A systemic self-modelling method and its application to material design and optimization

Abstract

A self-modelling system for material research has been developed based on discriminant analysis, artificial neural networks, classification mapping and genetic algorithms. It provides systemic methodologies for nonlinear multivariate modelling and multi-objective optimizing. It is designed to unveil connotative information from a limited experimental data set and gives qualitative, quantitative and geometry models of the object to be researched. In addition, optimized research schemes can be derived from these models by genetic algorithms and classification mapping. The technique is suitable for subjects that have some original study results but for the following reasons there are difficulties in doing further research. (i) The object researched has too many controlling factors and is too complex to analyse. (ii) The object is controlled by some unexplainable mechanisms and is difficult to analyse. (iii) The mathematical expression has strong nonlinearity and is difficult to resolve strictly.

Genetic algorithms in materials design and processing

“…Chen et al, 62 for example, described their expert system called KDPAG that makes simultaneous use of a knowledge base, a database, pattern recognition, and artificial neural network along with GAs, and has been used for designing materials of diverse kinds. Su et al 63 described a similar hybrid approach and demonstrated its capability for designing a composite material. Mahfouf et al 64 have also demonstrated a procedure where GAs were combined with artificial neural nets, for alloy steel design, with the variables as the carbon, manganese, chromium, and molybdenum content of the steel, along with its tempering temperature.…”