Materialwissenschaft und Werkstofftechnik最新文献

In this study, cryogenic-temperature formal machining technique was used to process solution-treated aluminum 7075 alloys, the mechanical properties, morphology, and microstructure of machined surface and produced chips are investigated. Results show machining temperature has a huge influence on chip and machined surface morphology, cryogenic-temperature machining chips and machined surfaces possess better surface integrity, chips are continuous and smoother, and machined surfaces are flatter. In contrast, room-temperature formal machining chips exhibit serrated cracks on their free surface and the machined surface produces more serious scaly spines phenomenon. Both cryogenic-temperature and room-temperature samples experience severe deformation, cryogenic-temperature machining alumium 7075 alloys max microhardness has enhanced from 98 HV 0.1 to 174 HV 0.1, and cryogenic-temperature samples’ microhardness is higher than corresponding room-temperature samples’ microhardness among all machining parameters. Cryogenic-temperature can effectively suppress dynamic recovery thus chips could store more dislocations and then possess smaller ultrafine-grained structures, accounting for higher microhardness. Besides cryogenic-temperature inhibits precipitation of solute cluster and second phase particles and plays a lubrication effect at tool-chip interface, thus cryogenic-temperature machining aluminum 7075 alloys could obtain superior machined surface quality/chip morphology to improve processability. (*: Equal contributi)

High energy consumption and poor processing quality are common problems in wood sawing. To address these issues, in this article, specific cutting energy and surface roughness were investigated with saw blade speed as control variables. Analysing the effect of parameters on specific cutting energy and surface roughness. The sawing parameters were optimised with the objectives of minimum specific cutting energy and minimum surface roughness. The findings indicate that specific cutting energy and surface roughness reduction with increasing rake angle; specific cutting energy and surface roughness decrease with increasing spindle speed; specific cutting energy decreases and surface roughness increases with increasing feed rate. ANOVA analysis reveals that sawing speed (n) has the most significant impact on specific cutting energy during oak cutting. The optimal solution derived from TOPSIS suggests a specific cutting energy of 2E7 J/m³ and a surface roughness of 1.758 μm. The innovation of this paper is the study of the specific cutting energy and the optimisation of parameters. These findings provide valuable theoretical and practical guidance for enhancing the efficiency and quality of oak processing while minimizing energy consumption.

The necessity to adapt sustainable development, green chemistry and industrial ecology leads to the creation of newer materials and the utilization of waste. In this context, this work aims at exploring the feasibility of the fabrication of hybrid composites comprised of a polymeric resin reinforced with a natural fiber (hemp) and an industry waste filler (dolomite dust). While the hemp fiber content is kept constant, epoxy-based hybrid composites are fabricated with different weight percentages of dolomite dust using the conventional hand lay-up route. The microstructural features of the constituent materials are studied using stereo and scanning electron microscopes which show the shape and size of dolomite particles and the arrangement of fibers. An x-ray diffraction analysis revealed the presence of hematite, graphite, lime and manganite. The presence of functional groups like hydroxyl, carboxyl and azide is ascertained from the transmittance spectra of Fourier transform infrared spectroscopy. The density and void fractions are increasing with the amount of filler particles in the composite. It is observed that the tensile and flexural strengths of the composites marginally drop with increase in dolomite dust content, but there is a reasonable improvement in their inter-laminar shear strength, micro-hardness and impact strength values.

This study investigated the effect of magnetic particle size and volume fraction on the shear yield stress and dynamic viscosity of magnetorheological fluids. Magnetorheological fluids with varying volume fractions of micro- and nanoscale magnetic particles were prepared. A plate-on-plate shear test bench was constructed to evaluate the fluids under a constant shear rate, with the applied current ranging from 0 A to 1.2 A. Results indicated that the shear yield stress initially increased and then decreased as the volume fraction of magnetic nanoparticles increased, reaching a maximum of 47 kPa at a volume fraction of 7 %. However, the excessive addition of magnetic particles or large-diameter particles led to settling and reduced stability of the fluids. The findings suggest that optimizing the size and volume fraction of magnetic particles is crucial for maximizing the shear yield stress of magnetorheological fluids.

The content of peroxide in acrylonitrile is critical for the production of polyacrylonitrile-based carbon fibers. Peroxide-free acrylonitrile is used as a blank solution for testing the peroxide content of acrylonitrile, which storage conditions are important to the test. Of course, for the change of peroxide content in acrylonitrile stored for a period of time has an effect on the production of carbon fiber. In order to study the effect of storage conditions on peroxide in peroxide-free acrylonitrile and acrylonitrile, the changes at refrigerator and room temperature were studied, respectively. Studies have shown that storage at temperatures below 20 °C for 3 months hardly affect the use of either peroxide-free acrylonitrile or acrylonitrile. The experimental results obtained are useful for practical production and testing, which can improve the accuracy of testing and stability of production.

Machine hammer peening is a surface treatment technique employed to enhance the surface properties of the treated samples. In this present work, the theoretical analysis, finite element simulation and experiment of single peening are carried out to investigate the relationship between the machine hammer peening force and geometric dimensions of the single peening indentation deformation, including diameter and depth. The single peening experimental results are in accordance with theoretical and simulation analysis. The machine hammer peening experiment with grate tool path are done and the results including surface topography, surface roughness, surface hardness, x-ray diffraction analysis and wear resistance are discussed. This work seeks to explore the effects of process parameters like machine hammer peening force, indentation and pitch spacing on surface roughness and hardness. The results reveal that the surface hardness and roughness are positively associated with machine hammer peening force and small indentation and pitch spacing lead to high hardness. The x-ray diffraction analysis validate machine hammer peening is a mechanical treatment without new compound generated. The friction test results in an enhancement in wear resistance of treated samples.

Additive manufacturing, notably laser powder bed fusion (LPBF), excels in producing complex geometries and is widely used in the automotive, aerospace, and naval industries. Laser powder bed fusion enables the creation of components with the required stiffness and strength at a lighter weight than traditional manufacturing methods. Aluminium alloys are particularly promising for laser powder bed fusion in the automotive and aerospace sectors. To enhance the effectiveness of laser powder bed fusion-produced components, optimized process parameters must be designed for specific materials. This study investigates the influence of processing parameters, scan speed, scan strategy, and hatch space, on the relative density, surface roughness, and microhardness of AlSi12 samples fabricated by laser powder bed fusion. A Taguchi L27 orthogonal array was used to systematically analyze the effects of these parameters. A regression model was developed and evaluated through analysis of variance using signal-to-noise (S/N) ratios to identify optimal parameter values. Results indicated that the scan pattern significantly affects relative density, while hatch space impacts surface roughness and microhardness. Optimal solutions were obtained through multi-objective optimization using the non-dominated sorting genetic algorithm (NSGA-II) and Pareto search algorithms. Experimental validation showed average errors of 0.483 % and 0.461 % for NSGA-II and Pareto search algorithms, respectively.