Investigations on Characteristics and Performance of Hard Thin Films Developed by Cathodic Arc Evaporation
Date
2019
Authors
Badiger, Pradeep V.
Journal Title
Journal ISSN
Volume Title
Publisher
National Institute of Technology Karnataka, Surathkal
Abstract
The fretting and adhesive wear behavior of Ti, Al and Fe based thin solid films deposited on
MDN121 steel substrate are studied. Plasma-assisted cathodic arc evaporation technique is
used to develop TiC-C, Ti/TiN/TiCN/TiN/TiCN, AlCN/AlC and FeCrN coatings. FESEMEDS, nanoindentation and Raman spectroscopy are used to characterize the coatings.
The fretting and adhesive wear tracks are investigated using an optical profiler, confocal
microscopy and electron microscopy. The diamond-like carbon (DLC) is observed in both
the coatings. Developed coatings exhibit better mechanical properties with increase in
hardness by 24.5 % in TiC-C and 29.4 % in Ti/TiN/TiCN/TiN/TiCN, 8.70 % in AlCN/AlC
and 50.79 % in FeCrN coatings compared to the uncoated SNMG120408 WC substrate.
During fretting wear analysis, TiC-C coating is exhibited lower coefficient of friction
(COF) compared to Ti-multilayer coating. Similarly, FeCrN coating exhibited lower
coefficient of friction (COF) compared to AlCN/AlC coating. The volumetric wear loss
of TiC-C monolayer coating is better than the multilayer coating. The volumetric wear
loss of FeCrN coating is better than AlCN/AlC coating. The wear surface morphology
revealed abrasive form of fretting wear mechanism in all coatings whereas galling
failure in the substrate. During adhesive wear analysis, TiC-C coating exhibited lower
coefficient of friction (COF) compared to Ti-multilayer coating. Similarly, FeCrN coating
exhibited lower coefficient of friction (COF) compared to AlCN/AlC coating. TiC-C,
Ti/TiN/TiCN/TiN/TiCN, AlCN/AlC and FeCrN coatings exhibited low friction and high
wear resistance.
Tungsten carbide cutting tool inserts are coated with customized composition of
Ti/TiCN/TiN/TiCN/TiN (multilayer), TiC-C AlCN/AlC and FeCrN (monolayer) thin films
using cathodic arc evaporation technique. Quality characteristics of coatings are evaluated
using calo and VDI3198 tests. Thickness of the coatings are found to be in the range
of 1.1-1.8 µm and adhesion quality of HF1 is attained. Machinability of highly alloyed
steel MDN431 is studied using the coatings developed on SNMG120408 inserts. The
iiiperformance of coated tool inserts are evaluated using cutting speed (59-118 m/min), feed
rate (0.062-0.125 mm/rev) and depth of cut (0.2-0.4 mm) as process parameters in turning
MDN431 steel. Experiments are conducted based on full factorial design and regression
analysis is used to analyze the cutting forces and surface roughness. Optimization of
the process parameters has been done with the combination of desirability approach and
PSO technique. Optimum machining condition for least cutting force and least surface
roughness are obtained at the condition of Vc=118 m/min, f=0.063 mm/rev and ap=0.2
mm for Ti-multilayer coatings, Vc=59 m/min, f=0.063 mm/rev and ap=0.2 mm for TiCC coatings, Vc=75 m/min, f=0.063 mm/rev and ap=0.3 mm for AlCN/AlC coatings and
Vc=118 m/min, f=0.063 mm/rev and ap=0.2 mm for FeCrN coatings. ANN modeling has
been adopted in order to improve the coefficients of determination (COD) and capability
of predictive regression models. ANN trained model and mathematical regression models
predict the responses, which follows the experimental data with minimum absolute error.
The predicted results are validated with minimum error and developed models are adequate
for further their usage. Tool wear was reduced by 3 times in Ti-multilayer, 3 times in TiC-C,
3.62 times in AlCN/AlC and 1.63times in FeCrN coated tools compared with commercially
available uncoated WC-Co inserts (SNMG120408).
Description
Keywords
Department of Mechanical Engineering, Ti-based thin films, monolayer and multi-layer, Fretting wear, Adhesive wear, Diamond-like carbon, cathodic arc evaporation, Al-coating, Fe-coating, Superalloy machining, PVD, Full factorial-optimization, ANN modeling, ANN modeling