

  28. Casem DT, Grunschel SE, Schuster BE (2011) Interferometric measurement techniques for small diameter kolsky bars. In: Dynamic behavior of materials, vol 1. Springer, pp 463–470

  29. Li W, Xu J (2009) Impact characterization of basalt fiber reinforced geopolymeric concrete using a 100-mm-diameter split hopkinson pressure bar. Mater Sci Eng A 513:145–153

    Google Scholar 

  30. Song B, Syn CJ, Grupido CL, Chen W, Lu W-Y (2008) A long split hopkinson pressure bar (lshpb) for intermediate-rate characterization of soft materials. Experiment Mechan 48(6):809

    Article  Google Scholar 

  31. Lok TS, Li XB, Liu D-S, Zhao PJ (2002) Testing and response of large diameter brittle materials subjected to high strain rate. J Mater Civ Eng 14(3):262–269

    Article  Google Scholar 

  32. Lindholm US (1964) Some experiments with the split hopkinson pressure bar. J Mechan Phys Sol 12(5):317–335

    Article  Google Scholar 

  33. Follansbee PS, Frantz C (1983) Wave propagation in the split hopkinson pressure bar. J Eng Mater Technol 105:61

    Article  Google Scholar 

  34. Li Z, Lambros J (1999) Determination of the dynamic response of brittle composites by the use of the split hopkinson pressure bar. Compos Sci Technol 59(7):1097–1107

    Article  Google Scholar 

  35. Francis DK, Whittington WR, Lawrimore WB, Allison PG, Turnage SA, Bhattacharyya JJ (2017) Split hopkinson pressure bar graphical analysis tool. Exp Mech 57(1):179–183

    Article  CAS  Google Scholar 

  36. Yao Zhenjie, Yi Weidong (2016) Curvature aided hough transform for circle detection. Expert Syst Appl 51:26–33

    Article  Google Scholar 

  37. Pătrăucean V, Gurdjos P, Von Gioi RG (2012) A parameterless line segment and elliptical arc detector with enhanced ellipse fitting. In: European conference on computer vision. Springer, pp 572–585

  38. Li D, Nan F, Xue T, Yu X (2017) Circle detection of short arc based on randomized hough transform. In: 2017 IEEE International conference on mechatronics and automation (ICMA). IEEE, pp 258–263

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Acknowledgements

This research was supported by the National Science Foundation CAREER award no. 1847653 and through the Undergraduate Research Opportunities Program (UROP) at the University of Utah awarded to W. Gilliland.

Funding

The efforts described here in was supported by the National Science Foundation CAREER under award no. 1847653, and through the Undergraduate Research Opportunities Program (UROP) at the University of Utah.

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Correspondence to O.T. Kingstedt.

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Salehi, SD., Gilliland, W. & Kingstedt, O. Application of the Hough Transform for Automated Analysis of Kolsky Bar Data. Exp Tech 46, 153–165 (2022). https://doi.org/10.1007/s40799-021-00458-0

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