{"title":"A model-based system for the classification and analysis of materials","authors":"A. Capelo, L. Ironi, S. Tentoni","doi":"10.1049/ISE.1993.0014","DOIUrl":null,"url":null,"abstract":"To build model-based systems capable of emulating the scientist's or engineer's way of reasoning about a given physical domain requires methods for automating the formulation or selection of a model which adequately captures the knowledge needed for solving a specific problem. To find and exploit such models requires the use and integration of different kinds of knowledge, formalisms and methods. This paper describes a system which aims at reasoning automatically about visco-elastic materials from a mechanical point of view. It integrates both domain-specific and domain-independent knowledge in order to classify and analyse the mechanical behaviour of materials. The classification task is based on qualitative knowledge, whereas the analysis of a material is performed at a quantitative level and is based on numerical simulation. The key ideas of the work are to automatically generate a library of models of ideal materials and their corresponding qualitative responses to standard experiments; to classify an actual material by selecting from within the library a class of models whose simulated qualitative behaviours towards standard loads match the observed behaviours; to identify a quantitative model of the material, and then to analyse the material by simulating its behaviour on any load. Each model in the library is automatically generated in two different forms; at the lowest level, as a symbolic description and, at a mathematical level, as an ordinary differential equation. This paper mainly concentrates on the methods and algorithms of model generation and qualitative simulation. >","PeriodicalId":55165,"journal":{"name":"Engineering Intelligent Systems for Electrical Engineering and Communications","volume":"167 1","pages":"145-158"},"PeriodicalIF":0.0000,"publicationDate":"1993-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Intelligent Systems for Electrical Engineering and Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1049/ISE.1993.0014","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12
Abstract
To build model-based systems capable of emulating the scientist's or engineer's way of reasoning about a given physical domain requires methods for automating the formulation or selection of a model which adequately captures the knowledge needed for solving a specific problem. To find and exploit such models requires the use and integration of different kinds of knowledge, formalisms and methods. This paper describes a system which aims at reasoning automatically about visco-elastic materials from a mechanical point of view. It integrates both domain-specific and domain-independent knowledge in order to classify and analyse the mechanical behaviour of materials. The classification task is based on qualitative knowledge, whereas the analysis of a material is performed at a quantitative level and is based on numerical simulation. The key ideas of the work are to automatically generate a library of models of ideal materials and their corresponding qualitative responses to standard experiments; to classify an actual material by selecting from within the library a class of models whose simulated qualitative behaviours towards standard loads match the observed behaviours; to identify a quantitative model of the material, and then to analyse the material by simulating its behaviour on any load. Each model in the library is automatically generated in two different forms; at the lowest level, as a symbolic description and, at a mathematical level, as an ordinary differential equation. This paper mainly concentrates on the methods and algorithms of model generation and qualitative simulation. >