Robust design of configurations and parameters of adaptable products
Jian ZHANG, Yongliang CHEN, Deyi XUE, Peihua GU
Robust design of configurations and parameters of adaptable products
An adaptable product can satisfy different customer requirements by changing its configuration and parameter values during the operation stage. Design of adaptable products aims at reducing the environment impact through replacement of multiple different products with single adaptable ones. Due to the complex architecture, multiple functional requirements, and changes of product configurations and parameter values in operation, impact of uncertainties to the functional performance measures needs to be considered in design of adaptable products. In this paper, a robust design approach is introduced to identify the optimal design configuration and parameters of an adaptable product whose functional performance measures are the least sensitive to uncertainties. An adaptable product in this paper is modeled by both configurations and parameters. At the configuration level, methods to model different product configuration candidates in design and different product configuration states in operation to satisfy design requirements are introduced. At the parameter level, four types of product/operating parameters and relations among these parameters are discussed. A two-level optimization approach is developed to identify the optimal design configuration and its parameter values of the adaptable product. A case study is implemented to illustrate the effectiveness of the newly developed robust adaptable design method.
adaptable product / robust design / optimization / uncertainties
[1] |
GuP, HasheminaM, NeeA Y C. Adaptable design. CIRP Annals – Manufacturing Technology, 2004, 53(2): 539–557
|
[2] |
KorenY, HuS J, GuP, ShpitalniM.Open-architecture products. CIRP Annals – Manufacturing Technology, 2013, 62(2): 719–729
|
[3] |
GuP, XueD, NeeA Y C. Adaptable design: concepts, methods and applications. Proceedings of the Institution of Mechanical Engineers. Part B, Journal of Engineering Manufacture, 2009, 223(11): 1367–1387
CrossRef
Google scholar
|
[4] |
LiY, XueD, GuP. Design for product adaptability. Concurrent Engineering: Research and Applications, 2008, 16(3): 221–232
|
[5] |
FletcherD, BrennanR W, GuP. A method for quantifying adaptability in engineering design. Concurrent Engineering: Research and Applications, 2009, 17(4): 279–289
|
[6] |
ChengQ, ZhangG, LiuZ, GuP, CaiL. A structure-based approach to evaluation product adaptability in adaptable design. Journal of Mechanical Science and Technology, 2011, 25(5): 1081–1094
CrossRef
Google scholar
|
[7] |
XueD, HuaG, MehradV, GuP. Optimal adaptable design for creating the changeable product based on changeable requirements considering the whole product life-cycle. Journal of Manufacturing Systems, 2012, 31(1): 59–68
CrossRef
Google scholar
|
[8] |
ZhangJ, GuP, BaoN, ZhangG. Analytical robust design of mechanical systems. In: Proceedings of the ASME 2008 international design engineering technical conferences & computers and information in engineering conference. New York, 2008
|
[9] |
TaguchiG. Performance analysis design. International Journal of Production Research, 1978, 16(6): 521–530
CrossRef
Google scholar
|
[10] |
TaguchiG. Taguchi on Robust Technology Development: Bringing Quality Engineering Upstream. New York: ASME Press, 1993
|
[11] |
ParkinsonA, SorensenC, PourhassanN. A general approach for robust optimal design. Journal of Mechanical Design, Transactions of the ASME, 1993, 115(1): 74–80
|
[12] |
ZhangJ, BaoN, ZhangG, GuP. Analytical approach to robust design of nonlinear mechanical system. Frontiers of Mechanical Engineering, 2009, 4(2): 203–214
CrossRef
Google scholar
|
[13] |
DuX, ChenW. Towards a better understanding of modeling feasibility robustness in engineering design. Journal of Mechanical Design, Transactions of the ASME, 2000, 122(4): 385–394
|
[14] |
FonsecaJ R, FriswellM I, LeesA W. Efficient robust design via Monte Carlo sample reweighting. International Journal for Numerical Methods in Engineering, 2007, 69(11): 2279–2301
CrossRef
Google scholar
|
[15] |
KumarA, NairP, KeaneA, ShahparS. Robust design using Bayesian Monte Carlo. International Journal for Numerical Methods in Engineering, 2008, 73(11): 1497–1517
CrossRef
Google scholar
|
[16] |
SamadianiE, JoshiY, AllenJ K, MistreeF. Adaptable robust design of multi-scale convective systems applied to energy efficient data centers. Numerical Heat Transfer, Part A, Application. An International Journal of Computation and Methodology, 2009, 57(2): 69–100
|
[17] |
ZhangJ, LiS, BaoN, ZhangG, XueD, GuP.A robust design approach to determination of tolerance of mechanical products. CIRP Annals – Manufacturing Technology, 2010, 59(1): 195–198
|
[18] |
HuW, AzarmS, AlmansooriA. New approximation assisted multi-objective collaborative robust optimization (new AA-McRO) under interval uncertainty. Structural and Multidisciplinary Optimization, 2013, 47(1): 19–35
CrossRef
Google scholar
|
[19] |
ZhangJ, XueD, GuP. Robust adaptable design considering changes of parameter values in product operation stage. In: Proceedings of the 22nd CIRP design conference. Bangalore, 2012
|
[20] |
HongG, HuL, XueD, TuY L, XiongY L. Identification of the optimal product configuration and parameters based on individual customer requirements on performance and costs in one-of-a-kind production. International Journal of Production Research, 2008, 46(12): 3297–3326
CrossRef
Google scholar
|
[21] |
ParameswaranM A, GanapathyS. Vibratory conveying – analysis and design: a review. Mechanism and Machine Theory, 1979, 14(2): 89–97
CrossRef
Google scholar
|
[22] |
LiY, ChenY. Technology improvement on electromagnetic vibration bowel feeder. Modern Manufacturing Engineering, 2005, 5: 109–111
|
[23] |
LuoY. Study on characteristic and control of electromagnetic vibration feeder. Packaging Engineering, 2005, 26(4): 54–56
|
[24] |
RaoS S. Mechanical Vibrations. New Jersey: Prentice Hall, 2011
|
/
〈 | 〉 |