Roughness analysis of a concave surface as a function of machining parameters and strategies for AISI 420 steel

Luis Christian Juiña, Emilio Josué Dávalos, Darío Sebastián Landazurí, Sonia Elizabeth Guaño, Nancy Verónica Moreno

Research output: Contribution to journalConference articlepeer-review

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In this study, the statistical methodology used to analyze and optimize the rough-ness values obtained experimentally is presented, which respond to machining parameters, such as strategy, cutting speed and feed per tooth of the tool; the same ones that were defined for the machining of test subjects with a concave surface, made from AISI 420 steel material. Using a CAD-CAM software, the modeling and the programming of the test subject was generated based on the parameters mentioned; each of these parameters contains 3 levels of variation, which gives 27 tests that were performed in a computerized numerical control machine. Applying the Taguchi methodology and the statistical analysis ANOVA on the roughness values obtained, it was possible to determine that the machining strategy has an influence on the roughness value on the study surface of 92.209%, followed by the cutting speed with 3661% and, finally, the advance per tooth of the tool with 0.511%; thus demonstrating that the optimal factors for machining this type of surface in AISI 420 steel are the use of a Radial Zigzag strategy at a cut-ting speed of 100 m/min and a feed rate per tooth of the tool of 0.05 mm/tooth. Also, by means of the statistical analysis ANOVA, it was possible to obtain a prediction equation of the average roughness value; this equation presents a confidence interval of ±0.460 lm, which adjusts with the real values obtained experimentally and allows the validation of the equation.

Original languageEnglish
Pages (from-to)85-92
Number of pages8
JournalMaterials Today: Proceedings
StatePublished - 2022
Event1st International Virtual Conference on Mechanical Engineering Trends, MET 2021 - Virtual, Online, Ecuador
Duration: 24 Mar 202126 Mar 2021

Bibliographical note

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© 2022 Elsevier Ltd. All rights reserved.


  • CAM
  • CNC
  • Surface
  • Taguchi


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