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This study aims to present a novel methodology for the evaluation of tribological properties of new nanocomposites with the A356 alloy matrix reinforced with aluminium oxide (Al₂O₃) nanoparticles.

Metal matrix nanocomposites (MMnCs) with varying amounts and sizes of Al₂O₃ particles were produced using a compocasting process. The influence of four factors, with different levels, on the wear rate, was analysed with the help of the design of experiments (DoE). A regression model was developed by using the response surface methodology (RSM) to establish a relationship between the observed factors and the wear rate. An artificial neural network was also applied to predict the value of wear rate. Adequacy of models was compared with experimental values. The extreme values of wear rate were determined with a genetic algorithm and particle swarm optimization using the RSM model.

The combination of optimization methods determined the values of the factors which provide the highest wear resistance, namely, reinforcement content of 0.44 wt.% Al₂O₃, sliding speed of 1 m/s, normal load of 100 N and particle size of 100 nm. Used methods proved as effective tools for modelling and predicting of the behaviour of aluminium matrix nanocomposites.

The specific combinations of the optimization methods has not been applied up to now in the investigation of MMnCs. In addition, using of small content of ceramic nanoparticles as reinforcement has been poorly investigated. It can be stated that the presented approach for testing and prediction of the wear rate of nanocomposites is a very good base for their future research.

A significant body of literature suggests that shipping companies operate in an extremely volatile and risky environment, relying on the effective use of information to remain competitive. However, decision-making in this market is demanding because of the high uncertainty, market competition and significant capital investments. Moreover, the rapid spread of COVID-19 renders information uncertainty a daunting challenge for companies engaged in global trade. Hence, this study aims to explore the information behavior of managers in a time of crisis seems compelling.

This study provides novel insights into the information behavior of senior managers by adopting a qualitative approach. Forty-nine semi-structured face-to-face interviews with individuals from Hellenic shipping companies were conducted. Moreover, this study explores the extant theory qualitatively, using the grounded theory methodology and shows that an unprecedented event (pandemic crisis) can redefine the information behavior of managers.

This study highlights the importance of information in decision-making. Moreover, the results show that, during a pandemic, managers resort to alternative information sources, adopt collaborative information behaviors and take advantage of digital technology.

There is limited research in exploring the information behavior of managers in times of pandemics. This research underscores the fact that during a crisis, managers seek information from digital information resources and decision-making assumes a more decentralized form. This study concludes with a discussion of the theoretical and practical implications of these findings.

This paper aims to investigate the feasibility of adding macro-textures to triangle meshes for additive manufacturing (AM) focusing on possible time and quality issues in both software processing and part fabrication.

A demonstrative software tool was developed to apply user-selected textures to existing meshes. The computational procedure is a three-dimensional extension of the solid texturing method used in computer graphics. The tool was tested for speed and quality of results, considering also the pre- and post-processing operations required. Some textured meshes were printed by different processes to test build speed and quality.

The tool can handle models with realistic complexity in acceptable computation times. Parts are built without difficulties or extra-costs achieving a good aesthetic yield of the texture.

The tool cannot reproduce sample patterns but requires the development of a generation algorithm for different type of textures. Mesh processing operations may take a long time when very fine textures are added to large parts.

Direct texturing can help obtain parts with aesthetic or functional textures without the need for surface post-treatments, which can be especially difficult and expensive for plastic parts.

The proposed method improves the uniformity and consistency of textures compared to existing approaches, and can support future systematic studies on the detail resolution of AM processes.

This paper aims to investigate the wear behaviour of different materials for cylinder liners and piston rings in a linear reciprocating tribometer with special focus on the wear of the cylinder liner in the boundary lubrication regime.

Conventional nitrided steel, as well as diamond-like carbon and chromium nitride-coated piston rings, were tested against cast iron, AlSi and Fe-coated AlSi cylinder liners. The experiments were carried out with samples produced from original engine parts to have the original surface topography available. Radioactive tracer isotopes were used to measure cylinder liner wear continuously, enabling separation of running-in and steady-state wear.

A ranking of the material pairings with respect to wear behaviour of the cylinder liner was found. Post-test inspection of the cylinder samples by scanning electron microscopy (SEM) revealed differences in the wear mechanisms for the different material combinations. The results show that the running-in and steady-state wear of the liners can be reduced by choosing the appropriate material for the piston ring.

The use of original engine parts in a closely controlled tribometer environment under realistic loading conditions, in conjunction with continuous and highly sensitive wear measurement methods and a detailed SEM analysis of the wear mechanisms, forms an intermediate step between engine testing and laboratory environment testing.