Friction moment prediction of HVOF coatings and Electroplated Hard Chromium

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Materials Letters 62 (2008) 473 – 477 www.elsevier.com/locate/matlet

Friction moment prediction of HVOF coatings and Electroplated Hard Chromium T. Sahraoui a , S. Guessasma b,⁎, N.E. Fenineche c a

b

LTSM, Département de Mécanique, Université de Blida, BP 270 route de Soumaâ, Blida 09000, Algeria Unité de Recherche Biopolymères, Interactions, Assemblages, INRA, BP 71627-44316, Nantes Cedex 3, France c LERMPS, Université de Technologie de Belfort-Montbéliard, Sévenans 90010, France Received 10 January 2007; accepted 28 May 2007 Available online 8 June 2007

Abstract Wear is one of the most critical problems in mechanics, oil, and gas industries among other industrial fields. Electrolytic Hard Chromium (EHC) and HVOF thermal spraying for applications of various materials have proved to be effective against wear. In this study, the results of wear tests for WC–12Co, Cr3C2–25NiCr, Tribaloy©-400, and EHC are predicted and compared using an artificial intelligence methodology. It has been shown that predicted wear analysis permits to determine friction moment for different loading conditions. The best candidate seems to be WC– 12Co for which worn surface reveals prevailing adhesion mechanism responsible for the lowering of the friction moment. © 2007 Elsevier B.V. All rights reserved. Keywords: Thermal spray; Friction moment; Artificial Neural Network

1. Introduction In oil and gas industries main problems exist related to tribological properties of the used materials: friction and wear of the equipment, for example, gas turbine shafts and bearings [1]. Electrolytic Hard Chromium is used by reduction of hexavalent ions (Cr6+) on new and refurbished parts as a protective coating [2]. However, its use is about to decrease due to the toxic and carcinogenic characteristics of hexavalent chromium. The increasing environmental protection and worker safety measures are causing companies in many fields to adopt alternatives. One potential alternative to EHC is the use of wear resistant materials such as HVOF coatings, which are more environmentally friendly and effective than EHC [3]. These enable drastic improvements in the in-service behavior of the component by improving surface characteristics through high hardness coupled with excellent cohesion and adhesion and very dense structure of the coatings [4,5].

⁎ Corresponding author. Tel.: +33 2 40 67 50 36; fax: +33 2 40 67 51 67. E-mail address: [email protected] (S. Guessasma). 0167-577X/$ - see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.matlet.2007.05.083

The aim of this study is to compare and to predict the wear resistance of HVOF sprayed coatings and EHC deposits by using Artificial Neural Network (ANN). This statistical technique is used to relate friction moment variation to wear test parameters, and has been shown previously to be an efficient methodology for wear test analysis [6,7]. 2. Experimental procedure In this work, four different coatings are deposited onto AFNOR 25CD4 low carbon steel substrates to have their tribological properties evaluated and compared. The feed stocks are WC–12%Co (Amdry 1301), Cr3C2–25%NiCr (Amdry 5260) and Tribaloy©-400 deposited by thermal spraying, and Electroplated Hard Chromium (EHC). High Velocity Oxy-Fuel (HVOF) coatings are all deposited using a METCO CDS equipment. The gas flow rates are 420, 180 and 20 SLPM for oxygen, methane gas (fuel) and powder carrier, respectively. The powder feed rate is 52 g min− 1. The spray distance is fixed to 300 mm with a gun nozzle diameter of 3 in. EHC coatings are deposited by a private company specialized in this field. Wear tests are performed using an Amsler machine, Fig. 1. Counter samples are brass discs of 50 mm diameter. All

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Fig. 1. Amsler wear test.

sample surfaces are ground prior to testing. The loads used are 240 N, 490 N and 735 N and the radial velocities are V1 = 0.52 m s− 1 for the coated samples and V2 = 0.47 m s− 1 for the brass disc. In order to study and to compare the wear behaviors of coatings in severe conditions, the wear tests are carried out without any lubrication. 3. Neural computation The methodology of Artificial Neural Network (ANN) technique is almost identical to a previous study dealing with Pin-On-Disk test [6]. In this study, 3 inputs are considered: - material type which is a classification variable with 4 states (Tribaloy-400, Cr3C2–25%NiCr,WC–12%Co, EHC); - sliding load varied in a large range (i.e., from 250 to 1000 N); - sliding distance: (range: 0–3000 m). This last parameter encodes wear mechanism and is an important factor for the assessment of friction moment variability. Thus, it should not be omitted from input variables [6].

A multi-layer perception (i.e., a normal feed forward) is considered with 2 hidden layers. In such a structure, input parameters feed the ANN without further preprocessing Oi ðXi Þ ¼ Xi

ð3Þ

where O() is the output O function () of neuron i. Whereas in the two hidden layers, each neuron input is related to its output as follows Oi ðIi Þ ¼

1 1 þ expðIi Þ

ð4Þ

where Ii and Oi are the input and output values of a given neuron, respectively. In Eq. (4), the sigmoid transformation of the neuron input is required in order to represent the non-linear causal correlations between the inputs and output of the problem. In the feed-forward scheme, neurons of a given layer are related to all neurons of the forward layer using connections called weights. The weights are real numbers translating, in some way, the strength of the connections between neurons. Ii ¼ wij Oj

The output parameter is the friction moment. Inputs and output are formatted in different ways Xi ¼

xi  ximin i ¼ 1; 2 ximax  ximin

y ¼ ðymax  ymin ÞTY þ ymin

ð1Þ

ð5Þ

where Ii is the input of neuron i in the forward layer, Oj is the output of neuron j in the backward layer. wij is the weight value associated to the connection between neuron i and neuron j. Weight values are not known and an initial guess is performed by using a random number generator. Weight configuration is

ð2Þ

where Eq. (1) scales the input values (load and sliding distance) between 0 and 1 whereas Eq. (2) sets the final value of the output. Xi is the input formatted value of parameter i in the input layer. ximax and ximin are the maximum and minimum values associated to parameter i (Table 1). Y is the output formatted value associated to the friction moment parameter y. ymin and ymax are the minimum and maximum values belonging to y (Table 1). Material type is introduced as a discrete variable with 4 different threshold states encoded by dividing the formatted input range (0–1) into 4 identical sub-ranges.

Table 1 Limits of the input/output variables Inputs (i) 1 2 3

xmin Sliding distance (m) Applied load (N) Material type

xmax

0 3000 250 1000 Tribaloy-400, Cr 3 C 2 – 25%NiCr, WC–12%Co, EHC

Output

ymin

ymax

Friction moment (N m)

0

1

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Table 2 Predicted and experimentally determined friction moments of several coatings vs the applied load relative to Amsler test after a sliding distance of 2500 m Material type

Load (N)

Friction moment (N m) Experimental

Predicted

Cr3C2–25NiCr

245 490 735 245 490 735 245 490 735 245 490 735

0.25 0.21 0.27 0.15 0.19 0.24 0.3 0.41 0.37 0.12 0.19 0.27

0.243 0.211 0.256 0.115 0.175 0.212 0.283 0.390 0.356 0.106 0.140 0.216

WC–12Co

Tribaloy©-400

EHC

Scatter

3 0 5 23 8 12 6 5 4 12 26 20

then optimized based on a quick propagation algorithm in which the decrease of the error between the predicted and experimental values is a function of the weight values [8]. DwðÞt ¼ a

j J ðÞt j J ðÞ

t1

 j Jy ðÞ

t

 DwðÞt1

ð6Þ

where Eq. (6) gives the update of any weight in the ANN knowing the error gradient ▽J(). In Eq. (6) the weight update at iteration t is sensitive to the previous update at iteration t − 1 and to the learning rate α which is less than 1. The gradient quantity ▿J() is expressed as the first derivative with respect to the weight values of the quadratic difference between the computed outputs and the real ones. In the case of the output layer, the gradient is expressed as AJ jJ ¼ ð7Þ Aw 1 J ¼ ðyr  yÞ2 ð8Þ 2

Fig. 3. Predicted and experimental friction moment evolution as function of sliding distance in the case of Tribaloy©-400.

The database needed for ANN optimization is divided into two distinct categories: training and test categories. The first one is used to update weights (connections) in the net structure and the second one is used to test the predicted response of the network. In order to build the database, sampling of wear curves is performed, which permitted up to 40 points per test condition to be obtained. Thus, the total sample number is 480 cases. The sampling allows that the ANN learns only from the half part of the database. The other part is used to ensure that ANN predictions are not far from those experimental sets that are not used for training. Two main criteria are monitored during the optimization, one for training and one for testing. ETrn ¼

1 ðyr  yðW ÞÞ2 NPtr i

i ¼ 1; NPtr

ETst ¼

1 ðyr  yðW ÞÞ2 NPtst i

i ¼ 1; NPtst

ð9Þ ð10Þ

where Etrn and Etst are the average training and test errors associated to the experimental sets NPtr and NPtst, respectively.

where yr is the real output associated to the experimental set. J is the cost function or the error function at the output layer. The half value in J is set for derivative purposes. Neuron number in both hidden layers is optimized based on the residual training and testing errors [9].

Fig. 2. Evolution of the coating weight loss vs the applied load.

Fig. 4. Predicted moment evolution vs applied load for the studied materials.

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Fig. 5. SEM micrograph of WC–12Co material worn surface offering the best performance among sprayed coatings (applied load is 410 N).

Training and testing are performed for an optimized computation duration of 2000 cycles, required to stabilize both training and test errors. The optimized network structure comprised of 6 neurons in the first hidden layer and 6 neurons in the second hidden layer. The overall residual error is 0.029 with a ratio of classification (samples for which output error is under 5%) of 100%. Table 2 compares experimental and predicted friction moments for the considered wear test conditions. Average error is found to be less than 10% with a maximum scatter of 26%. Such scatter is mainly attributed to high fluctuations in friction moment during sliding. 4. Results and discussion The wear tests performed on the HVOF sprayed coatings and the EHC deposits using Amsler machine produced relatively high initial weight loss value and a temporary increase of the friction moment [1]. At this stage, wear is not considered to be representative of the actual wear resistance of the bulk material [1]. Fig. 2 shows the evolution of the weight loss of the coated samples vs the applied load. The worst candidate seems to be Tribaloy where the weight loss increases significantly from 0.03 g to 0.06 g when increasing load value. In the same load range, WC–12Co material exhibits the lowest loss (less than 0.004 g). Fig. 3 shows an example of predicted and experimental evolutions of the Tribaloy friction moment up to a sliding distance of 2500 m with a fixed applied load of 245 N. Predicted curves are obtained by varying the sliding distance for the selected material under the load corresponding to the test condition. These curves are characterized by a grinding stage correlated by a temporary increase of the friction moment due to the high friction at the mating surfaces. A stability stage is reached thereafter, because of the reduction in friction (i.e., decrease of the moment variation rate) of the new mating polished surfaces. It appears when comparing the value of friction moment for the considered materials that wear is most significant in the case of Tribaloy-400, Cr3C2–25%NiCr, WC–12%Co and EHC, respectively. Both predicted and experimental evolutions agree in describing such trend. However, the predicted curves are smoother than the experimental ones because of the ability of the neural network to average friction moment fluctuations. Thus, it permits to distinguish more clearly friction

moment differences. The generalisation of the ANN predictions allow to predict the friction moment for combinations that are not present in the original database as can be seen in Fig. 4 where the predicted evolutions of the friction moment vs applied load for the four materials are reported. Such curves are created by varying the applied load at the network input continuously from 250 to 750 N. These evolutions can be explained by the high increase of friction phenomena when increasing the load. This trend is confirmed by several experimental works [10], where the difference of friction moment of these materials is due to the performances and high hardness of WC–12Co and Cr3C2–25NiCr HVOF sprayed coatings compared to Tribaloy-400. Wear mechanisms depend on the nature of each material. In the case of cermet based carbides, wear resistance is attributed to a complex function of carbide size, distribution, matrix hardness and toughness, and the solution of carbon in the metallic matrix [11,12]. For all the coatings examined, SEM observations of the worn surfaces indicated that worn surfaces of coatings show a continuous transferred brass film formed on the coated surfaces. Thus, the wear is taking place mainly by an adhesion mechanism (Fig. 5) and is enhanced by a severe delamination of the surface in case of Tribaloy©-400 and EHC and by particle pull-out in the case of cermet coatings [4]. This surface damage is related to the hardness or the ductility of the materials.

5. Summary The results of predictive and comparative studies on the friction and the wear behavior of EHC and HVOF thermal sprayed WC–12Co, Cr3C2–25NiCr and T-400 coatings have been presented. The methodology of Artificial Neural Network permitted the prediction of the moment friction evolution for the studied materials. Both predicted and experimental evolutions of friction moment agree in describing and comparing wear mechanisms of the considered materials. It has been established that HVOF coatings exhibit adequate properties for wear resistance, in spite of the unlubricated conditions and the extreme loading conditions. The best candidate is WC–12Co which exhibits the best wear performance from the selected sprayed materials.

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