Understanding the Bonding Process of Ultrasonic Additive Manufacturing

Ultrasonic additive manufacturing (UAM) is an additive manufacturing technolo-gy that combines an additive process of joining thin metal foils layer by layer using ultra-sound and a subtractive process of CNC contour milling. UAM can join similar or dis-similar materials and allows for embedded objects such as fibers and electronics. Despite these advantages, the UAM process exhibits a critical bonding failure issue as the height of the built feature approaches its width. Based on previous studies, we believe that the loss of bonding is due to complex dynamic interactions between the high frequency excitations of the sonotrode and the built feature. While the previous investigations have qualitatively explained the cause of the height to width ratio problem by showing the change of dynamic states as new layers of foils are deposited, they do not explain how the change of dynamics affects bond formation. Specifically, a UAM model is needed to be able to predict the bond quality, i.e. bond or debond, as the dynamics of the substrate state change. In order to establish the model, a comprehensive understanding of the welding process and bonding mechanisms is required. Due to the complexity of the bonding process, the model is first decomposed into several sub-models based on the different factors that affect the process. The key factors that govern the bonding process: material plasticity, heat transfer, friction, and dynamics need to be characterized. An experiment setup is designed to investigate and characterize the effects of ultrasound on aluminum 6061-O, 6061-T6, 1100-O, and Copper 11000-O. A plasticity model is proposed by modifying the Johnson-Cook plasticity model to introduce strain-rate hardening and acoustic softening effects. A lumped parameter model consisting of mass-spring network is proposed to re-place the finite element dynamic model for reducing computational cost. An asperity lay-er model based on sinusoidal shape solid asperities is proposed to associate the plastic deformation of the material to the linear weld density of the bonding at the interface. Other sub-models (thermal and friction models) are defined based on studies in the literature. The sub-models are implemented in the commercial software ABAQUS by using user subroutines and are integrated into one UAM model. The model is validated by comparing its prediction with experimental results in the literature. The proposed model can thus be used to understand the effects of dynamics on the stress state close to the bond inter-face, understand the energy flow within the UAM system, and evaluate the effects of different process parameters on the bond quality for process optimization

Efficacy of pegylated interferon α2a in patients without HBeAg loss after the withdrawal of long-term lamivudine therapy

BACKGROUND: Improving the HBe seroconversion rate of patients without HBeAg loss after long-term lamivudine therapy has become an urgent clinical problem that we have to face. Unfortunately, there is no consensus on the mananement of these patients. The aim of this study was to evaluate the efficacy of pegylated interferon (PEG-IFN) α2a in patients without HBeAg loss after the withdrawal of long-term lamivudine therapy. METHODS: Fifty patients with chronic hepatitis B without the loss of HBeAg after ≥96 weeks of lamivudine treatment were enrolled to withdraw from treatment to induce a biochemical breakthrough. Patients who achieved a biochemical breakthrough within 24 weeks received 48-weeks of PEG-IFN α2a therapy, and were then assessed during a subsequent 24-week follow-up period. RESULTS: Forty-three (86.0%) patients achieved a biochemical breakthrough within 24 weeks of lamivudine withdrawal. The rates of combined response (both undetectable HBV DNA and HBeAg loss) and HBsAg loss were alone 51.2% and 20.9%, respectively after 48 weeks of PEG-IFN α2a therapy, and 44.2% and 18.6%, respectively, at 24 weeks after treatment cessation. The end-of-treatment combined response rate was 65.4% among patients with a baseline HBsAg <20,000 IU/mL, which was significantly higher than 29.4% of patients with HBsAg ≥20,000 IU/mL (P=0.031). For patients with HBsAg levels <1,500 IU/mL at 12 and 24 weeks therapy, the end-of-treatment combined response rate was 68.2% and 69.0%, which were both significantly higher than patients with HBsAg ≥1,500 IU/mL (33.3% and 14.3%; P=0.048 and 0.001). The end-of-treatment combined response rate was significantly higher among patients with HBV DNA<10(5) copies/mL (76.2%) compared to patients with HBV DNA ≥10(5) copies/mL (27.3%) after 24 weeks of therapy (P=0.004). CONCLUSION: Retreatment with PEG-IFN α2a was effective and safe for patients without HBeAg loss after the withdrawal of long-term lamivudine therapy. HBsAg levels at the baseline, 12 and 24 weeks of therapy, and HBV DNA levels at 24 weeks of therapy, can predict the effect of PEG-IFN α2a after 48 weeks of therapy

Cellulase immobilization on superparamagnetic nanoparticles for reuse in cellulosic biomass conversion

Current cellulosic biomass hydrolysis is based on the one-time use of cellulases. Cellulases immobilized on magnetic nanocarriers offer the advantages of magnetic separation and repeated use for continuous hydrolysis. Most immobilization methods focus on only one type of cellulase. Here, we report co-immobilization of two types of cellulases, β-glucosidase A (BglA) and cellobiohydrolase D (CelD), on sub-20 nm superparamagnetic nanoparticles. The nanoparticles demonstrated 100% immobilization efficiency for both BglA and CelD. The total enzyme activities of immobilized BglA and CelD were up to 67.1% and 41.5% of that of the free cellulases, respectively. The immobilized BglA and CelD each retained about 85% and 43% of the initial immobilized enzyme activities after being recycled 3 and 10 times, respectively. The effects of pH and temperature on the immobilized cellulases were also investigated. Co-immobilization of BglA and CelD on MNPs is a promising strategy to promote synergistic action of cellulases while lowering enzyme consumption

Rolling bearing fault diagnosis by a novel fruit fly optimization algorithm optimized support vector machine

Based on the nonlinear and non-stationary characteristics of rotating machinery vibration, a FOA-SVM model is established by Fruit Fly Optimization Algorithm (FOA) and combining the Support Vector Machine (SVM) to realize the optimization of the SVM parameters. The mechanism of this model is imitating the foraging behavior of fruit flies. The smell concentration judgment value of the forage is used as the parameter to construct a proper fitness function in order to search the optimal SVM parameters. The FOA algorithm is proved to be convergence fast and accurately with global searching ability by optimizing the analog signal of rotating machinery fault. In order to improve the classification accuracy rate, built FOA-SVM model, and then to extract feature value for training and testing, so that it can recognize the fault rolling bearing and the degree of it. Analyze and diagnose actual signals, it prove the validity of the method, and the improved method had a good prospect for its application in rolling bearing diagnosis

Is Black-Scholes Model An Appropriate Option Pricing Tool in Chinese Stock Market?

In order to further develop the derivative markets, the Chinese government has decided to launch stock options as soon as possible. The Black-Scholes formula has been the most commonly used option pricing model today. But is the Black-Scholes model an appropriate tool for pricing of Chinese stock option? If the Black-Sholes model is perfect, implied volatilities should be exactly the same for options on the same underlying asset with different strike prices. This project, however, discovered that the pattern of the implied volatility across different exercise prices is negatively skewed, that is, the volatility decreases as the strike price increases, indicating that the Black-Sholes model is flawed to value Chinese stock options. This study also founds that the volatility skew arises from the unrealistic assumptions of the Black-Sholes model, such as normally-distributed return, risk-neutral investors and no-arbitrage opportunities. Additionally, China's unique business environment also weakens the reliability of the Black-Sholes model