An integrated quantitative-qualitative approach to automated modeling of visco-elastic materials from experimental data
by L. Ironi, S. Tentoni
in: R. Teti (ed.), Proc. ICME 98 - CIRP International
Seminar on Intelligent Computation in Manufacturing Engineering, Capri,
1-3 July 1998, CUES-Salerno & RES Communication-Naples, 381-388.
ABSTRACT
This paper illustrates, through a case study in the domain of Material Science, how AI and
Mathematics techniques can be suitably combined
within a unified
and robust approach to provide an intelligent tool for modeling.
Given a physical system, the modeling problem we address consists
in finding an adequate mathematical description (the constitutive equation) R(s,e)=0 of the cause-effect
relation between two state variables s(t), e(t) , where e(t) is the
response elicited on the system by an input signal s(t) applied over
time t.
The model structure, i.e. the mathematical form of operator R, is
closely related to the physical domain knowledge and the modeling assumptions. Much of the modeling activity performed by the skilled human
expert regards the definition of the model space, that is the set of all the model structures constrained by the physical and modeling assumptions.
The process of obtaining, usually through an optimization loop and parameter estimation techniques, from a given set of candidate model structures and experimental data, the ``best performing'' quantitative model is called as a whole System Identification (SI).
Download Postscript file.
Back to publication list
Liliana Ironi
1998