Wear is induced when two surfaces are in relative motion. The wear phenomenon is mostly data-driven and affected by various parameters such as load, sliding velocity, sliding distance, interface temperature, surface roughness, etc. Hence, it is difficult to predict the wear rate of interacting surfaces from fundamental physics principles. The machine learning (ML) approach has not only made it possible to establish the relation between the operating parameters and wear but also helps in predicting the behavior of the material in polymer tribological applications. In this study, an attempt is made to apply different machine learning algorithms to the experimental data for the prediction of the specific wear rate of glass-filled PTFE (Polytetrafluoroethylene) composite. Orthogonal array L25 is used for experimentation for evaluating the specific wear rate of glass-filled PTFE with variations in the operating parameters such as applied load, sliding velocity, and sliding distance. The experimental data are analysed using ML algorithms such as linear regression (LR), gradient boosting (GB), and random forest (RF). The R² value is obtained as 0.91, 0.97, and 0.94 for LR, GB, and RF, respectively. The R² value of the GB model is the highest among the models, close to 1.0, indicating an almost perfect fit on the experimental data. Pearson’s correlation analysis reveals that load and sliding distance have a considerable impact on specific wear rate as compared to sliding velocity.

Now a days polymer composites are used widely in automobile industry, replacing metal components based on their better mechanical and tribological properties. PTFE, PEEK, PPS filled with fillers like carbon, carbon fibre, CNTs, graphite, graphene etc. have shown greater impact in dry as well as wet wear conditions. The properties of polymer composites have considerable impact with type of filler, its size, shape, weight fraction. An attempt is made to investigate effect of carbon fiber filler on polymer composite. It was observed that use of nano sized carbon fibers with activated carbon in PTFE, can improve wear rate as low as ∼10⁻⁸ mm³/Nm in dry running condition. The synergetic effect was observed when two or more fillers used in polymer composites. PPS filled with carbon fibre it shows better wear resistance in wet condition. Wear resistance of carbon fibre filled polymer composite can be improved by proper surface treatment of the fibre with SiO₂. Doing surface treatment of carbon fibre improves wear resistance of polymer composites. The aim of this study is focused on tribological behavior of polymer composites.

Introduction. One of the most significant phenomena in every industrial sector is friction and wear, which inevitably occurs when there is relative motion between similar or dissimilar materials. A substantial portion of global energy production is estimated to be expended in overcoming friction and wear, making them critical factors in energy efficiency and sustainability. Recently, advances in materials science, lubrication technologies, and innovative design approaches have facilitated a significant reduction in friction and wear, leading to considerable energy savings and extended component lifespan. Polytetrafluoroethylene (PTFE), among other materials, has revolutionized the tribological field, emerging as a highly effective synthetic polymer. This is attributed to its exceptional properties, including a low coefficient of friction, chemical inertness, thermal stability, non-stick characteristics, and biocompatibility. These unique properties make PTFE an ideal material for various industrial applications, spanning from aerospace to biomedical sectors. The purpose of work. This study aims to conduct a comprehensive numerical and experimental investigation into the tribological properties of PTFE-based composites. The materials selected for investigation include pure PTFE, PTFE with 25% carbon (C) filler, and PTFE with 20% glass filler. Testing was performed using stainless steel (SS 304) as the counterbody. Tribological testing and subsequent evaluation were conducted under dry sliding friction conditions, considering key parameters such as load, sliding speed, and temperature. Response surface methodology (RSM) was employed to develop an empirical model, utilizing experimental data to predict the wear resistance of these materials. Empirical models were developed to understand the influence of process parameters on wear behavior and to optimize operating conditions for minimizing material loss. Method of investigation. Archard's wear model was used as the theoretical framework for predicting volume loss and specific wear rate based on numerical simulations. The wear coefficient (K) was determined through experimental testing and used as an input parameter in the numerical models. Numerical simulations were developed using the finite element analysis (FEA) software ANSYS, enabling the simulation of complex tribological interactions between the composite materials and the counterbody. A central composite rotatable design (CCRD) within the framework of RSM was used to structure the experiments. The experiments were conducted under dry sliding friction conditions using pin on disc tribometer. The input parameters for the experiments were load (ranging from 15 N to 200 N), sliding speed (ranging from 400 rpm to 1000 rpm), and temperature (ranging from 60 °C to 200 °C). Each experiment was conducted for a sliding distance of 5 km to ensure sufficient wear for analysis. A total of 20 experiments were performed for each material, providing a comprehensive dataset for statistical analysis and model validation. Result and discussion. The results of the study highlight the effectiveness of numerical simulation in predicting the wear resistance of PTFE-based composites under dry sliding friction conditions. Experimental investigations reveal that pure PTFE exhibits low mechanical strength, leading to a high wear rate, whereas PTFE with carbon and glass fillers demonstrates improved wear resistance characteristics. The addition of carbon to PTFE enhances the composite's performance by forming a stable transfer film on the counterbody, while the addition of glass promotes increased hardness and, consequently, reduced material loss. Empirical models developed using response surface methodology (RSM) confirm that the applied load on the pin is the most significant parameter affecting wear, followed by sliding speed and temperature. Numerical simulations based on Archard's wear model exhibit good agreement with experimental data, validating the accuracy of the numerical simulations. This research contributes to a deeper understanding of the application of PTFE-based composites in extending the service life and enhancing the reliability of industrial products.

The present study demonstrates the roller burnishing process of aluminum alloy 6061-T6 by using a combination of aluminum oxide and vegetable oil as a lubricant. Machining parameters were explored, varying speed (v) (range 100–300 rpm), feed (f) (range 0.1–0.3 mm), and number of passes (nop) (range 1 to 3). However, performance was measured in terms of surface roughness, microhardness, and roundness. According to the results obtained from experiments, it was found that lubrication had a significant impact on performance in terms of surface roughness, mmicrohardness and roundness. Under lubricated conditions, surface roughness ranged from 0.012 µm to 1.7 µm. However, an increase in mimicrohardnessrom 92 HV to 96 HV and an improvement in roundness from 0.07 mm up to 0.05 mm were observed. Additionally, the findings indicated that high speeds with low feed rates yielded the best results: for instance, at a feed of 0.1 mm/rev, speed (v) of 300 rpm, and number of passes of three, a surface roughness of about 0.8 µm, microhardness of approximately 94 HV, and roundness of about 0.02 mm were recorded when applying lubrication. This study demonstrates how minimal lubrication techniques can be used to improve the roller burnishing process, thereby achieving better mechanical properties and surface finishes while extending the lifespan of the burnishing tool. The study has brought about a conclusion that optimizing v and f during burnishing while including relevant lubricant helps manufacturers to realize significant product quality improvements and enhance production efficiency.