Polymer-composite materials have the characteristics of light weight, high load, corrosion resistance, heat resistance, and high oil resistance. In particular, graphene composite has better electrical conductivity and mechanical performance. However, the raw materials of graphene composite are processed into semi-finished products, directly affecting their performance and service life. The electromagnetic pulse compaction was initially studied to get the product Graphene/PEKK composite powder. Simultaneously, spark plasma sintering was used to get the bars to determine the electrical conductivity of Graphene/PEKK composite. On the basis of this result, conducting Graphene/PEKK composite powder can be processed by electromagnetic pulse compaction. Finite element numerical analysis was used to obtain process parameters during the electromagnetic pulse compaction. The results show that discharge voltage and discharge capacitance influence on the magnetic force, which is a main moulding factor affecting stress, strain and density distribution on the specimen during electromagnetic pulse compaction in a few microseconds.

Polymer composites are gradually replacing traditional metal materials in the fields of aviation, aerospace, automotive and medicine due to their corrosion resistance, light weight and high strength. Moulding technology and organization morphology of polymer composite are key elements affecting the quality of products and their application, so a vacuum hot pressing process for graphenex/poly(ether ketone ketone) (PEKK) (x = 0%, 2%, 3%, 4%, 5%, 6%) composite powders is explored with particularly designed moulding parameters to achieve high conductive properties and good mechanical properties in graphene/PEKK composite sheet with thickness of 1.25 mm and diameter of 80 mm. The vacuum environment ensures that the graphene is not oxidized by air during hot pressing molding, which is essential for achieving conductive property in the graphene/PEKK composite; The hot pressing temperature of each graphene/PEKK composite powder is higher than glass transition temperature but lower than melting temperature, which ensures the graphene/PEKK composite powders is fully compacted and then graphene is fully lapped in the composite sheet. In addition, the graphene/PEKK composite sheet shows conductive property when the graphene content increases to 3wt%, and then the conductivity of the composites increases and then decreases with a peak value at 5wt% with increasing graphene content. By comparing the mechanical properties and microstructure morphology of the graphene/PEKK composite sheets, it was obtained that graphene content has an obvious effect on the mechanical properties of the composites, e.g., the mechanical properties will be increased as the graphene content increasing when graphene content is more than 3%. The graphene distribution law of the composite material with different graphene contents is analysed using a scanning electron microscope (SEM).

Salinity is an important index of water quality in oilfield water injection engineering. To address the need for real-time measurement of salinity in water flooding solutions during oilfield water injection, a salinity measurement system that can withstand a high temperature environment was designed. In terms of the polarization and capacitance effects, the system uses an integrator circuit to collect information and fuzzy control to switch gears to expand the range. Experimental results show that the system can operate stably in a high-temperature environment, with an accuracy of 0.6% and an uncertainty of 0.2% in the measurement range of 1–10 g/L.

No abstract

Considering the crucial influence of feature selection on data classification accuracy, a grey wolf optimizer based on quantum computing and uncertain symmetry rough set (QCGWORS) was proposed. QCGWORS was to apply a parallel of three theories to feature selection, and each of them owned the unique advantages of optimizing feature selection algorithm. Quantum computing had a good balance ability when exploring feature sets between global and local searches. Grey wolf optimizer could effectively explore all possible feature subsets, and uncertain symmetry rough set theory could accurately evaluate the correlation of potential feature subsets. QCGWORS intelligent algorithm could minimize the number of features while maximizing classification performance. In the experimental stage, k nearest neighbors (KNN) classifier and random forest (RF) classifier guided the machine learning process of the proposed algorithm, and 13 datasets were compared for testing experiments. Experimental results showed that compared with other feature selection methods, QCGWORS improved the classification accuracy on 12 datasets, among which the best accuracy was increased by 20.91%. In attribute reduction, each dataset had a benefit of the reduction effect of the minimum feature number.