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Application of artificial neural networks to predict sliding wear resistance of Ni–TiN nanocomposite coatings deposited by pulse electrodeposition
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CERAMICS INTERNATIONAL
Ceramics International ] (]]]]) ]]]–]]] www.elsevier.com/locate/ceramint
Application of artificial neural networks to predict sliding wear resistance of Ni–TiN nanocomposite coatings deposited by pulse electrodeposition Xingyuan Lia,n, Yongyong Zhub, Guorong Xiaoc a College of Information Engineering, Ningbo Dahongying University, Ningbo 315175, PR China Department of Economics and Business Administration, Chongqing University of Education, Chongqing 400067, PR China c Department of Computer Science and Technology, GuangDong University of Finance, Guangzhou 510000, PR China
b
Received 21 March 2014; received in revised form 31 March 2014; accepted 1 April 2014
Abstract Ni–TiN composite coatings were prepared on 45 steel substrates by pulse electrodeposition. The effect of plating parameters on the sliding wear resistance of the Ni–TiN nanocomposite coatings was investigated using transmission electron microscopy (TEM), scanning electron microscopy (SEM), and X-ray diffraction (XRD). The sliding wear resistance of the Ni–TiN coatings was modeled using artificial neural networks (ANNs). TiN grains in Ni–TiN coatings were large when the average current density was relatively low and the pulse interval was long. At a given wear distance, with increasing TiN concentration in the bath, the wear loss of the coating initially decreased and subsequently increased. The average crystallite sizes for Ni and TiN in Ni–TiN coating were approximately 58 and 39 nm, respectively. The ANN model, which showed an error of approximately 4.2%, can effectively predict sliding wear resistance of Ni–TiN nanocomposite coatings. & 2014 Elsevier Ltd and Techna Group S.r.l. All rights reserved.
Keywords: Application; Artificial neural networks; Ni–TiN coating
1. Introduction Since the late 1990s, metal–ceramic composite coatings have been used on automobile parts, electrical switch gear, appliances, metal furniture, beverage containers, fasteners, and various other industrial products [1–3]. Composite coatings are formed by components with characteristic dimensionality, such as micro/nanometer-size setting in different matrixes [4]. Electrodeposition is a technique for the preparation of excellent performance composite coatings. A characteristic feature of this process is that ceramic particles (e.g., SiC, CNTs, TiN, and TiO2) suspended in a liquid medium migrate under the influence of an electric field (electrophoresis) and are deposited on an electrode. All charged ceramic particles used to form stable suspensions can be used in electrodeposition. However, electrodeposition is affected by parameters such as current density, particle concentration, and bath temperature. For n
Corresponding author. Tel./fax: þ 86 13777129213. E-mail address: [email protected] (X. Li).
example, Bebea et al. [5] found that Co–ZrO2 composite coatings were uniform and well bonded to the substrate, and the thickness of the coating increased with increasing current density. Xia et al. [6] demonstrated that the TiN nanoparticles that entered and homogeneously dispersed in a composite coating increased the number of nuclei for nucleation of Ni grains and inhibition of grain growth. Parida et al. [7] reported a Ni–TiO2 composite coating on steel substrates directly prepared by electrodeposition from a bath containing a dispersion of TiO2 power in Watt's bath. TiO2 particles less than 100 nm in size were homogeneously co-deposited with Ni on steel substrate, and microhardness values increased after incorporation of TiO2 compared with a pure Ni deposition. TiN, a metal nitride ceramic material, has high hardness, high elastic modulus, excellent chemical stability, and better wear and corrosion resistance. Consequently, electrodeposition of Ni–TiN composite coatings has been extensively examined [8–10]. Despite numerous investigations on composite coating electrodeposition, reports on the application of artificial neural networks (ANNs) in predicting the wear resistance of Ni–TiN
http://dx.doi.org/10.1016/j.ceramint.2014.04.005 0272-8842/& 2014 Elsevier Ltd and Techna Group S.r.l. All rights reserved.
Please cite this article as: X. Li, et al., Application of artificial neural networks to predict sliding wear resistance of Ni–TiN nanocomposite coatings deposited by pulse electrodeposition, Ceramics International (2014), http://dx.doi.org/10.1016/j.ceramint.2014.04.005
X. Li et al. / Ceramics International ] (]]]]) ]]]–]]]
2
nanocomposite coatings are few. In this study, Ni–TiN nanocomposite coatings were synthesized by pulse electrodeposition. The effect of plating parameters on sliding wear resistance of Ni–TiN nanocomposite coatings was investigated using transmission electron microscopy (TEM), scanning electron microscopy (SEM), and X-ray diffraction (XRD). The sliding wear resistance of Ni–TiN coatings was modeled using ANNs. 2. Experimental procedures
2.2. Characterization The surface morphology and microstructure of Ni–TiN composite coatings were investigated using SEM (JSM6480LV) and TEM (Tecnai-G2-20-S-Twin). To determine the crystal properties of the coatings, XRD analysis was performed on a Rigaku D/Max-2400 instrument using Cu Kα radiation (k ¼ 0.15418 nm). The operating target voltage was 40 kV and the tube current was 100 mA. Using the Scherrer equation, the average grains diameter could be calculated as follows: Kλ cos θ FWHM
2.1. Electrolyte composition and plating conditions
D¼
Ni–TiN nanocomposite coatings were deposited on 45 steel substrates using pulse electrodeposition. The anode was a pure Ni plate. The precursors used were TiN ceramic nanoparticles with purity above 99.99% and average primary particle size of 30 nm. One of the TEM images of TiN nanoparticles is shown in Fig. 1, which confirms their nanometer size and shape regularity. Prior to deposition, the substrates were mechanically polished to a 0.10–0.15 μm surface finish, sequentially cleaned to remove surface contamination, activated for 10 s in a mixed acidic bath, and rinsed with distilled water. The chemical composition of the electrolyte and the plating process parameters are listed in Table 1. To minimize the influence of the coating thickness on sliding wear tests, all samples were deposited to a similar thickness of approximately 60 μm. And the thickness of the coatings was measured by an ultrasonic thickness detector (TC830, 7 0.1 μm).
where K is the figure factor of the grains (K ¼ 0.89), λ the wavelength, θ is the Bragg angle and FWHM is the standard full width at half-maximum.
ð1Þ
2.3. Sliding wear tests The sliding wear tests were performed on a ring-on-block tester of MM200 controlled by a computer (Fig. 2). The lower sample comprising Si3N4 ceramic rings with 40 mm outer diameter, 16 mm inner diameter, and 10 mm width was rotated at 180 rpm, resulting in a relative sliding speed of 0.4 m/s. The upper sample, comprising a block-like specimen (20 20 10 mm3) coated with Ni–TiN composite coatings and fixed on the sample holder, was pressed under applied loads of 200 N. The total sliding distance for the test was 2000 m. At 500 m intervals, the weight loss of the specimens was measured and recorded. The wear mass loss of the samples was determined by an electronic analytical balance (ES120-4, 7 0.1 mg). To ensure the accuracy of measurement, the specimens were ultrasonically cleaned for 15 min before each weight measurement. All tests were performed at room temperature and under an atmospheric environment with lubrication (Machine Oil SEA 46). 2.4. ANNs setup
Fig. 1. TEM image of the TiN nanoparticles dispersed in water.
The original appearance for the ANNs was obtained from an examination of central nervous systems, including the neurons, dendrites, axons, and synapses, which constitute the processing
Table 1 Chemical composition and plating conditions. Chemicals
Content
Plating conditions
Parameters
NiSO4
300 g/l
2.5–5.0 A/dm2
NiCl2
50 g/l
H3BO3 Octyl phenol Cetrimonium bromide TiN nanoparticles
30 g/l 2 mg/l 0.5 mg/l 4, 6, 8, 10 g/l
Density of pulse current On-duty ratio of pulse current Temperature pH
20–60% 30 1C 4–5
Fig. 2. Schematic of a MM200 sliding wear tester. Please cite this article as: X. Li, et al., Application of artificial neural networks to predict sliding wear resistance of Ni–TiN nanocomposite coatings deposited by pulse electrodeposition, Ceramics International (2014), http://dx.doi.org/10.1016/j.ceramint.2014.04.005
X. Li et al. / Ceramics International ] (]]]]) ]]]–]]]
elements of biological neural networks as investigated by neuroscience. The ANN model comprises simple artificial nodes that are interconnected to form a network of nodes mimicking the biological neural networks. The framework of the ANN model, which comprises input, hidden, and output layers, is shown in Fig. 3. A feed-forward, multilayer perceptron, which was trained with back propagation algorithm, was used. Density of pulse current (Di), on-duty ratio of pulse current (Ro), and TiN particle concentrations in bath (Cp) were used as inputs, whereas wear mass loss (Mw) was considered as the output of the neural network model. Inputs and outputs were normalized within the range of 0–1 [11,12]. The output Mw produced
3
by neuron i in layer L is described as follows: ! Mw ¼ f
n
∑ W ij þ b
ð2Þ
j¼1
where f is the activation function, n is the number of elements in the layer L 1, and b is the offset or bias where the activation function shifts along the basic axis; Wij is the weight associated with the connection between neuron i in layer L and neuron j in layer L 1, which has an output of wi. The error (E) of ANNs model is expressed by the following relationship: E¼
1 M N ∑ ∑ ðdip yip Þ2 2M p¼1 i¼1
ð3Þ
where M is the number of training sets, N is the number of outputs, dip is the desired output, and yip is the actual output. 3. Results and analysis 3.1. Effect of plating parameters on wear mass loss of coatings
Fig. 3. Schematic description of ANNs configuration.
Fig. 4. Effect of plating parameters on wear mass loss of Ni–TiN coatings.
The effect of density and on-duty ratio of pulse current on the wear mass loss of the Ni–TiN composite coatings is indicated in Fig. 4. When the density is between 2.5 A/dm2 and 5.0 A/dm2, the wear mass losses of the coatings decrease with increasing on-duty ratio of pulse current. The coating prepared with the density of 4.5 A/dm2 and on-duty ratio of 30% shows the lowest wear mass loss (Mw ¼ 9.6 mg) because increased density and on-duty ratio of pulse current result in the strengthening of Ni2 þ ions and hastening of the deposition velocity of Ni2 þ ions. Subsequently, the TiN content deposited in Ni–TiN coatings increases. The high hardness of TiN particles enhances the properties of Ni–TiN composite coatings [4]. The Ni grains and dispersed TiN nanoparticles in the composite coatings obtained from different plating parameters were observed by TEM and are shown in Fig. 5. Large TiN
Fig. 5. TEM images of Ni–TiN composite coatings under different electrodepositing conditions: (a) Di ¼3.5 A/dm2, Ro ¼ 50%, and Cp ¼ 8 g/l; (b) Di ¼4.5A/dm2, Ro ¼ 30%, and Cp ¼8 g/l. Please cite this article as: X. Li, et al., Application of artificial neural networks to predict sliding wear resistance of Ni–TiN nanocomposite coatings deposited by pulse electrodeposition, Ceramics International (2014), http://dx.doi.org/10.1016/j.ceramint.2014.04.005
X. Li et al. / Ceramics International ] (]]]]) ]]]–]]]
4
grains are obtained under relatively low average current density and long pulse interval (Fig. 5(a)). Based on the principle of pulse electrodeposition, high-amplitude and narrow pulse current can accelerate nucleation and restrain the growth of the crystalline grains. Therefore, Fig. 5(b) shows that the nanosized TiN grains dispersed in Ni–TiN composite coatings are homogenous in size.
surface increases with sliding wear distance. Compared with the wear resistance of 45 steel substrates, the composite coatings have relatively high wear resistance. At a given wear distance, with increasing concentration of TiN particle in bath, the wear loss of coating initially decreases and subsequently increases. The coating with TiN particle concentration of 8 g/l exhibits the best wear resistance.
2.0
The wear mass loss data of Ni–TiN composite coatings and substrate material as functions of the sliding wear distance are shown in Fig. 6. For all samples, the wear mass loss of wear
1.5
E
3.2. Effect of TiN concentrations on wear mass loss of coatings
40
Mw (mg)
30 25
1.0
0.5
45 steel Ni, 4 g/l TiN Ni, 6 g/l TiN Ni, 8 g/l TiN Ni, 10 g/l TiN
35
2 layers 4 layers 8 layers 16 layers
0.0
20
0
5
10
15
20
25
Number of neurons
15
Fig. 9. Error for different hidden layers and neuron numbers.
10 5 0 0
500
1000
1500
2000
Wear distance (m)
Fig. 6. Effect of TiN particle concentrations in bath on the wear mass loss of Ni–TiN coatings prepared with Di ¼4.5 A/dm2, Ro ¼30%, and Cp ¼8 g/l.
Fig. 7. XRD patterns of the TiN powder and coatings: (a) TiN powder and (b) Ni–TiN coating prepared with Di ¼4.5 A/dm2, Ro ¼30%, and Cp ¼8 g/l.
Fig. 10. Training error curve of the ANNs model with 16 hidden layers.
Fig. 8. SEM morphologies of wear track of (a) 45 steel and (b) Ni–TiN coating prepared with Di ¼ 4.5 A/dm2, Ro ¼ 30%, and Cp ¼ 8 g/l. Please cite this article as: X. Li, et al., Application of artificial neural networks to predict sliding wear resistance of Ni–TiN nanocomposite coatings deposited by pulse electrodeposition, Ceramics International (2014), http://dx.doi.org/10.1016/j.ceramint.2014.04.005
X. Li et al. / Ceramics International ] (]]]]) ]]]–]]]
Scanning electron micrographs of the wear tracks on different samples are shown in Fig. 8. The wear track width of 45 steel samples is wide with smooth appearance mostly because of adhesive wear. In the case of Ni co-deposition and ceramic powder, the wear resistance increases in terms of the decreased wear track width.
14 Experimental value Predicted value
Mw (mg)
12
10 D =4.5 A/dm R =30%
8
3.4. ANNs model predictions
6
4 2
4
6
8
10
12
TiN particle concentration in bath (g/l)
12 Experimental value Predicted value
10
Mw (mg)
5
8 C =8 g/l R =30%
6
4 2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
Density of pulse current (A/dm2)
A major issue in designing ANN models is ensuring the quantity of hidden layers and neurons. The error in ANN models for various hidden layers is illustrated in Fig. 9. ANNs with 16 hidden layers and 15 neurons demonstrated the smallest error. The training error curve of the ANN model is shown in Fig. 10, and the curve indicates that the training epochs are 3440. Consequently, the ANN model with these properties was used to predict the sliding wear resistance of Ni–TiN coatings. The predicted values of weight mass losses in comparison with experimental values as a function of TiN particle concentration, density, and on-duty ratio of pulse current are shown in Fig. 11. The predicted values are consistent with the experimental values, and the smallest weight loss of Ni–TiN coatings is obtained at TiN particle concentration of 8 g/l, current density of 4.5 A/dm2, and on-duty ratio of 30%. According to the data, the average error for this ANN model is approximately 4.2%, as calculated using Eq. (3). 4. Conclusion
12 Experimental value Predicted value
Mw (mg)
10 D =4.5 A/dm C =8 g/l
8
6
4 10
20
30
40
50
60
70
On-duty ratio of pulse current (%)
Fig. 11. Relationship between the experimental and predicted sliding wear resistance of Ni–TiN nanocomposite coatings.
Ni–TiN composite coatings were prepared on 45 steel substrates by pulse electrodeposition, and numerical simulations for sliding wear resistance of coatings were forecasted using an ANN model. Results indicate that TiN grains in Ni– TiN coatings are large when the average current density is relatively low and the pulse interval is long. Compared with the wear resistance of 45 steel substrates, the composite coatings have relatively high wear resistance. The coating with TiN particle concentration of 8 g/l exhibits the best wear resistance. The average crystallite sizes for Ni and TiN in Ni– TiN coating are approximately 58 and 39 nm, respectively. The ANN model, which shows an error of approximately 4.2%, can effectively predict sliding wear resistance of Ni–TiN nanocomposite coatings. Acknowledgments
3.3. Microstructural analysis Fig. 7 illustrates XRD patterns of the TiN powder and Ni– TiN coating, which reveal the presence of TiN in the Ni–TiN coating. For Ni, the diffraction peaks at 44.821, 52.211 and 76.771 correspond to (111), (200) and (220). For TiN, the diffraction peaks at 36.661, 42.601 and 61.811 correspond to (111), (200) and (220). According to the XRD data, the average crystallite size for Ni and TiN calculated using Eq. (1) is approximately 58 nm and 39 nm, respectively.
The authors gratefully acknowledge the National Natural Science Foundation of China (Grant no. 51101027) and Scientific Research Fund of Zhejiang Provincial Education Department (Grant no. Y201329715). References [1] H. Schmidt, S. Langenfeld, R. Naß, A new corrosion protection coating system for pressure-cast aluminium automotive parts, Mater. Des. 18 (4– 6) (1997) 309–313.
Please cite this article as: X. Li, et al., Application of artificial neural networks to predict sliding wear resistance of Ni–TiN nanocomposite coatings deposited by pulse electrodeposition, Ceramics International (2014), http://dx.doi.org/10.1016/j.ceramint.2014.04.005
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X. Li et al. / Ceramics International ] (]]]]) ]]]–]]]
[2] R.I. Amaro, R.C. Martins, J.O. Seabra, N.M. Renevier, D.G. Teer, Molybdenum disulphide/titanium low friction coating for gears application, Tribol. Int. 38 (4) (2005) 423–434. [3] A. Naci Tankut, N. Tankut, Investigations the effects of fastener, glue, and composite material types on the strength of corner joints in case-type furniture construction, Mater. Des. 30 (10) (2009) 4175–4182. [4] F.F. Xia, H.B. Xu, C. Liu, J.W. Wang, J.J. Ding, C.H. Ma, Microstructures of Ni–AlN composite coatings prepared by pulse electrodeposition technology, Appl. Surf. Sci. 271 (2013) 7–11. [5] L. Bebea, P. Ponthiaux, F. Wenger, Co–ZrO2 electrodeposited composite coatings exhibiting improved micro hardness and corrosion behavior in simulating body fluid solution, Surf. Coat. Technol. 205 (2011) 5379–5386. [6] F. Xia, M. Wu, F. Wang, Z. Jia, A. Wang, Nanocomposite Ni–TiN coatings prepared by ultrasonic electrodeposition, Curr. Appl. Phys. 9 (2009) 44–47. [7] G. Parida, D. Chaira, M. Chopkar, A. Basu, Synthesis and characterization of Ni–TiO2 composite coatings by electro-co-deposition, Surf. Coat. Technol. 205 (2011) 4871–4879.
[8] M.H. Wu, N.N. Liu, Z. Li, Effects of electrodeposition process parameters on particle contents of Ni–TiN–CeO2 composite coating, J. Funct. Mater. 43 (19) (2012) 2657–2665. [9] X.B. Zhu, C. Cai, G.Q. Zheng, Z. Zhang, J.F. Li, Electrodeposition and corrosion behavior of nanostructured Ni–TiN composite films, Trans. Nonferr. Met. Soc. China 21 (2011) 2216–2224. [10] W. Wang, S.Q. Qian, X.Y. Zhou, Microstructure and properties of TiN/ Ni composite coating prepared by plasma transferred arc scanning process, Trans. Nonferr. Met. Soc. China 19 (2009) 1180–1184. [11] A. Shafyei, S.H. Mousavi Anijdan, A. Bahrami, Prediction of porosity percent in Al–Si casting alloys using ANN, Mater. Sci. Eng. A 431 (1–2) (2006) 206–210. [12] A.M. Rashidi, A.R. Eivani, A. Amadeh, Application of artificial neural networks to predict the grain size of nano-crystalline nickel coatings, Comput. Mater. Sci. 45 (2009) 499–504.
Please cite this article as: X. Li, et al., Application of artificial neural networks to predict sliding wear resistance of Ni–TiN nanocomposite coatings deposited by pulse electrodeposition, Ceramics International (2014), http://dx.doi.org/10.1016/j.ceramint.2014.04.005
CERAMICS INTERNATIONAL
Ceramics International ] (]]]]) ]]]–]]] www.elsevier.com/locate/ceramint
Application of artificial neural networks to predict sliding wear resistance of Ni–TiN nanocomposite coatings deposited by pulse electrodeposition Xingyuan Lia,n, Yongyong Zhub, Guorong Xiaoc a College of Information Engineering, Ningbo Dahongying University, Ningbo 315175, PR China Department of Economics and Business Administration, Chongqing University of Education, Chongqing 400067, PR China c Department of Computer Science and Technology, GuangDong University of Finance, Guangzhou 510000, PR China
b
Received 21 March 2014; received in revised form 31 March 2014; accepted 1 April 2014
Abstract Ni–TiN composite coatings were prepared on 45 steel substrates by pulse electrodeposition. The effect of plating parameters on the sliding wear resistance of the Ni–TiN nanocomposite coatings was investigated using transmission electron microscopy (TEM), scanning electron microscopy (SEM), and X-ray diffraction (XRD). The sliding wear resistance of the Ni–TiN coatings was modeled using artificial neural networks (ANNs). TiN grains in Ni–TiN coatings were large when the average current density was relatively low and the pulse interval was long. At a given wear distance, with increasing TiN concentration in the bath, the wear loss of the coating initially decreased and subsequently increased. The average crystallite sizes for Ni and TiN in Ni–TiN coating were approximately 58 and 39 nm, respectively. The ANN model, which showed an error of approximately 4.2%, can effectively predict sliding wear resistance of Ni–TiN nanocomposite coatings. & 2014 Elsevier Ltd and Techna Group S.r.l. All rights reserved.
Keywords: Application; Artificial neural networks; Ni–TiN coating
1. Introduction Since the late 1990s, metal–ceramic composite coatings have been used on automobile parts, electrical switch gear, appliances, metal furniture, beverage containers, fasteners, and various other industrial products [1–3]. Composite coatings are formed by components with characteristic dimensionality, such as micro/nanometer-size setting in different matrixes [4]. Electrodeposition is a technique for the preparation of excellent performance composite coatings. A characteristic feature of this process is that ceramic particles (e.g., SiC, CNTs, TiN, and TiO2) suspended in a liquid medium migrate under the influence of an electric field (electrophoresis) and are deposited on an electrode. All charged ceramic particles used to form stable suspensions can be used in electrodeposition. However, electrodeposition is affected by parameters such as current density, particle concentration, and bath temperature. For n
Corresponding author. Tel./fax: þ 86 13777129213. E-mail address: [email protected] (X. Li).
example, Bebea et al. [5] found that Co–ZrO2 composite coatings were uniform and well bonded to the substrate, and the thickness of the coating increased with increasing current density. Xia et al. [6] demonstrated that the TiN nanoparticles that entered and homogeneously dispersed in a composite coating increased the number of nuclei for nucleation of Ni grains and inhibition of grain growth. Parida et al. [7] reported a Ni–TiO2 composite coating on steel substrates directly prepared by electrodeposition from a bath containing a dispersion of TiO2 power in Watt's bath. TiO2 particles less than 100 nm in size were homogeneously co-deposited with Ni on steel substrate, and microhardness values increased after incorporation of TiO2 compared with a pure Ni deposition. TiN, a metal nitride ceramic material, has high hardness, high elastic modulus, excellent chemical stability, and better wear and corrosion resistance. Consequently, electrodeposition of Ni–TiN composite coatings has been extensively examined [8–10]. Despite numerous investigations on composite coating electrodeposition, reports on the application of artificial neural networks (ANNs) in predicting the wear resistance of Ni–TiN
http://dx.doi.org/10.1016/j.ceramint.2014.04.005 0272-8842/& 2014 Elsevier Ltd and Techna Group S.r.l. All rights reserved.
Please cite this article as: X. Li, et al., Application of artificial neural networks to predict sliding wear resistance of Ni–TiN nanocomposite coatings deposited by pulse electrodeposition, Ceramics International (2014), http://dx.doi.org/10.1016/j.ceramint.2014.04.005
X. Li et al. / Ceramics International ] (]]]]) ]]]–]]]
2
nanocomposite coatings are few. In this study, Ni–TiN nanocomposite coatings were synthesized by pulse electrodeposition. The effect of plating parameters on sliding wear resistance of Ni–TiN nanocomposite coatings was investigated using transmission electron microscopy (TEM), scanning electron microscopy (SEM), and X-ray diffraction (XRD). The sliding wear resistance of Ni–TiN coatings was modeled using ANNs. 2. Experimental procedures
2.2. Characterization The surface morphology and microstructure of Ni–TiN composite coatings were investigated using SEM (JSM6480LV) and TEM (Tecnai-G2-20-S-Twin). To determine the crystal properties of the coatings, XRD analysis was performed on a Rigaku D/Max-2400 instrument using Cu Kα radiation (k ¼ 0.15418 nm). The operating target voltage was 40 kV and the tube current was 100 mA. Using the Scherrer equation, the average grains diameter could be calculated as follows: Kλ cos θ FWHM
2.1. Electrolyte composition and plating conditions
D¼
Ni–TiN nanocomposite coatings were deposited on 45 steel substrates using pulse electrodeposition. The anode was a pure Ni plate. The precursors used were TiN ceramic nanoparticles with purity above 99.99% and average primary particle size of 30 nm. One of the TEM images of TiN nanoparticles is shown in Fig. 1, which confirms their nanometer size and shape regularity. Prior to deposition, the substrates were mechanically polished to a 0.10–0.15 μm surface finish, sequentially cleaned to remove surface contamination, activated for 10 s in a mixed acidic bath, and rinsed with distilled water. The chemical composition of the electrolyte and the plating process parameters are listed in Table 1. To minimize the influence of the coating thickness on sliding wear tests, all samples were deposited to a similar thickness of approximately 60 μm. And the thickness of the coatings was measured by an ultrasonic thickness detector (TC830, 7 0.1 μm).
where K is the figure factor of the grains (K ¼ 0.89), λ the wavelength, θ is the Bragg angle and FWHM is the standard full width at half-maximum.
ð1Þ
2.3. Sliding wear tests The sliding wear tests were performed on a ring-on-block tester of MM200 controlled by a computer (Fig. 2). The lower sample comprising Si3N4 ceramic rings with 40 mm outer diameter, 16 mm inner diameter, and 10 mm width was rotated at 180 rpm, resulting in a relative sliding speed of 0.4 m/s. The upper sample, comprising a block-like specimen (20 20 10 mm3) coated with Ni–TiN composite coatings and fixed on the sample holder, was pressed under applied loads of 200 N. The total sliding distance for the test was 2000 m. At 500 m intervals, the weight loss of the specimens was measured and recorded. The wear mass loss of the samples was determined by an electronic analytical balance (ES120-4, 7 0.1 mg). To ensure the accuracy of measurement, the specimens were ultrasonically cleaned for 15 min before each weight measurement. All tests were performed at room temperature and under an atmospheric environment with lubrication (Machine Oil SEA 46). 2.4. ANNs setup
Fig. 1. TEM image of the TiN nanoparticles dispersed in water.
The original appearance for the ANNs was obtained from an examination of central nervous systems, including the neurons, dendrites, axons, and synapses, which constitute the processing
Table 1 Chemical composition and plating conditions. Chemicals
Content
Plating conditions
Parameters
NiSO4
300 g/l
2.5–5.0 A/dm2
NiCl2
50 g/l
H3BO3 Octyl phenol Cetrimonium bromide TiN nanoparticles
30 g/l 2 mg/l 0.5 mg/l 4, 6, 8, 10 g/l
Density of pulse current On-duty ratio of pulse current Temperature pH
20–60% 30 1C 4–5
Fig. 2. Schematic of a MM200 sliding wear tester. Please cite this article as: X. Li, et al., Application of artificial neural networks to predict sliding wear resistance of Ni–TiN nanocomposite coatings deposited by pulse electrodeposition, Ceramics International (2014), http://dx.doi.org/10.1016/j.ceramint.2014.04.005
X. Li et al. / Ceramics International ] (]]]]) ]]]–]]]
elements of biological neural networks as investigated by neuroscience. The ANN model comprises simple artificial nodes that are interconnected to form a network of nodes mimicking the biological neural networks. The framework of the ANN model, which comprises input, hidden, and output layers, is shown in Fig. 3. A feed-forward, multilayer perceptron, which was trained with back propagation algorithm, was used. Density of pulse current (Di), on-duty ratio of pulse current (Ro), and TiN particle concentrations in bath (Cp) were used as inputs, whereas wear mass loss (Mw) was considered as the output of the neural network model. Inputs and outputs were normalized within the range of 0–1 [11,12]. The output Mw produced
3
by neuron i in layer L is described as follows: ! Mw ¼ f
n
∑ W ij þ b
ð2Þ
j¼1
where f is the activation function, n is the number of elements in the layer L 1, and b is the offset or bias where the activation function shifts along the basic axis; Wij is the weight associated with the connection between neuron i in layer L and neuron j in layer L 1, which has an output of wi. The error (E) of ANNs model is expressed by the following relationship: E¼
1 M N ∑ ∑ ðdip yip Þ2 2M p¼1 i¼1
ð3Þ
where M is the number of training sets, N is the number of outputs, dip is the desired output, and yip is the actual output. 3. Results and analysis 3.1. Effect of plating parameters on wear mass loss of coatings
Fig. 3. Schematic description of ANNs configuration.
Fig. 4. Effect of plating parameters on wear mass loss of Ni–TiN coatings.
The effect of density and on-duty ratio of pulse current on the wear mass loss of the Ni–TiN composite coatings is indicated in Fig. 4. When the density is between 2.5 A/dm2 and 5.0 A/dm2, the wear mass losses of the coatings decrease with increasing on-duty ratio of pulse current. The coating prepared with the density of 4.5 A/dm2 and on-duty ratio of 30% shows the lowest wear mass loss (Mw ¼ 9.6 mg) because increased density and on-duty ratio of pulse current result in the strengthening of Ni2 þ ions and hastening of the deposition velocity of Ni2 þ ions. Subsequently, the TiN content deposited in Ni–TiN coatings increases. The high hardness of TiN particles enhances the properties of Ni–TiN composite coatings [4]. The Ni grains and dispersed TiN nanoparticles in the composite coatings obtained from different plating parameters were observed by TEM and are shown in Fig. 5. Large TiN
Fig. 5. TEM images of Ni–TiN composite coatings under different electrodepositing conditions: (a) Di ¼3.5 A/dm2, Ro ¼ 50%, and Cp ¼ 8 g/l; (b) Di ¼4.5A/dm2, Ro ¼ 30%, and Cp ¼8 g/l. Please cite this article as: X. Li, et al., Application of artificial neural networks to predict sliding wear resistance of Ni–TiN nanocomposite coatings deposited by pulse electrodeposition, Ceramics International (2014), http://dx.doi.org/10.1016/j.ceramint.2014.04.005
X. Li et al. / Ceramics International ] (]]]]) ]]]–]]]
4
grains are obtained under relatively low average current density and long pulse interval (Fig. 5(a)). Based on the principle of pulse electrodeposition, high-amplitude and narrow pulse current can accelerate nucleation and restrain the growth of the crystalline grains. Therefore, Fig. 5(b) shows that the nanosized TiN grains dispersed in Ni–TiN composite coatings are homogenous in size.
surface increases with sliding wear distance. Compared with the wear resistance of 45 steel substrates, the composite coatings have relatively high wear resistance. At a given wear distance, with increasing concentration of TiN particle in bath, the wear loss of coating initially decreases and subsequently increases. The coating with TiN particle concentration of 8 g/l exhibits the best wear resistance.
2.0
The wear mass loss data of Ni–TiN composite coatings and substrate material as functions of the sliding wear distance are shown in Fig. 6. For all samples, the wear mass loss of wear
1.5
E
3.2. Effect of TiN concentrations on wear mass loss of coatings
40
Mw (mg)
30 25
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45 steel Ni, 4 g/l TiN Ni, 6 g/l TiN Ni, 8 g/l TiN Ni, 10 g/l TiN
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2 layers 4 layers 8 layers 16 layers
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Fig. 9. Error for different hidden layers and neuron numbers.
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Fig. 6. Effect of TiN particle concentrations in bath on the wear mass loss of Ni–TiN coatings prepared with Di ¼4.5 A/dm2, Ro ¼30%, and Cp ¼8 g/l.
Fig. 7. XRD patterns of the TiN powder and coatings: (a) TiN powder and (b) Ni–TiN coating prepared with Di ¼4.5 A/dm2, Ro ¼30%, and Cp ¼8 g/l.
Fig. 10. Training error curve of the ANNs model with 16 hidden layers.
Fig. 8. SEM morphologies of wear track of (a) 45 steel and (b) Ni–TiN coating prepared with Di ¼ 4.5 A/dm2, Ro ¼ 30%, and Cp ¼ 8 g/l. Please cite this article as: X. Li, et al., Application of artificial neural networks to predict sliding wear resistance of Ni–TiN nanocomposite coatings deposited by pulse electrodeposition, Ceramics International (2014), http://dx.doi.org/10.1016/j.ceramint.2014.04.005
X. Li et al. / Ceramics International ] (]]]]) ]]]–]]]
Scanning electron micrographs of the wear tracks on different samples are shown in Fig. 8. The wear track width of 45 steel samples is wide with smooth appearance mostly because of adhesive wear. In the case of Ni co-deposition and ceramic powder, the wear resistance increases in terms of the decreased wear track width.
14 Experimental value Predicted value
Mw (mg)
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10 D =4.5 A/dm R =30%
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3.4. ANNs model predictions
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8 C =8 g/l R =30%
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Density of pulse current (A/dm2)
A major issue in designing ANN models is ensuring the quantity of hidden layers and neurons. The error in ANN models for various hidden layers is illustrated in Fig. 9. ANNs with 16 hidden layers and 15 neurons demonstrated the smallest error. The training error curve of the ANN model is shown in Fig. 10, and the curve indicates that the training epochs are 3440. Consequently, the ANN model with these properties was used to predict the sliding wear resistance of Ni–TiN coatings. The predicted values of weight mass losses in comparison with experimental values as a function of TiN particle concentration, density, and on-duty ratio of pulse current are shown in Fig. 11. The predicted values are consistent with the experimental values, and the smallest weight loss of Ni–TiN coatings is obtained at TiN particle concentration of 8 g/l, current density of 4.5 A/dm2, and on-duty ratio of 30%. According to the data, the average error for this ANN model is approximately 4.2%, as calculated using Eq. (3). 4. Conclusion
12 Experimental value Predicted value
Mw (mg)
10 D =4.5 A/dm C =8 g/l
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On-duty ratio of pulse current (%)
Fig. 11. Relationship between the experimental and predicted sliding wear resistance of Ni–TiN nanocomposite coatings.
Ni–TiN composite coatings were prepared on 45 steel substrates by pulse electrodeposition, and numerical simulations for sliding wear resistance of coatings were forecasted using an ANN model. Results indicate that TiN grains in Ni– TiN coatings are large when the average current density is relatively low and the pulse interval is long. Compared with the wear resistance of 45 steel substrates, the composite coatings have relatively high wear resistance. The coating with TiN particle concentration of 8 g/l exhibits the best wear resistance. The average crystallite sizes for Ni and TiN in Ni– TiN coating are approximately 58 and 39 nm, respectively. The ANN model, which shows an error of approximately 4.2%, can effectively predict sliding wear resistance of Ni–TiN nanocomposite coatings. Acknowledgments
3.3. Microstructural analysis Fig. 7 illustrates XRD patterns of the TiN powder and Ni– TiN coating, which reveal the presence of TiN in the Ni–TiN coating. For Ni, the diffraction peaks at 44.821, 52.211 and 76.771 correspond to (111), (200) and (220). For TiN, the diffraction peaks at 36.661, 42.601 and 61.811 correspond to (111), (200) and (220). According to the XRD data, the average crystallite size for Ni and TiN calculated using Eq. (1) is approximately 58 nm and 39 nm, respectively.
The authors gratefully acknowledge the National Natural Science Foundation of China (Grant no. 51101027) and Scientific Research Fund of Zhejiang Provincial Education Department (Grant no. Y201329715). References [1] H. Schmidt, S. Langenfeld, R. Naß, A new corrosion protection coating system for pressure-cast aluminium automotive parts, Mater. Des. 18 (4– 6) (1997) 309–313.
Please cite this article as: X. Li, et al., Application of artificial neural networks to predict sliding wear resistance of Ni–TiN nanocomposite coatings deposited by pulse electrodeposition, Ceramics International (2014), http://dx.doi.org/10.1016/j.ceramint.2014.04.005
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[2] R.I. Amaro, R.C. Martins, J.O. Seabra, N.M. Renevier, D.G. Teer, Molybdenum disulphide/titanium low friction coating for gears application, Tribol. Int. 38 (4) (2005) 423–434. [3] A. Naci Tankut, N. Tankut, Investigations the effects of fastener, glue, and composite material types on the strength of corner joints in case-type furniture construction, Mater. Des. 30 (10) (2009) 4175–4182. [4] F.F. Xia, H.B. Xu, C. Liu, J.W. Wang, J.J. Ding, C.H. Ma, Microstructures of Ni–AlN composite coatings prepared by pulse electrodeposition technology, Appl. Surf. Sci. 271 (2013) 7–11. [5] L. Bebea, P. Ponthiaux, F. Wenger, Co–ZrO2 electrodeposited composite coatings exhibiting improved micro hardness and corrosion behavior in simulating body fluid solution, Surf. Coat. Technol. 205 (2011) 5379–5386. [6] F. Xia, M. Wu, F. Wang, Z. Jia, A. Wang, Nanocomposite Ni–TiN coatings prepared by ultrasonic electrodeposition, Curr. Appl. Phys. 9 (2009) 44–47. [7] G. Parida, D. Chaira, M. Chopkar, A. Basu, Synthesis and characterization of Ni–TiO2 composite coatings by electro-co-deposition, Surf. Coat. Technol. 205 (2011) 4871–4879.
[8] M.H. Wu, N.N. Liu, Z. Li, Effects of electrodeposition process parameters on particle contents of Ni–TiN–CeO2 composite coating, J. Funct. Mater. 43 (19) (2012) 2657–2665. [9] X.B. Zhu, C. Cai, G.Q. Zheng, Z. Zhang, J.F. Li, Electrodeposition and corrosion behavior of nanostructured Ni–TiN composite films, Trans. Nonferr. Met. Soc. China 21 (2011) 2216–2224. [10] W. Wang, S.Q. Qian, X.Y. Zhou, Microstructure and properties of TiN/ Ni composite coating prepared by plasma transferred arc scanning process, Trans. Nonferr. Met. Soc. China 19 (2009) 1180–1184. [11] A. Shafyei, S.H. Mousavi Anijdan, A. Bahrami, Prediction of porosity percent in Al–Si casting alloys using ANN, Mater. Sci. Eng. A 431 (1–2) (2006) 206–210. [12] A.M. Rashidi, A.R. Eivani, A. Amadeh, Application of artificial neural networks to predict the grain size of nano-crystalline nickel coatings, Comput. Mater. Sci. 45 (2009) 499–504.
Please cite this article as: X. Li, et al., Application of artificial neural networks to predict sliding wear resistance of Ni–TiN nanocomposite coatings deposited by pulse electrodeposition, Ceramics International (2014), http://dx.doi.org/10.1016/j.ceramint.2014.04.005