Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Multi-objective Optimisation Using Evolutionary Algorithms: An Introduction Read chapter Read first chapter

2011 | OriginalPaper | Chapter

6. Multi-objective Optimisation of a Family of Industrial Robots

Authors : Johan Ölvander, Mehdi Tarkian, Xiaolong Feng

Published in: Multi-objective Evolutionary Optimisation for Product Design and Manufacturing

Publisher: Springer London

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Product family design is a well recognised method to address the demands of mass customisation. A potential drawback of product families is that the performance of individual members are reduced because of the constraints added by the common platform, i.e., parts and components need to be shared by other family members. This chapter presents a framework where the product family design problem is stated as a multi-objective optimisation problem and where multi-objective evolutionary algorithms are applied to solve the problem. The outcome is a Pareto-optimal front that visualises the trade-off between the degree of commonality (e.g., number of shared components) and performance of individual family members. The design application is a family of industrial robots. An industrial robot is a mechatronic system that comprises a mechanical structure (i.e., a series of mechanical links), drive-train components (including motors and gears), electrical power units and control software for motion planning and control.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Product Portfolio Selection of Designs Through an Analysis of Lower-Dimensional Manifolds and Identification of Common Properties
next chapter Multi-objective Optimisation and Multi-criteria Decision Making for FDM Using Evolutionary Approaches
Literature
1.
Fellini, R., Kokoloras, M., Papalambros, P., & Perez-Duarte, A. (2005). Platform selection under performance bounds in optimal design of product families. Journal of Mechanical Design, 127, 524–535.CrossRef
2.
Nelson, S., Parkinson, M., & Papalambros, P. (2001). Multicriteria optimization in product platform design. Journal of Mechanical Design, 123, 199–204.CrossRef
3.
Jose, A., & Tollenaere, M. (2005). Modular and platform methods for product family design: literature analysis. Journal of Intelligent Manufacturing, 16, 371–390.CrossRef
4.
Fujita, K., & Yoshida, H. (2004). Product variety optimization simultaneously designing module combination and module attributes. Concurrent Engineering: Research and Applications, 12(2), 105–118.CrossRef
5.
Goldberg, D. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA: Addison-Wesley.
6.
Deb, K. (2001). Multi-Objective Optimization using Evolutionary algorithms. New York, NY: Wiley and Sons Ltd.
7.
Jiao, J., Zhang, Y., & Wang, Y. (2007). A generic genetic algorithm for product family design. Journal of Intelligent Manufacturing, 18(2), 233–247.CrossRef
8.
Simpson, T., D’Souza, B. (2004). Assessing variable levels of platform commonality within a product family using a multiobjective genetic algorithm. Concurrent Engineering: Research and Applications, 12(2) pp 199–129.
9.
Andersson, J. (2000). A Survey of Multi-objective Optimization in Engineering Design, Technical Report LiTH-IKP-R-1097, Department of Mechanical Engineering. Linköping, Sweden: Linköping University.
10.
Steuer, R. (2001). Multiple criteria optimization: Theory, computation and application. New York: John Wiley & Sons, Inc.
11.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transaction on Evolutionary Computation, 6(2), 181–197.CrossRef
12.
Simon, H. (1969). The Sciences of the Artificial. Cambridge: MIT Press.
13.
Cross, N. (2000). Engineering design methods (3rd edition). Chichester, Uk: John Wiley & sons.
14.
Pahl, G., Beitz, W. (1996). Engineering Design—A Systematic Approach. London: Springer-Verlag.
15.
Suh, N., (2001). Axiomatic Design—Advances and Applications. New York: Oxford University Press.
16.
Ullman, D. (1992). The Mechanical Design Process. New York: McGraw-Hill Inc.
17.
Ullrich, K.T., Eppinger, S′.D. (2000), Product design and development (2nd Edition). New York: McGraw-Hill Inc.
18.
Yoshikawa, T. (1985). Manipulability of robotic mechanisms. International Journal of Robotics Research, 4(2):pp. 3–9.
19.
Feng, X., Holmgren, B., Ölvander, J. (2009). Evaluation and optimization of industrial robot families using different kinematic measures. In proceedings of ASME Design Automation Conference. San Diego, August 30–September 2.
20.
Sicilano, B. (2001) Modeling and Control of Robot Manipulators. London: Springer Verlag.
21.
Spong, W. Mark & Vidyasagar M. (1989), Robot Dynamics and Control. New York: John Willey & Sons Inc.
22.
Tarkian, M., Ölvander, J., Feng, X., Petterson, M. (2009). Design automation of modular industrial robots. In proceedings of ASME Design Automation Conference. San Diego, August 30–September 2.
Metadata
Title
Multi-objective Optimisation of a Family of Industrial Robots
Authors
Johan Ölvander
Mehdi Tarkian
Xiaolong Feng
Copyright Year
2011
Publisher
Springer London
Book
Multi-objective Evolutionary Optimisation for Product Design and Manufacturing

Print ISBN: 978-0-85729-617-7

Electronic ISBN: 978-0-85729-652-8

Copyright Year: 2011

DOI
https://doi.org/10.1007/978-0-85729-652-8_6

Premium Partners

    Image Credits