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Mechanical properties testing of the hyperelastic thermoplastic polyurethane (TPU) produced by the continuous digital light processing (CDLP) method of additive manufacturing. Primarily, this paper aims to verify that 3D printed TPU still satisfies commonly assumed volumetric incompressibility and material isotropy in elastic range. The secondary aim is to investigate the accuracy and reproducibility of the CDLP method.

Cylindrical samples were printed and subjected to a volumetric compression test to reveal their bulk modulus K and maximal theoretical porosity (MTP). Dog bone specimens were oriented along different axes and printed. Their dimensions were measured, and they were subjected to cyclic uniaxial tests up to 100% strain to reveal the level of stress softening and possible anisotropy. The hyperelastic Yeoh model was fitted to the mean response.

The authors measured the bulk modulus of K = 1851 ± 184 MPa. The mean MTP was 0.9 ± 0.5%. The mean response was identical in both directions and the data could be fitted by the isotropic third order Yeoh function with R^2 = 0.996. The dimensions measurement revealed the largest error (above 5%) in the direction perpendicular to the direction of the digital light projection while the dimensions in other two dimensions were much more accurate (0.75 and 1%, respectively).

The TPU printed by CDLP can be considered and modelled as isotropic and practically volumetrically incompressible. The parts in the printing chamber should be positioned in a way that the important dimensions are not parallel to the direction of the digital light projection.

The authors experimentally confirmed the volumetric incompressibility and mechanical isotropy of the TPU printed using the CDLP method.

Emerging technologies commonly known as “rapid prototyping” fabricate solid objects directly from computer models by building parts in thin layers. Three‐dimensional printing is one such process that creates engineering prototypes and tooling by joining powder particles selectively on a layer‐by‐layer basis. The powder‐based approach offers tremendous flexibility in geometry and materials, but it makes layer position accuracy a fundamental concern for dimensional control in the vertical direction. Ideally, each powder layer is generated at a vertical position that remains fixed, at a prescribed distance with respect to a machine reference. However, compressive loads imparted to a stack of layers (by the weight of subsequent layers, for example) may cause the layers to displace downward. Develops a model for layer displacement using experimental data for compressibility and applied load. Compares predictions made from the model to measured displacements, and the predictions successfully captured the relative magnitudes of actual errors at various positions within layered powder beds. Position changes were most severe in the middle regions of the powder beds, with diminishing magnitude towards the top and bottom. Uses aluminium oxide powder in two different sizes (approximately of 10‐micron and 30‐micron diameter) and two different shapes (platelet and spherical) in the studies. The average measured displacement in a 76.2mm deep bed ranged from 23 microns for a 30‐micron platelet‐shaped powder to over 260 microns for a 9‐micron platelet‐shaped sample.

The purpose of the paper is to analyse the performances of closures and compressibility corrections classically used in turbulence models when applied to highly-compressible turbulent boundary layers (TBLs) over flat plates.

A direct numerical simulation (DNS) database of TBLs, covering a wide range of thermodynamic conditions, is presented and exploited to perform a priori analyses of classical and recent closures for turbulent models. The results are systematically compared to the “exact” terms computed from DNS.

The few compressibility corrections available in the literature are not found to capture DNS data much better than the uncorrected original models, especially at the highest Mach numbers. Turbulent mass and heat fluxes are shown not to follow the classical gradient diffusion model, which was shown instead to provide acceptable results for modelling the vibrational turbulent heat flux.

The main originality of the present paper resides in the DNS database on which the a priori tests are conducted. The database contains some high-enthalpy simulations at large Mach numbers, allowing to test the performances of the turbulence models in the presence of both chemical dissociation and vibrational relaxation processes.

This study aims to investigate the prediction of the nonlinear response of three-dimensional-printed polymeric lattice structures with and without structural defects. Unlike metallic structures, the deformation behavior of polymeric components is difficult to quantify through the classical numerical analysis approach as a result of their nonlinear behavior under mechanical loads.

Geometric models of periodic lattice structures were designed via PTC Creo. Imperfections in the form of missing unit cells are introduced in the replica of the lattice structure. The perfect and imperfect lattice structures have the same dimensions – 10 mm × 14 mm × 30 mm (w × h × L). The fused deposition modelling technique is used to fabricate the parts. The fabricated parts were subjected to physical compression tests to provide a measure of their transverse compressibility resistance. The ensuing nonlinear response from the experimental tests is deployed to develop a support vector machine surrogate model.

Results from the surrogate model’s performance, in terms of correlation coefficient, rose to as high as 99.91% for the nonlinear compressive stress with a minimum achieved being 98.51% across the four datasets used. In the case of deflection response, the model accuracy rose to as high as 99.74% while the minimum achieved is 98.56% across the four datasets used.

The developed model facilitates the prediction of the quasi-static response of the structures in the absence and presence of defects without the need for repeated physical experiments. The structure investigated is designed for target applications in hierarchical polymer packaging, and the methodology presents a cost-saving method for data-driven constitutive modelling of polymeric parts.

Under this heading are published regularly abstracts of all Reports and Memoranda of the Aeronautical Research Committee, Reports and Technical Notes of the U.S. National Advisory Committee for Aeronautics and publications of other similar research bodies as issued

Design of bedding textiles that contact the human body affects the sleep quality. Bedding textiles contribute to comfort sense during the sleep duration, in addition to ambient and bed microclimate. The purpose of this study is to evaluate the effects of different layer properties on the compression recovery and thermal characteristics of multilayer bedding textiles.

In this study, woven and knitted multilayer bedding textiles were manufactured from fabric, fiber, sponge and interlining, respectively. Different sponge thickness, fiber and interlining weight were used in the layers of samples. Later, the pilling resistance, compression and recovery, air permeability and thermal conductivity of multilayer bedding textiles were investigated.

The results indicated that samples with the higher layer weight and thickness provide better compression recovery and lower air permeability properties. It was also found that knitted surfaces show the higher air permeability than the woven surfaces depending on the fabric porosity. Layer properties have insignificant effect on the thermal conductivity values.

While researchers mostly focus on thermal comfort properties of garments, there are limited studies about comfort properties of bedding textiles in the literature. Furthermore, compression recovery properties of bedding textiles have also a great importance in terms of comfort. Originality of this study is that these properties were analyzed together.

Seam pucker can only be assessed subjectively, by using photographic standards, since instrumental techniques have so far failed to produce accurate results. A low cost means of predicting the degree of pucker has been provided, by correlating measured values of fabric and thread mechanical properties and geometrical relationships with the degree of pucker obtained in the seams. These properties were experimentally measured, for a set of commercial lightweight woven fabrics, and when their values were suitably plotted, three distinct clusters were obtained which, when compared with fabric grading, were found to correspond to severe, critical and acceptable seam pucker. The most important properties were found to be fabric and thread bending, fabric shear, fabric thickness and weight, and certain fabric geometrical parameters using Peirce geometry. The various methods of plotting these properties provide, within the test set of fabrics, a means of predicting the tendency of a fabric to exhibit structural jamming seam pucker.

A swinging pendulum is arranged to impact and bounce from a fabric sample. The energy losses during impact compression are calculated from the changes in potential energy of the pendulum after allowing for other losses. It is found that the compression energy loss, calculated from the static characteristics, gives only a rough guide to the impact compression losses, so other factors must be considered. Further experimental work is proposed to determine more precisely the contribution of the fabric properties to these differences in the characteristics.

Under this heading are published regularly abstracts of all Reports and Memoranda of the Aeronautical Research Committee, Reports and Technical Notes of the U.S. National Advisory Committee for Aeronautics, and publications of other similar research bodies as issued

The purpose of this article is to analyze the state-of-the art in a systematic way, identifying the main research groups and their related topics. The types of studies found are fundamental for understanding the application of artificial neural networks (ANNs) in cemented soils and the potential for using the technique, as well as the feasibility of extrapolation to new geotechnical or civil and environmental engineering segments.

This work is characterized as being bibliometric and systematic research of an exploratory perspective of state-of-the-art. It also persuades the qualitative and quantitative data analysis of cemented soil improvement, biocemented or microbially induced calcite precipitation (MICP) soil improvement by prediction/modeling by ANN. This study sought to compile and study the state of the art of the topic which possibilities to have a critical view about the theme. To do so, two main databases were analyzed: Scopus and Web of Science. Systematic review techniques, as well as bibliometric indicators, were implemented.

This paper connected the network between the achievements of the researches and illustrated the main application of ANNs in soil improvement prediction, specifically on cemented-based soils and biocemented soils (e.g. MICP technique). Also, as a bibliometric and systematic review, this work could achieve the key points in the absence of researches involving soil-ANN, and it provided the understanding of the lack of exploratory studies to be approached in the near future.

Because of the research topic the article suggested other applications of ANNs in geotechnical engineering, such as other tests not related to geomechanical resistance such as unconfined compression test test and triaxial test.

This article systematically and critically presents some interesting points in the direction of future research, such as the non-approach to the use of ANNs in biocementation processes, such as MICP.

Regarding the social environment, the paper brings approaches on methods that somehow mitigate the computational use, or elements necessary for geotechnical improvement of the soil, thereby optimizing the same consequently.

Neural networks have been studied for a long time in engineering, but the current computational power has increased the implementation for several engineering applications. Besides that, soil cementation is a widespread technique and its prediction modes often require high computational strength, such parameters can be mitigated with the use of ANNs, because artificial intelligence seeks learning from the implementation of the data set, reducing computational cost and increasing accuracy.