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Modeling of chip–tool interface temperature using response surface methodology and artificial neural network in HPC-assisted turning and tool life investigation

  • M. Kamruzzaman
  • Saadman Sakib RahmanEmail author
  • Md. Zurais Ibne Ashraf
  • Nikhil Ranjan Dhar
ORIGINAL ARTICLE

Abstract

This study focuses on chip–tool interface temperature modeling of C-60, 17CrNiMo4, and 42CrMo4 steel alloys in dry-cut and high-pressure coolant (HPC)-assisted turning at various cutting speed-feed rates with SNMG and SNMM inserts. Improvement of tool life in turning C-60 steel under the application of jet at elevated pressure over dry machining is investigated. Scanning electron microscope (SEM) views of principal and auxiliary flank worn out tip (48-min) of cutting inserts divulge the effectiveness of high-pressure coolant emulsion over dry and conventional wet cooling. The experimental runs were conducted in full factorial orientation and response surface methodology (RSM) has been employed for subsequent modeling to formulate mathematical equations to devise accurate predictions of chip–tool interface temperature. Analysis of variance (ANOVA) is conducted to perceive the effects of each individual factors and their interactions terms and measure the significance of the proposed model. Later, optimization with desirability function concluded factor settings (cutting speed = 93 m/min, feed rate = 0.10 mm/rev, environment = HPC, material = C-60, and insert = SNMM) that minimize the response within the experimental domain satisfying desired goals. Further, artificial neural network (ANN) model has been developed and the prediction performance was compared with the cubic equations of RSM. It was observed that during testing phase MAPE and coefficient of determination (R 2) for RSM is 1.947 and 94.48 %, respectively; and the corresponding values for ANN (4-22-1) is 2.669 and 93.25 %. Results also reveal that cutting speed and environment have ∼38.82 and ∼37.82 % contribution on chip–tool interface temperature formation during machining. In addition, the results showed that HPC-assisted machining reduces chip–tool interface temperature significantly as well as prolong tool life.

Keywords

Chip–tool interface temperature HPC-assisted machining Scanning electron microscope Response surface methodology Analysis of variance Artificial neural network 

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Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • M. Kamruzzaman
    • 1
  • Saadman Sakib Rahman
    • 2
    Email author
  • Md. Zurais Ibne Ashraf
    • 2
  • Nikhil Ranjan Dhar
    • 3
  1. 1.Mechanical EngineeringDhaka University of Engineering and TechnologyGazipurBangladesh
  2. 2.Mechanical and Production EngineeringAhsanullah University of Science and TechnologyDhakaBangladesh
  3. 3.Industrial and Production EngineeringBangladesh University of Engineering and TechnologyDhakaBangladesh

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