An Equivalent Structural Stress-Based Frequency-Domain Fatigue Assessment Approach for Welded Structures under Random Loading.

Welded structures under random loadings are usually susceptible to fatigue-induced failures that lead to significant economic and safety effects. However, accurately predicting these structures' fatigue damage and life in the frequency domain remains challenging due to the limitations associated with using traditional weld stress extrapolation methods, such as nominal, hotspot, and notch stress methods. These methods struggle with precisely defining and characterizing the stresses at the weld toe and root as they vary depending on factors like weld stress concentration effects, joint geometry, and loading modes. This research introduces an Equilibrium Equivalent Structural Stress (EESS)-based frequency-domain fatigue analysis approach for welded structures subjected to random loading. The proposed method utilizes the EESS formulations, which are based on the decomposition and characterization of weld toe stresses with a single stress parameter, together with incorporating structural dynamic properties' effects on the stresses acting on the weld joints and the corresponding accumulated fatigue damage of the structure. The numerical demonstration and validation of the proposed method have been performed using a welded Rectangular Hollow Section (RHS) T-joint structure subjected to stationary random fatigue loading. The proposed method's fatigue damage and life results are compared with the fatigue test data and the equivalent hotspot stress extrapolation-based technique results.

Application of Solution Method to Prepare High Performance Multicomponent Oxide Thin Films.

Capacitors play an increasingly important role in hybrid integrated circuits, while the MIM capacitors with high capacitance density and small thickness can meet the needs of high integration. Generally speaking, the films prepared with a single metal oxide dielectric often achieve a breakthrough in one aspect of performance, but dielectric layers are required to be improved to get better performance in leakage current, capacitance density, and transmittance simultaneously in modern electronic devices. Therefore, we optimized the performance of the dielectric layers by using multiple metal oxides. We combined zirconia, yttria, magnesium oxide, alumina, and hafnium oxide with the solution method to find the best combination of these five metal oxides. The physical properties of the multi-component films were measured by atomic force microscopy (AFM), ultraviolet-visible spectrophotometer, and other instruments. The results show that the films prepared by multi-component metal oxides have good transmittance and low roughness. The thicknesses of all films in our experiment are less than 100 nm. Then, metal-insulator-metal (MIM) devices were fabricated. In addition, we characterized the electrical properties of MIM devices. We find that multi-component oxide films can achieve good performances in several aspects. The aluminum-magnesium-yttrium-zirconium-oxide (AMYZOx) group of 0.6 M has the lowest leakage current density, which is 5.03 × 10-8 A/cm2 @ 1.0 MV/cm. The hafnium-magnesium-yttrium-zirconium-oxide (HMYZOx) group of 0.8 M has a maximum capacitance density of 208 nF/cm2. The films with a small thickness and a high capacitance density are very conducive to high integration. Therefore, we believe that multi-component films have potential in the process of dielectric layers and great application prospects in highly integrated electronic devices.

Coordinated control for path-following of an autonomous four in-wheel motor drive electric vehicle.

Coordination of Active Front Steering (AFS) and Direct Yaw Moment Control (DYC) has been widely used for non-autonomous vehicle lateral stability control. Recently, some researchers used it (AFS/DYC) for path-following of autonomous vehicles. However, current controllers are not robust enough with respect to uncertainties and different road conditions to guarantee lateral stability of Autonomous Four In-wheel Motor Drive Electric Vehicles. Thus, a coordinated control is proposed to address this issue. In this paper, a two-layer hierarchical control strategy is utilized. In the upper-layer, a self-tunable super-twisting sliding mode control is utilized to deal with parametric uncertainties, and a Model Predictive Control (MPC) is used in order to allocate the control action to each AFS and DYC. Parametric uncertainties of tires' cornering stiffness, vehicle mass and moment of inertia are considered. Simulations with different road conditions for path-following scenario have been conducted in MATLAB/Simulink. An autonomous vehicle equipped with Four In-wheel Motor and two degrees of freedom vehicle dynamics model is used in this study. In the end, the performance of the proposed controller is compared with the MPC controller. Simulation results reveal that the proposed controller provides better path-following in comparison with the MPC controller.

Information Theory and Its Application in Machine Condition Monitoring.

Rotating machinery is part and parcel of modern industrial applications [...].

A Full Evaporation Static Headspace Gas Chromatography Method with Nitrogen Phosphorous Detection for Ultrasensitive Analysis of Semi-volatile Nitrosamines in Pharmaceutical Products.

The recent detection of potent carcinogenic nitrosamine impurities in several human medicines has triggered product recalls and interrupted the supply of critical medications for hundreds of millions of patients, illuminating the need for increased testing of nitrosamines in pharmaceutical products. However, the development of analytical methods for nitrosamine detection is challenging due to high sensitivity requirements, complex matrices, and the large number and variety of samples requiring testing. Herein, we report an analytical method for the analysis of a common nitrosamine, N-nitrosodimethylamine (NDMA), in pharmaceutical products using full evaporation static headspace gas chromatography with nitrogen phosphorous detection (FE-SHSGC-NPD). This method is sensitive, specific, accurate, and precise and has the potential to serve as a universal method for testing all semi-volatile nitrosamines across different drug products. Through elimination of the detrimental headspace-liquid partition, a quantitation limit of 0.25 ppb is achieved for NDMA, a significant improvement upon traditional LC-MS methods. The extraction of nitrosamines directly from solid sample not only simplifies the sample preparation procedure but also enables the method to be used for different products as is or with minor modifications, as demonstrated by the analysis of NDMA in 10+ pharmaceutical products. The in situ nitrosation that is commonly observed in GC methods for nitrosamine analysis was completely inhibited by the addition of a small volume solvent containing pyrogallol, phosphoric acid, and isopropanol. Employing simple procedures and low-cost instrumentation, this method can be implemented in any analytical laboratory for routine nitrosamine analysis, ensuring patient safety and uninterrupted supply of critical medications. Graphical Abstract.

A new deep domain adaptation method with joint adversarial training for online detection of bearing early fault.

For online early fault detection of rolling bearings in non-stop scenarios, one of the main concerns is the model bias caused by the distribution shift between offline and online working conditions. Under such concern, how to improve the feature sensitivity to early faults and the robustness of detection model has become a key challenge of improving the effectiveness of online detection. To solve this problem, a new online early fault detection method is proposed in this paper based on a strategy of deep transfer learning. First, a new robust state assessment method is presented. By introducing priori degradation information in the anomaly detection process of the isolated forest algorithm, this method can accurately assess the normal state and early fault state under noise interference. Second, a new deep domain adaptation algorithm is proposed. The algorithm uses the results of state assessment as output labels, and designs a deep domain adaptation neural network for joint adversarial training at feature level and model level simultaneously. Then a domain-invariant feature representation can be extracted from the data of different working conditions, and an online detection model can then be constructed. Comparative experiments are run on two bearing datasets IEEE PHM Challenge 2012 and XJTU-SY, and the results verifies the effectiveness of the proposed method in false alarm number and detection location.

Studies on Carrier Recombination in GaN/AlN Quantum Dots in Nanowires with a Core-Shell Structure.

GaN quantum dots embedded in nanowires have attracted much attention due to their superior optical properties. However, due to the large surface-to-volume ratio of the nanowire, the impacts of surface states are the primary issue responsible for the degradation of internal quantum efficiency (IQE) in heterostructured dot-in-nanowires. In this paper, we investigate the carrier recombination mechanism of GaN/AlN dot-in-nanowires with an in situ grown AlN shell structure. Ultraviolet photoelectron spectroscopy (UPS) measurements were performed to describe the band bending effect on samples with different shell thicknesses. Temperature-dependent photoluminescence (TDPL) data support that increasing the AlN shell thickness is an efficient way to improve internal quantum efficiency. Detailed carrier dynamics was analyzed and combined with time-resolved photoluminescence (TRPL). The experimental data are consistent with our physical model that the AlN shell can effectively flatten the band bending near the surface and isolate the surface non-radiative recombination center. Our systematic research on GaN/AlN quantum dots in nanowires with a core-shell structure may significantly advance the development of a broad range of nanowire-based optoelectronic devices.

Development of a simple liquid chromatography method for dissolution testing of low dose desogestrel and ethinylestradiol tablets.

Accurate quantitation of low dose, multi-active dissolution samples poses unique challenges in the pharmaceutical industry, often resulting in separate HPLC methods for each active or the use of multiple detectors for increased sensitivity. In this study, we report a fast, isocratic HPLC method utilizing only UV detection for dissolution testing of low dose desogestrel and ethinylestradiol tablets. Rapid separation is completed in 5 min using isocratic elution at a flow rate of 0.45 mL/min, with a column temperature at 30 °C, an injection volume of 50 µL and the detection wavelength at 200 nm. After extensive method development and optimization, the cyano stationary phase was used to overcome the large difference in hydrophobicity for desogestrel and ethinylestradiol, providing balanced retention for both analytes under isocratic elution. Chromatography modeling software was used to provide a rapid analysis of multiple columns and chromatography conditions. The optimized method boasts fast and efficient separation through use of a short, small I.D. column and a large injection volume of dissolution solution to achieve high sensitivity. The stable baseline from an isocratic separation allows low detection wavelengths to be used, resulting in accurate and precise quantitation of both desogestrel and ethinylestradiol. The method has been successfully validated for specificity, linearity, accuracy and precision in the range of 75 - 600 ng/mL for desogestrel and 10 - 80 ng/mL for ethinylestradiol using both HPLC and UHPLC systems. The method robustness was characterized using a design of experiment approach, and the operational design region of the method was established.

Analysis of thermolabile residual solvents in a spray dried dispersion using static headspace gas chromatography.

In this study, we discuss the development of a static headspace gas chromatography method for the analysis of residual acetone as well as its enriched impurities including mesityl oxide and diacetone alcohol, in a spray dried dispersion. The major challenges include the instability of mesityl oxide and diacetone alcohol at high temperature and peak tailing of diacetone alcohol. It was found that the headspace oven temperature has to be controlled to 150°C or below to prevent degradation beyond an acceptable level (< 1%). The peak tailing of diacetone alcohol was attributed to the "Phase Soaking" effect due to excessive diluent, which may condense and temporarily modify the stationary phase. The peak shape of diacetone alcohol is dependent on the column loading capacity and the peak area of N-methyl pyrrolidone, the solvent that elutes after diacetone alcohol. The headspace oven temperature was set at 140°C, where the highest response ratio of diacetone alcohol/N-methyl pyrrolidone at 1.46 and thus the best sensitivity was obtained. The calculated quantitation limits were 1 ppm for acetone, 3 ppm for mesityl oxide and 31 ppm for diacetone alcohol. The method successfully passed validation criteria for specificity, linearity, accuracy, and precision.

Health condition identification of planetary gearboxes based on variational mode decomposition and generalized composite multi-scale symbolic dynamic entropy.

This paper proposes a novel fault diagnosis method based on variational mode decomposition (VMD) and generalized composite multi-scale symbol dynamic entropy (GCMSDE) to identify the different health conditions of planetary gearboxes. First, VMD is adopted to remove the noises and highlight the fault symptoms. Second, GCMSDE is utilized to extract the fault features from the denoised vibration signals. Third, the Laplacian score (LS) approach is employed to refine the fault features. Finally, the new features are fed into Softmax regression to identify the health conditions of planetary gearboxes. The proposed method is numerically and experimentally demonstrated to be able to differentiate seven localized fault types on the sun gear, planet gear and ring gear of planetary gearboxes.

The growth of sulfur adlayers on Au(100).

We have studied the growth of S layers adsorbed on Au(100) with low-energy electron diffraction (LEED), X-ray photoemission spectra (XPS), and scanning tunneling microscope (STM). Three phases of S/Au(100)-(2 × 2), trimer, and c(2 × 4)-are identified; the latter two are not previously reported. A dose of S2 at 300 K transformed Au(100)-(5 × 20) initially into the (2 × 2) phase and formed the c(2 × 4) phase at a saturation coverage. The STM results show that monolayer Au islands formed during the initial S dose and remained throughout the growth, resulting in a rough c(2 × 4) surface. We show that a highly ordered c(2 × 4) phase can be obtained with a flat (2 × 2) phase as an intermediate step during growth. Based on the evolution of XPS and STM images with varied S2 dose, the components of S 2p are assigned and structural models for the various S/Au(100) phases are proposed. In the (2 × 2) phase, one S atom resides on a four-fold hollow site in each (2 × 2) unit cell, corresponding to a S coverage of 0.25 ML; in the trimer phase, three S atoms form a trimer residing on a four-fold hollow site in each (2 × 2) unit cell, corresponding to a S coverage of 0.75 ML; in the c(2 × 4) phase, there are five S atoms in each primitive unit cell of c(2 × 4); three of them form a trimer residing on a four-fold hollow site, and the other two form a dimer located on the top of the trimer, corresponding to a nominal S coverage of 1.25 ML. With the proposed structural models, the growth of S on Au(100) at 300 K is described in detail.