Effect of infill pattern, density and material type of 3D printed cubic structure under quasi-static loading

The present research work is aimed to investigate the effect of infill pattern, density and material types of 3D printed cubes under quasi-static axial compressive loading. The proposed samples were fabricated though 3D printing technique with two different materials, such as 100% polylactic acid (PLA) and 70% vol PLA mixed 30% vol carbon fiber (PLA/CF). Four infill pattern structures such as triangle, rectilinear, line and honeycomb with 20%, 40%, 60%, and 80% infill density were prepared. Subsequently, the quasi-static compression tests were performed on the fabricated 3D printed cubes to examine the effect of infill pattern, infill density and material types on crushing failure behaviour and energy-absorbing characteristics. The results revealed that the honeycomb infill pattern of 3D printed PLA cubic structure showed the best energy-absorbing characteristics compared to the other three infill patterns. From the present research study, it is highlighted that the proposed 3D printed structures with different material type, infill pattern and density have great potential to replace the conventional lightweight structures, which could provide better energy-absorbing characteristics.

Relationship between well pattern density and variation function of stochastic modelling and database establishment

For 3D geological modelling of oil and gas reservoirs, well pattern density is directly related to the number of samples involved in the calculation, which determines the variation function of stochastic modelling and has great impacts on the results of reservoir modelling. This paper focuses on the relationship between well pattern density and the variogram of stochastic modelling, selects the large Sulige gas field with many well pattern types as the research object, and establishes a variogram database of stochastic models for different well pattern densities. First, the well pattern in the study area is divided into three different types (well patterns A, B, and C) according to well and row space. Several different small blocks (model samples) are selected from each type of well pattern to establish the model, and their reasonable variogram values (major range, minor range and vertical range) are obtained. Then, the variogram values of all model samples with similar well pattern densities are analysed and counted, and the variogram database corresponding to each type of well pattern is established. Finally, the statistical results are applied to the modelling process of other blocks with similar well pattern density to test their accuracy. The results show that the reservoir model established by using the variation function provided in this paper agrees well with the actual geological conditions and that the random model has a high degree of convergence. This database has high adaptability, and the model established is reliable.