Third Party Funds Group - Sub project
Acronym: SLASSY
Start date : 01.01.2009
End date : 31.12.2020
Extension date: 31.03.2021
Website: https://www.tr-73.de
The processes of sheet metal forming, which are being researched within the framework of the Collaborative Research Centre Transregio 73 (SFB/TR73), offer new possibilities for adapting components to increased requirements due to their increased design freedom. The combination of solid and sheet metal forming offers enormous potential for product development, and suitable computer-aided tools must be made available to exploit this potential.
Subproject B1 is dedicated to this overall goal with the
development of a self-learning assistance system (SLASSY). SLASSY
enables the knowledge-based support of the design engineer in the
development of sheet metal formed components with complex secondary form
elements. The necessary design-relevant manufacturing knowledge is
collected simultaneously with the manufacturing process development by
using machine learning methods. It is then stored in the
multidimensional knowledge base in the form of meta or prediction
models. The architecture of SLASSY supports the synthesis of a component
from main and secondary form elements as well as its knowledge-based
analysis.
A major goal of the third phase is the use of spatially
resolved meta models for the prediction of local component and tool
properties. For example, to directly predict the degree of deformation
on the component surface.
The aim of the sub-project is the simultaneous development (i.e. at the same time as and parallel to the development of the new manufacturing technology) of a self-learning assistance system with an integrated multidimensional knowledge base, whereby the product developer receives the necessary support at an early stage in the production-oriented design of sheet-metal formed components. The focus of the third phase is on researching and providing methods for analysing component designs in terms of manufacturability, taking into account the influences of the entire manufacturing process chain.