1. Introduction
Aluminum alloy has been widely used in the mechanical manufacturing and aerospace fields due to its advantages of low density, superior electrical and thermal conductivity and optimal ductility [
1,
2,
3]. However, certain disadvantages, such as low hardness and the fact that it is easily oxidized at high temperature, limit its service life and wide application [
4]. Protective coating can be prepared on the surface of aluminum alloy to solve this problem. In this case, Ni–W coating has attracted extensive attention due to its excellent hardness, corrosion resistance and high temperature oxidation resistance as well as other properties [
5,
6,
7,
8,
9]. In particular, pulse electrodeposition of Ni–W coatings shows the superiority of fine crystallization, smooth and bright and fast film formation rate, which could enhance the hardness, high temperature oxidation resistance of aluminum alloy substrates [
10,
11,
12,
13]. Nevertheless, prior research has shown that the mechanical differences between a soft-ductile substrate and hard-brittle coating could generate high mechanical mismatch between coatings and substrate, resulting in high-stress concentration and finally failure of the interface [
14,
15,
16,
17], as shown in
Figure 1.
Ni–W graded coating with W gradient structure which can improve the bonding strength of coating and substrate [
18], thermal shock resistance and fracture toughness [
19]. In the preparation of Ni–W graded coatings, the process parameters can be determined by multiple tests which will cause an increase in cost, reduced efficiency as well as the polluting of the environment [
20,
21,
22,
23,
24]. Therefore, Artificial Neural Network (ANN) can be introduced into the preparation of pulsed electrodeposition Ni–W graded coating. ANN technology can simulate some intelligent behaviors of the human brain and has the abilities of self-organization, self-learning, self-adaptation, high fault tolerance and highly nonlinear description. In conclusion, ANN is an effective method to deal with highly nonlinear and non-obvious functional relationship problems. It can model complex nonlinear processes efficiently and accurately [
25,
26].
Jiang et al. [
25] applied a three-layer backward propagation (BP) model to predict the hardness of Ni-TiN nano coatings fabricated by pulse electrodeposition. Liu et al. [
26] studied an ANN model to predict the HVOF of sprayed Cr
3C
2-25NiCr coating and to analyze the influence of operating parameters.
However, there are few studies on the relationships between the pulse electrodeposition and properties of Ni–W graded coating prepared by ANN. This paper established a network model of different process parameters to counter the influence of high-temperature oxidation performance using back-propagation neural network technology. The model was applied to predict and analyze performance on Ni–W coating. The technological parameters of pulsed electrodeposition Ni–W coating were optimized. Furthermore, the Ni–W graded coating was prepared by the optimized parameters. The microstructure, high-temperature oxidation resistance as well as micro-hardness of the coatings were observed and analyzed, respectively.
2. Materials and Methods
2.1. Materials
Ni–W graded coatings were prepared by pulse electrodeposition technique. The purity of 99% nickel plate was used as the anode and LY12 aluminum alloy substrate as the cathode, with the size of 12 mm × 10 mm × 10 mm. The pulse electrodeposition parameters such as duty cycle, frequency, temperature and current density hold great influence on the properties of Ni–W coatings. The general pulse electrodeposition battery schematic diagram is shown in
Figure 2.
2.2. Bath Composition and Plating Parameters
The chemical composition of bath and pulse electrodeposition parameters are listed in
Table 1. The sample was immersed in 3.5 wt % NaCl solution at 25 °C, and the Ni–W graded coatings were subjected to corrosion test. The high temperature oxidation resistance of the coating was measured by cyclic oxidation weight gain method, the quality difference of the sample before and after heating was measured. The oxidation rate was the weight gain of the sample per unit area, and the unit was mg/cm
2. The KSY muffle furnace is used in the experiment. The temperature of the electric furnace is automatically controlled by the temperature controller. Heat the resistance furnace to 400 °C, and put the prepared sample according to certain process parameters into the box resistance furnace to heat preservation. After cooling at room temperature, the samples were weighed on the Sartorius electronic balance (precision 0.01 mg). The mass of the samples after high-temperature oxidation was recorded. The difference between the initial mass and the initial mass was divided by the surface area of the sample, which was the oxidation weight gain of the sample, which was calculated by the Equation (1). The weight loss was measured by a sensitive electronic balance with an accuracy of 0.01 mg.
where Δ
M is the oxidation weight gain of the coating (mg/cm
2), Δ
M1 is the mass of the sample before high-temperature oxidation (g), Δ
M2 is the mass of the sample after high-temperature oxidation (g),
S is the surface area of the sample (cm
2). Model inputs and symbols are shown in
Appendix A,
Table A1.
2.3. Composite Coating Characterization
Ni–W coating phase structures were analyzed by XRD-7000 from a Japan-Shimadzu X ray diffractometer using the Cu K alpha radiation to determine, with scanning speed of 4°/min, and scanning angle ranging from 20°–80°. Scanning electron microscope (SEM) and energy disperse spectroscopy (EDS) (JSM-6390A, Osaka, Japan) methods were also used to investigate the influence of pulse electrodeposition parameters on the microstructure and elements of Ni–W coatings. Abbreviation explanation are shown in
Appendix A,
Table A2.
3. Design of Test
As for the high temperature oxidation resistance of Ni–W coatings, the oxidation rate is mainly manifested as the oxidation weight gain of the coating surface. The smaller the oxidation weight gain of the plating layer, the better the oxidation resistance. The prediction of the BP neural network is generally analyzed by taking the maximum point. Therefore, in order to cooperate with the training and learning of BP neural network, Equation (2) is adopted to conduct reciprocal processing of the data.
Among them, m
x is the oxidative weight gain, and V is the reciprocal of the oxidative weight gain. The data processed by Equation (2) were shown in
Table 2. In this paper, L9 (3
3) orthogonal table is used for orthogonal experiments. The factors and level selection of orthogonal tests are shown in
Table 3, and the test results are shown in
Table 4.
4. BP Neural Network Structure
BP neural network is the most widely used and mature ANN that can deal with complex nonlinear systems rapidly. It is generally composed of three layers: the hidden layer, the input layer and the output layer. BP neural network also includes the forward propagation and the back propagation. Through the input layer, the experimental data are gradually propagated to the output layer of the hidden layer under the action of the excitation function. If the expected data are not obtained, it is transferred to back propagation by correcting the corresponding weights and thresholds several times, and finally the output value is within the allowable range of error.
4.1. Design of BP Neural Network
There are many factors affecting the Ni–W coating, such as the bath temperature, PH, density, deposition time, duty ratio and so on. In this paper, the three factors of bath temperature, current density and duty cycle are set as the input sample, and the output sample is the reciprocal V of oxidation weight gain of the coating, as shown in
Figure 3.
4.2. Network Training and Prediction
4.2.1. Data Processing
After the input and output variables are determined, premnmx is used for normalization processing so that the results are between [0, 1]. After training and simulation, the data are subjected to anti-normalization processing.
4.2.2. Network Training and Prediction
In this paper, the momentum BP algorithm with variable learning rate is adopted. The training function of this algorithm is Traingdx, and the hidden layer function adopts the differentiable hyperbolic tangent function. The hidden layer and the output layer adopted a linear function. During the training process, the maximum training times were selected to be 1000, and the learning rate was 0.05.
It can be seen from
Table 5 that the predicted value is quite consistent with the experimental value, and the relative error is small. This indicates that the model can well describe the mapping relationship between input and output. A relatively stable network was obtained, which could be used to predict the high-temperature oxidation resistance of Ni–W coating and provide an effective basis for the actual process, as shown in
Figure 4a. The fitting effect of BP neural network is shown in
Figure 4b, and its value is R = 0.9996.
4.3. Prediction of BP Neural Network
It can be seen from
Figure 5 that the BP neural network prediction curve is undulating, indicating that the combination at different technological levels has a significant impact on the rate of antioxidant weight gain of the coating. The 9th parameter combination at the highest point of BP neural network obtained the oxidation weight gain of 2.869 (10
−2 mg/cm
2) after treatment, which was not different from the actual measured value of 2.861 (10
−2 mg/cm
2). Therefore, the group 9th (T
1C
3D
3), with the bath temperature of 30 °C, current density of 15 mA/cm
2 and duty cycle of 0.3, can be determined as the best pulse electrodeposition parameter combination which can prepare the best oxidation resistance Ni–W coating. The Ni–W alloy coating and Ni–W gradient coating were prepared by using the best parameter combination simulated by the BP neural network.
5. Results and Discussion
5.1. The Cross-Sectional Morphology and EDS Spectra Analysis of the Prepared Ni–W Coatings
The Ni–W alloy coating and further Ni–W graded coating were prepared by the optimal pulse electrodeposition parameters (T
1C
3D
3), with the bath temperature of 30 °C, current density of 15 mA/cm
2 and duty cycle of 0.3 simulated by BP neural network. The surface morphology of the Ni–W alloy coating was observed by scanning electron microscopy (SEM), as shown in
Figure 6a. The light-colored area was Ni–W alloy coating, and the dark area was aluminum alloy matrix. Moreover, the distribution of the elements was uniform with the thickness of 136 μm. In addition, the obvious cracks, pores or other defects induced by high mechanical mismatch of soft-ductile substrate and hard-brittle coating are observed in
Figure 6a. As shown in
Figure 6b, the coatings consist of Ni element of 58.51% and W element of 41.49%, which further prove that the BP neural network successfully simulates the superior pulse electrodeposition parameters to prepare the Ni–W coating efficiently.
Figure 6c,d show the surface morphology of Ni–W coatings before and after 400 °C heat treatment. The surface morphology of Ni–W coating shows the typical tightly packed cellular structure [
27]. Apparently, Ni–W coating shows grain refinement after 400 °C heat treatment, which proves that the optimal parameters simulated by the BP neural network were effective in the experiments [
28].
Based on the BP neural network simulating the optimal pulse electrodeposition parameters, Ni–W graded coatings were further successfully prepared, as shown in
Figure 7. The coating was divided into seven layers in total, and every layer was prepared using the optimal parameters simulated by BP neural network with different Na
2WO
4·2H
2O addition, as shown in
Table 1.
Figure 7a shows the SEM image of Ni–W graded coating cross section morphology. It can be seen that Ni–W graded coatings were both closely bound to the matrix. The total thickness of the coating was about 137 m, the most left black area was aluminum alloy matrix, and the gradient ramp part was Ni–W graded coating (the brighter part of the coating, the higher the content of W, the lower the content of Ni) [
29]. The boundary between the layers was tight and uniform without cracks.
Figure 7b shows the line energy spectrum analysis which further proves the gradient W content in the coating. Compared with high mechanical mismatched Ni–W alloy coating, Ni–W graded coating shows better adhesion between coatings and matrix as well as different layers [
30].
The EDS spectra of seven layers of Ni–W graded coatings are shown in
Figure 8a–h. The content of W increased significantly from the 1st layer of 2.51% to the seventh layer of 45.29% among the Ni–W graded coating. The gradient W content not only affects the color of the coating, but also changes the micro-structure of Ni–W coating [
5].
Figure 8i shows the gradient increase of W element trend and gradient decrease of Ni element trend in the prepared Ni–W graded coating. Several researchers demonstrate that the coating displays an amorphous state when the W% > 44% in the coatings [
31]. Amorphous alloy coatings exhibit excellent physical and chemical properties due to their unique structural characteristics, such as excellent mechanical properties, wear and corrosion resistance, high resistivity, high activity and catalytic properties, and many special magnetic properties [
10].
5.2. The XRD of Ni–W Graded Coatings
To further investigate the phase composition of Ni–W graded coating, XRD of the prepared samples before and after heat treatment are shown in
Figure 9. Three diffraction peaks observed in Ni–W graded coating appear at diffraction angles of 2θ
1 = 43.86°, 2θ
2 = 51.40°, 2θ
3 = 75.40°. As we know, the main diffraction peaks of pure Ni are located at 2θ
1 = 44.62°, 2θ
2 = 51.94°, 2θ
3 = 76.14°, corresponding to the crystal planes of (1 1 1), (2 0 0) and (2 2 0). It can be seen that peaks of the Ni–W graded coating are similar to that of pure Ni. The atom of tungsten is larger than that of nickel [
32]. Therefore, the solution of tungsten causes the expansion of the Ni lattice, which leads to the shift of the Ni diffraction peak, further indicating that the Ni–W coating is the solid solution structure [
33,
34]. Obviously, the content of W in the coating significantly influences the structure and the properties of the coating. The gentle diffraction peaks of the Ni–W graded coating further prove that it has an amorphous structure [
35]. Apparently, when the samples were processed by heat treatment of 400 °C, 8 h, the three diffraction peaks become sharper, indicating that Ni–W graded coating becomes crystallized after heat treatment [
26].
5.3. The High Temperature Oxidation Resistance of Ni–W Graded Coatings
To further verify the optimal pulse electrodeposition simulated by the BP neural network, we also weighed the samples after heat treatment for 400 °C, 8 h, as shown in
Figure 10. The oxidative weight gains were calculated by Equation (1) and the average weight gain is 2.4324 mg/cm
2, which is lower than original samples of average weight gain of 3.3194 mg/cm
2, indicating the better high temperature oxidation resistance optimized by BP neural network. The reason for this enhancement is that the graded coating results in a denser coating with increased bonding and thus improved high temperature resistance to oxidation compared to a normal alloy coating with a relaxed bond.
5.4. The Micro-Hardness of Ni–W Graded Coatings
Figure 11 shows the Vickers hardness of the obtained Ni–W graded coatings, and the micro-hardness changes with W content in Ni–W graded coating. As presented in
Figure 10, the pure Al alloy without Ni–W coating shows 98.7 HV, and it soars to 383.3 HV with the 1st layer Ni–W graded coating. As the W content increases from 2.51% to 45.29%, the hardness of the coating enhances from 383.3 HV to 437.5 HV, indicating that the hard tungsten contributes to the hardness of coatings [
36,
37]. The study shows that when the content of W in the coating increases, the degree of lattice distortion of the alloy increases, and the resistance of dislocation movement also increases, which leads to the increase of the micro-hardness of the alloy [
38,
39].
6. Conclusions
This work successfully prepared the Ni–W coatings by pulse electrodeposition. A BP neural network was used to optimize the parameters of pulse electrodeposition with high temperature oxidation resistance as index. The optimization results show that the ninth group 9th (T1C3D3), with the bath temperature of 30 °C, current density of 15 mA/cm2 and duty cycle of 0.3, were simulated as the optimal pulse electrodeposition parameter. The predicted value of the model agrees well with the experimental value curve, and the relative error is minor. The maximum error is less than 3%, and the correlation coefficient is 0.9996. According to the SEM and EDS test, Ni–W graded coatings including seven layers of gradient W content were successfully prepared, which effectively decreases the internal stress difference between soft-ductile substrate and hard-brittle coating. Furthermore, the weight gain test proves that BP neural networks can truly optimize the pulse electrodeposition parameters to prepare better high temperature oxidation resistance samples. Consequently, using BP neural networks to select the optimal preparation parameters and apply the optimal parameters to scientific research can greatly reduce cost and improve efficiency.
Author Contributions
Conceptualization, P.F. and P.S.; Methodology, P.F., Y.S. and H.W.; Investigation, P.S., T.P. and W.Z.; Resources, P.F. and H.W.; Formal Analysis, P.F., P.S. and L.P.; Writing—Original Draft Preparation, P.F., Y.S. and P.S; Data Curation, H.W., T.P., Y.S. and P.F.; writing—review and editing, W.Z., P.S. and Y.S.; visualization, H.W., T.P. and R.F.; All authors have read and agreed to the published version of the manuscript.
Funding
This work is supported by the National Natural Science Foundation of China (Grant No. 51805407), Natural Science Foundation in Shaanxi Province of China (Grant No. 2018JQ5148) and Basic Research Fund of Engineering University of PAP (WJY202153).
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The data presented in this study are available on request from the corresponding author.
Conflicts of Interest
The authors declare no conflict of interest.
Appendix A
Table A1.
Model Inputs and Symbols.
Table A1.
Model Inputs and Symbols.
Symbols | Definition | Unit |
---|
ΔM | The oxidation weight gain of the coating | mg/cm2 |
M1 | The mass of the sample before high-temperature oxidation | g |
M2 | The mass of the sample after high-temperature oxidation | g |
S | The surface area of the sample | cm2 |
mx | The oxidative weight gain | g |
V | The reciprocal of the oxidative weight gain | 1/g |
Table A2.
Abbreviations in articles.
Table A2.
Abbreviations in articles.
Symbols | Definition |
---|
BP | Backward propagation |
ANN | Artificial neural network |
Ni–W | Nickel and tungsten |
HVOF | High velocity oxygen fuel |
LY-12 | A high strength duralumin |
KSY | A muffle furnace model |
SEM | Scanning electron microscope |
EDX | Energy dispersive X-ray spectroscopy |
EDS | Energy disperse spectroscopy |
XRD | X-ray diffraction |
VH | Vickers hardness |
References
- Pan, C.-Q.; Zhong, Q.-D.; Yang, J.; Cheng, Y.F.; Li, Y.-L. Investigating crevice corrosion behavior of 6061 Al alloy using wire beam electrode. J. Mater. Res. Technol. 2021, 14, 93–107. [Google Scholar] [CrossRef]
- Qi, Y.; Liu, S.; Luo, L.; Peng, Z.; Liang, J.; Wang, P. Plasma electrolytic fluorination on Al alloys: Coating growth and plasma discharge behavior. Ceram. Int. 2021, 47, 29758–29770. [Google Scholar] [CrossRef]
- Thapliyal, S.; Shukla, S.; Zhou, L.; Hyer, H.; Agrawal, P.; Agrawal, P.; Komarasamy, M.; Sohn, Y.; Mishra, R.S. Design of heterogeneous structured Al alloys with wide processing window for laser-powder bed fusion additive manufacturing. Addit. Manuf. 2021, 42, 1002002. [Google Scholar]
- Xin, G.; Wu, C.; Liu, W.; Rong, Y.; Huang, Y. Anti-corrosion superhydrophobic surfaces of Al alloy based on micro-protrusion array structure fabricated by laser direct writing. J. Alloys Compd. 2021, 881, 160649. [Google Scholar] [CrossRef]
- Li, B.; Li, D.; Mei, T.; Xia, W.; Zhang, W. Fabrication and characterization of boron nitride reinforced Ni–W nanocomposite coating by electrodeposition. J. Alloys Compd. 2019, 777, 1234–1244. [Google Scholar] [CrossRef]
- Neethu, R.M.; Hegde, A.C. Development of Ni–W alloy coatings and their electrocatalytic activity for water splitting reaction. Phys. B Condens. Matter 2020, 597, 412359. [Google Scholar] [CrossRef]
- Shajahan, S.; Basu, A. Corrosion, oxidation and wear study of electro-co-deposited ZrO2-TiO2 reinforced Ni–W coatings. Surf. Coat. Technol. 2020, 393, 125729. [Google Scholar] [CrossRef]
- Wasekar, N.P.; Bathini, L.; Ramakrishna, L.; Rao, D.S.; Padmanabham, G. Pulsed electrodeposition, mechanical properties and wear mechanism in Ni–W/SiC nanocomposite coatings used for automotive applications. Appl. Surf. Sci. 2020, 527, 146896. [Google Scholar] [CrossRef]
- Zhang, M.-F.; Zhu, C.-F.; Yang, C.-P.; Lv, Y.-N. Preparation of amorphous Ni–W coating for the current collector of Na/S battery by electrodeposition. Surf. Coat. Technol. 2018, 346, 40–47. [Google Scholar] [CrossRef]
- Wasekar, N.P.; Hebalkar, N.; Jyothirmayi, A.; Lavakumar, B.; Ramakrishna, M.; Sundararajan, G. Influence of pulse parameters on the mechanical properties and electrochemical corrosion behavior of electrodeposited Ni–W alloy coatings with high tungsten content. Corros. Sci. 2020, 165, 108409. [Google Scholar] [CrossRef]
- Zhang, W.; Li, B.; Ji, C. Synthesis and characterization of Ni–W/TiN nanocomposite coating with enhanced wear and corrosion resistance deposited by pulse electrodeposition. Ceram. Int. 2019, 45, 14015–14028. [Google Scholar] [CrossRef]
- Wang, C.; Hossain Bhuiyan, M.E.; Moreno, S.; Minary-Jolandan, M. Direct-Write Printing Copper-Nickel (Cu/Ni) Alloy with Controlled Composition from a Single Electrolyte Using Co-Electrodeposition. ACS Appl Mater. Interfaces 2020, 12, 18683–18691. [Google Scholar] [CrossRef] [PubMed]
- Behroozfar, A.; Hossain Bhuiyan, M.E.; Daryadel, S.; Edwards, D.; Rodriguez, B.J.; Minary-Jolandan, M. Additive printing of pure nanocrystalline nickel thin films using room environment electroplating. Nanotechnology 2020, 31, 55301. [Google Scholar] [CrossRef] [PubMed]
- Panchal, S.; Mathew, M.; Dincer, I.; Agelin-Chaab, M.; Fraser, R.; Fowler, M. Thermal and electrical performance assessments of lithium-ion battery modules for an electric vehicle under actual drive cycles. Electr. Power Syst. Res. 2018, 163, 18–27. [Google Scholar] [CrossRef]
- Panchal, S.; Dincer, I.; Agelin-Chaab, M.; Fraser, R.; Fowler, M. Design and simulation of a lithium-ion battery at large C-rates and varying boundary conditions through heat flux distributions. Measurement 2018, 116, 382–390. [Google Scholar] [CrossRef] [Green Version]
- Panchal, S.; Dincer, I.; Agelin-Chaab, M.; Fraser, R.; Fowler, M. Experimental and theoretical investigation of temperature distributions in a prismatic lithium-ion battery. Int. J. Therm. Sci. 2016, 99, 204–212. [Google Scholar] [CrossRef]
- Panchal, S.; Dincer, I.; Agelin-Chaab, M.; Fraser, R.; Fowler, M. Experimental investigation and simulation of temperature distributions in a 16Ah-LiMnNiCoO2 battery during rapid discharge rates. Heat Mass Transf. 2016, 53, 937–946. [Google Scholar] [CrossRef]
- Allahyarzadeh, M.H.; Aliofkhazraei, M.; Rouhaghdam, A.R.S.; Torabinejad, V. Gradient electrodeposition of Ni-Cu-W(alumina) nanocomposite coating. Mater. Des. 2016, 107, 74–81. [Google Scholar] [CrossRef]
- Zhang, M.; Li, M.; Chi, J.; Wang, S.; Yang, S.; Yang, J.; Wei, Y. Effect of Ti on microstructure characteristics, carbide precipitation mechanism and tribological behavior of different WC types reinforced Ni-based gradient coating. Surf. Coat. Technol. 2019, 374, 645–655. [Google Scholar] [CrossRef]
- Ma, Z.; Zhang, W.; Luo, Z.; Sun, X.; Li, Z.; Lin, L. Ultrasonic characterization of thermal barrier coatings porosity through BP neural network optimizing Gaussian process regression algorithm. Ultrasonics 2020, 100, 105981. [Google Scholar] [CrossRef]
- Becker, A.; Fals, H.D.C.; Roca, A.S.; Siqueira, I.B.A.F.; Caliari, F.R.; da Cruz, J.R.; Vaz, R.F.; de Sousa, M.J.; Pukasiewicz, A.G.M. Artificial neural networks applied to the analysis of performance and wear resistance of binary coatings Cr3C237WC18M and WC20Cr3C27Ni. Wear 2021, 477, 203797. [Google Scholar] [CrossRef]
- Xu, Y.; Zhu, Y.; Xiao, G.; Ma, C. Application of artificial neural networks to predict corrosion behavior of Ni–SiC composite coatings deposited by ultrasonic electrodeposition. Ceram. Int. 2014, 40, 5425–5430. [Google Scholar] [CrossRef]
- Li, X.; Zhu, Y.; Xiao, G. Application of artificial neural networks to predict sliding wear resistance of Ni–TiN nanocomposite coatings deposited by pulse electrodeposition. Ceram. Int. 2014, 40 Pt A, 11767–11772. [Google Scholar] [CrossRef]
- Paturi, U.M.R.; Cheruku, S.; Geereddy, S.R. Process modeling and parameter optimization of surface coatings using artificial neural networks (ANNs): State-of-the-art review. Mater. Today Proc. 2021, 38, 2764–2774. [Google Scholar] [CrossRef]
- Jiang, M.; Ma, C.; Xia, F.; Zhang, Y. Application of artificial neural networks to predict the hardness of Ni–TiN nanocoatings fabricated by pulse electrodeposition. Surf. Coat. Technol. 2016, 286, 191–196. [Google Scholar] [CrossRef]
- Liu, M.; Yu, Z.; Zhang, Y.; Wu, H.; Liao, H.; Deng, S. Prediction and analysis of high velocity oxy fuel (HVOF) sprayed coating using artificial neural network. Surf. Coat. Technol. 2019, 378, 124988. [Google Scholar] [CrossRef]
- Elias, L.; Chitharanjan Hegde, A. A comparative study on the electrocatalytic activity of electrodeposited Ni–W and Ni-P alloy coatings. Mater. Today Proc. 2018, 5, 21156–21161. [Google Scholar] [CrossRef]
- Elias, L.; Hegde, A.C. Effect of magnetic field on corrosion protection efficacy of Ni–W alloy coatings. J. Alloys Compd. 2017, 712, 618–626. [Google Scholar] [CrossRef]
- Shreeram, D.D.; Li, S.; Bedekar, V.; Cong, H.; Doll, G.L. Effect of reverse pulse time on electrodeposited Ni–W coatings. Surf. Coat. Technol. 2017, 325, 386–396. [Google Scholar] [CrossRef]
- Elias, L.; Chitharanjan Hegde, A. Electrodeposition of laminar coatings of Ni–W alloy and their corrosion behaviour. Surf. Coat. Technol. 2015, 283, 61–69. [Google Scholar] [CrossRef]
- Lee, D.B.; Ko, J.H.; Kwon, S.C. High temperature oxidation of Ni–W coatings electroplated on steel. Mater. Sci. Eng. A 2004, 380, 73–78. [Google Scholar] [CrossRef]
- De Lima-Neto, P.; Correia, A.N.; Santana, R.A.C.; Colares, R.P.; Barros, E.B.; Casciano, P.N.S.; Vaz, G.L. Morphological, structural, microhardness and electrochemical characterisations of electrodeposited Cr and Ni–W coatings. Electrochim. Acta 2010, 55, 2078–2086. [Google Scholar] [CrossRef]
- Panagopoulos, C.N.; Plainakis, G.D.; Lagaris, D.A. Nanocrystalline Ni–W coatings on copper. Mater. Sci. Eng. B 2011, 176, 477–479. [Google Scholar] [CrossRef]
- Argañaraz, M.P.Q.; Ribotta, S.B.; Folquer, M.E.; Gassa, L.M.; Benítez, G.; Vela, M.E.; Salvarezza, R.C. Ni–W coatings electrodeposited on carbon steel: Chemical composition, mechanical properties and corrosion resistance. Electrochim. Acta 2011, 56, 5898–5903. [Google Scholar] [CrossRef]
- Allahyarzadeh, M.H.; Aliofkhazraei, M.; Rezvanian, A.R.; Torabinejad, V.; Sabour Rouhaghdam, A.R. Ni–W electrodeposited coatings: Characterization, properties and applications. Surf. Coat. Technol. 2016, 307, 978–1010. [Google Scholar] [CrossRef]
- Sunwang, N.; Wangyao, P.; Boonyongmaneerat, Y. The effects of heat treatments on hardness and wear resistance in Ni–W alloy coatings. Surf. Coat. Technol. 2011, 206, 1096–1101. [Google Scholar]
- Lee, D.B.; Ko, J.H.; Kwon, S.C. Oxidation of Ni–W coatings at 700 and 800 °C in air. Surf. Coat. Technol. 2005, 193, 292–296. [Google Scholar] [CrossRef]
- Orrillo, P.A.; Ribotta, S.B.; Gassa, L.M.; Benítez, G.; Salvarezza, R.C.; Vela, M.E. Phosphonic acid functionalization of nanostructured Ni–W coatings on steel. Appl. Surf. Sci. 2018, 433, 292–299. [Google Scholar] [CrossRef] [Green Version]
- Hou, K.-H.; Chang, Y.-F.; Chang, S.-M.; Chang, C.-H. The heat treatment effect on the structure and mechanical properties of electrodeposited nano grain size Ni–W alloy coatings. Thin Solid Film. 2010, 518, 7535–7540. [Google Scholar]
| Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).