Corrosion: Simulating and Render-ing

Stephane Merillou, Jean-Michel Dischler, Djamchid Ghazanfarpour

20071114 이승주

1/15

-Introduction

-Physics of Corrosion

-Modeling and Rendering corrosion

-Result

Contents

2/15

• corrosion reactions can be divided into two main categories: metallic patinas and rusty layers.

• we propose a model to simulate and render new forms of corrosion that correspond to the second category.

• we describe our random-walkbased spreading process, resulting in a “corrosion map”

Introduction

3/15

• Corrosion can be defined as the deterioration of a material against its environment.

• On a physical point of view, corrosion is an electrochemical process requiring the presence of an electrolyte.

• The chemical reactions in the case of iron for example imply the creation of many different oxides composing rust.

• These oxides all have their own colors, explaining the usually noisy and non-uniform aspect of rust color.

Physics of Corrosion

Schematic atmospheric corrosion princi-ple (a) metallic patinas, (b) destructive cor-rosion.

Some rust constituents and their colors

Introduction

4/15

• Uniform corrosion: corrosive attack over the entire surface area (at least a large proportion).

• Galvanic corrosion appears when two different materials are cou-pled in a corrosive electrolyte

• Pitting corrosion is a process which produces localized cavities in the material

• Crevice corrosion is often due to a differential aeration: that is, a sudden difference of the oxygen accessibility over two parts of the same material

Physics of Corrosion

Corrosion: qualitative models

5/15

• sufficient estimations of the steel corrosion rate are given in [14] according to a simple specific atmosphere corrosivity classifica-tion.

• These values permit us to estimate the amount of wasted matter according to a certain time

• the following equation (its coefficients permits the experimental results of table 2 to be well matched [17]– w(t) = k ⅹ tⁿ

Quantification of corrosion

Physics of Corrosion

steel corrosion mean rate (g=m2=year) vs atmosphere corrosivity as a function of time.

Analytic functions to evaluate corrosion rate by evaluating the lost weight in (g=year)

6/15

• For each metallic object, the user selects the amount no of starting points.

• Assume that the object is given in the form of a mesh.• Initially each face Fi has a probability coefficient pi.• The probability coefficients are modified by the two conditions (in-

ternal and external).

• Concerning internal conditions– There is no simple way to physically quantify micro-structural imperfections.– We empirically propose to use two new coefficients.

• A microstructure imperfections factor kstr;i

• A greasy factor kgr;i

Modeling and Rendering corrosion

Starting points of corrosion

7/15

• Concerning external conditions– the pi coefficients are modified according to the global scene and object geome-

try– Two objects are in contact (collision) with each other.– The object is isolated– Isolated metallic objects (no collision and no significant differential aeration) are

affected by uniform corrosion

Modeling and Rendering corrosion

Starting points of corrosion

Galvanic corrosion and differential aeration corrosion princi-ples

8/15

• Once all faces Fi of all objects have their own probability coeffi-cient pi given by

pi = pi;initial ⅹ ksrt;i ⅹ kgr;i ⅹ kext

• starting points simply can be selected randomly according to these coefficients

• The coefficients kext, kstr;i, kgr;i are selected by the user

Modeling and Rendering corrosion

Starting points of corrosion

9/15

• Our approach consists in simulating the corrosion process on a “thick plate” (corrosion map), then mapping this plate onto the ob-ject.

• With these starting points, the spreading process can be run onto the map.

• Each corrosion map pixel contains following information: a color, a porosity coefficient, a roughness coefficient

Modeling and Rendering corrosion

Computing the corrosion-map

10/15

• The first step consists of computing a list lcp of “already” corroded pixels.

• the corrosion spreading process is applied to ps by randomly choosing one of its non-corroded neighboring pixels.

• Concerning pv, the spreading process goes directly in depth, that is, we decrease the depth value by .

Modeling and Rendering corrosion

Computing the corrosion-mapcorrosion spread

11/15

• Corrosion does not affect all the pixels of the map at the same time as induced by the use of a time dependent spreading technique with preferential starting points.

• we need for each pixel to evaluate a specific δ• This is achieved by calculating for each pixel a “local corrosion

time” tlocal– weq = pixel heigth ⅹ Sp ⅹ ρiron

– tlocal = e^(1/n)ⅹln(weq/k)– Δw = w(tlocal + 1) - w(tlocal)

– hl = Δ w/(ρiron ⅹSp)

Computing the corrosion-map

Calculating δ

12/15

• On non-corroded parts :use the Cook and Torrance BRDF model [3] for iron.

• On corroded parts :use the model which accounts for both porosity and roughness.– Porosity is empirically chosen to be 80%, since rust is a very porous matter– Roughness factor : measured using a HommelWerke HommelTester T2000

Affecting roughness and porosity

Modeling and Rendering corrosion

13/15

Result

14/15

Result

15/15