Additive manufacturing (AM) has recently become an increasingly popular form of production due to its advantages over traditional manufacturing methods, such as accessibility, the potential to produce parts with complex geometry, and reduced waste. For the widespread industry adoption of AM components, metal AM has the most potential. The most popular methods of metal AM are powder-based manufacturing techniques. Due to the layer-by-layer nature of AM, the mechanical and tribological properties of an additive manufactured part differs from those of traditionally manufactured components. For the technology to develop and grow further, the tribological properties of AM components must be fully explored and characterized. The choice of material, surface textures, and post-processing methods are shown to have significant impact on friction and wear. Therefore, this paper focuses on reviewing the existing literature with an emphasis on the development of advanced materials for AM applications as well as the optimization of the resulting surface quality via post-processing and presents areas of interest for further examination in this prospective technology.

As modern devices and systems continue to advance, device wear remains a key factor in limiting their performance and lifetime, as well as environmental and health effects. Traditional approaches often rely on wear prediction based on physical models, but due to device complexity and uncertainty, these methods often fail to provide accurate predictions and accurate wear identification. Machine learning, as a data-driven approach based on its ability to discover patterns and correlations in complex systems, has enormous potential for monitoring and predicting device wear. Here, we review recent advances in applying machine learning for predicting the wear of mechanical components. Machine learning for wear prediction shows significant potential in optimizing material selection, manufacturing processes, and equipment maintenance, ultimately enhancing productivity and resource efficiency. Successful implementation relies on careful data collection, standardized evaluation methods, and the selection of effective algorithms, with artificial neural networks (ANNs) frequently demonstrating notable success in predictive accuracy.

Gaining grounds as a potential heat transfer fluid due to its superior thermal and tribological properties, Nanofluid Minimum Quantity Lubrication (NMQL) has been classified as an environmentally friendly technique and has already been successfully applied in several machining processes. This paper presents a review of the role of NMQL for different machining processes. The mechanisms of the MQL technique are thoroughly explained for achieving optimal performance based on parameters like nozzle feed position, angle of elevation, distance from the nozzle tip to cutting zone, flow rate, and air pressure. NMQL is shown to enhance cooling performance and lubrication, as well as the tribological properties of the fluid and cutting performance. With government legislative and public opinion pushing manufacturing companies towards sustainable production techniques and practices, the implementation of MQL-nanofluid can slowly prevent the adverse effects that conventional cutting fluids contribute.

This study investigates the viscoelastic deformation mechanisms of bone as a response to Vickers hardness indentation. We utilized advanced high-resolution scanning electron microscopy (SEM) to investigate a distinct deformation pattern that originates from the indentation site within the bone matrix. The focus of our research was to analyze a unique deformation mechanism observed in bone tissue, which has been colloquially termed as “screw-like” due to its resemblance to a screw thread when viewed under an optical microscope. The primary goals of this research are to investigate the distinctive characteristics of the “screw-like” deformation pattern and to determine how the microstructure of bone influences the initiation and control of this mechanism. These patterns, emerging during the dwell period of indentation, underscore the viscoelastic nature of bone, indicating its propensity for energy dissipation and microstructural reconfiguration under load. This study uncovered a direct correlation between the length of the “screw-like” deformation and the duration of the indentation dwell time, providing quantifiable evidence of the bone’s viscoelastic behavior. This finding is pivotal in understanding the mechanical properties of bone, including its fracture toughness, as it relates to the complex interplay of factors such as energy dissipation, microstructural reinforcement, and stress distribution. Furthermore, this study discusses the implications of viscoelastic properties on the bone’s ability to resist mechanical challenges, underscoring the significance of viscoelasticity in bone research.

Many grease-lubricated machines operate in wet environments, and are vulnerable to contamination because of water exposure. Reports suggest that even the presence of 1% water in grease reduces the life of a bearing by 90%. Nevertheless, only a few qualitative tests and standards are available to characterize the water resistance properties of greases. In this paper, we propose a standard for evaluating the water resistance properties of greases by studying their hydrophobic and hydrophilic nature via a custom-designed apparatus for measuring the grease contact angle. In this approach, a water droplet is dispensed onto the surface of the grease and the contact angle of the droplet is studied. For this purpose, an apparatus was designed, built, and tested with twelve different greases. To validate the efficacy of the test method and setup, tests were performed at two different locations by independent operators. From the obtained contact angle values, the authors propose categorizing a grease’s water-resistance properties into five different grades that can be set as guidelines for the industrial user when selecting a grease for machinery operation in a wet environment. The classification of the water-repellent properties of greases, using the proposed standard is compared with existing ASTM standards used for evaluation of grease properties in the presence of water.

This study investigates the effects of a newly developed gradient severe shot peening treatment (GSSP) on the mechanical properties and tribological behavior of AISI 304 steel. Some experimental tests, including microhardness and ball-on-disc wear tests, were conducted to evaluate the mechanical and tribological properties of both treated and untreated samples. The microstructure of the tested materials was analyzed using X-ray diffraction, optical microscopy, and scanning electron microscopy. Specimens treated with GSSP exhibited a reduction in mean crystallite size and an increase in hardness compared to specimens treated with conventional shot peening at a constant pressure. Samples treated by both conventional and gradient shot peening processes exhibited 50% to 75% of the wear experienced by non-treated steel specimens. An important finding of this study is that while the microhardness improvement in conventional shot pinning diminishes at a depth of 0.3 mm, it is sustained at depth of 0.4 mm and beyond in the GSSP. This study demonstrates the potential of the gradient severe shot peening technique to improve the mechanical properties of AISI 304 steel, primarily by reducing crystallite size and increasing hardness.