Digital tools for material selection

Finite Element Analysis (FEA) of functionally material objects are generally regarded as separate domains of interest in CAD and Computer-Aided Engineering (CAE) community. With CAD modeling tools only,the end users are still uncertain whether or not the designed objects can really meet the functional requirements in terms of structural, thermal or other prescribed properties. Considering the main factors with important influence on material paramaters, the support vector machine (SVM) model is established, which can really meet the functional requirements in terms of structural, thermal or other prescribed properties. Taking surving data investigating the relationships that exist between the structures and properties of materials, as samples for training and learning, some functions are obtained in identification of optimized model. It is shown that the identification model of SVM analysis is an effective method with high prediction accuracy and could be used in practice.