Practice of Simulation and Life Cycle Assessment in Tribology-A Review

A Review on the Effect of Metal Oxide Nanoparticles on Tribological Properties of Biolubricants

This review provides a comprehensive and accessible literature review on the integration of nanoparticles into biolubricants to enhance wear and friction regulation, thus improving the overall lubricated system performance. Nanotechnology has significantly impacted various industries, particularly in lubrication. Nanobiolubricants offer promising avenues for enhancing tribological properties. This review focuses on oxide nanoparticles, such as zinc oxide (ZnO), aluminum oxide (Al2O3), copper oxide (CuO), titanium dioxide (TiO2), zirconium dioxide (ZrO2), and graphene oxide (GO) nanoparticles, for their ability to enhance lubricant performance. The impact of nanoparticle concentration on biolubricant properties, including viscosity, viscosity index, flash point temperature, and pour point temperature, is analyzed. The review also addresses potential obstacles and limitations in nanoparticle incorporation, aiming to propose effective strategies for maximizing their benefits. The findings underscore the potential of nanobiolubricants to improve operational efficiency and component lifespan. This review aims to provide valuable insights for researchers, engineers, and professionals in exploring and leveraging nanotechnology's potential in the lubrication industry. This review paper explores the basics of tribology along with its significance, green principles, mechanisms, and energy savings because of friction, wear, and lubrication. Condition monitoring techniques are also explored to achieve brief knowledge about maintaining reliability and safety of the industrial components. Recent advances in tribology including superconductivity, biotribology, high-temperature tribology, tribological simulation, hybrid polymer composite's tribology, and cryogenic tribology are investigated, which gives a thorough idea about the subject.

A review of recent advances and applications of machine learning in tribology

In tribology, a considerable number of computational and experimental approaches to understand the interfacial characteristics of material surfaces in motion and tribological behaviors of materials have been considered to date. Despite being useful in providing important insights on the tribological properties of a system, at different length scales, a vast amount of data generated from these state-of-the-art techniques remains underutilized due to lack of analysis methods or limitations of existing analysis techniques. In principle, this data can be used to address intractable tribological problems including structure-property relationships in tribological systems and efficient lubricant design in a cost and time effective manner with the aid of machine learning. Specifically, data-driven machine learning methods have shown potential in unraveling complicated processes through the development of structure-property/functionality relationships based on the collected data. For example, neural networks are incredibly effective in modeling non-linear correlations and identifying primary hidden patterns associated with these phenomena. Here we present several exemplary studies that have demonstrated the proficiency of machine learning in understanding these critical factors. A successful implementation of neural networks, supervised, and stochastic learning approaches in identifying structure-property relationships have shed light on how machine learning may be used in certain tribological applications. Moreover, ranging from the design of lubricants, composites, and experimental processes to studying fretting wear and frictional mechanism, machine learning has been embraced either independently or integrated with optimization algorithms by scientists to study tribology. Accordingly, this review aims at providing a perspective on the recent advances in the applications of machine learning in tribology. The review on referenced simulation approaches and subsequent applications of machine learning in experimental and computational tribology shall motivate researchers to introduce the revolutionary approach of machine learning in efficiently studying tribology.

The Twelve Principles of Green Tribology: Studies, Research, and Case Studies—A Brief Anthology

Sustainability has become of paramount importance, as evidenced by the increasing number of norms and regulations concerning various sectors. Due to its intrinsic trans-sectorial nature, tribology has drawn the attention of the supporters of sustainability. This discipline allows the environmental, economic, and social impacts to be decreased in a wide range of applications following the same strategies. In 2010, Nosonovsky and Bhushan drew up 12 approaches based on the 12 principles of green chemistry and the 12 principles of green engineering, defining the “12 principles of green tribology.” This review exploits the 12 principles of green tribology to fathom the developed research related to sustainability and tribology. Different approaches and innovative studies have been proposed in this short selection as references to consider for further development, pursuing the efforts of the scientific community for a sustainable future through the contribution also of tribosystems. The manuscript aims to provide practical examples of materials, lubricants, strategies, and technologies that have contributed to the overall progress of tribology, decreasing wear and friction and increasing efficiency, and at the same time promoting sustainable development, lowering toxicity, waste production, and loss of energy and resources.

Friction-Wear Characteristics of Carbon Fiber Reinforced Paper-Based Friction Materials under Different Working Conditions

To study the friction and wear performance of carbon fiber reinforced friction materials under different working conditions, paper-based friction materials with different fibers were prepared. Experiments on the SAE#2 test bench were conducted to study the infectors including friction torques, surface temperature, coefficient of friction (COF), and surface morphologies. The results were analyzed, which indicated that the carbon fiber reinforced friction material could provide a higher friction torque and a lower temperature rising rate under the applied high pressure and high rotating speed conditions. As the pressure increased from 1 MPa to 2.5 MPa, the friction torque of plant fiber reinforced material increased by 150%, the friction torque of carbon fiber reinforced material increased by 400%, and the maximum temperature of plant fiber reinforced and carbon fiber reinforced material reached the highest value at 1.5 MPa. Thus, carbon fibers not only improved the COF and friction torque performance but also had advantages in avoiding thermal failure. Meanwhile, carbon fiber reinforced friction materials can provide a more stable COF as its variable coefficient (α) only rose from 38.18 to 264.62, from 1 MPa to 2.5 MPa, which was much lower than the natural fiber reinforced friction materials. Simultaneously, due to the good dispersion and excellent mechanical properties of PAN chopped carbon fibers, fewer pores formed on the initial surface, which improved the high wear resistance, especially in the intermedia disc.

Effect of Silicon Carbide Nanoparticles on the Friction-Wear Properties of Copper-Based Friction Discs

To study the influence of nano-additives on the friction-wear characteristics of friction materials, the nano-sized silicon carbide particles which have excellent chemical and physical properties are considered to add in composite to form the modified friction material. The influence of the silicon carbide nanoparticles (SCN) on the friction-wear characteristics of copper-based friction materials (CBFM) is investigated via the SAE#2 (made in Hangzhou, China) clutch bench test with the applied pressure, rotating speed, and automatic transmission fluid (ATF) temperature taken into account. Moreover, the variations of friction torque and temperature are considered to evaluate the friction performance, and the variable coefficient is employed to describe the friction stability. The wear characteristics of friction materials are investigated by the disc changes in thickness and micro-morphology. The results show that the CBFM with SCN can provide a higher friction torque, which increased by 30% to 50% compared with CBFM. The variable coefficient of CBFM with SCN changes from 674 to 52 with the rotating speed raised from 600 rpm to 3000 rpm, which shows that the friction stability is relatively worse. Furthermore, the micromorphology shows that the CBFM with SCN has lower porosity and surface roughness, which increases the microscopic contact area and the coefficient of friction (COF). Simultaneously, the reduction in porosity also leads to a decrease in the cooling quality, bringing about a rapid temperature rise. Thus, the wear amount of CBFM with SCN increases significantly, especially for the friction disc in the axial middle position.

Influence of the 6061 Aluminium Alloy Thermo-Viscoplastic Behaviour on the Load-Area Relation of a Contact

The contact between solids in metal-forming operations often involves temperature-dependent viscoplasticity of the workpiece. In order to estimate the real contact area in such contexts, both the topography and the deformation behaviour should be taken into account. In this work, a deterministic approach is used to represent asperities in appropriately shaped quadratic surfaces. Such geometries are implemented in indentation finite element simulations, in which the indented material has thermo-viscoplastic properties. By creating a database of simulation data, investigations in terms of contact load and area for the specifically shaped asperities allow for an analysis on the influence of the material properties on the load–area relation of the contact. The temperature and viscoplasticity greatly define how much load is supported by a substrate due to an indenting asperity, but the description of the deformation behaviour at small values of strain and strain rate is also relevant. The pile-up and sink-in regions are very dependent on the thermo-viscoplastic conditions and material model, which consequently affect the real contact area calculation. The interplay between carried load and contact area of a full surface analysis indicates the role that different sized asperities play in the contact under different thermomechanical conditions.