A strategy for resolving evolutionary performance coupling at the early stages of complex engineering design

In the performance-driven complex engineering design process, at an early design stage, some required design parameters or equations cannot be determined precisely, which can have significant impact on the chain of design decisions and have to be validated with the outcome of design results from the later stages requiring redesign effort to resolve inconsistencies. So it is imminent to evaluate the extent of, and systematically manage, the couplings of design models and develop an appropriate resolving strategy in order to obtain more accurate design results in the shortest time period. This paper presents the research work carried out via the analyses of performance evolution and potential performance coupling at the early stage of complex engineering design. Four strategies based on performance model transformation for resolving evolutionary performance coupling are studied (i.e. decoupling, coupling, first-decoupling-then-coupling and first-coupling-then-decoupling). A selection method for resolving performance coupling based on the synthetic analysis of sensitivity of uncertainty propagation, solvability of coupled models, coupling strength and performance interface and availability of design information is proposed. To demonstrate the related concepts and method, the solving process of a complex design problem related to a suspension system design for tracked vehicle is given.

[1]  Saltelli Andrea,et al.  Global Sensitivity Analysis: The Primer , 2008 .

[2]  Joseph A. C. Delaney.  Sensitivity analysis , 2018, The African Continental Free Trade Area: Economic and Distributional Effects.

[3]  Crispin Hales,et al.  Engineering design: a systematic approach , 1989 .

[4]  David G. Ullman,et al.  The Mechanical Design Process , 1992 .

[5]  Karl T. Ulrich,et al.  Product Design and Development , 1995 .

[6]  Amaresh Chakrabarti,et al.  A scheme for functional reasoning in conceptual design , 2001 .

[7]  Michael A. Orloff,et al.  Inventive Thinking through TRIZ: A Practical Guide , 2006 .

[8]  Christiaan J. J. Paredis,et al.  Managing Design-Process Complexity: A Value-of-Information Based Approach for Scale and Decision Decoupling , 2009, J. Comput. Inf. Sci. Eng..

[9]  Jeffrey W. Herrmann,et al.  Separating Design Optimization Problems Into Decision-Based Design Processes , 2009 .

[10]  Shapour Azarm,et al.  Interval Uncertainty Reduction and Single-Disciplinary Sensitivity Analysis With Multi-Objective Optimization , 2009, DAC 2009.

[11]  Yong Zeng,et al.  A science-based approach to product design theory Part I: formulation and formalization of design process , 1999 .

[12]  Jaroslaw Sobieszczanski-Sobieski,et al.  Optimization of coupled systems : A critical overview of approaches , 1994 .

[13]  Robert P. Smith,et al.  A model-based method for organizing tasks in product development , 1994 .

[14]  Jaroslaw Sobieszczanskisobieski,et al.  On the sensitivity of complex, internally coupled systems , 1988 .

[15]  Steven R. Schmid,et al.  Fundamentals of Machine Elements , 1999 .

[16]  D. N. P. Murthy,et al.  Product performance and specification in new product development , 2006 .

[17]  Su Mi Dahlgaard-Park,et al.  Inventive Thinking through TRIZ: A Practical Guide , 2006 .

[18]  Steven D. Eppinger,et al.  A Model-Based Framework to Overlap Product Development Activities , 1997 .

[19]  Semyon Savransky,et al.  Engineering of Creativity: Introduction to TRIZ Methodology of Inventive Problem Solving , 2000 .

[20]  Hideaki Takeda,et al.  Representation of Design Object Based on the Functional Evolution Process Model , 1998 .

[21]  G. A. Britton,et al.  Associative assembly design features: concept, implementation and application , 2007 .

[22]  Daniel A. McAdams,et al.  A Methodology for Model Selection in Engineering Design , 2005 .

[23]  Yehuda E. Kalay,et al.  Performance-based design , 1999 .