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Multidisciplinary Design Optimization

of Modular Industrial Robots by

Utilizing High Level CAD Templates

1、Introduction

In the design of complex and tightly integrated engineering products, it

is essential to be able to handle interactions between different subsystems

of multidisciplinary nature [1]. To achieve an optimal design, a product must

be treated as a complete system instead of developing subsystems independently

[2]. MDO has been established as a convincing concurrent design optimization

technique in development of such complex products [3,4].

Furthermore, it has been pointed out that, regardless of discipline,

basically all analyses require information that has to be extracted from a

geometry model [5]. Hence, according to Bow-cutt [1], in order to enable

integrated design analysis and optimization it is of vital importance to be

able to integrate an automated parametric geometry generation system into the

design framework. The automated geometry generation is a key enabler for

so-called geometry-in-the-loop[6] multidisciplinary design frameworks,

where the CAD geometries can serve as framework integrators for other

engineering tools.

To eliminate noncreative work, methods for creation and automatic

generation of HLCt have been suggested by Tarkian [7].The principle of high

HLCts is similar to high level primitives(HLP) suggested by La Rocca and van

Tooren [8], with the exception that HLCts are created and utilized in a CAD

environment.Otherwise, the basics of both HLP and HLCt can, as suggested byLa

Rocca, be compared to parametric LEGOV Rblocks containing a set of design and

analysis parameters. These are produced and stored in libraries, giving

engineers or a computer agent the possibility to first topologically select

the templates and then modify the morphology, meaning the ——

欢迎下载 2 shape,of each template parametrically.

2、Multidisciplinary Design Framework

MDO is a “systematic approach to design space exploration”[17], the

implementation of which allows the designer to map the interdisciplinary

relations that exist in a system. In this work, the MDO framework consists

of a geometry model, a finite element(FE) model, a dynamic model and a basic

cost model. The geometry model provides the analysis tools with geometric

input. The dynamic model requires mass properties such as mass, center of

gravity, and inertia. The FE model needs the meshed geometry of the robot as

well as the force and torque interactions based on results of dynamic

simulations.

High fidelity models require an extensive evaluation time which has be

taken into account. This shortcoming is addressed by applying surrogate models

for the FE and the CAD models. The models are briefly presented below.

2.1 High Level CAD Template—Geometry Model

Traditionally, parametric CAD is mainly focused on morphological

modifications of the geometry. However, there is a limit to morphological

parameterization as follows:

•The geometries cannot be radically modified.

•Increased geometric complexity greatly increases parameterization

complexity.

The geometry model of the robot is generated with presaved HLCts, created

in CATIA V5. These are topologically instantiated with unique internal design

variables. Topological parameterization allows deletion, modification, and

addition of geometricelements which leads to a much greater design space

captured.Three types of HLCts are used to define the industrial robot

topologically; Datum HLCt which includes wireframe references required for

placement for the Actuator HLCTs and Structure HLCts, as seen Fig.2. ——

欢迎下载 3

Fig. 2 An industrial robot (left) and a modular industrial robot(right)

The names of the references that must be provided for each HLCt

instantiation are stored in the knowledge base (see Appen-dix A.4), which is

searched through by the inference engine. In Appendix A, pseudocode examples

describes how the references are retrieved and how they are stored in the

knowledge base.

The process starts by the user defining the number of degrees of freedom

(DOF) of the robot (see Fig. 3) and is repeated until the number of axis (i)

is equal to the user defined DOF.In order to instantiate the first Structure

HLCt, two Datum and two actuator instances are needed. References from the

two Datum instances help orienting the structure in space, while the

geometries of the actuator instances, at both ends of the link, are used to

construct the actuator attachments, as seen in Figs. 2 and 3. For the remaining

links, only one new instance of both datum and actuator HLCts are required,

since the datum and actuator instances from adjacent links are already