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Research Papers: Design Automation

Multi-Objective Optimal Design of Obstacle-Avoiding Two-Dimensional Steiner Trees With Application to Ascent Assembly Engineering

[+] Author and Article Information
Alexandru-Ciprian Zăvoianu

Department of Knowledge-Based
Mathematical Systems,
Johannes Kepler University Linz,
Altenbergerstraße 69,
Linz 4040, Austria
e-mail: Ciprian.Zavoianu@jku.at

Susanne Saminger-Platz

Department of Knowledge-Based
Mathematical Systems,
Johannes Kepler University Linz,
Altenbergerstraße 69,
Linz 4040, Austria
e-mail: Susanne.Saminger-Platz@jku.at

Doris Entner

Design Automation,
V-Research GmbH—Industrial
Research and Development,
Stadtstraße 33,
Dornbirn 6850, Austria
e-mail: Doris.Entner@v-research.at

Thorsten Prante

Design Automation,
V-Research GmbH—Industrial
Research and Development,
Stadtstraße 33,
Dornbirn 6850, Austria
e-mail: Thorsten.Prante@v-research.at

Michael Hellwig

Research Centre Process and Product Engineering,
Vorarlberg University of Applied Sciences,
Hochschulstraße 1,
Dornbirn 6850, Austria
e-mail: Michael.Hellwig@fhv.at

Martin Schwarz

Technology Management,
Liebherr-Werk Nenzing GmbH,
Dr.-Hans-Liebherr-Str. 1,
Nenzing 6710, Austria
e-mail: Martin.Schwarz@liebherr.com

Klara Fink

Technology Management,
Liebherr-Werk Nenzing GmbH,
Dr.-Hans-Liebherr-Str. 1,
Nenzing 6710, Austria
e-mail: Klara.Fink@liebherr.com

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received September 9, 2017; final manuscript received January 8, 2018; published online March 26, 2018. Assoc. Editor: Mian Li.

J. Mech. Des 140(6), 061401 (Mar 26, 2018) (11 pages) Paper No: MD-17-1615; doi: 10.1115/1.4039009 History: Received September 09, 2017; Revised January 08, 2018

We present an effective optimization strategy that is capable of discovering high-quality cost-optimal solution for two-dimensional (2D) path network layouts (i.e., groups of obstacle-avoiding Euclidean Steiner trees) that, among other applications, can serve as templates for complete ascent assembly structures (CAA-structures). The main innovative aspect of our approach is that our aim is not restricted to simply synthesizing optimal assembly designs with regard to a given goal, but we also strive to discover the best tradeoffs between geometric and domain-dependent optimal designs. As such, the proposed approach is centered on a variably constrained multi-objective formulation of the optimal design task and on an efficient coevolutionary solver. The results we obtained on both artificial problems and realistic design scenarios based on an industrial test case empirically support the value of our contribution to the fields of optimal obstacle-avoiding path generation in particular and design automation in general.

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Figures

Grahic Jump Location
Fig. 1

An example of an offshore crane where different ascent assembly modules highlighted in red (a) are combined to form a fairly complex CAA-structure (b)

Grahic Jump Location
Fig. 2

A 3D model and the corresponding 2D design plane obtained after unfolding. Access points are marked with circles and obstacles are marked with rectangles.

Grahic Jump Location
Fig. 3

Example of 2D path network layouts that link definition points while avoiding the rectangular obstacle areas

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Fig. 4

Example of a solution candidate x for which k* = 5 and m = 3 and of the resulting MTn,x

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Fig. 5

A computer-aided design model of the gantry of a Liebherr mobile harbor crane with highlighted user-specified access points (a), expert-designed ascent assembly solution (b), and complementary 3D and unfolded 2D model abstraction (c)

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Fig. 6

Results for the four academic test cases (a) A1 test case, (b) A2 test case, (c) A3 test case, (d) A4 test case, (e) A5 test case, and (f) A6 test case

Grahic Jump Location
Fig. 7

CAA-structure optimization results for the LWN TC1 optimization scenario using different cost functions: (a) C1 cost setting, (b) C2 cost setting, (c) C3 cost setting, and (d) C4 cost setting

Grahic Jump Location
Fig. 8

CAA-structure optimization results for the LWN TC2 optimization scenario using different cost functions: (a) C1 cost setting, (b) C2 cost setting, (c) C3 cost setting, and (d) C4 cost setting

Grahic Jump Location
Fig. 9

CAA-structure optimization results for the LWN TC3 optimization scenario using different cost functions: (a) C1 cost setting, (b) C2 cost setting, (c) C3 cost setting, and (d) C4 cost setting

Grahic Jump Location
Fig. 10

CAA-structure optimization results for the LWN TC4 optimization scenario using different cost functions: (a) C1 cost setting, (b) C2 cost setting, (c) C3 cost setting, and (d) C4 cost setting

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Fig. 11

Pareto optimal solutions for LWN TC1 when considering the C1 and C3 cost settings

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