Model Test on Mechanical and Deformation Property of a Geomembrane Surface Barrier for a High Rockfill Dam

The cushion of a geomembrane surface barrier of a high rockfill dam built on deep overburden is prone to crack and fail because of excessive flexural deformation. This study proposes a geomembrane surface barrier for a high rockfill dam on deep overburden. The proposed geomembrane surface barrier uses polyurethane bonded aggregates as the cushion material. The loading and deformation performance of the barrier system under uniform water pressure was investigated using a self-developed structure model test device. The mechanical and deformation property of each layer of the barrier, and the interaction mode between adjacent layers, were obtained through external videos and internal sensor monitoring. The results demonstrated that the polyurethane bonded aggregate cushion exhibited good adaptability to flexural deformation during the entire loading process and maintained good contact and coordinate deformation with the upper protective and the lower transition layers. The geomembrane surface barrier created using polyurethane bonded aggregates as the cushion material can adapt to the flexural deformation of a high rockfill dam surface on deep overburden.

Deformation Property of Dense Oxide Coated Magnesium Alloy Surface

Dense oxide coated AZ 31 magnesium alloy surfaces were uniformly formed using a radio frequency magnetron sputtering method. The magnesium oxide thin film thickness was about 240 nm. XRD results of the film indicated that film of magnesium oxide single phase was deposited. The surface of the film was uniform and no crack was observed. The Vickers hardness measured by the nanoindenter was about Hv80 and Hv200 for the AZ31 substrate and the sample coated with the thin film, respectively. The dynamic hardness of the AZ31 substrate and the sample coated with the thin film were almost the same. In the curve at the time of pressurization, a step was observed in the sample coated with the thin film. On the other hand, many steps were observed in the data curve for the thin film deposited on the glass substrate.

Enhancing 3D Printing Producibility in Polylactic Acid Using Fused Filament Fabrication Fused Deposition Modelling and Machine Learning

Polylactic Polylactic acid (PLA) is one of the high applicable material which is used in the 3D printers due to some significant features like its deformation property and affordable costacid (PLA) is brittle in nature with extensive deformation property. For improvement of the end-use quality, it is of significant importance to enhance the quality of Fused Filament Fabrication (FFF)fused deposition modeling (FDM)-printed objects in PLA. The purpose of this investigation is to boost toughness and to reduce the production cost of the FDMFFF-printed tensile test samples with the desired part thickness. Due to prevent from many numerous and idle printing samples the response Surface Method (RSM) is used.To attain the research purpose number of experiments are designed and analyzed by the Response Surface Method (RSM). The statistical analysis is performed to deal with this concern considering extruder temperature (ET), infill percentage (IP), and layer thickness (LT) as controlled factors. The tensile test specimens are printed based on the designed experiments, and the tensile strength tests are conducted by SANTAM 150 universal testing machine based on ASTM D638. The pattern for filling is designed based on honeycomb which is applied to produce lightweight and high-strength specimens. The area under Force- Extension curve up to fracture is acquired as the toughness of the printed specimens. This study also developed a modeling process using artificial neural network (ANN) and artificial neural network- genetic algorithm (ANN-GA) techniques to develop an accurate estimation for toughness, part thickness, and production cost dependent variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA as a hybrid machine learning (ML) technique could could successfully improveenhances the accuracy of modeling about 7.5, 11.5 and 4.5 % for toughness, part thickness, and production cost, respectively, in comparison with those for the single ANN method. On the other side, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which enables the usability of printed PLA objects. The research is accomplished under the constraints of PLA compatibility with existing Fused Filament Fabrication fused deposition modeling installation, in the absence of the functional assistant of the machine in the absence of the functional assistant of the machine. Although the mechanical properties and dimensional accuracy of PLA have already been studied, there is little literature on the toughness of the printed PLA with honeycomb internal fill pattern.