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Multidisciplinary design optimization of modular Industrial Robots
Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
ABB Corporate Research, Västerås, Sweden.
2011 (English)In: Proceedings of the ASME 2011 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, IDETC/CIE 2011, August 28- 31, 2011, Washington, DC, USA, The American Society of Mechanical Engineers (ASME) , 2011, Vol. 5, 867-876 p.Conference paper, Published paper (Refereed)
Abstract [en]

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 frameworkutilizes well-established methods such as metamodeling and multi-level optimization inorder to speed up the design optimization process. The contributionof the paper is to show that by applying amerger of well-established methods, the computational cost can be cutsignificantly, enabling search for truly novel concepts.

Place, publisher, year, edition, pages
The American Society of Mechanical Engineers (ASME) , 2011. Vol. 5, 867-876 p.
Keyword [en]
MDO, CAD, Optimization
National Category
Other Mechanical Engineering
Identifiers
URN: urn:nbn:se:liu:diva-71765DOI: 10.1115/DETC2011-48196ISI: 000324076700080ISBN: 978-0-7918-5482-2 (print)OAI: oai:DiVA.org:liu-71765DiVA: diva2:453948
Conference
The 37th Design Automation Conference (DAC), ASME 2011 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, Washington DC, USA, August 28-31
Available from: 2011-11-10 Created: 2011-11-04 Last updated: 2016-05-13Bibliographically approved
In thesis
1. Design and Optimization under Uncertainties: A Simulation and Surrogate Model Based Approach
Open this publication in new window or tab >>Design and Optimization under Uncertainties: A Simulation and Surrogate Model Based Approach
2012 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis deals with development of complex products via modeling and simulation, and especially the use of surrogate models to decrease the computational efforts when probabilistic optimizations are performed. Many methods that can be used to perform probabilistic optimizations exist and this thesis strives to present and demonstrate the capabilities of a few of them. Hopefully, this information can be helpful for someone who wants to choose a method.

Knowledge about several different topics is required to perform a probabilistic optimization. First, it is necessary to incorporate the probabilistic behavior into the analysis by estimating how the uncertainties and variations in the model and its parameters are affecting the performance of the system. The focus in this thesis is on sampling based methods to estimate these probabilities. Secondly, an optimization algorithm should be chosen so that the computer can search for and present an optimal solution automatically.

The probabilistic optimization process can be computationally demanding since numerous simulations of the model are performed each time the value of the objective function is estimated. It is therefore desirable to speed up the process by incorporating computationally effective surrogate models. This is especially important if the simulated model is computationally demanding on its own, e.g. a finite element model with many nodes.

Each of these topics is presented in its own chapter of this thesis. A few  methods are presented and their performances demonstrated for each topic.

Surrogate models can also be used to improve the performances of optimization algorithms when the desire is to optimize computationally expensive objective functions. With this in mind, efforts have been made to improve the Complex-RF optimization algorithm. A modified algorithm is presented in this thesis and the main difference is that it creates and utilizes surrogate models iteratively during the optimization process. The modified algorithm is compared with Complex-RF and is demonstrated to be superior for computationally expensive models.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2012. 81 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1556
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-84850 (URN)978-91-7519-753-1 (ISBN)
Presentation
2012-11-02, Mass, A-huset, Campus Valla, Linköpings universitet, Linköping, 10:15 (Swedish)
Opponent
Supervisors
Funder
EU, FP7, Seventh Framework Programme, 234344
Available from: 2012-10-24 Created: 2012-10-24 Last updated: 2012-10-24Bibliographically approved

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Tarkian, MehdiPersson, JohanÖlvander, JohanFeng, Xiaolong

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