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
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