Advertisement

Surface Monitoring

  • Shichang Du
  • Lifeng Xi
Chapter

Abstract

As an effective tool for quality control, statistical process control (SPC) has been widely used in various industries for special cause identification, removal and variation reduction (Montgomery in Introduction to statistical quality control. Wiley, 2007 [1].

References

  1. 1.
    Montgomery DC (2007) Introduction to statistical quality control. WileyGoogle Scholar
  2. 2.
    Colosimo BM, Semeraro Q, Pacella M (2008) Statistical process control for geometric specifications: on the monitoring of roundness profiles. J Qual Technol 40(1):1–18CrossRefGoogle Scholar
  3. 3.
    Williams JD, Woodall WH, Birch JB (2007) Statistical monitoring of nonlinear product and process quality profiles. Qual Reliab Eng Int 23(8):925–941CrossRefGoogle Scholar
  4. 4.
    Colosimo BM, Mammarella F, Petrò S (2010) Quality control of manufactured surfaces. Front Stat Qual Control: 55–70Google Scholar
  5. 5.
    Chen S, Nembhard HB (2011) A high-dimensional control chart for profile monitoring. Qual Reliab Eng Int 27(4):451–464CrossRefGoogle Scholar
  6. 6.
    Colosimo BM, Cicorella P, Pacella M, Blaco M (2014) From profile to surface monitoring: SPC for cylindrical surfaces via gaussian processes. J Qual Technol 46(2):95–113CrossRefGoogle Scholar
  7. 7.
    Wang A, Wang K, Tsung F (2014) Statistical surface monitoring by spatial-structure modeling. J Qual Technol 46(4):359–376CrossRefGoogle Scholar
  8. 8.
    Woodall WH (2007) Current research on profile monitoring. Produção 17(3):420–425Google Scholar
  9. 9.
    Etesami F (1988) Tolerance verification through manufactured part modeling. J Manuf Syst 7(3):223–232CrossRefGoogle Scholar
  10. 10.
    Xia H, Ding Y, Wang J (2008) Gaussian process method for form error assessment using coordinate measurements. IIE Trans 40(10):931–946CrossRefGoogle Scholar
  11. 11.
    Wang H, Suriano S, Zhou L, Hu SJ (2009) High-definition metrology based spatial variation pattern analysis for machining process monitoring and diagnosis. In: ASME 2009 International manufacturing science and engineering conference, pp 471–480Google Scholar
  12. 12.
    Suriano S, Wang H, Hu SJ (2012) Sequential monitoring of surface spatial variation in automotive machining processes based on high definition metrology. J Manuf Syst 31(1):8–14CrossRefGoogle Scholar
  13. 13.
    Suriano S, Wang H, Shao C, Hu SJ, Sekhar P (2015) Progressive measurement and monitoring for multi-resolution data in surface manufacturing considering spatial and cross correlations. IIE Trans 47(10):1033–1052CrossRefGoogle Scholar
  14. 14.
    Wang K, Tsung F (2010) Using profile monitoring techniques for a data-rich environment with huge sample size. Qual Reliab Eng Int 21(7):677–688CrossRefGoogle Scholar
  15. 15.
    Wells LJ, Megahed FM, Niziolek CB, Camelio JA, Woodall WH (2013) Statistical process monitoring approach for high-density point clouds. J Intell Manuf 24(6):1267–1279CrossRefGoogle Scholar
  16. 16.
    He K, Zhang M, Zuo L, Alhwiti T, Megahed FM (2017) Enhancing the monitoring of 3D scanned manufactured parts through projections and spatiotemporal control charts. J Intell Manuf 28(4):899–911CrossRefGoogle Scholar
  17. 17.
    Roth JT, Djurdjanovic D, Yang X, Mears L, Kurfess T (2010) Quality and inspection of machining operations: tool condition monitoring. J Manuf Sci Eng 132(4):575–590CrossRefGoogle Scholar
  18. 18.
    Castejón M, Alegre E, Barreiro J, Hernández LK (2007) On-line tool wear monitoring using geometric descriptors from digital images. Int J Mach Tools Manuf 47(12):1847–1853CrossRefGoogle Scholar
  19. 19.
    Jurkovic J, Korosec M, Kopac J (2005) New approach in tool wear measuring technique using CCD vision system. Int J Mach Tools Manuf 45(9):1023–1030CrossRefGoogle Scholar
  20. 20.
    Kerr D, Pengilley J, Garwood R (2006) Assessment and visualisation of machine tool wear using computer vision. Int J Adv Manuf Technol 28(7–8):781–791CrossRefGoogle Scholar
  21. 21.
    Pfeifer T, Wiegers L (2000) Reliable tool wear monitoring by optimized image and illumination control in machine vision. Measurement 28(3):209–218CrossRefGoogle Scholar
  22. 22.
    Shahabi HH, Ratnam MM (2009) In-cycle monitoring of tool nose wear and surface roughness of turned parts using machine vision. Int J Adv Manuf Technol 40(11–12):1148–1157CrossRefGoogle Scholar
  23. 23.
    Wang X, Kwon PY (2014) WC/Co tool wear in dry turning of commercially pure aluminium. J Manuf Sci Eng 136(3):031006-1-7Google Scholar
  24. 24.
    Kious M, Ouahabi A, Boudraa M, Serra R, Cheknane A (2010) Detection process approach of tool wear in high speed milling. Measurement 43(10):1439–1446CrossRefGoogle Scholar
  25. 25.
    Oraby SE, Al-Modhuf AF, Hayhurst DR (2004) A diagnostic approach for turning tool based on the dynamic force signals. J Manuf Sci Eng 127(3):463–475CrossRefGoogle Scholar
  26. 26.
    Kaya B, Oysu C, Ertunc HM (2011) Force-torque based on-line tool wear estimation system for CNC milling of Inconel 718 using neural networks. Adv Eng Softw 42(3):76–84CrossRefGoogle Scholar
  27. 27.
    Alonso FJ, Salgado DR (2008) Analysis of the structure of vibration signals for tool wear detection. Mech Syst Signal Process 22(3):735–748CrossRefGoogle Scholar
  28. 28.
    Bovic K, Pierre D, Xavier C (2011) Tool wear monitoring by machine learning techniques and singular spectrum analysis. Mech Syst Signal Process 25(1):400–415CrossRefGoogle Scholar
  29. 29.
    Salgado DR, Alonso FJ (2007) An approach based on current and sound signals for in-process tool wear monitoring. Int J Mach Tools Manuf 47(14):2140–2152CrossRefGoogle Scholar
  30. 30.
    Wang H (2015) Progressive measurement and monitoring for multi-resolution data in surface manufacturing considering spatial and cross correlations. IIE Trans 47(10):1–20Google Scholar
  31. 31.
    Marinescu I, Axinte DA (2008) A critical analysis of effectiveness of acoustic emission signals to detect tool and workpiece malfunctions in milling operations. Int J Mach Tools Manuf 48(10):1148–1160CrossRefGoogle Scholar
  32. 32.
    Yen CL, Lu MC, Chen JL (2013) Applying the self-organization feature map (SOM) algorithm to AE-based tool wear monitoring in micro-cutting. Mech Syst Signal Process 34(1–2):353–366CrossRefGoogle Scholar
  33. 33.
    Attanasio A, Ceretti E, Giardini C, Cappellini C (2013) Tool wear in cutting operations: experimental analysis and analytical models. J Manuf Sci Eng 135(5):051012-1-11Google Scholar
  34. 34.
    Dutta S, Datta A, Chakladar ND, Pal SK, Mukhopadhyay S, Sen R (2012) Detection of tool condition from the turned surface images using an accurate grey level co-occurrence technique. Precis Eng 36(3):458–466CrossRefGoogle Scholar
  35. 35.
    Kassim AA, Mannan MA, Zhu M (2007) Texture analysis methods for tool condition monitoring. Image Vis Comput 25(7):1080–1090CrossRefGoogle Scholar
  36. 36.
    Wilkinson P, Reuben RL, Jones JDC, Barton JS, Hand DP, Carolan TA, Kidd SR (1997) Surface finish parameters as diagnostics of tool wear in face milling. Wear 205(1–2):47–54CrossRefGoogle Scholar
  37. 37.
    Dutta S, Pal SK, Mukhopadhyay S, Sen R (2013) Application of digital image processing in tool condition monitoring: A review. CIRP J Manufact Sci Technol 6(3):212–232CrossRefGoogle Scholar
  38. 38.
    ISO 25178-602:2012 (2010) Geometrical product specifications (GPS)—surface texture: areal-part 602: nominal characteristics of non-contact (confocal chromatic probe) instrumentsGoogle Scholar
  39. 39.
    Huang Z, Shih AJ, Ni J (2006) Laser interferometry hologram registration for three-dimensional precision measurements. J Manuf Sci Eng 128(4):887–896CrossRefGoogle Scholar
  40. 40.
    Stephenson DA, Ni J (2010) A multifeature approach to tool wear estimation using 3D workpiece surface texture parameters. J Manuf Sci Eng 132(6):1033–1041Google Scholar
  41. 41.
    Wang M, Xi L, Du S (2014) 3D surface form error evaluation using high definition metrology. Precis Eng 38(1):230–236CrossRefGoogle Scholar
  42. 42.
    Jr AMDS, Sales WF, Santos SC, Machado AR (2005) Performance of single Si 3N 4 and mixed Si 3N 4 +PCBN wiper cutting tools applied to high speed face milling of cast iron. Int J Mach Tools Manuf 45(3):335–344CrossRefGoogle Scholar
  43. 43.
    Astakhov VP (2004) The assessment of cutting tool wear. Int J Mach Tools Manuf 44(6):637–647CrossRefGoogle Scholar
  44. 44.
    Dutta S, Kanwat A, Pal SK, Sen R (2013) Correlation study of tool flank wear with machined surface texture in end milling. Measurement 46(10):4249–4260CrossRefGoogle Scholar
  45. 45.
    Al-Kindi Ghassan, Zughaer Hussien (2012) An approach to improved CNC machining using vision-based system. Adv Manuf Process 27(7):765–774CrossRefGoogle Scholar
  46. 46.
    Hai TN, Wang H, Hu SJ (2012) Chacterization of cutting force induced surface shape variation using high-definition metrology. J Manuf Sci Eng 135:641–650Google Scholar
  47. 47.
    Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621CrossRefGoogle Scholar
  48. 48.
    Huang DL, Du SC, Li GL, Wu ZQ (2017) A systemic approach for on-line minimizing volume difference of multiple chambers with casting surfaces in machining processes based on high definition metrology. J Manuf Sci Eng 139(8):081003-1-17Google Scholar
  49. 49.
    Wang K, Wei J, Bo L (2015) A spatial variable selection method for monitoring product surface. Int J Prod Res 54(14):1–21Google Scholar
  50. 50.
    He Z, Zuo L, Zhang M, Megahed FM (2012) An image-based multivariate generalized likelihood ratio control chart for detecting and diagnosing multiple faults in manufactured products. Int J Prod Res 54(6):1771–1784CrossRefGoogle Scholar
  51. 51.
    Sullivan JH (2002) Detection of multiple change points from clustering individual observation. J Qual Technol 34(4):374–383CrossRefGoogle Scholar
  52. 52.
    Woodall WH, Dan JS, Montgomery DC, Gupta S (2004) Using control charts to monitor process and product quality profiles. J Qual Technol 36(3):309–320CrossRefGoogle Scholar
  53. 53.
    Du S, Liu C, Huang D (2015) A shearlet-based separation method of 3D engineering surface using high definition metrology. Precis Eng 40:55–73CrossRefGoogle Scholar
  54. 54.
    Du S, Liu C, Xi L (2015) A selective multiclass support vector machine ensemble classifier for engineering surface classification using high definition metrology. J Manuf Sci Eng 137(1):011003-1-15Google Scholar
  55. 55.
    Du SC, Huang DL, Wang H (2015) An adaptive support vector machine-based workpiece surface classification system using high-definition metrology. IEEE Trans Instrum Meas 64(10):2590–2604CrossRefGoogle Scholar
  56. 56.
    Du S, Fei L (2016) Co-kriging method for form error estimation incorporating condition variable measurements. J Manuf Sci & Eng 138(4):o41003-1-16Google Scholar
  57. 57.
    Wang M, Ken T, Du S, Xi L (2015) Tool wear monitoring of wiper inserts in multi-insert face milling using three-dimensional surface form indicators. J Manuf Sci Eng 137(3):031006-1-8Google Scholar
  58. 58.
    Wang M, Shao YP, Du SC, Xi LF (2015) A diffusion filter for discontinuous surface measured by high definition metrology. Int J Precis Eng Manuf 16(10):2057–2062CrossRefGoogle Scholar
  59. 59.
    Hai N, Wang H, Tai BL, Ren J, Hu SJ, Shih AJ (2016) High-definition metrology enabled surface variation control by cutting load balancing. J Manuf Sci Eng 138(2):021010-1-11Google Scholar
  60. 60.
    Wells, L. J., Shafae, M. S., and Camelio, J. A., 2016, “Automated surface defect detection using high-density data,” Journal of Manufacturing Science & Engineering, 138(7), pp. 071001-1-10Google Scholar
  61. 61.
    Chen Q, Yang S, Li Z (1999) Surface roughness evaluation by using wavelets analysis. Precis Eng 23(3):209–212CrossRefGoogle Scholar
  62. 62.
    Lu C, Troutman JR, Schmitz TL, Ellis JD, Tarbutton JA (2016) Application of the continuous wavelet transform in periodic error compensation. Precis Eng 44:245–251CrossRefGoogle Scholar
  63. 63.
    Xu J, Yamada K, Seikiya K, Tanaka R, Yamane Y (2014) Effect of different features to drill-wear prediction with back propagation neural network. Precis Eng 38(4):791–798CrossRefGoogle Scholar
  64. 64.
    Barnhill RE, Pottmann OH (1992) Fat surfaces: a trivariate approach to triangle-based interpolation on surfaces. Comput Aided Geom Des 9(5):365–378CrossRefGoogle Scholar
  65. 65.
    Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395CrossRefGoogle Scholar
  66. 66.
    Raguram R, Chum O, Pollefeys M, Matas J, Frahm JM (2013) USAC: a universal framework for random sample consensus. IEEE Trans Pattern Anal Mach Intell 35(8):2022–2038CrossRefGoogle Scholar
  67. 67.
    Rodriguez A, Laio A (2014) Clustering by fast search and find of density peaks. Science 344(6191):1492–1496CrossRefGoogle Scholar
  68. 68.
    Flitney RK (2011) Seals and sealing handbook. ElsevierGoogle Scholar
  69. 69.
    Persson BNJ, Yang C (2008) Theory of the leak-rate of seals. J Phys Condens Matter 20Google Scholar
  70. 70.
    Aharony A, Stauffer D (2003) Introduction to percolation theory. Taylor & FrancisGoogle Scholar
  71. 71.
    Persson BNJ, Albohr O, Creton C, Peveri V (2004) Contact area between a viscoelastic solid and a hard, randomly rough, substrate. J Chem Phys 120:8779–8793CrossRefGoogle Scholar
  72. 72.
    Lorenz B, Persson BNJ (2009) Leak rate of seals: comparison of theory with experiment. EPL 86Google Scholar
  73. 73.
    Lorenz B, Persson BNJ (2010) Leak rate of seals: effective-medium theory and comparison with experiment. Eur Phys J E 31:159–167CrossRefGoogle Scholar
  74. 74.
    Bottiglione F, Carbone G, Mangialardi L, Mantriota G (2009) Leakage mechanism in flat seals. J Appl Phys 106CrossRefGoogle Scholar
  75. 75.
    Bottiglione F, Carbone G, Mantriota G (2009) Fluid leakage in seals: an approach based on percolation theory. Tribol Int 42:731–737CrossRefGoogle Scholar
  76. 76.
    Marie C, Lasseux D (2007) Experimental leak-rate measurement through a static metal seal. J Fluids Eng 129:799–805CrossRefGoogle Scholar
  77. 77.
    Robbe-Valloire F, Prat M (2008) A model for face-turned surface microgeometry. Application to the analysis of metallic static seals. Wear 264:980–989CrossRefGoogle Scholar
  78. 78.
    Okada H, Itoh T, Suga T (2008) The influence of surface profiles on leakage in room temperature seal-bonding. Sens Actuators A 144:124–129CrossRefGoogle Scholar
  79. 79.
    Haruyama S, Nurhadiyanto D, Choiron MA, Kaminishi K (2013) Influence of surface roughness on leakage of new metal gasket. Int J Press Vessels Pip 111–112:146–154CrossRefGoogle Scholar
  80. 80.
    Marie C, Lasseux D, Zahouani H, Sainsot P (2003) An integrated approach to characterize liquid leakage through metal contact seal. Eur J Mech Environ Eng 48:81–86Google Scholar
  81. 81.
    Ledoux Y, Lasseux D, Favreliere H, Samper S, Grandjean J (2011) On the dependence of static flat seal efficiency to surface defects. Int J Press Vessels Pip 88:518–529CrossRefGoogle Scholar
  82. 82.
    Malburg MC (2003) Surface profile analysis for conformable interfaces. J Manuf Sci Eng 125:624–627CrossRefGoogle Scholar
  83. 83.
    Liao Y, Stephenson DA, Ni J (2012) Multiple-scale wavelet decomposition, 3D surface feature exaction and applications. J Manuf Sci Eng 134Google Scholar
  84. 84.
    Ren J, Park C, Wang H (2018) Stochastic modeling and diagnosis of leak areas for surface assembly. J Manuf Sci Eng 140:041011–10Google Scholar
  85. 85.
    Arghavani J, Derenne M, Marchand L (2002) Prediction of gasket leakage rate and sealing performance through fuzzy logic. Int J Adv Manuf Technol 20:612–620CrossRefGoogle Scholar
  86. 86.
    Xin L, Gaoliang P (2016) Research on leakage prediction calculation method for static seal ring in underground equipments. J Mech Sci Technol 30:2635–2641CrossRefGoogle Scholar
  87. 87.
    Du S, Liu T, Huang D, Li G (2018) A fast and adaptive bi-dimensional empirical mode decomposition approach for filtering of workpiece surfaces using high definition metrology. J Manuf Syst 46:247–263CrossRefGoogle Scholar
  88. 88.
    Shao Y, Du S, Xi L (2017) 3D machined surface topography forecasting with space-time multioutput support vector regression using high definition metrology, V001T02A69Google Scholar
  89. 89.
    ISO 16610-22 (2015) Geometrical product specifications (GPS)-filtration part 22: linear profile filters: spline filterGoogle Scholar
  90. 90.
    ISO 16610-1 (2015) Geometrical product specifications (GPS)-filtration part 1: overview and basic conceptsGoogle Scholar
  91. 91.
    Krystek M (1996) Form filtering by splines. Measurement 18:9–15CrossRefGoogle Scholar
  92. 92.
    Maragos P, Schafer R (1987) Morphological filters–part I: their set-theoretic analysis and relations to linear shift-invariant filters. IEEE Trans Acoust Speech Signal Process 35:1153–1169CrossRefGoogle Scholar
  93. 93.
    ISO 16610-40 (2015) Geometrical product specifications (GPS)-filtration part 40: morphological profile filters: basic conceptsGoogle Scholar
  94. 94.
    ISO 16610-41 (2015) Geometrical product specifications (GPS)-filtration part 41: morphological profile filters: disk and horizontal line-segment filtersGoogle Scholar
  95. 95.
    ISO 16610-85 (2015) Geometrical product specifications (GPS)-filtration part 85: morphological areal filters: segmentationGoogle Scholar
  96. 96.
    ISO 25178-2 (2012) Geometrical product specifications (GPS)-surface texture: areal part 2: terms, definitions and surface texture parametersGoogle Scholar
  97. 97.
    ISO 4287 (1997) Geometrical product specifications (GPS)-surface texture: profile method: terms, definitions and surface texture parameters.Google Scholar
  98. 98.
    Hyun S, Pel L, Molinari JF, Robbins MO (2004) Finite-element analysis of contact between elastic self-affine surfaces. Phys Rev E-Stat, Nonlinear, Soft Matter Phys 70:026117CrossRefGoogle Scholar
  99. 99.
    Megalingam A, Mayuram MM (2012) Comparative contact analysis study of finite element method based deterministic, simplified multi-asperity and modified statistical contact models. J Tribol 134:014503CrossRefGoogle Scholar
  100. 100.
    Johnson KL (1985) Contact mechanics. Cambridge University Press, New YorkGoogle Scholar
  101. 101.
    Huang D, Du S, Li G et al (2018) Detection and monitoring of defects on three-dimensional curved surfaces based on high-density point cloud data. Precis Eng 53:79–95CrossRefGoogle Scholar
  102. 102.
    Shao Y, Yin Y, Du S et al (2018) Leakage monitoring in static sealing interface based on three dimensional surface topography indicator. J Manuf Sci Eng 140(10):101003CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Shichang Du
    • 1
  • Lifeng Xi
    • 2
  1. 1.School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghaiChina

Personalised recommendations