A study on high ratio cup drawing by Maslennikov’s process

Deep drawing of sheet metals using Maslennikov’s technique has been analyzed by analytical and finite element simulation approaches. A new friction model based on local contact conditions has been used in the finite element (FE) simulations of the process. Compared to traditional Coulomb friction model, the results of FE simulations with the new friction model show good correlation with analytical calculations. The effects of key process parameters such as rubber ring thickness, ring inner diameter, die hole diameter, and die profile radius on the results have been investigated. The results showed that very deep cups without thinning in the side wall portion can be achieved with this process. Based on the results of FE analysis, it was found that the maximum drawing ratio can be achieved by adopting a combination of process parameters which correspond to points nearest to the fracture limit.     

Analysis of deep drawing of sheet metal using the Marform process

This paper deals with the deep drawing of metal cups using the Marform process. Using this technique, higher limiting drawing ratios can be obtained compared with the conventional deep drawing process. The analytical model of the process is presented initially, followed by the finite element simulations using ABAQUS software. A new friction model based on local contact conditions is presented and used in the finite element (FE) simulations of the process. Compared with traditional Coulomb friction model, the results of the FE simulations with the new friction model showed good correlation with experimental results. The results showed that the maximum thinning occurs at the punch profile portion, and by increasing the forming pressure, thinning of the sheet metal propagates from the punch profile portion to the side wall. At low forming pressures, wrinkles appear in the flange, whilst at higher pressures, fracture is the main defect of the Marform process.     

Concrete bridge surface damage detection using a single‐stage detector

Early and timely detection of surface damages is important for maintaining the functionality, reliability, and safety of concrete bridges. Recent advancement in convolution neural network has enabled the development of deep learning‐based visual inspection techniques for detecting multiple structural damages. However, most deep learning‐based techniques are built on two‐stage, proposal‐driven detectors using less complex image data, which could be restricted for practical applications and possible integration within intelligent autonomous inspection systems. In this study, a faster, simpler single‐stage detector is proposed based on a real‐time object detection technique, You Only Look Once (YOLOv3), for detecting multiple concrete bridge damages. A field inspection images dataset labeled with four types of concrete damages (crack, pop‐out, spalling, and exposed rebar) is used for training and testing of YOLOv3. To enhance the detection accuracy, the original YOLOv3 is further improved by introducing a novel transfer learning method with fully pretrained weights from a geometrically similar dataset. Batch renormalization and focal loss are also incorporated to increase the accuracy. Testing results show that the improved YOLOv3 has a detection accuracy of up to 80% and 47% at the Intersection‐over‐Union (IoU) metrics of 0.5 and 0.75, respectively. It outperforms the original YOLOv3 and the two‐stage detector Faster Region‐based Convolutional Neural Network (Faster R‐CNN) with ResNet‐101, especially for the IoU metric of 0.75.     

Crush Behavior of Conical Composite Shells: Effect of Cone Angle and Diameter/Wall Thickness Ratio

The crush behavior of truncated, conical, foam-filled, empty shells subjected to quasi-static axial crushing is investigated experimentally. Epoxy resin/E-glass shells with cone angles of 0°, 11°, and 22° and with three different diameter-wall thickness ratios were tested. Polyurethane foam with a density of 55 kg/m³ was used in filled specimens. For empty shells showing an unstable failure mode, energy absorption was increased with the cone angle. In contrast, foam-filled shells showed a stable progressive crushing failure and a reverse trend in energy absorption with the cone angle.     

Effect of Stearic Acid as a Co‐solvent on the Solubility Enhancement of Aspirin in Supercritical CO2

The solubility of aspirin in supercritical CO₂ (SC‐CO₂) with stearic acid as a co‐solvent was measured at various pressures and temperatures. The experimental data were obtained by a static method. Stearic acid had a significant effect on the enhancement of solubility, as the aspirin solubility increased by up to 16 times. Further, the effect of stearic acid on the solubility enhancement of aspirin was compared with that of other co‐solvents. Different semi‐empirical models from the literature were applied for correlating the experimental data, proving good agreement with the experimental data. The model of Sung and Shim exhibited the lowest deviation from the obtained data. The results of the solubility test can be employed to produce aspirin‐based pharmaceuticals using supercritical fluid technology (SFT).     

A set theoretical approach for configuration processing

A mathematical theory for configuration processing is developed. This theory is based on some simple concepts of set algebra. Examples are included to clarify the presented concepts.     

Room temperature electroabsorption in a GexSi1-x PIN photodiode

We present room temperature electroabsorption measurements in a Ge _0.2Si_0.8 pin photodiode. The results appear to be very similar to those reported earlier on Ge_xSi_1-x/Si multiple quantum wells     

Highly Stable Ag–Au Core–Shell Nanowire Network for ITO-Free Flexible Organic Electrochromic Device

Solid and flexible electrochromic (EC) devices require a delicate design of every component to meet the stringent requirements for transparency, flexibility, and deformation stability. However, the electrode technology in flexible EC devices stagnates, wherein brittle indium tin oxide (ITO) is the primary material. Meanwhile, the inflexibility of metal oxide usually used in an active layer and the leakage issue of liquid electrolyte further negatively affect EC device performance and lifetime. Herein, a novel and fully ITO-free flexible organic EC device is developed by using Ag–Au core–shell nanowire (Ag–Au NW) networks, EC polymer and LiBF₄/propylene carbonate/poly(methyl methacrylate) as electrodes, active layer, and solid electrolyte, respectively. The Ag–Au NW electrode integrated with a conjugated EC polymer together display excellent stability in harsh environments due to the tight encapsulation by the Au shell, and high area capacitance of 3.0 mF cm⁻² and specific capacitance of 23.2 F g⁻¹ at current density of 0.5 mA cm⁻². The device shows high EC performance with reversible transmittance modulation in the visible region (40.2% at 550 nm) and near-infrared region ( − 68.2% at 1600 nm). Moreover, the device presents excellent flexibility ( > 1000 bending cycles at the bending radius of 5 mm) and fast switching time (5.9 s).     

Three-dimensional solution of low-velocity impact on sandwich panels with hybrid nanocomposite face sheets

In this article, a three-dimensional solution based on Fourier's series and the generalized differential quadrature method is presented to model the low-velocity impact on sandwich panels with hybrid nanocomposite face sheets. Navier's equations are derived and displacements are substituted by their corresponding Fourier's series. The contact force is considered as a Fourier's series of impactor displacement and deflection of contact point. To verify the theoretical model, experiments are performed on a polyurethane foam-cored sandwich panel with epoxy/woven-fiberglass/nanosilica hybrid nanocomposite face sheets. Contact force and lateral displacement of contact point histories are compared with the theoretical model.