Loading...
Please wait, while we are loading the content...
Similar Documents
A multivariate surface roughness modeling and optimization under conditions of uncertainty
| Content Provider | Semantic Scholar |
|---|---|
| Author | Lopes, Luiz Gustavo Dias Gomes, José Henrique F. Paiva, Paulo Vinícius De Farias Barca, Luiz Fernando Ferreira, João Roberto Balestrassi, Pedro Paulo |
| Copyright Year | 2013 |
| Abstract | Correlated responses can be written in terms of principal component scores, but the uncertainty in the original responses will be transferred and will influence the behavior of the regression function. This paper presents a model building strategy that consider the multivariate uncertainty as weighting matrix for the principal components. The main objective is to increase the value of R predicted to improve model’s explanation and optimization results. A case study of AISI 52100 hardened steel turning with Wiper tools was performed in a Central Composite Design with three-factors (cutting speed, feed rate and depth of cut) for a set of five correlated metrics (Ra, Ry, Rz, Rq and Rt). Results indicate that different modeling methods conduct approximately to the same predicted responses, nevertheless the response surface to Weighted Principal Component – case b – (WPC1) presented the highest predictability. 2013 Elsevier Ltd. All rights reserved. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://pedro.unifei.edu.br/Artigos%20Publicados/2013Measurement.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Central composite design Mathematical optimization Principal component analysis Response surface methodology Stainless Steel |
| Content Type | Text |
| Resource Type | Article |
