Estimation of microhardness and crystal grain size values of electrodeposited Ni-B/TiC nanocomposite coatings by artificial neural networks (ANN) method

Ünal E., YAŞAR A., KARAHAN İ. H.

Journal of Alloys and Compounds, cilt.966, 2023 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 966
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1016/j.jallcom.2023.171677
  • Dergi Adı: Journal of Alloys and Compounds
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Chemical Abstracts Core, Chimica, Communication Abstracts, Compendex, INSPEC, Metadex, Public Affairs Index, Civil Engineering Abstracts
  • Anahtar Kelimeler: ANN, Crystal grain size, Electrodeposited coatings, Microhardness, Ni-B alloy, TiC
  • Hatay Mustafa Kemal Üniversitesi Adresli: Evet

Özet

In this study, composite coatings with Ni-B alloy main structure reinforced with TiC nanoparticles were coated on a stainless steel substrate by electrodeposition method. The microhardness and crystal structures of the produced nanocomposite films were examined and the relations between the obtained results and the production parameters were discussed in detail. In addition, the surface and section morphologies of the coatings were analyzed by SEM and EDS. Furthermore the complex relationship between the experimentally obtained microhardness, crystal grain size values and the production parameters was modeled by artificial neural networks (ANN) method. According to the hardness results, nanocomposite coatings microhardness values varied between about 470 HV and 820 HV. In XRD examinations, the crystal grain size values of nanocomposite coatings were found ranging from 6.6 nm to 17.4 nm. From the morphology results, it is understood that the surface structure and coating thicknesses are remarkably affected by the production parameters. Considering the EDS results, it was observed that the alloying with boron and the TiC reinforcement were successfully applied in nanocomposite coatings. With respect to the results obtained with ANN, the best results were obtained in the model with 10 neurons in the hidden layer and the highest error value of 2.02% was achieved for microhardness values. For the crystal grain size, the most successful results were acquired in the model with 12 neurons and the highest error value was obtained as 3.397%.