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Multivariate Optimization of the Cutting Parameters when Turning Slender Components

Multivariate Optimization of the Cutting Parameters when Turning Slender Components

A.R. Silva Filho, A.M. Abrão, A.P. Paiva, J.R. Ferreira
Copyright: © 2012 |Volume: 2 |Issue: 4 |Pages: 20
ISSN: 2156-1680|EISSN: 2156-1672|EISBN13: 9781466613409|DOI: 10.4018/ijmmme.2012100102
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MLA

Filho, A.R. Silva, et al. "Multivariate Optimization of the Cutting Parameters when Turning Slender Components." IJMMME vol.2, no.4 2012: pp.12-31. http://doi.org/10.4018/ijmmme.2012100102

APA

Filho, A. S., Abrão, A., Paiva, A., & Ferreira, J. (2012). Multivariate Optimization of the Cutting Parameters when Turning Slender Components. International Journal of Manufacturing, Materials, and Mechanical Engineering (IJMMME), 2(4), 12-31. http://doi.org/10.4018/ijmmme.2012100102

Chicago

Filho, A.R. Silva, et al. "Multivariate Optimization of the Cutting Parameters when Turning Slender Components," International Journal of Manufacturing, Materials, and Mechanical Engineering (IJMMME) 2, no.4: 12-31. http://doi.org/10.4018/ijmmme.2012100102

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Abstract

The geometric features of the work piece and the cutting parameters considerably affect the quality of a finished part subjected to any machining operation owing to the imposed elastic and plastic deformations, especially when slender components are produced. This work is focused on the influence of the work piece slenderness ratio and cutting parameters on the quality of the machined part, assessed in terms of surface roughness and both geometric (run-out) and dimensional (diameter) deviations. Turning tests with coated tungsten carbide tools were performed using AISI 1045 medium carbon steel as work material. Differently from the published literature, a statistical analysis based on the multivariate one-way analysis of variance (MANOVA) was applied to the data obtained using a Box-Behnken experimental design. In order to identify the combination of parameters (slenderness ratio, cutting speed, feed rate and depth of cut) levels which simultaneously optimize the responses of interest (surface roughness, run-out and diameter deviation), a multivariate optimization method based on principal component analysis (PCA) and generalized reduced gradient (GRG) was employed.

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