Journal
CERAMICS INTERNATIONAL
Volume 41, Issue 8, Pages 9350-9360Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ceramint.2015.03.306
Keywords
Duplex surface treatment; Thermo-reactive deposition and diffusion (TRD); Thermodynamics; Kinetics; Modeling
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Specimens of DIN 100MnCrW4 steel (type 01 tool steel) have been cut and prepared for performing a duplex surface treatment involving nitriding and low temperature vanadium thermo-reactive deposition and diffusion (TRD) technique. The TRD process was performed in a molten salt bath at different temperatures of 575, 650 and 725 degrees C for 1-30 h. The treatment formed a vanadium carbonitride coating with the thickness up to 10.5 mu m on a hardened diffusion zone. Characterizations by means of an optical microscope (OM), scanning electron microscope equipped with energy dispersive X-ray spectrometer (SEM-EDS) and X-ray diffraction analysis (XRD) indicated that the compact and dense coating mainly consisted of V(C,N) and V-2(C,N) phases. All the growth processes of the formed vanadium carbonitride layer obtained by TRD followed a parabolic kinetics while the calculated activation energy (Q) for the treatment was 181.1 kJ/mol. An artificial neural network (ANN) based model for predicting the layer thickness of ceramic coatings was presented. Constructing the model, training, validating and testing of experimental results from 72 different specimens were conducted. The data used as inputs in the proposed model were arranged in a format of five parameters that comprised of pre-nitriding time, ferro-vanadium particle size, ferro-vanadium weight percent, salt bath temperature and coating time. Accordingly, the thickness of duplex coating in each specimen was estimated accurately. Finally, the proposed ANN-based model showed a strong potential for predicting the layer thickness of duplex ceramic coating performed by the TRD technique on the substrate of cold work tool steel. (C) 2015 Elsevier Ltd and Techna Group S.r.l. All rights reserved.
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