Sustainable Hard Turning of High Chromium AISI D2 Tool Steel Using CBN and Ceramic Inserts

Rafighi M., Özdemir M., Al Shehabi S., Tuncay Kaya M.

TRANSACTIONS OF THE INDIAN INSTITUTE OF METALS, vol.74, pp.1639-1653, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 74
  • Publication Date: 2021
  • Doi Number: 10.1007/s12666-021-02245-2
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, INSPEC, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.1639-1653
  • Keywords: High chromium AISI D2 tool steel, Sustainable hard turning, Surface roughness, Cutting force components, Response surface methodology, Artificial neural network
  • Yozgat Bozok University Affiliated: Yes


In this experimental study, the effects of cutting parameters and insert types on the surface roughness and cutting force components were investigated during hard turning of high chromium AISI D2 tool steel under dry cutting conditions. Three different cutting speeds, feed rates, and cutting depths were chosen as machining parameters, while cubic boron nitride and ceramic inserts with two different nose radii were selected as tool material. The design of the experiment was carried out based on the Taguchi L-36 mixed orthogonal array. The response surface method was used to establish the relation between input and output parameters. Analysis of variance was performed to show the most significant parameters on the response. In addition, an artificial neural network was implemented for output modeling. The results revealed that surface roughness was mainly affected by the feed rate with almost 90.53%. Following feed rate, the nose radius was also significant on the surface roughness. Based on the results, the cubic boron nitride insert exhibited better performance than the ceramic insert in terms of minimum surface roughness. The cutting force components were mostly affected by the insert type. Cubic boron nitride insert caused greater forces during machining compared to the ceramic insert. The results revealed that the artificial neural network and response surface methodology exhibited very good accuracy with experimental data. However, the artificial neural network shows better accuracy and can predict the responses with 99.51% accuracy.