Vol. 24 No. 3 2022 3 EDITORIAL COUNCIL EDITORIAL BOARD EDITOR-IN-CHIEF: Anatoliy A. Bataev, D.Sc. (Engineering), Professor, Rector, Novosibirsk State Technical University, Novosibirsk, Russian Federation DEPUTIES EDITOR-IN-CHIEF: Vladimir V. Ivancivsky, D.Sc. (Engineering), Associate Professor, Department of Industrial Machinery Design, Novosibirsk State Technical University, Novosibirsk, Russian Federation Vadim Y. Skeeba, Ph.D. (Engineering), Associate Professor, Department of Industrial Machinery Design, Novosibirsk State Technical University, Novosibirsk, Russian Federation Editor of the English translation: Elena A. Lozhkina, Ph.D. (Engineering), Department of Material Science in Mechanical Engineering, Novosibirsk State Technical University, Novosibirsk, Russian Federation The journal is issued since 1999 Publication frequency – 4 numbers a year Data on the journal are published in «Ulrich's Periodical Directory» Journal “Obrabotka Metallov” (“Metal Working and Material Science”) has been Indexed in Clarivate Analytics Services. We sincerely happy to announce that Journal “Obrabotka Metallov” (“Metal Working and Material Science”), ISSN 1994-6309 / E-ISSN 2541-819X is selected for coverage in Clarivate Analytics (formerly Thomson Reuters) products and services started from July 10, 2017. Beginning with No. 1 (74) 2017, this publication will be indexed and abstracted in: Emerging Sources Citation Index. Journal “Obrabotka Metallov” (“Metal Working & Material Science”) has entered into an electronic licensing relationship with EBSCO Publishing, the world's leading aggregator of full text journals, magazines and eBooks. The full text of JOURNAL can be found in the EBSCOhost™ databases. Novosibirsk State Technical University, Prospekt K. Marksa, 20, Novosibirsk, 630073, Russia Tel.: +7 (383) 346-17-75 http://journals.nstu.ru/obrabotka_metallov E-mail: metal_working@mail.ru; metal_working@corp.nstu.ru
OBRABOTKAMETALLOV Vol. 24 No. 3 2022 4 EDITORIAL COUNCIL EDITORIAL COUNCIL CHAIRMAN: Nikolai V. Pustovoy, D.Sc. (Engineering), Professor, President, Novosibirsk State Technical University, Novosibirsk, Russian Federation MEMBERS: The Federative Republic of Brazil: Alberto Moreira Jorge Junior, Dr.-Ing., Full Professor; Federal University of São Carlos, São Carlos The Federal Republic of Germany: Moniko Greif, Dr.-Ing., Professor, Hochschule RheinMain University of Applied Sciences, Russelsheim Florian Nürnberger, Dr.-Ing., Chief Engineer and Head of the Department “Technology of Materials”, Leibniz Universität Hannover, Garbsen; Thomas Hassel, Dr.-Ing., Head of Underwater Technology Center Hanover, Leibniz Universität Hannover, Garbsen The Spain: Andrey L. Chuvilin, Ph.D. (Physics and Mathematics), Ikerbasque Research Professor, Head of Electron Microscopy Laboratory “CIC nanoGUNE”, San Sebastian The Republic of Belarus: Fyodor I. Panteleenko, D.Sc. (Engineering), Professor, First Vice-Rector, Corresponding Member of National Academy of Sciences of Belarus, Belarusian National Technical University, Minsk The Ukraine: Sergiy V. Kovalevskyy, D.Sc. (Engineering), Professor, Vice Rector for Research and Academic Affairs, Donbass State Engineering Academy, Kramatorsk The Russian Federation: Vladimir G. Atapin, D.Sc. (Engineering), Professor, Novosibirsk State Technical University, Novosibirsk; Victor P. Balkov, Deputy general director, Research and Development Tooling Institute “VNIIINSTRUMENT”, Moscow; Vladimir A. Bataev, D.Sc. (Engineering), Professor, Novosibirsk State Technical University, Novosibirsk; Vladimir G. Burov, D.Sc. (Engineering), Professor, Novosibirsk State Technical University, Novosibirsk; Aleksandr N. Gerasenko, Director, Scientifi c and Production company “Mashservispribor”, Novosibirsk; Sergey V. Kirsanov, D.Sc. (Engineering), Professor, National Research Tomsk Polytechnic University, Tomsk; Aleksandr N. Korotkov, D.Sc. (Engineering), Professor, Kuzbass State Technical University, Kemerovo; Evgeniy A. Kudryashov, D.Sc. (Engineering), Professor, Southwest State University, Kursk; Dmitry V. Lobanov, D.Sc. (Engineering), Associate Professor, I.N. Ulianov Chuvash State University, Cheboksary; Aleksey V. Makarov, D.Sc. (Engineering), Corresponding Member of RAS, Head of division, Head of laboratory (Laboratory of Mechanical Properties) M.N. Miheev Institute of Metal Physics, Russian Academy of Sciences (Ural Branch), Yekaterinburg; Aleksandr G. Ovcharenko, D.Sc. (Engineering), Professor, Biysk Technological Institute, Biysk; Yuriy N. Saraev, D.Sc. (Engineering), Professor, Institute of Strength Physics and Materials Science, Russian Academy of Sciences (Siberian Branch), Tomsk; Alexander S. Yanyushkin, D.Sc. (Engineering), Professor, I.N. Ulianov Chuvash State University, Cheboksary
Vol. 24 No. 3 2022 5 CONTENTS OBRABOTKAMETALLOV TECHNOLOGY Permyakov G.L., Davlyatshin R.P., Belenkiy V.Y., Trushnikov D.N., Varushkin S.V., Pang S. Numerical analysis of the process of electron beam additive deposition with vertical feed of wire material...................... 6 Ilinykh A.S., Banul V.V., Vorontsov D.S. Theoretical analysis of passive rail grinding.................................. 22 Chinchanikar S. Modeling of sliding wear characteristics of Polytetrafl uoroethylene (PTFE) composite reinforced with carbon fi ber against SS304........................................................................................................ 40 EQUIPMENT. INSTRUMENTS Abbasov V.A., Bashirov R.J. Features of ultrasound application in plasma-mechanical processing of parts made of hard-to-process materials...................................................................................................................... 53 MATERIAL SCIENCE Stolyarov V.V., Andreev V.A., Karelin R.D., Ugurchiev U.Kh., Cherkasov V.V., Komarov V.S., Yusupov V.S. Deformability of TiNiHf shape memory alloy under rolling with pulsed current....................... 66 Vorontsov A.V., Filippov A.V., Shamarin N.N., Moskvichev E.N., Novitskaya O.S., Knyazhev E.O., Denisova Yu.A., Leonov A.A., Denisov V.V. Microstructure and residual stresses of ZrN/CrN multilayer coatings formed by the plasma-assisted vacuum-arc method........................................................................... 76 Ivanov I.V., Safarova D.E., Bataeva Z.B., Bataev I.A. Comparison of approaches based on the WilliamsonHall method for analyzing the structure of an Al0.3CoCrFeNi high-entropy alloy after cold deformation....... 90 Kryukov D.B. Structural features and technology of light armor composite materials with mechanism of brittle cracks localization.......................................................................................................................... 103 EDITORIALMATERIALS 112 FOUNDERS MATERIALS 123 CONTENTS
OBRABOTKAMETALLOV Vol. 24 No. 3 2022 TECHNOLOGY Modeling of sliding wear characteristics of Polytetrafl uoroethylene (PTFE) composite reinforced with carbon fi ber against SS304 Satish Chinchanikar * Vishwakarma Institute of Information Technology, Survey No. 3/4, Kondhwa (Budruk), Pune - 411039, Maharashtra, India https://orcid.org/0000-0002-4175-3098, satish.chinchanikar@viit.ac.in Obrabotka metallov - Metal Working and Material Science Journal homepage: http://journals.nstu.ru/obrabotka_metallov Obrabotka metallov (tekhnologiya, oborudovanie, instrumenty) = Metal Working and Material Science. 2022 vol. 24 no. 3 pp. 40–52 ISSN: 1994-6309 (print) / 2541-819X (online) DOI: 10.17212/1994-6309-2022-24.3-40-52 ART I CLE I NFO Article history: Received: 14 July 2022 Revised: 26 July 2022 Accepted: 27 July 2022 Available online: 15 September 2022 Keywords: PTFE Wear Artifi cial neural network Pin-on-disk SS304 ABSTRACT Introduction. Over the last decade, composite materials based on polytetrafl uoroethylene (PTFE) have been increasingly used as alternative materials for automotive applications. PTFE is characterized by a low coeffi cient of friction, hardness and corrosion resistance. However, this material has a high wear rate. A group of researchers attempted to improve the wear resistance of PTFE material by reinforcing it with different fi llers. The purpose of the work: This study experimentally investigates the dry sliding wear characteristics of a PTFE composite reinforced with carbon fi ber (35 wt.%) compared to SS304 stainless steel. In addition, experimental mathematical and ANN models are developed to predict the specifi c wear rate, taking into account the infl uence of pressure, sliding speed, and interface temperature. The methods of investigation. Dry sliding experiments were performed on a pin-on-disk wear testing machine with varying the normal load on the pin, disk rotation, and interface temperature. Experiments were planned systematically to investigate the effect of input parameters on specifi c wear rates with a wide range of design space. In total, fi fteen experiments were carried out at a 5-kilometer distance without repeating the central run experiment. Sliding velocities were obtained by selecting the track diameter on the disk and corresponding rotation of the disk. A feedforward back-propagation machine learning algorithm was used to the ANN model. Results and Discussion. This study fi nds better prediction accuracy with the ANN architecture having two hidden layers with 150 neurons on each layer. This study fi nds an increase in specifi c wear rates with normal load, sliding velocity, and interface temperature. However, the increase is more prominent at higher process parameters. The normal load followed by sliding velocity most signifi cantly affects the specifi c wear rate. The results predicted by the developed models for specifi c wear rates are in good agreement with the experimental values with an average error close to 10%. This shows that the model could be reliably used to obtain the wear rate of PTFE composite reinforced with carbon fi ber (35 wt.%) compared to SS304 stainless steel. This study fi nds scope for further studies considering the effect of varying ANN architectures, different amount of neurons, and hidden layers on the prediction accuracy of the wear rate. For citation: Chinchanikar S. Modeling of sliding wear characteristics of Polytetrafl uoroethylene (PTFE) composite reinforced with carbon fi ber against SS304. Obrabotka metallov (tekhnologiya, oborudovanie, instrumenty) = Metal Working and Material Science, 2022, vol. 24, no. 3, pp. 40–52. DOI: 10.17212/1994-6309-2022-24.3-40-52. (In Russian). ______ * Corresponding author Chinchanikar Satish, Ph.D. (Engineering), Professor Vishwakarma Institute of Information Technology, Pune - 411039, Maharashtra, India Tel.: 91-2026950441, e-mail: satish.chinchanikar@viit.ac.in Introduction Tribological behavior of sliding contact surfaces have prominent effect on power loss, heat generation, and the overall performance of the system. Researchers have made several attempts to replace conventional material with a composite one that is lighter and more economical, suitable for a particular application. Over the last decade, composite materials based on polytetrafl uoroethylene (PTFE) have been increasingly used as alternative materials for automotive applications. PTFE commercially known as Tefl on is mostly preferred as an alternative material when having sliding contact. PTFE is characterized by a low coeffi cient of friction, hardness, and corrosion resistance. However,
OBRABOTKAMETALLOV TECHNOLOGY Vol. 24 No. 3 2022 this material has a high wear rate. A group of researchers attempted to improve the wear resistance of PTFE material by reinforcing it with different fi llers, considering its wide range of automotive applications having sliding contact [1-5]. Sonawane et al. [1] observed better sliding wear properties for 35% carbon fi ber fi lled PTFE material against 25% carbon fi lled PTFE when using Al6061 as countersurface. AISI 304 is the most used austenitic stainless steel in household, automotive and industrial applications. With a view to consider PTFE composite as an alternative material for automotive applications, Chinchanikar et al. [2] performed dry sliding wear characteristics of PTFE composite reinforced with carbon fi ber (35 wt.%) against AISI 304 stainless steel. Their study observed development of transfer fi lmwith increase in pressure at sliding interfaces that assisted in decreasing specifi c wear rate. However, further studies are required on the development of transfer fi lm on the sliding surface considering the effect of normal load, sliding velocity, and temperature. Unal et.al. [3] investigated the wear of PTFE, PTFE+17% glass fi ber, PTFE+25% bronze, PTFE+35% carbon fi ber. Their study found a decrease in friction coeffi cient for the PTFE and composites up to a certain normal load beyond which friction and wear rate increased. Their investigation observed the formation of thin and uniform transfer fi lm in the case of PTFE and disruption of transfer fi lm in the case of bronze- and carbon-fi lled composite. Sachin [4] studied the wear behavior of PTFE and its composites including glass and carbon as fi ller. Their study observed an increase in volume loss with the increase in load and distance. However, volume loss decreased with the increase in grit size and was considered to be a dominant factor for the wear resistance of the materials. Their study showed that carbon-fi lled composites had greater wear resistance than fi berglass-reinforced PTFE matrix. Venkateswarlu et al. [5] investigated mechanical properties such as hardness, tensile strength, and elongation of pure PTFE and different PTFE-composites with varying fi ller concentrations. Their study observed an increase in hardness with the optimum fi ller content and beyond this value hardness was decreased. On the other hand, tensile strength and elongation of PTFE-composites decreased with the increase in fi ller content. Their study found bronze as a promising fi ller material for obtaining higher tensile strength and lower elongation. Wang et al. [6] experimental study revealed that single incorporation of short carbon fi ber and graphite signifi cantly reduces friction in the case of composites based on PI and its wear resistance. Song et al. [7] investigated the effect of addition of glass fi ber and molybdenum disulphide (MoS2) on wear and friction of PTFE-composite with chopped carbon fi ber (20 wt.%) as fi ller. Their study found an increase in friction coeffi cient with the sliding speed and its decrease with the load when used steel ring as counter surface. The addition of MoS2 to PTFE composite increased its scratch resistance and therefore reduced the wear rate. Gujrathi et al. [8] experimental studies also observed reduction in the wear rate due to fi ller materials addition. Their study observed that the development of a protective layer between the pin and counterface assisted in decreasing the wear volume loss. Shen et al. [9] investigated the tribological performance of PTFE fi lled SiO2 particles-epoxy composites. Their study observed that adding 10-15% of PTFE yields in lowest coeffi cient of friction and wear rate under dry sliding with bearing steel balls as counterface. In another study, Shen et al. [10] compared the abrasion resistance of PTFE using Al2O3 particles with sizes in the range 5 to 200 μm. Their study revealed that the abrasive size signifi cantly infl uences the tribological characteristics of tribo-pairs. Sawyer et al. [11] observed the wear resistance of PTFE composite reinforced with 40 nm alumina particles increased with fi ller concentration. Kim et al. [12] study found a decrease in friction coeffi cients with the normal load and sliding velocity. Wear rates observed as decreasing with the rise in normal load. However, initially wear rate increased with the sliding velocity and then decreased. Wang et al. [13] investigated the wear properties of textured stainless steel opposed to polymer surfaces. EDX analysis performed by them showed different wear behavior. Desale and Pawar [14] studied the wear and friction characteristics of solid lubricant PTFE reinforced with carbon, MoS2, glass fi ber, polyether ether ketone particles under dry and wet conditions against SS304 stainless steel. They observed the minimum wear rate for the PTFE composite fi lled with 15% glass fi ber and 5% MoS2 particles.
OBRABOTKAMETALLOV Vol. 24 No. 3 2022 TECHNOLOGY Artifi cial neural network (ANN) model has been considered as potential and good tool for mathematical modelling of complex and nonlinear wear behavior [15]. The ANN approach, inspired by the biological nervous system, simulates many complicated real-life nonlinear and complex relationships. Ibrahim et al. [15] developed an ANN model to determine wear of PTFE composites. Further, the performance of the models was compared with conventional multilinear regression model (MLR). Their study showed that the ANN model has higher predictive accuracy. Sensitive analysis showed that the volume fraction of the reinforcing fi ller, the sliding distance and the density of the composites tend to be signifi cant parameters. ANN helps to ensure the accuracy in modelling nonlinear relations of composite material properties. And further helps to evaluate the infl uence of many input parameters on material’s performance. A group of researchers found that ANNs are highly accurate in modelling the mechanical behavior of composite materials [16]. Researchers have put a lot of effort into modeling sliding wear characteristics using ANNs. A group of researchers observed that the performance of an ANN model depends on the quantity and type of data provided while training. Further, it is reported that it is necessary to determine the signifi cant set of parameters to save time and train an ANN model effectively [17]. The ANN modelling assists in understanding the process physics that would improve the process performance by facilitating better process control. Although suffi cient work has been carried out by the researchers to evaluate the performance of reinforced composites, very few have modeled sliding wear characteristics of PTFE composite reinforced with carbon fi ber against SS304 stainless steel. With this view, this study develops experimental-based mathematical and ANN models to predict the sliding wear characteristics of PTFE composite reinforced with carbon fi ber against SS304 stainless steel taking into account the impact of normal load, interface temperature, and sliding velocity. Experimental Details Carbon-fi lled PTFE has excellent frictional properties, mechanical and wear properties. During manufacturing, carbon may be added in the form of powder or fi bre. A hot compression moulding process is used to prepare a PTFE pin reinforced with carbon fi bre (35 wt.%). The reinforced PTFE composite specimens had diameter and length of 10 mm and 40 mm, respectively. Cylindrical pins were further machined to have an individual length of 31 mm considering the position of the pin heater holder in which the test specimens (pin) get fi tted. Three sets of SS304 stainless steel plates were used as the material for discs having an outer diameter of 165 mm and a thickness of 8 mm. All plates were hardened to 60 HRC and machined to get an almost equal surface roughness of 1.6 μm. A pin-on-disk machine was used to perform dry sliding experiments (Fig. 1). This machine has a facility to vary the speed in the range from 200–2000 rpm and normal load in the range of 20-200 N. The machine is equipped with a heater for obtaining the effect of interface temperature on wear characteristics of sliding surfaces. A thermocouple is used to obtain information about the temperature of the pin. This machine also has a facility to carry out wear tests taking into account the impact of lubrication. Cylindrical pins used as test specimens varied in size and had a diameter of 3, 6, 8 and 10 mm. Each pin size required a different holder type. This holder was mounted on a rod that has a seesaw arrangement. The weights attached at the other end of the rod was transferred to cylindrical pin and hence, plate (disk) through steel wire. Friction force and the linear wear (in μm) were measured by sensors that the machine Fig. 1. A pin-on-disk machine showing disk arrangement
OBRABOTKAMETALLOV TECHNOLOGY Vol. 24 No. 3 2022 was equipped with. Proximity sensor that the machine is equipped with helped in measuring the speed of the disk (rpm) having least count of 1 rpm with 1 % accuracy. In general, in the compression process pressure on the piston ring varied in the range 2 to 25 bar and temperature in the range 50–200oC with a sliding velocity of 5 m/s. Based on this, ranges of normal load, interface temperature and sliding speed were selected, which are shown in Table 1. Experiments were planned systematically to investigate the effect of input parameters on specifi c wear rates with a wide range of design space. In total, fi fteen experiments were carried out at a 5-kilometer distance without repeating the central run experiment. Sliding velocities were obtained by selecting the track diameter on the disk and corresponding rotation of the disk. Ta b l e 1 Levels of parameters selected to evaluate specifi c wear rate Parameter Low level Moderate level High level Normal load (FN) (N) 20 100 180 Interface temperature (T) (oC) 50 100 150 Sliding velocity (v) (m/s) 2 5 8 Track distance: 5 km Results and Discussion Dry sliding wear characteristics of PTFE composite (a pin material) against SS304 stainless steel plate (a disk material) were performed on a pin-on-disk machine. Experiments were performed as per DoE; normal load, interface temperature, and sliding velocity were varied in the ranges as shown in Table 1. On the pin-on-disk machine, the normal load was applied to the pin by transferring (seesaw arrangement) the weights attached at the other end of the rod. The corresponding temperature was set by turning on the heater and the temperature attained was measured by a thermocouple. The required sliding speed was obtained by selecting the appropriate track diameter on the disk and selecting the corresponding rotation speed of the disk. The test was carried out at a 5-kilometer track distance (approx. 14–17 min). A digital readout for wear, friction force corresponding to process parameters such as normal load, temperature, and disk rotation speed was monitored from the Control panel. The Control panel was attached to a desktop computer. Variation in friction force and wear with respect to test time to cover track distance of 5 km was also monitored on a desktop computer using Windcom software. Experimental matrix with process parameters such as normal load, interface temperature, sliding speed and corresponding results is shown in Table 2. Theoretically, the wear rate was calculated by Eq. 1. However, volume loss was obtained by measuring the weight loss of the pin prior to and following the test. Volume loss is calculated by using Eq. 2. volume loss Specific wear rate = , load sliding distance (1) where mass loss Volume loss = . density (2) An experimental-based mathematical model as shown in Eq. 3 was developed to predict wear rate in terms of normal load (FN), interface temperature (T), and sliding speed (v). The developed model is also
OBRABOTKAMETALLOV Vol. 24 No. 3 2022 TECHNOLOGY Ta b l e 2 Experimental matrix and results Expt. No. FN (N) T (oC) v (m/s) Weight (gm) Weight loss (gm) Volume loss (mm3) Specifi c wear rate (× 10–5) (mm3/Nm) Before test After test 1 50 70 7 5.191 5.185 0.006 2.65 1.06 2 100 100 5 5.223 5.207 0.016 7.75 1.55 3 50 130 7 5.251 5.244 0.007 3.15 1.26 4 150 130 3 5.196 5.168 0.028 13.275 1.77 5 100 50 5 5.134 5.122 0.012 5.9 1.18 6 180 100 5 5.061 5.017 0.044 20.97 2.33 7 150 130 7 5.172 5.130 0.042 19.875 2.65 8 100 100 2 5.211 5.200 0.011 5.2 1.04 9 20 100 5 5.183 5.181 0.002 0.77 0.77 10 150 70 7 5.214 5.181 0.033 15.675 2.09 11 100 100 8 5.252 5.232 0.020 9.4 1.88 12 150 70 3 5.211 5.185 0.026 12.525 1.67 13 100 150 5 5.133 5.114 0.019 9.05 1.81 14 50 130 3 5.183 5.178 0.005 2.35 0.94 15 50 70 3 5.221 5.217 0.004 1.725 0.69 useful to understand the parametric impact on wear. In the equation k, a, b, and c are constants that are obtained by developing polynomial regression model based on the experimental data. Specific wear rate ( ) . a b c s N W k F T v (3) A DataFit software was used to obtain the correlation between wear, normal load, temperature, and sliding velocity as expressed in Eq. 4. The correlation coeffi cient obtained (R2 value) is 0.9791 showed that the developed empirical expression could be effectively used to know wear rate of a PTFE composite reinforced with carbon fi ber (35 wt.%) against SS304 stainless steel in the range of parameters selected in this study. 8 0.6307 0.333 0.403 Specific wear rate ( ) 9.89 1 . 0 s N W F T v (4) From the exponents of normal load, interface temperature, and speed, it can be seen that specifi c wear rate is signifi cantly affected by normal load and after that by sliding speed, and temperature. To have a clear understanding of the effect of input parameters on specifi c wear rate 3-D graphs are plotted for specifi c wear rate using empirical Eq. (4), varying with normal load, interface temperature, and sliding speed. 3-D surface curves are plotted by varying the two process parameters at a time, keeping the other parameter constant at the mid-value of the ranges of the parameters as depicted in Table 1. The 3-D plots refl ecting the variation in the specifi c wear rate are shown in Figs. 2, a–c. Fig. 2, a depicts the variation in the wear rate with the normal load and interface temperature considering the sliding velocity of 5 m/s. Fig. 2, b shows the variation in wear rate with the sliding speed and normal load, and Fig. 12, c depicts variation with the interface temperature and sliding speed. The plots are based on varying two process parameters while maintaining a constant value of the third parameter (FN = 100N, T = 100oC, and v = 5 m/s). This study found an interaction effect of the process parameters on the PTFE composite wear rate against SS304 stainless steel.
OBRABOTKAMETALLOV TECHNOLOGY Vol. 24 No. 3 2022 It is apparent that the specifi c wear rate increases with the normal load, interface temperature, and sliding velocity. However, the increase in specifi c wear rate will become more noticeable at higher process parameters. The normal load followed by sliding velocity and interface temperature can be seen as most signifi cant parameters affecting the wear rate. This can be also confi rmed by the higher exponent value for the normal load followed by for sliding speed and then for interface temperature in Eq. (4). This study fi nds that wear is prominently affected by the normal load, especially at higher values of interface temperature and sliding speed. Artifi cial neural network (ANN) is a computational technique that can model relationships between input parameters and output responses. A typical MLP architecture which is most commonly used is shown in Fig. 3. MLP is characterized by three different layers namely input layer, hidden layer, and output layer, which consist of an interconnected group of artifi cial neurons. The number of neurons present in the input layer and output layer is equal to the number of input variables and corresponding output values. To predict output with higher accuracy, training of the developed network is essential. In the training process of a model, the synaptic weights of the network are modifi ed in an orderly fashion to attain the desired output. Most used training algorithms is the error backpropagation algorithm. For a typical ANN algorithm, at the fi rst step the weights and thresholds are initialized. Then, the output of each neuron is calculated Fig. 2. 3-D plots showing specifi c wear rate varying with: a – Normal load and interface temperature; b – Normal load and sliding speed; c – Interface temperature and sliding speed a b c Fig. 3. Typical ANN architecture
OBRABOTKAMETALLOV Vol. 24 No. 3 2022 TECHNOLOGY from the input data and initialized weights which lead to the fi nal output prediction of the network. Then, the error at output node is calculated and based on an error the weights are modifi ed. And weights in the previous layers are modifi ed by back-propagating errors calculated at output layer nodes [18]. This process is repeated for a set of input and output of training data. The training stops when the ANN output is suffi ciently close to the expected output for each set. ANN model is built to obtain the wear considering the input parameters as the normal load, interface temperature, and sliding speed using MATLAB Toolbox. The ANN architecture has three layers namely input, output, and hidden layers (Fig. 4). The input layer has 3 neurons, the output layer has 1 neuron, and there is appropriate number of neurons on the hidden layer. The neurons are selected by checking the network accuracy. The number of neurons on the hidden layer can be changed if the network does not perform well after training. Fig. 4. ANN architecture to obtain wear rate A feed-forward neural network maps a data set of numeric inputs with a set of numeric targets. The Neural Fitting app of MATLAB Toolbox helps to select data and create and train a network and evaluate its performance using mean square error and regression analysis. A two-layer feed-forward network with sigmoid hidden neurons and linear output neurons is selected that fi ts multi-dimensional problems arbitrarily well, given consistent data and enough neurons in its hidden layer. The network has been trained with the Levenberg-Marquardt backpropagation algorithm. In a neural network, three kinds of samples are used for the training and validation of test data. In the present work, around 70 % of the data is used for training the neural network. The network is adjusted according to its error. Around 15 % of the data is used for validation of the results predicted by the trained neural network. These validation data sets are used to measure network generalization, and to halt training when generalization stops improving. And around 15 % data is used for testing the results predicted by the neural network. These data sets do not affect training and so provide an independent estimation of network performance during and after training. The next important step is to determine network architecture to obtain better accuracy of the predicted results. In this study, a better-predicted accuracy of 0.9747 has been observed with eight neurons in the hidden layer. Further, the network is to be trained using either the Levenberg-Marquardt algorithm or Bayesian Regularization, or Scaled Conjugate Gradient algorithm. However, the researchers have mostly used the Levenberg-Marquardt algorithm. This algorithm is comparatively faster than other algorithms. However, this algorithm requires more memory. Neural network training performance is measured in terms of mean squared error (the average squared error between targets and outputs). Lower values are better. Regression (R) values measure the correlation between outputs (predicted values) and targets (inputs). Neural network regression graphs with regression coeffi cients obtained while training the model, during validation, testing, and for the entire data set are shown in Figs. 5, a–d respectively. The values of regression coeffi cients close to one for training, validation, testing, and for the entire data set shows that the developed neural network model could be reliably used for predicting PTFE composite wear rate reinforced with carbon fi bre (35 wt.%) against SS304 stainless steel within the domain of the parameters selected in this study.
OBRABOTKAMETALLOV TECHNOLOGY Vol. 24 No. 3 2022 Further, the validation experiments were performed using the process parameters different than that are used for developing the models. A comparative of the predicted results with the experimental-based mathematical model and artifi cial neural network (ANN) is shown in Table 3. The model accuracy is assessed by obtaining % error between the predicted and experimental values of wear rate for different process parameters. The % error is obtained using Eq. (5). Predicted value –Expt value 100 Average error = . Expt value (5) Table 3 presents data on the specifi c wear rate predicted by the developed models. Predicted results are seen in good agreement with the experimental values with average error of 10.16 % for experimental-based model and 3.57 % for ANN model. It is apparent that the results predicted by the ANN model are having a better agreement with the experimental results as compared to experimental-based model. Conclusions This study attempted modelling sliding wear characteristics of PTFE composite reinforced with carbon fi ber (35 % by weight) against SS304 stainless steel. Experiments were carried out on the pin-on-disk at different normal loads, interface temperature, and sliding velocities. An experimental-based mathematical a b c d Fig. 5. Neural network (a) Training; (b) Validation; (c) Test; (d) All data set
OBRABOTKAMETALLOV Vol. 24 No. 3 2022 TECHNOLOGY Ta b l e 3 Validation experiments and modeling results Expt. no. FN (N) T ( oC) v (m/s) Specifi c wear rate (x 10-5) (Ws) (mm3/Nm) |% Error| Expt. value Statistical model ANN model Statistical model ANN model 1 130 1.72 1.72 1.72 1.72 1.72 5.06 1.72 2 90 4.97 4.97 4.97 4.97 4.97 19.16 4.97 3 40 5.04 5.04 5.04 5.04 5.04 15.33 5.04 4 140 1.29 1.29 1.29 1.29 1.29 7.72 1.29 5 170 3.24 3.24 3.24 3.24 3.24 7.61 3.24 6 70 5.13 5.13 5.13 5.13 5.13 6.10 5.13 Average error 10.16 3.57 model and ANN model were developed to predict specifi c wear rates to understand the parametric effect on specifi c wear rate. The followings conclusions could be drawn from the present study: It has been observed that the wear rate increased with the normal load, interface temperature, and sliding velocity. However, the increase was more prominent at higher process parameters. The normal load followed by sliding velocity and interface temperature were found as most signifi cant parameters affecting the wear rate. This was also confi rmed by the higher exponent value for the normal load followed by for sliding speed and then for interface temperature. The correlation coeffi cient of 0.97 observed for both the developed experimental-based mathematical and ANN models shows that the model could be reliably used to obtain wear rate of PTFE composite reinforced with carbon fi ber (35% by weight) against SS304 stainless steel. The results predicted by the developed models for specifi c wear rate were in good agreement with the experimental values with an average error close to 10%. However, the results predicted by the ANN model showed better agreement (avg. error of 3.57 %) with the experimental results than statistical-based models (avg. error of 10.16 %). References 1. Sonawane A., Deshpande A., Chinchanikar S., Munde Y. Dry sliding wear characteristics of carbon fi lled polytetrafl uoroethylene (PTFE) composite against Aluminium 6061 alloy. Materials Today: Proceedings, 2021, vol. 44, pp. 3888–3893. DOI: 10.1016/j.matpr.2020.12.929. 2. Chinchanikar S, Barade A, Deshpande A. Sliding wear characteristics of carbon fi lled polytetrafl uoroethylene (PTFE) composite against AISI 304 stainless steel counterface. Materials Science Forum, 2021, vol. 1034, 51–60. DOI: 10.4028/www.scientifi c.net/MSF.1034.51. 3. Unal H., Mimarolu A., Kadioglu U., Ekiz H. Sliding friction and wear behavior of PTFE and its composite under dry sliding conditions. Materials and Design, 2004, vol. 25, pp. 239–245. DOI: 10.1016/j.matdes.2003.10.009. 4. Sahin Y. Analysis of abrasive wear behavior of PTFE composite using Taughi’s technique. Cogent Engineering, 2015, vol. 2, no. 1, pp. 1–15. DOI: 10.1080/23311916.2014.1000510. 5. Venkateswarlu G., Sharada R., Rao M.B. Effect of fi llers on mechanical properties of PTFE based composites. Archives of Applied Science Research, 2015, vol. 7, no. 7, pp. 48–58. 6. Wang Q., Zhang X., Pei X. Study on the synergistic effect of carbon fi ber and graphite and nanoparticle on the friction and wear behavior of polyimide composites. Materials and Design, 2010, vol. 31, no. 8, pp. 3761–3768. DOI: 10.1016/j.matdes.2010.03.017. 7. Song F., Wang Q., Wang T. Effect of glass fi ber and MoS2 on tribological behaviour and PV limit of chopped carbon fi ber reinforced PTFE composite. Tribology International, 2016, vol. 104, pp. 392–401. DOI: 10.1016/j.triboint.2016.01.015.
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