AIMS Mathematics, 2020, 5(4): 3256-3273. doi: 10.3934/math.2020209

Research article

Export file:

Format

  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text

Content

  • Citation Only
  • Citation and Abstract

Mathematical morphology approach to internal defect analysis of A356 aluminum alloy wheel hubs

1 Science and Technology on Electronic Test and Measurement Laboratory, North University of China,Taiyuan 030051, China
2 Department of Electronic engineering, Taiyuan Institute of Technology, Taiyuan 030008, China

A356 aluminum alloy is a material widely used in the production of automobile wheels. Internal defects such as gas holes and shrinkage cavities are likely to develop in the process of low pressure casting. X-ray images of the hub are able to provide some information on such defects. This paper proposes a defect extraction method which is built on mathematical morphology. It involves three operations, i.e., the top-hat transform, the top-hat reconstruction transform and the dilation reconstruction. A larger square structuring element and a small threshold are used firstly to obtain all potential defect areas of the hub. A structuring element of a suitable size are applied to different defect areas in subsequent extraction. A new threshold is then decided to get the final defect extraction results. The experimental results show that the above defect extraction method not only works on X-ray hub images, but is robust against the interference caused by noises and hub geometry, and hence can potentially be extensively applied to X-ray detection of hubs.
  Figure/Table
  Supplementary
  Article Metrics

References

1. P. Li, D. M. Maijer, T. C. Lindley, et al. Simulating the Residual Stress in an A356 Automotive Wheel and Its Impact on Fatigue Life, Metall. Mater. Trans. B, 38 (2007), 505-515.    

2. S. Wang, N. Zhou, W. Qi, et al. Microstructure and mechanical properties of A356 aluminum alloy wheels prepared by thixo-forging combined with a low superheat casting process, T. Nonferr. Metal. Soc., 24 (2014), 2214-2219.    

3. N. K. Kund, P. Dutta, Numerical study of influence of oblique plate length and cooling rate on solidification and macrosegregation of A356 aluminum alloy melt with experimental comparison, J. Alloy. Compod., 678 (2016), 343-354.    

4. L. Ming, Y. Li, G. Bi, et al. Effects of melt treatment temperature and isothermal holding parameter on water-quenched microstructures of A356 aluminum alloy semisolid slurry, T. Nonferr. Metal. Soc., 28 (2018), 393-403.    

5. P. Fan, S. Cockcroft, D. Maijer, et al. Examination and Simulation of Silicon Macrosegregation in A356 Wheel Casting, Metals, 8 (2018), 503.

6. B. Zhang, S. L. Cockcroft, D. M. Maijer, et al. Casting defects in low-pressure die-cast aluminum alloy wheels, JOM, 57 (2005), 36-43.

7. H. Boerner, H. Strecher, Automated X-Ray Inspection of Aluminum Castings. IEEE T. Pattern Anal., 10 (1988), 79-91.

8. D. Mery, T. Jaeger, D. Filbert, A review of methods for automated recognition of casting defects, Insight, 44 (2002), 428-436.

9. X. Li, S. K. Tso, X. Guan, et al. Improving Automatic Detection of Defects in Castings by Applying Wavelet Technique, IEEE T. Ind. Electron., 53 (2006), 1927-1934.    

10. T. Saravanan, S. Bagavathiappan, J. Philip, et al. Segmentation of defects from radiography images by the histogram concavity threshold method, Insight, 49 (2007), 578-584.    

11. Y. Wang, Y. Sun, P. Lv, et al. Detection of line weld defects based on multiple thresholds and support vector machine, NDT E Int., 41 (2008), 517-524.    

12. Y. Tang, X. Zhang, X. Li, et al. Application of a new image segmentation method to detection of defects in castings, Int. J. Adv. Manuf. Technol., 43 (2009), 431-439.    

13. X. Yuan, L. Wu, Q. Peng, An improved Otsu method using the weighted object variance for defect detection, Appl. Surf. Sci., 349 (2015), 472-484.    

14. M. Malarvel, G. Sethumadhavan, P. C. R. Bhagi, et al. An improved version of Otsu's method for segmentation of weld defects on X-radiography images, Optik, 142 (2017), 109-118.    

15. J. Zhang, Z. Guo, T. Jiao, et al. Defect Detection of Aluminum Alloy Wheels in Radiography Images Using Adaptive Threshold and Morphological Reconstruction, Appl. Sci., 8 (2018), 2365.

16. D. Mery, D. Filbert, Automated flaw detection in aluminum castings based on the tracking of potential defects in a radioscopic image sequence, IEEE Trans. Robot. Autom., 18 (2002), 890-901.    

17. M. Carrasco, D. Mery, Automatic multiple view inspection using geometrical tracking and feature analysis in aluminum wheels, Mach. Vis. Appl., 22 (2011), 157-170.    

18. A. Osman, V. Kaftandjian, U. Hassler, Improvement of x-ray castings inspection reliability by using Dempster-Shafer data fusion theory, Pattern Recogn. Lett., 32 (2011), 168-180.    

19. X. Zhao, Z. He, S. Zhang, Defect detection of castings in radiography images using a robust statistical feature, J. Opt. Soc. Am. A, 31 (2014), 196-205.    

20. X. Zhao, Z. He, S. Zhang, et al. A sparse-representation-based robust inspection system for hidden defects classification in casting components, Neurocomputing, 153 (2015), 1-10.    

21. D. Mery, C. Arteta, Automatic Defect Recognition in X-ray Testing using Computer Vision. In: Proceedings of the 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), Santa Rosa, CA, USA, March 2017, 24-31.

22. J. Lin, Y. Yao, L. Ma, et al. Detection of a casting defect tracked by deep convolution neural network, Int. J. Adv. Manuf. Technol., 97 (2018), 573-581,    

23. R. Alaknanda, S. Anand, P. Kumar, Flaw detection in radio-graphic weld images using morphological approach, NDT E Int., 39 (2006), 29-33.    

© 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Article outline

Show full outline
Copyright © AIMS Press All Rights Reserved