Many properties of the use of paper are influenced by its surface quality. The surface quality can be measured by different techniques. We aim to characterise its topography and the link between its structural and physical properties.

1. Characterisation of the properties use of paper by topographical analysis of its surface Gérard Baudin et Jean-Francis Bloch - LGP2 - PAGORA Contact: mercier.christophe1@googlemail.com Christophe Mercier – September 2004

2. Outline  Introduction  Presentation of topics  Pertinence and interpretation of topographical parameter s  Application  Conclusion and perspectives

3. Paper surface / PhD 23 May 2010 PhD defense " Paper is really not a perfect substrate for printing. A number of optical and physical imperfections, such as local variations in colour , optical density and surface topography , degrade the image each in their own manner . " Gustavson S. , 1995 " Although print gloss has been used as a quality factor for many years, the relation between the visual apparence of a printed paper and its surface roughness is not well understood " Bélan M.C., 2001 " It is mainly the surface properties like roughness and distribution of flocs that determine the quality of print. " Johansson J.O., 2002 Printing Gloss Profile model

4. Acquisition technique 23 May 2010 PhD defense

5. Measured surface 23 May 2010 PhD defense

6. Outline  Introduction  Presentation of topics  Pertinence and interpretation of topographical parameters  Applications  Conclusion and perspectives

7. Objectives 23 May 2010 PhD defense I nterpretation of parameters Calend e ring Offset printing Acquisition Treatments

8. Measurement methods 23 May 2010 PhD defense  Determination of spacing and acquisition length in order to obtain a statistically representative surface.  Raw data treatment before exploitation.

9. Calendering 23 May 2010 PhD defense Objectives:  Characterisation of surface variations due to successive passages in calendaring nip with : * 3D parameters, * ray-tracing.

10. Offset printing 23 May 2010 PhD defense  Determination of the repartition of ink at the surface. Objectives:  Determination of surface modifications due to printing.

11. Pertinence and interpretation 23 May 2010 PhD defense  Discriminate different surfaces or profiles, with standard parameters . Objectives:  For quality control, detect the variations of paper surface.

12. Outline  Introduction  Presentation of topics  Pertinence and interpretation of topographical parameter s  Application  Conclusion and perspectives

13. Outline / param e t er interpr e tation 23 May 2010 PhD defense  Introduction of 1 D and 2 D param e t er s  Interpr e tations of 1 D et 2 D param e t er s Conventions : 1D for profile z = f(x) 2D for surface z = f(x,y)

14. Characterisation techniques 23 May 2010 PhD defense [Stout et al] 1993

15. Functional Characterisation 1D 23 May 2010 PhD defense Notations: P : Raw profile R: Roughness profile W: waviness profile Standard: NF EN ISO 4287, Dec 1998 Form Form + waviness Form + waviness + r oughness Y X

16. Functional Characterisation 1D 23 May 2010 PhD defense mean line Xs i Z p Z v Z t dz(x) dx Height distribution X Abbott curve Z i

17. Functional Characterisation 1D 23 May 2010 PhD defense

18. Characterisation techniques 23 May 2010 PhD defense [Stout et al]

19. Illustration of Fourier Transform 23 May 2010 PhD defense T1 = N/9; T2 = N/16; T3 = N/33; Industrial application: wire marks N : number of points

20. Fourier: limitation 23 May 2010 PhD defense

21. Fourier limitation : L # N.  23 May 2010 PhD defense T1 = 323 (15); T2 = 212(24); T3 = 145(34);

22. Power Densit y S pectr um 23 May 2010 PhD defense Ajouter la définition Ind e pendance / acquisition length T1 = 323 (15) T2 = 212 (24) T3 = 145 (34)

23. A utocorrelation fu nction 23 May 2010 PhD defense Is c orrelation l ength a c h aract e risti c scale for paper ? l c

24. 2D classi cal ch aract e risation 23 May 2010 PhD defense Me thod s are similar to those used for p rofil e s : – Fourier T ransform – Areal A utocorrelation function (AACF)

25. Fourier: spectrum 23 May 2010 PhD defense Original surface Frequency spectrum Std

26. Areal A utocorrelation function (AACF) 23 May 2010 PhD defense Normalisation: Maximum = 1

27. Areal A utocorrelation function (AACF) 23 May 2010 PhD defense Simulated s urface AACF of the simulated surface AACF characteristic parameter

28. Areal A utocorrelation function (AACF) 23 May 2010 PhD defense Parameters: Sal and Str Representation of central lobe

29. 2 D Recommandation s 23 May 2010 PhD defense Five famil ies of param e t er s: * amplitude : 4 * spaci al (Sal, Str,….) : 4 * hybrid : 3 * Functional (Bearing Area) : 3 * Functional (Volumes) : 3 :17 Five famil ies of param e t er s: * amplitude : 4 * spaci al (Sal, Str,….) : 4 * hybrid : 3 * Functional (Bearing Area) : 3 * Functional (Volumes) : 3 :17

30. A mplitude p aram e t ers 23 May 2010 PhD defense

31. S pa tial p aram e t er s 23 May 2010 PhD defense

32. H ybrid p aram e t er s 23 May 2010 PhD defense

33. Outline / param e t er interpr e tation 23 May 2010 PhD defense  Introduction of 1 D and 2 D param e t er s  Interpr e tation of 1 D and 2 D param e t er s

34. Inter p r e tation of 1 D param e t er s 23 May 2010 PhD defense I nfluence of the n u mb er of param e t er s in order to c h aract e ris e a surface B ijectivit y profiles / param e t er s Objecti ve : P rofil e models :  three fixed param e t er s  four fixed param e t er s  five fixed param e t er s  five fixed param e t er s + Abbott curve

35. Studied (me a sure d ) p rofil e 23 May 2010 PhD defense Abbott curve

36. T hree param e t er s: mod el 23 May 2010 PhD defense Lm Psm h X Z X Principle Three targeted parameters : Pa, Psk, PSm Overhead

37. Three param e t ers : r e sults 23 May 2010 PhD defense

38. Four param e t er s: mod el 23 May 2010 PhD defense L1 L4 L3 L2 Psm hv hp Z X Remar k : Sku is constant F our targeted parameters : Pq, Psk, PSm , PDq

39. Four param e t er s: r e sults 23 May 2010 PhD defense

40. Five param e t er s: mod e l 23 May 2010 PhD defense Five targeted parameters : Pq, Psk, PSm, PDq, Pku d d p 1 p 3 p 2 V 2 V 1 V 3

41. Five param eters : r e sult s 23 May 2010 PhD defense Altitude d istribution

42. Five param e t er s + Abbott: mod e l 23 May 2010 PhD defense Five targeted parameters : Pq, Psk, Pku, PSm, PDq + Abbott curve C onstant height , variable ellipticit y

43. Five param e t er s + Abbott: r e sult s 23 May 2010 PhD defense Measured profile Virtual profile

44. Conclusion / profile 23 May 2010 PhD defense  Models of profiles : non-bijectivity (discrimination / Fourier T ransform or autocorr e lation f u nction …)

45. Outline / param e t er interpr e tation 23 May 2010 PhD defense  Introduction of 1 D and 2 D param e t er s  Interpr e tation of 1 D and 2 D param e t er s

46. 2D param e t er s i nterpr e tation 23 May 2010 PhD defense S urface m od e l s : * either with rectangular or curved e l e ments / fibres * or with sinuso idal functions Study bijectivit y surfaces param eters Objecti ve s:

47. Texture n°1: m e thod 23 May 2010 PhD defense rectangular or curved e l e ments + random noise

48. Texture n°1: r e sults 23 May 2010 PhD defense 12 parameters are similar Overhead

49. Texture n°1: r es ults 23 May 2010 PhD defense Measured s urface Virtual s urface

50. Texture n°1bis: m e thod 23 May 2010 PhD defense

51. Texture n°1bis: r e sults 23 May 2010 PhD defense

52. Texture n°1bis: r e sults 23 May 2010 PhD defense Measured s urface Simulated s urface 12 parameters and Abbott curve are similar !

53. Second method 23 May 2010 PhD defense Condition : S tructura l e l e ment of the model surface: fibre. Question: Other s tructura l e l e ment s ? Surface mod e l Measured Surface

54. Texture n°2: m e thod 23 May 2010 PhD defense µm µm A mplitude d istribution Orientation Noise

55. Texture n°2: r e sults 23 May 2010 PhD defense Param e t er s A mplitude d istribution Measured s urface S imul ated s urface

56. Texture n°2: r e sults 23 May 2010 PhD defense 12 parameters and Abbott curve are similar !

57. Texture n°2: limitations 23 May 2010 PhD defense Areal A utocorrelation function S imul ated s urface Measured s urface

58. Conclusion / surface 23 May 2010 PhD defense  Non bijectivity parameters / surfaces  Visu al perception

59. Outline  Introduction  Presentation of topics  Pertinence and interpretation of topographical parameter s  Application  calandering  offset printing  Conclusion and perspectives

60. Photogoniometer 23 May 2010 PhD defense Sample Source Sensor

61. Measured indicatrix of diffusion 23 May 2010 PhD defense

62. Light re fle ction 23 May 2010 PhD defense Objecti ve :  Simulation of 1 D and 2 D indicatrix of diffusion Measured indicatrix of diffusion S urface topography

63. Surface light reflection 23 May 2010 PhD defense Normal lobe Specular reflection Incident ray Forescatter lobe Net reflexion 2µm Backscatter lobe

64. BRDF : Bidirectionnal Reflectance Distribution Function 23 May 2010 PhD defense  3  2  1 d  i d  r X N Y ds

65. Reflection models 23 May 2010 PhD defense Re fle ct ion m od e l E mpiri cal a ppro a ch Ge om e tri cal a ppro a ch P hysi cal a ppro a ch Th e or y pertubati on Th e or y Kirchhoff Mod e l Lamberti a n Mod e l Phong Mod e l Ward Mod e l Torrance-Sparrow Mod el Cook and Torrance Mod e l Nayar Mod e l Beckmann and Spizzichino

66. Cook and Torrance 23 May 2010 PhD defense D: surface model  Length of correlation  root mean square of altitudes

67. RMS + Length of correlation 23 May 2010 PhD defense Profils measure Filering Calculation rms and lc plot Measure of indicatrix /

68. RMS + Length of correlation 23 May 2010 PhD defense / Profils measure Filering Calculation rms and lc plot Measure of indicatrix

69. Ray tracing - profiles - principle 23 May 2010 PhD defense Source Mean plan of surface

70. Ray tracing - profiles - simulation 23 May 2010 PhD defense Simulation of profiles (RMS and L c ) Control of RMS and L c Trace of curve Ray-tracing / Trace of curve RMS constant

71. Ray tracing - profiles - simulation 23 May 2010 PhD defense L c constant

72. Ray tracing – measured profiles 23 May 2010 PhD defense

73. Ray tracing - measured profiles 23 May 2010 PhD defense

74. Ray tracing - measured profiles 23 May 2010 PhD defense Examples : Measured indicatrix of diffusion Simulated indicatrix of reflection sb 0 sb 1*60 sb2*60 sb 3*60

75. Conclusion ray tracing 23 May 2010 PhD defense  Ray-tracing: qualitative effect of calandering  RMS and correlation length # paper profile

76. Solid printing (1) 23 May 2010 PhD defense Objective: Spatial non- uniformit y ch aract e risation of ink distribution , mean ink thickness , … Only one crater considered : Contact angle controled

77. Solid printing(2) 23 May 2010 PhD defense Application:

78. Solid printing(3) 23 May 2010 PhD defense Results: 0,5 µm 1 µm 1,5 µm

79. Conclusion / Solid printing 23 May 2010 PhD defense  This model is useful to study industrial problems linked to non- homogeneous repartition of ink

80. Halftone printing (1) 23 May 2010 PhD defense Objective: Determination of ink distribution in surface thanks to modification of amplitude distribution before/after printing. % surface

81. Halftone printing(2) 23 May 2010 PhD defense Method: determination of mean and RMS ink thickness repartition

82. Halftone printing(3) 23 May 2010 PhD defense Results:

83. Conclusion / Halftone printing 23 May 2010 PhD defense  From a paper surface measurement, we obtain a number of points and a thickness of deposed ink in order to superpose the amplitude distribution of both measured and simulated printed surface.

84. Outline  Introduction  Presentation of topics  Pertinence and interpretation of topographical parameter s  Application  Conclusion and perspectives

85. Conclusion 23 May 2010 PhD defense  From a paper surface measurement, we obtain a number of points and a thickness of deposed ink in order to superpose the amplitude distribution of both measured and simulated printed surface.  Printing model are useful to study industrial problems linked to non- homogeneous repartition of ink  Ray-tracing: qualitative effect of calandering , RMS and correlation length # paper profile

86. Perspectives 23 May 2010 PhD defense  Spatial repartition of ink  Ray-tracing taking account spectral reflection  Surface models / virtual materials

87. Thanks 23 May 2010 PhD defense

88. Thanks

89. Questions Questions

90. Paramètres de volumes 23 May 2010 PhD defense

91. Paramètres fonctionnels 23 May 2010 PhD defense

92. Fourier: spectres 23 May 2010 PhD defense Surface simulée TF de la surface simulée

93. Classical characterisation 2D 23 May 2010 PhD defense Spectral analysis : periodic components of the profile are pointed out using Fourier transform, due periodogramme and power spectrum density Spatial analysis : des correlations entre une réalisation à une position x t et les positions précédentes sont recherchées. La fonction d’autocorrélation est utilisée l’analyse temps/fréquence : elle a pour vocation de mettre en évidence des inhomogénéités présentes dans le profil. (/O ndelettes)

94. Ray tracing - surfaces - principe 23 May 2010 PhD defense

95. Ray tracing - surfaces - cas réels 23 May 2010 PhD defense Papier Alpastar Gloss Semi-mat Mat

96. Ray tracing - surfaces - cas réels 23 May 2010 PhD defense Papier Hello Gloss Semi-mat Mat

97. Ray tracing - surfaces - cas réels 23 May 2010 PhD defense Papier Alpanova Gloss Semi-mat Mat

98. Ray tracing - surfaces - cas réels 23 May 2010 PhD defense Papier Econova Gloss Semi-mat Mat

99. BRDF : Bidirectionnal Reflectance Distribution Function 23 May 2010 PhD defense  3  2  1 d  i d  r X N Y

100. Cook and Torrance 23 May 2010 PhD defense D: surface model  Length of correlation  root mean square of altitudes

101. Correlation length vs Length of statistical stability 23 May 2010 PhD defense C:arteauorel_calandreiltre_5mmb3f100_5mm.pro Measured profile Correlation length Length of statistical stability