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Technical Briefs

Multidisciplinary Design Optimization of Modular Industrial Robots by Utilizing High Level CAD Templates

[+] Author and Article Information
Mehdi Tarkian

e-mail: mehdi.tarkian@liu.se

Johan Persson

e-mail: johan.persson@liu.se

Johan Ölvander

e-mail: johan.olvander@liu.se
Department of Management and Engineering,
Linköping University,
SE-581 83 Linköping Sweden

Xiaolong Feng

ABB Corporate Research,
SE-721 78 Västerås Sweden
e-mail: xiaolong.feng@se.abb.com

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received November 10, 2011; final manuscript received August 28, 2012; published online October 19, 2012. Assoc. Editor: Shinji Nishiwaki.

J. Mech. Des 134(12), 124502 (Oct 19, 2012) (8 pages) doi:10.1115/1.4007697 History: Received November 10, 2011; Revised August 28, 2012

This paper presents a multidisciplinary design optimization (MDO) framework for automated design of a modular industrial robot. The developed design framework seamlessly integrates high level computer aided design (CAD) templates (HLCt) and physics based high fidelity models for automated geometry manipulation, dynamic simulation, and structural strength analysis. In the developed framework, methods such as surrogate models and multilevel optimization are employed in order to speed up the design optimization process. This work demonstrates how a parametric geometric model, based on the concept of HLCt, enables a multidisciplinary framework for multi-objective optimization of a modular industrial robot, which constitutes an example of a complex heterogeneous system.

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Figures

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Fig. 1

An industrial robot (left) and a modular industrial robot (right)

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Fig. 2

Actuator (left), datum (center), and structure HLCt (right). Shown are also the references required by each HLCt. In order for the actuator HLCt instance to be fully defined, a reference point from datum HLCt, which is not visualized, is also required.

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Fig. 3

The high level CAD template instantiation process

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Fig. 4

FE models of the even and odd links

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Fig. 5

Graph of the NRMSEs for different surrogate models, fitted using 50 and 100 samples

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Fig. 6

Relationships between variables (ac, α, and ω and t), objectives (W and CT) and constraints (AL and MS) in the integrated design framework

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Fig. 7

The SL optimization process

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Fig. 8

The ML optimization process with one global and six local optimizations in sequence

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Fig. 9

Pareto frontier (marked) for the ML strategy and SL strategy

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Fig. 10

Pareto frontiers of the SL (dark dots) and the ML (light dots) strategies

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Fig. 11

Optimal robot variants from the ML and SL pareto frontiers

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