Volume 5, Issue 1, January 2016, Page: 27-38
Tempering Process Optimization in Sae 9254 Wires Through Generalized Reduced Gradient, Genetic Algorithms and Simulated Annealing
Cristie Diego Pimenta, Department of Production, University of Guaratinguetá (Feg-Unesp), São Paulo, Brazil
Messias Borges Silva, Department of Production, University of Guaratinguetá (Feg-Unesp), São Paulo, Brazil
Rosinei Batista Ribeiro, Department of Production, University of Guaratinguetá (Feg-Unesp), São Paulo, Brazil
Rose Lima de Morais Campos, Department of Production, University of Guaratinguetá (Feg-Unesp), São Paulo, Brazil
Walfredo Ribeiro de Campos Junior, Department of Marketing, College ESPM, São Paulo, Brazil
Jorge Luiz Rosa, Department of Production, University of Guaratinguetá (Feg-Unesp), São Paulo, Brazil
Received: Jan. 15, 2016;       Accepted: Feb. 3, 2016;       Published: Feb. 23, 2016
DOI: 10.11648/j.ajtas.20160501.15      View  3838      Downloads  73
Abstract
The purpose of this work was the creation of a statistical modeling able to replace the process used to setup of the ovens of the quench hardening and tempering that is traditionally accomplished through adjustments made based on the results of mechanical properties as tested in laboratory and required in customer specifications. We sought to understand the influence of the input variables (factors) on the mechanical properties tensile strength and hardness, in SAE 9254 draw steel wires, with diameters 2.00 mm and 6.50 mm, used in the manufacture of valve springs and clutch for automobile tracking. Were investigated the input variables of the process speed and tempering temperature. Design of Experiments with block Analysis, Quadratic Multiple Regression, Analysis of Variance (ANOVA) and Response Surface Methodology (RSM). For the optimization of statistical models were used the Generalized Reduced Gradient methods (GRG), Genetic Algorithm (AG) and the Meta-heuristics Simulated Annealing (SA). The results revealed that all variables considered have significant influence and models obtained were validated using appropriate statistical methods. This new modeling and its optimization, if properly implemented and enforced, could lead scientific advances which would provide the automation of this process, and consequently cause great impact on increasing productivity and product quality.
Keywords
Heat Treatment, Generalized Reduced Gradient, Design of Experiments, Response Surface Method, Genetic Algorithms, Meta-Heuristic
To cite this article
Cristie Diego Pimenta, Messias Borges Silva, Rosinei Batista Ribeiro, Rose Lima de Morais Campos, Walfredo Ribeiro de Campos Junior, Jorge Luiz Rosa, Tempering Process Optimization in Sae 9254 Wires Through Generalized Reduced Gradient, Genetic Algorithms and Simulated Annealing, American Journal of Theoretical and Applied Statistics. Vol. 5, No. 1, 2016, pp. 27-38. doi: 10.11648/j.ajtas.20160501.15
Copyright
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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