Tempering Process Optimization in Sae 9254 Wires Through Generalized Reduced Gradient, Genetic Algorithms and Simulated Annealing

Cristie Diego Pimenta; Messias Borges Silva; Rosinei Batista Ribeiro; Rose Lima de Morais Campos; Walfredo Ribeiro de Campos Junior; Jorge Luiz Rosa

doi:doi:10.11648/j.ajtas.20160501.15

Tempering Process Optimization in Sae 9254 Wires Through Generalized Reduced Gradient, Genetic Algorithms and Simulated Annealing

Cristie Diego Pimenta, Messias Borges Silva, Rosinei Batista Ribeiro, Rose Lima de Morais Campos, Walfredo Ribeiro de Campos Junior, Jorge Luiz Rosa

Published in American Journal of Theoretical and Applied Statistics (Volume 5, Issue 1)

Received: 15 January 2016 Accepted: 3 February 2016 Published: 23 February 2016

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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.

Published in	American Journal of Theoretical and Applied Statistics (Volume 5, Issue 1)
DOI	10.11648/j.ajtas.20160501.15
Page(s)	27-38
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Heat Treatment, Generalized Reduced Gradient, Design of Experiments, Response Surface Method, Genetic Algorithms, Meta-Heuristic

References

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APA Style

Cristie Diego Pimenta, Messias Borges Silva, Rosinei Batista Ribeiro, Rose Lima de Morais Campos, Walfredo Ribeiro de Campos Junior, et al. (2016). Tempering Process Optimization in Sae 9254 Wires Through Generalized Reduced Gradient, Genetic Algorithms and Simulated Annealing. American Journal of Theoretical and Applied Statistics, 5(1), 27-38. https://doi.org/10.11648/j.ajtas.20160501.15

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ACS Style

Cristie Diego Pimenta; Messias Borges Silva; Rosinei Batista Ribeiro; Rose Lima de Morais Campos; Walfredo Ribeiro de Campos Junior, et al. Tempering Process Optimization in Sae 9254 Wires Through Generalized Reduced Gradient, Genetic Algorithms and Simulated Annealing. Am. J. Theor. Appl. Stat. 2016, 5(1), 27-38. doi: 10.11648/j.ajtas.20160501.15

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AMA Style

Cristie Diego Pimenta, Messias Borges Silva, Rosinei Batista Ribeiro, Rose Lima de Morais Campos, Walfredo Ribeiro de Campos Junior, et al. Tempering Process Optimization in Sae 9254 Wires Through Generalized Reduced Gradient, Genetic Algorithms and Simulated Annealing. Am J Theor Appl Stat. 2016;5(1):27-38. doi: 10.11648/j.ajtas.20160501.15

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@article{10.11648/j.ajtas.20160501.15,
  author = {Cristie Diego Pimenta and Messias Borges Silva and Rosinei Batista Ribeiro and Rose Lima de Morais Campos and Walfredo Ribeiro de Campos Junior and Jorge Luiz Rosa},
  title = {Tempering Process Optimization in Sae 9254 Wires Through Generalized Reduced Gradient, Genetic Algorithms and Simulated Annealing},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {5},
  number = {1},
  pages = {27-38},
  doi = {10.11648/j.ajtas.20160501.15},
  url = {https://doi.org/10.11648/j.ajtas.20160501.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20160501.15},
  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.},
 year = {2016}
}

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TY  - JOUR
T1  - Tempering Process Optimization in Sae 9254 Wires Through Generalized Reduced Gradient, Genetic Algorithms and Simulated Annealing
AU  - Cristie Diego Pimenta
AU  - Messias Borges Silva
AU  - Rosinei Batista Ribeiro
AU  - Rose Lima de Morais Campos
AU  - Walfredo Ribeiro de Campos Junior
AU  - Jorge Luiz Rosa
Y1  - 2016/02/23
PY  - 2016
N1  - https://doi.org/10.11648/j.ajtas.20160501.15
DO  - 10.11648/j.ajtas.20160501.15
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 27
EP  - 38
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20160501.15
AB  - 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.
VL  - 5
IS  - 1
ER  -

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Author Information

Cristie Diego Pimenta

Department of Production, University of Guaratinguetá (Feg-Unesp), S?o Paulo, Brazil
Messias Borges Silva

Department of Production, University of Guaratinguetá (Feg-Unesp), S?o Paulo, Brazil
Rosinei Batista Ribeiro

Department of Production, University of Guaratinguetá (Feg-Unesp), S?o Paulo, Brazil
Rose Lima de Morais Campos

Department of Production, University of Guaratinguetá (Feg-Unesp), S?o Paulo, Brazil
Walfredo Ribeiro de Campos Junior

Department of Marketing, College ESPM, S?o Paulo, Brazil
Jorge Luiz Rosa

Department of Production, University of Guaratinguetá (Feg-Unesp), S?o Paulo, Brazil

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APA Style

Cristie Diego Pimenta, Messias Borges Silva, Rosinei Batista Ribeiro, Rose Lima de Morais Campos, Walfredo Ribeiro de Campos Junior, et al. (2016). Tempering Process Optimization in Sae 9254 Wires Through Generalized Reduced Gradient, Genetic Algorithms and Simulated Annealing. American Journal of Theoretical and Applied Statistics, 5(1), 27-38. https://doi.org/10.11648/j.ajtas.20160501.15

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ACS Style

Cristie Diego Pimenta; Messias Borges Silva; Rosinei Batista Ribeiro; Rose Lima de Morais Campos; Walfredo Ribeiro de Campos Junior, et al. Tempering Process Optimization in Sae 9254 Wires Through Generalized Reduced Gradient, Genetic Algorithms and Simulated Annealing. Am. J. Theor. Appl. Stat. 2016, 5(1), 27-38. doi: 10.11648/j.ajtas.20160501.15

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AMA Style

Cristie Diego Pimenta, Messias Borges Silva, Rosinei Batista Ribeiro, Rose Lima de Morais Campos, Walfredo Ribeiro de Campos Junior, et al. Tempering Process Optimization in Sae 9254 Wires Through Generalized Reduced Gradient, Genetic Algorithms and Simulated Annealing. Am J Theor Appl Stat. 2016;5(1):27-38. doi: 10.11648/j.ajtas.20160501.15

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@article{10.11648/j.ajtas.20160501.15,
  author = {Cristie Diego Pimenta and Messias Borges Silva and Rosinei Batista Ribeiro and Rose Lima de Morais Campos and Walfredo Ribeiro de Campos Junior and Jorge Luiz Rosa},
  title = {Tempering Process Optimization in Sae 9254 Wires Through Generalized Reduced Gradient, Genetic Algorithms and Simulated Annealing},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {5},
  number = {1},
  pages = {27-38},
  doi = {10.11648/j.ajtas.20160501.15},
  url = {https://doi.org/10.11648/j.ajtas.20160501.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20160501.15},
  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.},
 year = {2016}
}

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TY  - JOUR
T1  - Tempering Process Optimization in Sae 9254 Wires Through Generalized Reduced Gradient, Genetic Algorithms and Simulated Annealing
AU  - Cristie Diego Pimenta
AU  - Messias Borges Silva
AU  - Rosinei Batista Ribeiro
AU  - Rose Lima de Morais Campos
AU  - Walfredo Ribeiro de Campos Junior
AU  - Jorge Luiz Rosa
Y1  - 2016/02/23
PY  - 2016
N1  - https://doi.org/10.11648/j.ajtas.20160501.15
DO  - 10.11648/j.ajtas.20160501.15
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 27
EP  - 38
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20160501.15
AB  - 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.
VL  - 5
IS  - 1
ER  -

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