Identification of the scraping quality for the machine tool using the smartphone

  • Ming-Fei Chen
  • Cheng-Wen Chen
  • Chun-Jung Su
  • Wei-Lun Huang
  • Wen-Tse HsiaoEmail author


The technology for scraped surface detection remains experimental. It is mainly performed visually by experienced technicians, which is neither objective nor standardized. Since the scraping technique is the key to the accuracy and the performance of the machine, a method to determine the characteristics of the scraping surface in a quick, objective way is proposed, in order to ensure the quality of scraping. The scraping surface is photographed using a smartphone, and an Android application is developed using a Java image processing algorithm. A square inch of image is then captured on the screen, the RGB image is isolated, and its histogram is read. Using a watershed algorithm, the background image is calculated from the boundary, and then binary, filter and morphological image processing algorithms are used. If the light is uniform, a split binary technique is used. The scraping appearance is then calculated and analyzed in points per inch (PPI) and percentage of points (POP). The experimental results indicated that the total area for recognition was approximately 1-in2; PPI = 13; and POP = 30.9%, and recognition time was less than 1 sec. The experimental analysis results in this study were compared to those of two scraping experts; the two errors were both lower than 5%.


Scraping surface Image processing Android Points per inch Percentage of points 



  1. 1.
    Yoshimi T, Masafumi S, Tetsuya Y, Masahiro C (1986) The recognition of bearings by means of a CCD line sensor and the automation of scraping works. J Jpn Soc Precis Eng 52:2087–2092CrossRefGoogle Scholar
  2. 2.
    Zhao ZQ, Ma LH, Cheung Y, Wu X, Tang Y, Chen CL (2015) An efficient android-based plant leaf identification system. Neurocomputing 151:1112–1119CrossRefGoogle Scholar
  3. 3.
    Otsu NA (1979) Threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics 9:62–66CrossRefGoogle Scholar
  4. 4.
    Khanna V, Gupta P, Hwang CJ (2002) Finding connected components in digital images by aggressive reuse of labels. Image Vision Comput 20:557–568CrossRefGoogle Scholar
  5. 5.
    Tan GB, Liu SH, Wang DG, Zhang SW (2016) Measurement and analysis of wax–oil gel scraping process at contact area under pure sliding conditions. Measurement 80:29–43CrossRefGoogle Scholar
  6. 6.
    Fan KC, Torng J, Jywe W, Chou RC, Ye JK (2012) 3-D measurement and evaluation of surface texture produced by scraping process. Measurement 45:384–392CrossRefGoogle Scholar
  7. 7.
    Hsieh TH, Jywe WY, Huang HL, Chen SL (2011) Development of a laser-based measurement system for evaluation of the scraping workpiece quality. Opt Laser Eng 49:1045–1053CrossRefGoogle Scholar
  8. 8.
    Hsieh TH, Jywe WY, Tsai YC, Lee MT (2017) Design, manufacture, and development of a novel automatic scraping machine. Int J Adv Manuf Technol 90:2617–2630CrossRefGoogle Scholar
  9. 9.
    Chung WC (2009) The study and implementation of operating system porting for android. National Taipei University of Technology, Master Thesis.Google Scholar
  10. 10.
    King R (2008) Scraping technique training notes. King-Way Machine Consultants, Inc.Google Scholar
  11. 11.
    Yeganeh H, Ziaei A, Rezaie A (2008) A novel approach for contrast enhancement based on histogram equalization. Proc IEEE ICCCE 256.

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Mechatronics EngineeringNational Changhua University of EducationChanghuaTaiwan
  2. 2.Precision Machinery Research and Development CenterTaichungTaiwan
  3. 3.National Applied Research LaboratoriesTaiwan Instrument Research InstituteHsinchuTaiwan

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