Multidisciplinary design optimization of modular industrial robots |

Multidisciplinary design optimization of modular industrial robots

Mehdi Tarkian, Johan Persson (Linköping University), Johan Ölvander and Xiaolong Feng (ABB Corporate Research)

This paper presents a multidisciplinary design optimization framework for modular industrial robots. An automated design framework, containing physics based high fidelity models for dynamic simulation and structural strength analyses are utilized and seamlessly integrated with a geometry model.

The proposed framework utilizes well-established methods such as meta-modeling and multi-level optimization in order to speed up the design optimization process. The contribution of the paper is to show that by applying a merger of well-established methods, the computational cost can be cut significantly, enabling search for truly novel concepts. MDO is a “systematic approach to design space exploration”, the implementation of which allows the designer to map the interdisciplinary relations that exist in a system. In this paper the MDO framework consists of geometry model inCATIA V5, a FE model in CATIA V5 and a Dynamic model in Dymola and a basic cost model, see Figure 2. The model integration is established with modeFRONTIER. Dymola integration is however not supported, therefore the dynamic model is host simulated in MATLAB Simulink.