Optimizing production in machining of hardened steels using response surface methodology

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DOI:

https://doi.org/10.4025/actascitechnol.v41i1.38091

Palavras-chave:

design of experiments, response surface methodology, production optimization

Resumo

This paper presents the modeling of tool life and surface roughness for machining AISI 52100 steel with a hardness of 50 HRC through Design of Experiments and Response Surface Methodology (RSM) with a view to enhance the quality and productivity. Knowing that the tool life and surface roughness are factors that influence the quality of the product, this study used the statistical tool of RSM in the search of factors that better determine optimal models. The models obtained prioritize the product quality and the cutting productivity. Results from Analysis of Variance demonstrated that the mathematical models elaborated allowed the prediction of surface roughness parameters´ values and tool life (T) with a precision of 95% confidence interval and a coefficient of determination above 94%. The wiper geometry of the tool led to the achievement of low average surface roughness (Ra) ranging from 0.2 to 0.4 µm with relatively high advances (0.2-0.4 mm rev-1) and maximum height of the profile surface roughness (Rt) in the range of 1.4 to 2.8 µm, without making use of the cutting fluid.

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Biografia do Autor

  • Paulo Henrique da Silva Campos, Universidade Federal de Itajubá
    Industrial Engineering and Management Institute
  • Vinicius de Carvalho Paes, Universidade Federal de Itajubá
    Bachelor degree in Computer Science (2008) and master degree in Computer and Technology Science from Universidade Federal de Itajubá. Founder of information technology companies primarily focused on SaaS (software as a service). Practical and professional experience in project management, server management, network management, information security, programming, server high availability, database, web analytics, search engine optimization, web crawler, web indexer, return of investment, data mining, artificial intelligence. PhD student in Industrial Engineering at UNIFEI and researcher at NOMATI with thesis focused on Design of Experiments on Artificial Neural Network´s Parameterization for Nonlinear Problems Solution.
  • Ernany Daniel de Carvalho Gonçalves, Universidade Federal de Itajubá
    Industrial Engineering and Management Institute
  • João Roberto Ferreira, Universidade Federal de Itajubá
    Industrial Engineering and Management Institute
  • Pedro Paulo Balestrassi, Universidade Federal de Itajubá
    Industrial Engineering and Management Institute
  • João Paulo Davim Tavares da Silva, Universidade de Aveiro
    Department of Mechanical Engineering

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Publicado

2019-05-29

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Seção

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Como Citar

Campos, P. H. da S., Paes, V. de C., Gonçalves, E. D. de C., Ferreira, J. R., Balestrassi, P. P., & Silva, J. P. D. T. da. (2019). Optimizing production in machining of hardened steels using response surface methodology. Acta Scientiarum. Technology, 41(1), e38091. https://doi.org/10.4025/actascitechnol.v41i1.38091

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