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Development of a dynamic surface roughness monitoring system based on artificial neural networks (ANN) in milling operation

  • AmirMahyar Khorasani
  • Mohammad Reza Soleymani Yazdi
ORIGINAL ARTICLE

Abstract

Dynamic surface roughness prediction during metal cutting operations plays an important role to enhance the productivity in manufacturing industries. Various machining parameters such as unwanted noises affect the surface roughness, whatever their effects have not been adequately quantified. In this study, a general dynamic surface roughness monitoring system in milling operations was developed. Based on the experimentally acquired data, the milling process of Al 7075 and St 52 parts was simulated. Cutting parameters (i.e., cutting speed, feed rate, and depth of cut), material type, coolant fluid, X and Z components of milling machine vibrations, and white noise were used as inputs. The original objective in the development of a dynamic monitoring system is to simulate wide ranges of machining conditions such as rough and finishing of several materials with and without cutting fluid. To achieve high accuracy of the resultant data, the full factorial design of experiment was used. To verify the accuracy of the proposed model, testing and recall/verification procedures have been carried out and results showed that the accuracy of 99.8 and 99.7 % were obtained for testing and recall processes.

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Graphical Abstract

A dynamic surface roughness monitoring system in milling operations of Al 7075 and St 52 is developed based on the ANNs using cutting conditions, vibrations in X and Z directions, and cutting fluid as inputs and surface roughness as output.

Keywords

Artificial neural networks Milling Process Simulation Surface Roughness 

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Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  • AmirMahyar Khorasani
    • 1
  • Mohammad Reza Soleymani Yazdi
    • 2
  1. 1.School of EngineeringDeakin UniversityWaurn PondsAustralia
  2. 2.Mechanical Engineering DepartmentIH UniversityTehranIran

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