Process capability surrogate model-based tolerance synthesis for multi-station manufacturing systems
Huang, Wenzhen, Phoomboplab, Tirawat and Ceglarek, Dariusz. (2009) Process capability surrogate model-based tolerance synthesis for multi-station manufacturing systems. IIE Transactions, Vol.41 (No.4). pp. 309-322. ISSN 0740-817XFull text not available from this repository.
Official URL: http://dx.doi.org/10.1080/07408170802510408
The main challenges in tolerance synthesis for complex assembly design currently are: (i) to produce a simplified deterministic model that is able to formulate general statistic models in complex assembly problems; (ii) to lower the high computation intensity required in optimization studies when the process capability (yield) model is used for key product characteristics. In this paper, tolerance synthesis for complex assemblies is defined as a probabilistic optimization problem which allows the modeling of assemblies with a general multivariate statistical model and complex tolerance regions. An approach is developed for yield surrogate model generation based on an assembly model in multi-station manufacturing systems, computer experiments, multivariate distribution transformation and regression analysis. Therefore, efficient gradient-based approaches can be applied to avoid the intensive computation in direct optimization. Industrial case studies are presented to illustrate and validate the proposed methodology and compared with the existing tolerance synthesis methods. [Supplementary materials are available for this article. Go to the publisher's online edition of IIE Transactions for the following free supplemental resource: Appendix].
|Item Type:||Journal Article|
H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
|Divisions:||Faculty of Science > WMG (Formerly the Warwick Manufacturing Group)|
|Journal or Publication Title:||IIE Transactions|
|Publisher:||Taylor & Francis Inc.|
|Number of Pages:||14|
|Page Range:||pp. 309-322|
|Access rights to Published version:||Restricted or Subscription Access|
|Funder:||National Institute of Standards and Technology (NIST) UK EPSRC Star Award, US NSF-CAREER Award|
|Grant number:||EP/E044506/1, DMII-0239244|
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