0
Research Papers

Formal Process to Support Resolution of Functional Trade-Offs in Complex Product Development

[+] Author and Article Information
Kazuya Oizumi

Department of Systems Innovation,
School of Engineering,
The University of Tokyo,
Engineering Building 3, 308, 7-3-1 Hongo,
Bunkyo-ku, Tokyo 113-8656, Japan
e-mail: oizumi@m.sys.t.u-tokyo.ac.jp

Keita Ishida

Department of Systems Innovation,
School of Engineering,
The University of Tokyo,
Engineering Building 3, 308, 7-3-1 Hongo,
Bunkyo-ku, Tokyo 113-8656, Japan
e-mail: ishida@m.sys.t.u-tokyo.ac.jp

Muyo Tai

Department of Systems Innovation,
School of Engineering,
The University of Tokyo,
Engineering Building 3, 308, 7-3-1 Hongo,
Bunkyo-ku, Tokyo 113-8656, Japan
e-mail: muyou@m.sys.t.u-tokyo.ac.jp

Kazuhiro Aoyama

Department of Systems Innovation,
School of Engineering,
The University of Tokyo,
Engineering Building 3, 330, 7-3-1 Hongo,
Bunkyo-ku, Tokyo 113-8656, Japan
e-mail: aoyama@sys.t.u-tokyo.ac.jp

Manuscript received September 15, 2018; final manuscript received May 17, 2019; published online June 7, 2019. Assoc. Editor: Mahesh Mani.

J. Comput. Inf. Sci. Eng 19(3), 031013 (Jun 07, 2019) (14 pages) Paper No: JCISE-18-1250; doi: 10.1115/1.4043822 History: Received September 15, 2018; Revised May 17, 2019

This research study proposes a method to resolve issues with trade-offs between functionalities, which hinder the unconventional improvement of a product. As products have become increasingly complex, it has become difficult to grasp all the aspects of a product. To resolve the problematic trade-off issues of a complex product, it is necessary to model the product in an appropriate form and to gather knowledge from experts in each domain. Although there have been several models to tackle this issue, modeling still poses difficulties due to a lack of clear guidelines. This paper classifies models into three types: function-based, cognition-based, and physics-based models. Next, their roles and description guidelines are clarified. As a function-based model depicts the functionality of a product in a rather simple description, it is employed to specify significant trade-offs. A cognition-based model depicts the designers' recognition of physical phenomena, whereas a physics-based model rigorously depicts the physical phenomena. A cognition-based model is appropriate for ideation, while the physics-based model contributes to the objectivity of a model. This study proposes a strategy of complementary modeling and the use of cognition-and physics-based models. To support the ideation of a solution to the trade-offs, the theory of inventive problem solving (TRIZ) is applied. The proposed method is validated by a case study of continuously variable transmissions (CVT).

Copyright © 2019 by ASME
Your Session has timed out. Please sign back in to continue.

References

Tai, M. , Ishida, K. , Oizumi, K. , and Aoyama, K. , 2018, “ A Formal Process to Support Resolution of Functional Trade-Offs in Complex Product Development,” ASME Paper No. DETC2018-86317.
Zanni-Merk, C. , Cavallucci, D. , and Rousselot, F. , 2009, “ An Ontological Basis for Computer Aided Innovation,” Comput. Ind., 60(8), pp. 563–574. [CrossRef]
Dubois, S. , Lutz, P. , Rousselot, F. , and Vieux, G. , 2007, “ A Model for Problems' Representation at Various Generic Levels to Assist Inventive Design,” Int. J. Comput. Appl. Technol., 30(1/2), pp. 105–112. [CrossRef]
Becattini, N. , Borgianni, Y. , Cascini, G. , and Rotini, F. , 2012, “ Model and Algorithm for Computer-Aided Inventive Problem Analysis,” Comput.-Aided Des., 44(10), pp. 961–986. [CrossRef]
Bariani, P. F. , Berti, G. A. , and Lucchetta, G. , 2004, “ A Combined DFMA and TRIZ Approach to the Simplification of Product Structure,” Proc. Inst. Mech. Eng., Part B, 218(8), pp. 1023–1027. [CrossRef]
Yamashina, H. , Ito, T. , and Kawada, H. , 2002, “ Innovative Product Development Process by Integrating QFD With TRIZ,” Int. J. Prod. Res., 40(5), pp. 1031–1050. [CrossRef]
Yamashina, H. , Ishida, K. , and Mizuyama, H. , 2005, “ An Innovative Product Development Process for Resolving Fundamental Conflicts,” J. Jpn. Soc. Precis. Eng., 71(2), pp. 216–222.
Yeh, C. H. , Huang, J. C. Y. , and Yu, C. K. , 2011, “ Integration of Four-Phase QFD and TRIZ in Product R&D: A Notebook Case Study,” Res. Eng. Des., 22(1), pp. 125–141. [CrossRef]
Albers, A. , and Zingel, C. , 2013, “ Challenges of Model-Based Systems Engineering: A Study Towards Unified Term Understanding and the State of Usage of SysML,” Smart Product Engineering: 23rd CIRP Design Conference, Bochum, Germany, May 11–13, pp. 83–92.
Kasser, J. E. , 2010, “ Seven Systems Engineering Myths and the Corresponding Realities,” Systems Engineering A Test and Evaluation Conference, Adelaide, Australia.
Friedenthal, S. , Moore, A. , and Steiner, R. , 2008, A Practical Guide to SysML: The Systems Modeling Language, Morgan KaufMann OMG Press, San Francisco, CA.
Weilkiens, T. , 2007, Systems Engineering With SysML/UML Modeling, Analysis, Design, Morgan KaufMann OMG Press, San Francisco, CA.
INCOSE, 2015, Systems Engineering Handbook: A Guide for System Life Cycle Processes and Activities, Wiley, Hoboken, NJ.
Dori, D. , 2002, Object-Process Methodology, Springer, Berlin.
Barbieri, G. , Kernschmidt, K. , Fantuzzi, C. , and Vogel-Heuser, B. , 2014, “ A SysML Based Design Pattern for the High-Level Development of Mechatronic Systems to Enhance Re-Usability,” 19th World Congress, the International Federation of Automatic Control, Cape Town, South Africa, Aug. 24–29, pp. 3431–3437.
Shah, A. A. , Kerzhner, A. A. , Schaefer, D. , and Paredis, C. J. J. , 2010, “ Multi-View Modeling to Support Embedded Systems Engineering in SysML,” Graph Transformations and Model-Driven Engineering, G. Engels , C. Lewerentz , W. Schäfer , A. Schürr , and B. Westfechtel , eds., Springer, Berlin.
Tiller, M. , 2001, Introduction to Physical Modeling With Modelica, Springer, Berlin.
Odo, Y. , and Koga, T. , 2012, “ Computer Aided Early Design Based on Integration of Modelica and SysML,” 22nd Design Engineering System Department Conference, Hiroshima, Japan, Sept. 26–28.
Sutherland, J. , Oizumi, K. , Aoyama, K. , Eguchi, T. , and Takahashi, N. , 2016, “ System-Level Design Tools Utilizing OPM and Modelica,” ASME Paper No. DETC2016-60101.
Arai, K. , Oizumi, K. , and Aoyama, K. , 2015, “ Finding Break Through Points in Platformed Product Family Design,” ASME Paper No. DETC2015-47238.
Inoue, K. , Tai, M. , Oizumi, K. , and Aoyama, K. , 2017, “ Ideation Support for Innovative Product Design Focusing on the Physical Causal Relationship Causing Functional Tradeoff,” 2017 Design Engineering System Department Conference of the Japan Society of Mechanical Engineers, Shimonoseki, Japan, Sept. 13–15.
Oizumi, K. , Kamiyama, H. , and Aoyama, K. , 2016, “ Specification of Design Problem Through Trade-Off Structure Analysis on Product System,” 2016 Design Engineering System Department Conference of the Japan Society of Mechanical Engineers, Yokohama, Japan, Oct. 8–10.
Oizumi, K. , Ishida, K. , and Aoyama, K. , 2017, “ Construction Support for Appropriately Detailed Simulation Models Based on Product Design Requirements,” 2017 Design Engineering System Department Conference of the Japan Society of Mechanical Engineers, Shimonoseki, Japan, Sept. 13–15.
Oizumi, K. , and Aoyama, K. , 2015, “ Comprehending Design Change Propagation by Utilizing Information on Quality Function Deployment,” J. Jpn. Soc. Des. Eng., 50(11), pp. 35–44.

Figures

Grahic Jump Location
Fig. 1

Process proposed in this paper

Grahic Jump Location
Fig. 2

House of quality: function-based model

Grahic Jump Location
Fig. 3

Physical causal relationship model: cognition-based model

Grahic Jump Location
Fig. 4

Actor-component-dynamics model: physics-based model

Grahic Jump Location
Fig. 5

Flow of trade-off solution and utilized models of a product

Grahic Jump Location
Fig. 6

Specification of significant trade-offs: (a) house of quality, (b) calculation of propagation sensitivity, (c) expected propagation sensitivity DSM, and (d) propagation contribution matrix

Grahic Jump Location
Fig. 7

Flow of trade-off descriptions

Grahic Jump Location
Fig. 8

Mutually complemental modeling between physics- and cognition-based models: (a) from physics to cognition and (b) from cognition to physics

Grahic Jump Location
Fig. 9

Mapping elements from ACD model to Modelica model

Grahic Jump Location
Fig. 10

Resolution of trade-off problems by TRIZ utilizing simulation results

Grahic Jump Location
Fig. 11

House of quality of the CVT

Grahic Jump Location
Fig. 12

Significant trade-offs specified for the CVT

Grahic Jump Location
Fig. 13

Mapping elements from physical causal relationship model to ACD model

Grahic Jump Location
Fig. 14

Modelica simulation model of the CVT

Grahic Jump Location
Fig. 15

Prioritized physical contradiction and inventive principles deduced for the CVT: (a) simulation results, (b) physical causal relationship model, and (c) prioritized physical contradictions and inventive principles

Tables

Errata

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In