Pro-active Management of Visual

Appearance of Products: from the

Automotive Sector to other Industries

Fco M. Martínez-Verdú

Color & Vision Group: http://web.ua.es/en/gvc

University of Alicante (Spain)

verdu@ua.es

Visual appearance of products Color & Texture managed currently in the automotive sector

Challenges for its optimal and efficient management Multi-scale approach (bottom-up vs. top-down)

Foundations for pro – active Quality Management: Visual and instrumental correlation

Multivariate statistics: visual psychophysics, DoE, etc.

Conclusions

OUTLINE

Great variety of visual attributes in daily products

VISUAL APPEARANCE OF PRODUCTS

VISUAL APPEARANCE OF PRODUCTS

Dyes & Pigments

New visual appearance

attributes

Multi-functional properties

Coloration processes

Market forces: performance – cost balance,

customer preferences, etc.

Continuous loop

Color & Texture

Reflection & Transmission

Goniochromatism: BRDF

Sparkle & Graininess

VISUAL APPEARANCE IN AUTOMOTIVE

® Wikipedia

MSc degree in Color Technology for the Automotive Sector

VISUAL APPEARANCE IN AUTOMOTIVE

• Bottom – up:

• Many variables

• Impracticable

• Top – down:

• Feasible

• How?

CHALLENGES: MULTI – SCALE APPROACH

Color, Texture

Radiative

transfer theory

Particles

interaction

Light – Matter

interaction

particle models

Light sources tech.,

Pigments, dyes

Gloss, sparkle, etc.

Color differences

Visual appearance

Emission SPD(l)

Reflection r(l)

Transmission t(l)

Coefficients:

Absorption K

Scattering S

Substrate

Coloration

application

processes:

no. layers, etc.

Phys. + Chem.

Particles & Substrate:

Size, Shape, Thickness

Refraction index,

Extinction index,

Roughness, etc.

TH

EO

RE

TIC

AL

AP

PR

OA

CH

EX

PE

RIM

EN

TA

L A

PP

RO

AC

H

• But, in this case (empirical approach = top – down), the

typical challenge is how we can understand and manage

by a pro-active way the relevance and interplay of

nano/micro (structural) parameters, and other ones

(coloration application processes, optical, etc.), on final

visual appearance attributes (color, texture, etc.).

• HOW?

• Metrology, Visual Psychophysics, and Statistics

• inter and multi-disciplinary (hybrid) approach

CHALLENGES: MULTI – SCALE APPROACH

• IDEAL CONTEXT:

• BiRD motto:

• What You See Is What

You Measure Rightly = WYSIWYMR

• ICC profile format (Graphics Arts)

• Objective: WYSIWYG

VISUAL & INSTRUMENTAL CORRELATION

Instrumental scaling

Vis

ual assessm

en

t

?

VISUAL & INSTRUMENTAL CORRELATION

Visual appearance of materials

DT = f(DE, DG, DS, ...) is the “GOAL”

• Human visual perception tasks:• Detection

• Influence of viewing distance and geometry

• Spatio-chromatic dithering

• Scaling (ordering: from less to more)• Color (from spectral data to 3 dim.),

• Sparkle (2 dim.), Graininess (1 dim.), etc.• Color & Texture palettes

• Discrimination (differences):• Perceptibility vs. Acceptability

• FAIL vs. PASS controls by tolerance ellipses

VISUAL & INSTRUMENTAL CORRELATION

Special equipment: Tele-spectro-radiometer

Radiometric, photometric and colorimetric measurements

without contact, and adjusted to the target size

Spectrofluorimeter

Multi-angle spectrophotometers

Lighting cabinets for visual assessments

VISUAL & INSTRUMENTAL CORRELATION

UA – Research Technical Services:

XPS, WDX, FRX, SEM, FT-IR, ATR, Raman, etc.

Pending advanced instrumentation

multi – angle spectroscopic ellipsometry

spectral constants of absorption (K) and scattering (S) to different measurement

geometries (irradiation / observation)

multi-angle micro-spectrophotometer

X-CT (tomography)

(3D) transversal scanning of nanomaterials, etc.

interferometric microscopy using white light

3D surface contactless profilometer

VISUAL & INSTRUMENTAL CORRELATION

• Current challenges in color industries:• Gonio – appearance: color & texture

• Spectral BRDF own color palette

• Formulation of new colors outside Rösch – McAdam solid

• Tolerances Total Visual Appearance (color, gloss, sparkle, etc.)

• Measurement without contact (by tele – spectroradiometer, etc.)• Reversible or irreversible electro / thermo- chromism, etc.

• Real colored products vs. its efficient digital simulation• Color gamut of displays technologies

• Pro – active prediction models for visual quality of products

VISUAL & INSTRUMENTAL CORRELATION

Products: why? Earn money being competitive (Porter)

by differentiation: and better than … , impossible to be copied, etc.

faith perceptually digital simulation to the original

specific colors & textures functional (added value from color: resistance, etc.)

gonio – apparent

fluorescent, thermochromic, etc.

viewing distance effect: spatio – chromatic dithering near vs. far

lighting conditions changes effect: type of light source (wLED, etc.)

type of measurement geometry: diffuse vs. directional (gonio - )

MULTIVARIATE STATISTICS

Processes: why? how? when? Design and production easy to be managed

Feasibility & stability of original product model (std. or master)

Ease for creativity & innovation

Repeatability & accuracy of batches Measure to save time & money:

Comparison with error range TOLERANCE

Multi – scale process: nano/micro visual From bottom – up approach top – down

Predictive model of pro – active management by: Statistical design of experiments (DoE)

Regression models

MULTIVARIATE STATISTICS

DEAUDI2000 < 2 = 1.41 OK

DEAUDI2000 [ 2 , 3 = 1.73] cOK

DEAUDI2000 > 1.73 FAIL

• Statistical Design of Experiments (DoE)• Statistical technique used in quality control for planning,

conducting, analyzing, and interpreting sets of experiments

aimed at making sound decisions without incurring a too high

cost or taking too much time• Qualitative and quantitative variables optimization objective

• Selection of the minimal number of samples

• Non-linear / linear multidimensional regression models• Increasing sampling for an optimal prediction model

• even combining qualitative and quantitative (measureable) variables

MULTIVARIATE STATISTICS: DoE

• Problem formulation• Aim (reproducible and measurable)

• Relevant factors (qualitative and quantitative)

• Screening design• Selection of levels for each factor

• Experiments (no. of samples)

• Analysis of the raw data

• Data analysis (Pareto, regression, etc.)

• Optimization & Robustness studies

MULTIVARIATE STATISTICS: DoE BASICS

1 – Sparkle detection distance vs. metallic pigment size & shape

2 – Sparkle detection distance vs. concentration, achromatic

background, illuminance level & pigment type

3 – Sparkle detection distance vs. colored background

4 – Color matching vs. silver finishing process on a coated plastic

5 – Gonio-appearance of 3D printed parts vs. 3D printing technology

and its sub – processes

FIVE DoE EXAMPLES

• Relevance and interplay of colored backgrounds by CIE-L*C*abhab

• Fixed structural and environmental data (factors)• Color mix: variable solid pigment + fixed effect pigment

• L*: 3 levels• C*ab: 3 levels• hab: 4 levels

SPARKLE DETECTION DISTANCE

Complete multi-level factorial table of experiments (samples)

Sample no. C L h Sample description [Hue / Lightness / Chroma]

1 0 1 1,00 RED / LIGHT / MEDIUM

2 1 -1 1,00 RED / DARK / STRONG

… … … … …

13 -1 1 -1,00 GREEN / LIGHT / WEAK

14 -1 -1 0,33 BLUE / DARK/ WEAK

… …

23 0 1 0,33 BLUE / LIGHT / MEDIUM

24 0 -1 -0,33 YELLOW / DARK / MEDIUM

… … … … …

34 1 0 1,00 RED / GRAY / STRONG

35 0 0 0,33 BLUE / GRAY / MEDIUM

36 -1 1 1,00 RED / LIGHT / WEAK

• Goal: color matching (DEab = 0), L* = 82 , & maximum transparency

• Initial DoE proposal: Taguchi L16 (215-11) Matrix, before analysis

COLOR MATCH vs. SILVER FINISHING

Worksheet MEASURED RESPONSES

Nº experim. MaterialPVD

Thickness

PVD

Conc.Topcoat

Topcoat

RobotBasecoat

Basecoat

Robot DEab L* Transparency (T)

1 Metal A

Low

Low Low

translucent

white

Low Low Low

2Metal B

HighHigh High

3High

Low

4 Metal AHigh Low Low

5 Metal CLow

High

translucent

white

6Metal D

LowHigh High

7High

High

8 Metal CLow Low

Low

9 Metal A

High

LowHigh

10Metal B

HighHigh Low

11High

Low

12 Metal AHigh Low High

13 Metal CLow Low

translucent

white

14Metal D

LowHigh Low

15High

High

16 Metal C Low Low High

• Can 3D printed parts for cars (body or interior) equal or better

color & texture without losing phys – chem performance?

• DoE aims: high sparkle, flop, chroma, colorfastness, etc.

• Factors:

• Qualitative:

• Technologies: FFF or FDM, MultiJet Fusion, ColorJet, Powder-bed, living AM, etc.

• Materials: (bio)polymers, pigments, additives, process sequence, etc.

• Quantitative:

• Temperature, irradiation, speed, layer height, infill, head size, etc.

GONIO-APPEARANCE IN 3D PRINTED PARTS

• FFF experiment table (Taguchi L9): PLA fixed, simple interactions

• Head size (mm): 3 levels

• 100, 200 & 300

• Speed (mm/s): 3 levels

• 20, 40 & 60

• Infill (%): 3 levels

• 0, 20 & 100

• Color: 3 levels

• Without pigment

• Solid or special-effect pigment

GONIO-APPEARANCE IN 3D PRINTED PARTS

Sample no. HEAD SPEED INFILL COLOR

1 1 3 2 3

2 3 2 2 1

3 1 2 3 2

4 3 1 3 3

5 3 3 1 2

6 2 1 2 2

7 2 3 3 1

8 1 1 1 1

9 2 2 1 3

Plane printed samples for measuring flop

• FFF experiment tables:

• Complete multi-level factorial:

• All previous factors with 2 levels, except color set = 24, all possible interactions

• Multi-level factorial + D – optimal design

• Only speed with 2 levels complete set = 54, but optimally reduced to 21

• Multi-level factorial + D – optimal design:

• All factors with 3 levels + new factor (polymer: ABS & PLA) complete set = 162, but

optimally reduced to 21, and simple interactions well detected

• Multi-level V2 factorial + D – optimal design:

• Only speed and polymer with 2 levels from 108 to 21, quadratic interactions

GONIO-APPEARANCE IN 3D PRINTED PARTS

Hybrid multi – scale approach for visual appearance of materials

applied successfully in automotive can be extended to other

industries as ceramics, coatings, cosmetics, plastics, printing, etc.

Structural elements (pigments, etc.), advanced instrumental techniques,

visual and instrumental correlation methods, statistics (DoE, etc.), can save

time and money to implement new color & texture quality controls

successfully, etc., and even to make easy new competitive advantages for

companies.

CONCLUSIONS

COUNT ON US

Pro-active Management of Visual

Appearance of Products: from the

Automotive Sector to other Industries

Fco M. Martínez-Verdú

Color & Vision Group: http://web.ua.es/en/gvc

University of Alicante (Spain)

verdu@ua.es