- author = "Umberto Armani and Dirk Jan Boon and Vassili V. Toropov and Andrey Polynkin and Leslie J. Clark and Mary B. Stowe",
- title = "Generation of models related to aluminium surface treatment using genetic programming",
- booktitle = "Proceedings of the 9th world congress on structural and multidisciplinary optimization (WCSMO9),",
- year = "2011",
- address = "Shizuoka, Japan",
- month = jun # " 13-17",
- keywords = "genetic algorithms, genetic programming, hybrid genetic programming, corrosion, model",
- broken = "http://pbl.eng.kagawa-u.ac.jp/kani/index4.php",
-
URL = "
http://pbl.eng.kagawa-u.ac.jp/kani/p/paper246_1.pdf",
- size = "9 pages",
-
abstract = "Surface treatment in aerospace industry is of
paramount importance for protection of metallic
structures against corrosion, in particular aluminium
alloys. One of the common techniques consists of the
generation of a surface coating (through chemical
conversion or anodising) followed by the application of
a primer paint containing a water soluble chromate
salt, such as barium chromate BaCrO4 or strontium
chromate SrCrO4.
Such treatment allows for corrosion protection of the aluminium alloy in the presence of moisture even in the case of damage to the protective coating, through chemical and mass transfer processes involving the primer, water, the exposed alloy and the chromate salts. The availability of empirical models describing the quantity of chromate dissolving into the aqueous medium is therefore important for understanding the corrosion protection process and it could lead to improvements in the development and qualification of new corrosion protection systems.
The main aim of this paper is to provide improved models to describe the quantity of dissolved chromate in water for three different chromate-based primers, considering as independent variables the time treated aluminium alloy samples are left in an aqueous solution and the acidity of the solution. To produce the models a hybrid genetic programming technique is used. Its role is to generate models through symbolic regression on experimental data provided by industry. Being a non-parametric regression technique, genetic programming is successful in finding a range of models whose mathematical structure is different from existing ones",
