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Optimization of coating variables for hardness of industrial tools by using artificial neural networks
Expert Systems with Applications 38 (2011) 12116–12127
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Optimization of coating variables for hardness of industrial tools by using artificial neural networks M. Reza Soleymany Yazdi a, A. Mahyar Khorasani b,⇑, Mehdi Faraji b a b
Mech. Eng. Dept., Imam Hussein University, Tehran, Iran Faculty of Hi-tech and Eng., IUIM, Tehran, Iran
a r t i c l e
i n f o
Keywords: Artificial neural networks Optimization of coating Hardening CVD and PVD approach Multi layer perceptron
a b s t r a c t Thin-film coating plays a prominent role on the manufacture of many industrial devices. Coating can increase material performance due to the deposition process. This paper proposes the estimation of hardness of titanium thin-film layers as protective industrial tools by using multi layer perceptron (MLP) neural network. Based on the experimental data obtained during the process of chemical vapor deposition (CVD) and physical vapor deposition (PVD), the optimization of the coating variables for achieving the maximum hardness of titanium thin-film layers, is performed. Then, the obtained results are experimentally verified. During titanium coating, improvements of up to 16.75% of the layers hardness are accessible. Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction CVD/PVD coating or ion-injection processes have become one of the most important and significant key-technologies to protect the surface of mechanical or electric devices against various wear mechanisms and to prolong the life-time. Titanium or some ceramic materials due to their significant hardness are the typical tool materials to be used as thin layer for die materials in the metal forming (Aizawa & Kihara, 1992; Prengel, Jindal, & Wendt, 2001; Polini et al., 2006; Ranea, 2002). For the proper use of these materials and other related materials in coating process, the optimization of coating variables is indispensable. Various methods for the prediction of maximum hardness of titanium thin film layers have been proposed. They were not universally successful due to the complex spontaneous of the coating processes. However, the applied indirect methods suffered from the fact that not only the coated but other process parameters also influence the measurement results (Assis & Costa, 2007; Lima and coworkers, 2006, Quirynen, Bollen, Willems, & van Steenberghe, 1994; Singh, Grover, Totlani, & Suri, 2001; Speelman, Collaert, & Klinge, 1992). Alger et al. (Donald & Steinberg, 1972) utilized an X-ray device that was capable of measuring titanium film thickness from 0.1 to 30 lm. Photocatalytic measurements of titanium dioxide films deposited with metal organic decomposition have been used by Marko et al. (1997). Also, ellipsometry has been introduced as a
⇑ Corresponding author. Tel.: +98 02188032724; fax: +98 02188032725. E-mail addresses: [email protected] (M.R.S. Yazdi), khorasanimahyar@ yahoo.com (A.M. Khorasani), [email protected] (M. Faraji). 0957-4174/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2011.03.002
precise technique for measuring thin film properties by some researchers (Morton et al., 2004; Roth, 2000; Woollam, 1999). Furthermore, ultrasonic nondestructive testing has been presented as a useful method for characterization of microstructures, assessment of defects, and evaluation of material properties (Buitrago, Irausquín, & Mendoza, 2007; Kumar, Jayakumar, Raj, & Ray, 2003; Kumar, Laha, Jayakumar, Rao, & Raj, 2002). In recent years, Wincheski (2008) used eddy current impedance plane instrument with custom wound eddy current sensors to measure the thickness and hardness of material. Although these methods are capable of measuring titanium thin-film hardness, but the major problem arises when the maximum hardness has to be characterized. In this paper, the titanium alloys such as TiN,1 TiC2 and TiC–N,3 using different pressure and temperature conditions, were deposited on 192 specimens of high speed cutting tools. Then, the hardness of deposited thin layers was measured. Based on acquired experimental data, multi layer perceptron (MLP) neural networks have been used for process modeling and achieving optimal coating parameters for maximum hardness of protective layers. Then, the obtain results are experimentally validated. 2. Experimentations To find out the hardness and corrosion of parts coated with TiC–N, TiN and TiC material and to obtain optimal coating
1 2 3
Titanium Nitride. Titanium Carbide. Titanium Carbide Nitride.
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Fig. 1. (Right) PVD coating samples, (Left) CVD coating samples.
Fig. 4. Schematic of PVD coating.
Table 1 Measured hardness of coated layers. Hardness of the rectangular sample, (Vickers)
Hardness of the circular samples (Vickers)
Sublayer roughness
Material type
490
450
TiN
650 565
630 470
770 810
740 790
900
830
Nonpolished polished Nonpolished polished Nonpolished polished
Fig. 2. Schematic of CVD coating.
Fig. 3. Coated layers before and after corrosion test.
Fig. 5. Proposed perceptron neural networks.
TiC
TiC–N
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Fig. 6. RMS error.
parameters, a large number of specimens including two groups of circular and rectangular samples have been prepared. Using several tests which not presented in this paper, the values of coating parameters are chosen. So, during coating process, the methane gas with a temperature of 700°–1100° has been injected to the layers. Voltage and coating time are selected 65 V and 30 min, respectively. In all experiments, ion bombarding time and sublayer temperature were 20 min and 150° respectively. In first group, the circular steel sublayers with a diameter and thickness of 20 and 4 mm, respectively (see Fig. 1), have been used. Using CVD method in form of rotational sampling is applied. Specimens have been deposited by using methane gas for TiC layers, nitrogen gas for TiN layers, and titanium tetra chlorine gas for TiC–N layers. The coating process is illustrated in Fig. 2.
In second group, the rectangular steel sublayers with two different sizes of 75 25 2 mm and 50 25 2 mm were used (see Fig. 3). PVD approach has been used for coating of rectangular samples by using nitrogen and methane gas. Fig. 4 schematically illustrates the PVD approach. During each coating steps, a glass sublayer under same conditions was prepared for identifying the crystalline phases and measuring thickness of the coated layers. XRD analysis using Tolanski approach was applied. For whole specimens, the obtained thickness of layer was about 4 microns. The hardness of 192 specimens in both groups was measured using Micro Hardening Measurer Instrument with 500 magnifications. The results are shown in Table A-1 (see Appendix A). Furthermore, to study the influence of sublayer roughness on
Fig. 7. Correlation and curve fitting analyses.
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Fig. 8. Correlation before optimization (in TiC coating).
hardness of thin layers, two groups of parts involving the polished and non polished surfaces have been prepared and deposited by Using TiN, TiC and TiC–N materials. The results are shown in Table 1. Corrosion and erosion testing (i.e. high speed test) of all specimens have been carried out under various conditions (i.e. time, pressure and speed changing). Fig. 3 illustrates the qualitative comparison of layers corrosion.
3. Modeling of the titanium coating process Artificial neural networks (ANN) have emerged as a well known solution for tackling pattern recognition, classification and optimization tasks (Devijver & Kittler, 1982; Karayel, 2009; Zain, Haron, & Sharif, 2010).
In this section, based on the experimental data, the coating process modeling for hardness of titanium thin layers is carried out by using MLP neural networks. MLP neural network is one of the most popular supervised artificial neural networks which have the ability to save nonlinear problems. 156 exemplars have been used for the off-line training and performance checking of the proposed model. As shown in Fig. 5, a three-layer MLP is used, including 6 inputs (i.e. ion bombard time, sublayer roughness, material type, sublayer temperature, work and chamber pressure), 2 hidden layers containing 12 neurons, and an output layer with a single neuron (i.e. hardness of thin layer), 6 6 6 1. Hidden layers must have equitable covering rate on learning data and number of the neurons on hidden layers must be optimized (Mursec, Zuperl, Cus, & Ploj, 2004; Zuperi & Cus, 2003). Therefore, the best architecture and parameters of the MLP model are chosen through several tests
Fig. 9. Correlations after optimization (in TiC coating).
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Fig. 10. RMS error before optimization (in TiC coating).
which are not presented in this paper. Sigmoid and Hyperbolic Scant transfer functions have been applied for neurons of hidden and output layers, respectively. The obtained Root Mean Squared (RMS) error and correlation between the estimated and experimental data were 0.034999 and 0.982426, respectively (cf. Figs. 6 and 7). As shown in Figs. 6 and 7, the proposed MLP neural network has provided proper modeling results. Indeed, this method can be reliably and successfully used for modeling of the titanium coating process.
Shakeri, & Abadi, 2007; Sori, Narimani, & Rohani, 2007). Based on the experimental data, MLP neural networks were used to found out the correlation between the coating parameters and the hardness of each TiN, TiC and TiC–N thin layers. Then, the optimal values of effective parameters for maximizing the hardness of thin layers were selected. Due to positive effect of the polished sublayer on the hardness of coated thin layers, the type of surface roughness and materials have been considered to be constant. Therefore, four inputs including the ion bombarding time, work and chamber pressure and temperature of sublayers have been selected.
4. Optimization of the coating parameters 4.1. TiC coating case To obtain the maximum hardness of the coated thin layer, under some constraints, the optimal coating parameters are estimated by using the proposed statistical approach (Abaspor,
Figs. 8 and 9 illustrate correlations between the experimental and estimated outputs (i.e. hardness of thin layers) using non-opti-
Fig. 11. RMS error after optimization (in TiC coating).
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Fig. 12. Correlations before optimization (in TiN coating).
Fig. 13. Correlations after optimization (in TiN coating).
Fig. 14. RMS error before optimization (in TiN coating).
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mal and optimal coating parameters, respectively. RMS errors of outputs before and after optimization of coating parameters have been shown respectively in Figs. 10 and 11. As shown in Figs. 8–11, after using optimal coating parameters, correlations and RMS errors of outputs have been improved by 2% and 40%, respectively.
Figs. 14 and 15 illustrate RMS errors of the estimated hardness, respectively before and after optimization of coating parameters. As shown in Figs. 12–15, after using optimal coating parameters, the correlations and RMS errors of outputs have been improved by 1% and 4% respectively.
4.2. TiN coating case
4.3. TiC–N coating case
Correlations between the experimental and estimated hardness of thin layers using non-optimal and optimal coating parameters have been demonstrated respectively in Figs. 12 and 13.
Figs. 16 and 17 show correlations between the experimental and estimated outputs respectively, using non-optimal and optimal coating parameters. RMS errors of outputs before and after
Fig. 15. RMS error after optimization (in TiN coating).
Fig. 16. Correlations before optimization (in TiC–N coating).
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Fig. 17. Correlations after optimization (in TiC–N coating).
Fig. 18. RMS error before optimization (in TiC–N coating).
optimization of coating parameters have been illustrated respectively in Figs. 18 and 19. After using optimal coating parameters, correlations and RMS errors of outputs have been improved by 11% and 95%, respectively (cf. Figs. 16–19).
6. Conclusions
5. Experimental validation and discussion
The use of neural networks for estimation of the maximum hardness of titanium thin film was investigated. The statistical approach is applied to optimize the coating parameters in coating of TiC, TiN, and TiC–N materials. Then, the obtained results have been experimentally validated. The results can be concluded as:
To verify the accuracy of the obtained results, several experimental tests have been implemented. As shown in Table 2, the optimal coating parameters, not only have provided the maximum hardness of coated layers among the obtained initial experimental data, but also they have improved the hardness of coated layers of up to 2.3%.
1. The predicted results are in a proper agreement with the experimental data which illustrates the capability of the proposed neural models as a tool for accurate estimation of the hardness of coated layers. 2. The proposed statistical approach was successfully implemented to estimate the optimal coating parameters.
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Fig. 19. RMS error after optimization (in TiC–N coating).
Table 2 Experimental validation results. Martial type
TiN TiC TiC–N
Hardness of rectangular samples (Vickers) Before optimization
After optimization
650 770 900
665 780 915
Average improvement (%)
2.31 1.3 1.67
3. Using optimal coating parameters, not only have provided the maximum hardness of coated layers among the initial tests, but also have properly improved this variable.
Hardness of circular samples (Vickers) Before optimization
After optimization
2.31 740 830
640 750 840
Average improvement (%)
1.59 1.35 1.2
Appendix A See Table A-1.
Table A-1 Measured hardness of coated layers. Material type
Ion bombard time
Work pressure
Chamber pressure
Sub-layer temperature
Sub-layer roughness
Hardness of thin layer
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
20 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 20 17.5 15 20 17.5 15
0.056 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.056 0.046 0.036 0.056 0.046 0.036
0.003 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.003 0.025 0.002 0.003 0.025 0.002
300 290 280 270 260 250 240 230 220 210 200 190 180 170 160 150 300 290 280 270 260 250 240
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1
470 450 435 420 435 420 405 420 405 390 405 390 375 390 375 360 740 720 705 690 705 690 675
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M.R.S. Yazdi et al. / Expert Systems with Applications 38 (2011) 12116–12127 Table A-1 (continued) Material type
Ion bombard time
Work pressure
Chamber pressure
Sub-layer temperature
Sub-layer roughness
Hardness of thin layer
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
20 17.5 15 20 17.5 15 20 17.5 15 20 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 20
0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.056
0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.003
230 220 210 200 190 180 170 160 150 300 290 280 270 260 250 240 230 220 210 200 190 180 170 160 150 300 290 280 270 260 250 240 230 220 210 200 190 180 170 160 150 300 290 280 270 260 250 240 230 220 210 200 190 180 170 160 150 300 290 280 270 260 250 240 230 220 210 200 190 180 170 160 150 300 290
1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0
690 675 660 675 660 645 660 645 630 565 545 530 515 530 515 500 515 500 485 500 485 470 485 470 455 770 750 735 710 735 710 695 710 695 680 695 680 665 680 665 650 450 430 415 400 415 400 385 400 385 370 385 370 355 370 355 340 630 610 595 580 595 580 565 580 565 550 565 550 535 550 535 520 490 470 (continued on next page)
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Table A-1 (continued) Material type
Ion bombard time
Work pressure
Chamber pressure
Sub-layer temperature
Sub-layer roughness
Hardness of thin layer
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15
0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036
0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002
280 270 260 250 240 230 220 210 200 190 180 170 160 150 300 290 280 270 260 250 240 230 220 210 200 190 180 170 160 150 300 290 280 270 260 250 240 230 220 210 200 190 180 170 160 150 300 290 280 270 260 250 240 230 220 210 200 190 180 170 160 150 300 290 280 270 260 250 240 230 220 210 200 190 180
0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0
455 440 455 440 425 440 425 410 425 410 395 410 395 370 650 630 615 600 615 600 585 600 585 570 585 570 555 570 555 540 790 770 755 740 755 740 725 740 725 710 725 710 695 710 695 680 830 810 795 780 795 780 765 780 765 750 765 750 735 750 735 720 810 790 775 760 775 760 745 760 745 730 745 730 715
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M.R.S. Yazdi et al. / Expert Systems with Applications 38 (2011) 12116–12127 Table A-1 (continued) Material type
Ion bombard time
Work pressure
Chamber pressure
Sub-layer temperature
Sub-layer roughness
Hardness of thin layer
3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
20 17.5 15 20 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15
0.056 0.046 0.036 0.056 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036
0.003 0.025 0.002 0.003 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002
170 160 150 300 290 280 270 260 250 240 230 220 210 200 190 180 170 160 150
0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
730 715 700 900 880 865 850 865 850 835 850 835 820 835 820 805 820 805 790
For material type 1 illustrates TIC, 2 illustrates TIN and 3 shows TICN. For sub-layer roughness 0 demonstrates non polish and 1 illustrates polished.
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Contents lists available at ScienceDirect
Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa
Optimization of coating variables for hardness of industrial tools by using artificial neural networks M. Reza Soleymany Yazdi a, A. Mahyar Khorasani b,⇑, Mehdi Faraji b a b
Mech. Eng. Dept., Imam Hussein University, Tehran, Iran Faculty of Hi-tech and Eng., IUIM, Tehran, Iran
a r t i c l e
i n f o
Keywords: Artificial neural networks Optimization of coating Hardening CVD and PVD approach Multi layer perceptron
a b s t r a c t Thin-film coating plays a prominent role on the manufacture of many industrial devices. Coating can increase material performance due to the deposition process. This paper proposes the estimation of hardness of titanium thin-film layers as protective industrial tools by using multi layer perceptron (MLP) neural network. Based on the experimental data obtained during the process of chemical vapor deposition (CVD) and physical vapor deposition (PVD), the optimization of the coating variables for achieving the maximum hardness of titanium thin-film layers, is performed. Then, the obtained results are experimentally verified. During titanium coating, improvements of up to 16.75% of the layers hardness are accessible. Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction CVD/PVD coating or ion-injection processes have become one of the most important and significant key-technologies to protect the surface of mechanical or electric devices against various wear mechanisms and to prolong the life-time. Titanium or some ceramic materials due to their significant hardness are the typical tool materials to be used as thin layer for die materials in the metal forming (Aizawa & Kihara, 1992; Prengel, Jindal, & Wendt, 2001; Polini et al., 2006; Ranea, 2002). For the proper use of these materials and other related materials in coating process, the optimization of coating variables is indispensable. Various methods for the prediction of maximum hardness of titanium thin film layers have been proposed. They were not universally successful due to the complex spontaneous of the coating processes. However, the applied indirect methods suffered from the fact that not only the coated but other process parameters also influence the measurement results (Assis & Costa, 2007; Lima and coworkers, 2006, Quirynen, Bollen, Willems, & van Steenberghe, 1994; Singh, Grover, Totlani, & Suri, 2001; Speelman, Collaert, & Klinge, 1992). Alger et al. (Donald & Steinberg, 1972) utilized an X-ray device that was capable of measuring titanium film thickness from 0.1 to 30 lm. Photocatalytic measurements of titanium dioxide films deposited with metal organic decomposition have been used by Marko et al. (1997). Also, ellipsometry has been introduced as a
⇑ Corresponding author. Tel.: +98 02188032724; fax: +98 02188032725. E-mail addresses: [email protected] (M.R.S. Yazdi), khorasanimahyar@ yahoo.com (A.M. Khorasani), [email protected] (M. Faraji). 0957-4174/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2011.03.002
precise technique for measuring thin film properties by some researchers (Morton et al., 2004; Roth, 2000; Woollam, 1999). Furthermore, ultrasonic nondestructive testing has been presented as a useful method for characterization of microstructures, assessment of defects, and evaluation of material properties (Buitrago, Irausquín, & Mendoza, 2007; Kumar, Jayakumar, Raj, & Ray, 2003; Kumar, Laha, Jayakumar, Rao, & Raj, 2002). In recent years, Wincheski (2008) used eddy current impedance plane instrument with custom wound eddy current sensors to measure the thickness and hardness of material. Although these methods are capable of measuring titanium thin-film hardness, but the major problem arises when the maximum hardness has to be characterized. In this paper, the titanium alloys such as TiN,1 TiC2 and TiC–N,3 using different pressure and temperature conditions, were deposited on 192 specimens of high speed cutting tools. Then, the hardness of deposited thin layers was measured. Based on acquired experimental data, multi layer perceptron (MLP) neural networks have been used for process modeling and achieving optimal coating parameters for maximum hardness of protective layers. Then, the obtain results are experimentally validated. 2. Experimentations To find out the hardness and corrosion of parts coated with TiC–N, TiN and TiC material and to obtain optimal coating
1 2 3
Titanium Nitride. Titanium Carbide. Titanium Carbide Nitride.
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Fig. 1. (Right) PVD coating samples, (Left) CVD coating samples.
Fig. 4. Schematic of PVD coating.
Table 1 Measured hardness of coated layers. Hardness of the rectangular sample, (Vickers)
Hardness of the circular samples (Vickers)
Sublayer roughness
Material type
490
450
TiN
650 565
630 470
770 810
740 790
900
830
Nonpolished polished Nonpolished polished Nonpolished polished
Fig. 2. Schematic of CVD coating.
Fig. 3. Coated layers before and after corrosion test.
Fig. 5. Proposed perceptron neural networks.
TiC
TiC–N
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Fig. 6. RMS error.
parameters, a large number of specimens including two groups of circular and rectangular samples have been prepared. Using several tests which not presented in this paper, the values of coating parameters are chosen. So, during coating process, the methane gas with a temperature of 700°–1100° has been injected to the layers. Voltage and coating time are selected 65 V and 30 min, respectively. In all experiments, ion bombarding time and sublayer temperature were 20 min and 150° respectively. In first group, the circular steel sublayers with a diameter and thickness of 20 and 4 mm, respectively (see Fig. 1), have been used. Using CVD method in form of rotational sampling is applied. Specimens have been deposited by using methane gas for TiC layers, nitrogen gas for TiN layers, and titanium tetra chlorine gas for TiC–N layers. The coating process is illustrated in Fig. 2.
In second group, the rectangular steel sublayers with two different sizes of 75 25 2 mm and 50 25 2 mm were used (see Fig. 3). PVD approach has been used for coating of rectangular samples by using nitrogen and methane gas. Fig. 4 schematically illustrates the PVD approach. During each coating steps, a glass sublayer under same conditions was prepared for identifying the crystalline phases and measuring thickness of the coated layers. XRD analysis using Tolanski approach was applied. For whole specimens, the obtained thickness of layer was about 4 microns. The hardness of 192 specimens in both groups was measured using Micro Hardening Measurer Instrument with 500 magnifications. The results are shown in Table A-1 (see Appendix A). Furthermore, to study the influence of sublayer roughness on
Fig. 7. Correlation and curve fitting analyses.
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Fig. 8. Correlation before optimization (in TiC coating).
hardness of thin layers, two groups of parts involving the polished and non polished surfaces have been prepared and deposited by Using TiN, TiC and TiC–N materials. The results are shown in Table 1. Corrosion and erosion testing (i.e. high speed test) of all specimens have been carried out under various conditions (i.e. time, pressure and speed changing). Fig. 3 illustrates the qualitative comparison of layers corrosion.
3. Modeling of the titanium coating process Artificial neural networks (ANN) have emerged as a well known solution for tackling pattern recognition, classification and optimization tasks (Devijver & Kittler, 1982; Karayel, 2009; Zain, Haron, & Sharif, 2010).
In this section, based on the experimental data, the coating process modeling for hardness of titanium thin layers is carried out by using MLP neural networks. MLP neural network is one of the most popular supervised artificial neural networks which have the ability to save nonlinear problems. 156 exemplars have been used for the off-line training and performance checking of the proposed model. As shown in Fig. 5, a three-layer MLP is used, including 6 inputs (i.e. ion bombard time, sublayer roughness, material type, sublayer temperature, work and chamber pressure), 2 hidden layers containing 12 neurons, and an output layer with a single neuron (i.e. hardness of thin layer), 6 6 6 1. Hidden layers must have equitable covering rate on learning data and number of the neurons on hidden layers must be optimized (Mursec, Zuperl, Cus, & Ploj, 2004; Zuperi & Cus, 2003). Therefore, the best architecture and parameters of the MLP model are chosen through several tests
Fig. 9. Correlations after optimization (in TiC coating).
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Fig. 10. RMS error before optimization (in TiC coating).
which are not presented in this paper. Sigmoid and Hyperbolic Scant transfer functions have been applied for neurons of hidden and output layers, respectively. The obtained Root Mean Squared (RMS) error and correlation between the estimated and experimental data were 0.034999 and 0.982426, respectively (cf. Figs. 6 and 7). As shown in Figs. 6 and 7, the proposed MLP neural network has provided proper modeling results. Indeed, this method can be reliably and successfully used for modeling of the titanium coating process.
Shakeri, & Abadi, 2007; Sori, Narimani, & Rohani, 2007). Based on the experimental data, MLP neural networks were used to found out the correlation between the coating parameters and the hardness of each TiN, TiC and TiC–N thin layers. Then, the optimal values of effective parameters for maximizing the hardness of thin layers were selected. Due to positive effect of the polished sublayer on the hardness of coated thin layers, the type of surface roughness and materials have been considered to be constant. Therefore, four inputs including the ion bombarding time, work and chamber pressure and temperature of sublayers have been selected.
4. Optimization of the coating parameters 4.1. TiC coating case To obtain the maximum hardness of the coated thin layer, under some constraints, the optimal coating parameters are estimated by using the proposed statistical approach (Abaspor,
Figs. 8 and 9 illustrate correlations between the experimental and estimated outputs (i.e. hardness of thin layers) using non-opti-
Fig. 11. RMS error after optimization (in TiC coating).
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Fig. 12. Correlations before optimization (in TiN coating).
Fig. 13. Correlations after optimization (in TiN coating).
Fig. 14. RMS error before optimization (in TiN coating).
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mal and optimal coating parameters, respectively. RMS errors of outputs before and after optimization of coating parameters have been shown respectively in Figs. 10 and 11. As shown in Figs. 8–11, after using optimal coating parameters, correlations and RMS errors of outputs have been improved by 2% and 40%, respectively.
Figs. 14 and 15 illustrate RMS errors of the estimated hardness, respectively before and after optimization of coating parameters. As shown in Figs. 12–15, after using optimal coating parameters, the correlations and RMS errors of outputs have been improved by 1% and 4% respectively.
4.2. TiN coating case
4.3. TiC–N coating case
Correlations between the experimental and estimated hardness of thin layers using non-optimal and optimal coating parameters have been demonstrated respectively in Figs. 12 and 13.
Figs. 16 and 17 show correlations between the experimental and estimated outputs respectively, using non-optimal and optimal coating parameters. RMS errors of outputs before and after
Fig. 15. RMS error after optimization (in TiN coating).
Fig. 16. Correlations before optimization (in TiC–N coating).
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Fig. 17. Correlations after optimization (in TiC–N coating).
Fig. 18. RMS error before optimization (in TiC–N coating).
optimization of coating parameters have been illustrated respectively in Figs. 18 and 19. After using optimal coating parameters, correlations and RMS errors of outputs have been improved by 11% and 95%, respectively (cf. Figs. 16–19).
6. Conclusions
5. Experimental validation and discussion
The use of neural networks for estimation of the maximum hardness of titanium thin film was investigated. The statistical approach is applied to optimize the coating parameters in coating of TiC, TiN, and TiC–N materials. Then, the obtained results have been experimentally validated. The results can be concluded as:
To verify the accuracy of the obtained results, several experimental tests have been implemented. As shown in Table 2, the optimal coating parameters, not only have provided the maximum hardness of coated layers among the obtained initial experimental data, but also they have improved the hardness of coated layers of up to 2.3%.
1. The predicted results are in a proper agreement with the experimental data which illustrates the capability of the proposed neural models as a tool for accurate estimation of the hardness of coated layers. 2. The proposed statistical approach was successfully implemented to estimate the optimal coating parameters.
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Fig. 19. RMS error after optimization (in TiC–N coating).
Table 2 Experimental validation results. Martial type
TiN TiC TiC–N
Hardness of rectangular samples (Vickers) Before optimization
After optimization
650 770 900
665 780 915
Average improvement (%)
2.31 1.3 1.67
3. Using optimal coating parameters, not only have provided the maximum hardness of coated layers among the initial tests, but also have properly improved this variable.
Hardness of circular samples (Vickers) Before optimization
After optimization
2.31 740 830
640 750 840
Average improvement (%)
1.59 1.35 1.2
Appendix A See Table A-1.
Table A-1 Measured hardness of coated layers. Material type
Ion bombard time
Work pressure
Chamber pressure
Sub-layer temperature
Sub-layer roughness
Hardness of thin layer
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
20 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 20 17.5 15 20 17.5 15
0.056 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.056 0.046 0.036 0.056 0.046 0.036
0.003 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.003 0.025 0.002 0.003 0.025 0.002
300 290 280 270 260 250 240 230 220 210 200 190 180 170 160 150 300 290 280 270 260 250 240
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1
470 450 435 420 435 420 405 420 405 390 405 390 375 390 375 360 740 720 705 690 705 690 675
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Ion bombard time
Work pressure
Chamber pressure
Sub-layer temperature
Sub-layer roughness
Hardness of thin layer
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
20 17.5 15 20 17.5 15 20 17.5 15 20 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 20
0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.056
0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.003
230 220 210 200 190 180 170 160 150 300 290 280 270 260 250 240 230 220 210 200 190 180 170 160 150 300 290 280 270 260 250 240 230 220 210 200 190 180 170 160 150 300 290 280 270 260 250 240 230 220 210 200 190 180 170 160 150 300 290 280 270 260 250 240 230 220 210 200 190 180 170 160 150 300 290
1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0
690 675 660 675 660 645 660 645 630 565 545 530 515 530 515 500 515 500 485 500 485 470 485 470 455 770 750 735 710 735 710 695 710 695 680 695 680 665 680 665 650 450 430 415 400 415 400 385 400 385 370 385 370 355 370 355 340 630 610 595 580 595 580 565 580 565 550 565 550 535 550 535 520 490 470 (continued on next page)
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Table A-1 (continued) Material type
Ion bombard time
Work pressure
Chamber pressure
Sub-layer temperature
Sub-layer roughness
Hardness of thin layer
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15
0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036
0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002
280 270 260 250 240 230 220 210 200 190 180 170 160 150 300 290 280 270 260 250 240 230 220 210 200 190 180 170 160 150 300 290 280 270 260 250 240 230 220 210 200 190 180 170 160 150 300 290 280 270 260 250 240 230 220 210 200 190 180 170 160 150 300 290 280 270 260 250 240 230 220 210 200 190 180
0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0
455 440 455 440 425 440 425 410 425 410 395 410 395 370 650 630 615 600 615 600 585 600 585 570 585 570 555 570 555 540 790 770 755 740 755 740 725 740 725 710 725 710 695 710 695 680 830 810 795 780 795 780 765 780 765 750 765 750 735 750 735 720 810 790 775 760 775 760 745 760 745 730 745 730 715
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Ion bombard time
Work pressure
Chamber pressure
Sub-layer temperature
Sub-layer roughness
Hardness of thin layer
3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
20 17.5 15 20 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15 20 17.5 15
0.056 0.046 0.036 0.056 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036 0.056 0.046 0.036
0.003 0.025 0.002 0.003 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002 0.003 0.025 0.002
170 160 150 300 290 280 270 260 250 240 230 220 210 200 190 180 170 160 150
0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
730 715 700 900 880 865 850 865 850 835 850 835 820 835 820 805 820 805 790
For material type 1 illustrates TIC, 2 illustrates TIN and 3 shows TICN. For sub-layer roughness 0 demonstrates non polish and 1 illustrates polished.
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