Evaluating the influence of various friction stir processing strategies on surface integrity of hybrid nanocomposite Al6061.

To fundamentally investigate the influence of different friction stir processing (FSP) strategies, namely raster, spiral, and parallel in various passes on the surface integrity of hybrid aluminum nanocomposites reinforced by titanium oxide (TiO), silicon carbide (SiC), and zirconium oxide (ZrO) nanoparticles, various examinations were conducted. The surface integrity, comprising microstructural characterization, elemental composition, surface topography, roughness, waviness, and microhardness was studied by different analyses, including scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), optical microscopy (OM), atomic force microscopy (AFM), and Vickers microhardness machine in different zones. Results demonstrated that surface integrity and quality are dependent on the type of FSP strategy.

Tuning of scleroglucan adsorption on carbonate surfaces grafting alkyl side chains of different lengths: a theoretical and experimental study.

The semi-flexible polysaccharide scleroglucan (Sclg) is a well-known viscosity modifier and its highly hydrophilic nature limits its utilization as a polymeric surfactant of high efficiency. Grafting hydrophobic alkyl moieties onto the chain backbone provides a convenient means to reduce the hydrophilicity of Sclg. Herein, by using a self-consistent field theory (SCFT) that considers both the semi-flexibility of the Sclg main chain and the presence of grafted alkyl chains, we study the influences of the grafted alkyl moieties with different lengths (C6, C10 or C18) on the adsorption behaviors of the resultant polymeric surfactants on a carbonate surface.

Prediction of micro-hardness in thread rolling of St37 by convolutional neural networks and transfer learning.

Unlabelled: This study introduces a non-destructive method by applying convolutional neural networks (CNN) to predict the micro-hardness of the thread-rolled steel. Material microstructure images were collected for our research, and micro-hardness tests were conducted to label the extracted microstructure images. In recent years, researchers have used machine learning (ML) and deep learning (DL) models to predict material properties for forming, machining, additive manufacturing, and other processes.

Milling Al520-MMC Reinforced with SiC Particles and Additive Elements Bi and Sn.

In recent years and due to advanced fabrication techniques of composites, many of these functional materials have been brought to the forefront with more benefits. Amongst composites, special attention has been paid to metal matrix composites (MMCs). Reinforced aluminum MMCs with nanoparticles are among the new MMCs with a wide range of industry applications.

Risk assessment for hazardous lubricants in machining industry.

Knowing that over two-thirds of lubricant disposals return to the environment with no purification process, adequate strategies are demanded to reduce their risks. For this reason, the main focus of the present study is to describe an environment-friendly approach. In the first part, two widely used lubricants (mineral-based and vegetable-based) were introduced, and the reasons for their hazards were investigated.