Characterization of PA-12 specimens fabricated by projection sintering at various sintering parameters

Large area projection sintering (LAPS) promises to be a new method in the field of additive manufacturing. Developed in the Mechanical Engineering Department, University of South Florida, LAPS uses long exposure times over a broad area of powder to fuse into dense, reproducible materials. In contrast, LS, a common powder-based additive manufacturing, uses a focused beam of light scanned quickly over the material. Local regions of concentrated high-energy bursts of light lead to higher peak temperatures and differing cooling dynamics and overall crystallinity. The mechanical properties of laser sintered specimens suffer because of uneven particle fusion. LAPS offers the capacity to fine-tune fusion properties through enhanced thermodynamic control of the heating and cooling profiles for sintering. Further research is required to identify the relationship between LAPS build settings and part properties to enable the fabrication of custom parts with desired properties. This study examines the influence of LAPS sintering parameters on chemical structures, crystallinity, mechanical, and thermal properties of polyamide-12 specimens using powder X-ray diffraction, Fourier transform infrared spectroscopy, differential scanning calorimetry, small-angle X-ray scattering, scanning electron microscopy, and microhardness testing. It was observed that higher crystallinity was imparted to specimens that were sintered for a shorter time and vice versa.     

Relationship between microstructure and mechanical properties of polyether block amide foams

The mechanical behaviors of five polyether block amide foams, obtained by mold-opening foam injection process, were investigated with regard to their microstructures. The materials vary in mass ratios of hard versus soft segments, and/or in process packing time. The resulting microstructures have been characterized in terms of cavity size and shape ratios, by analyzing scanning electron microscope images after careful sample preparation. The foam mechanical responses have been characterized in compression at small and large strain. At small strain, the initial linear part of the stress–strain curve is enhanced firstly by the hard segment mass ratio and secondly by the fineness of the microstructure. Similar results have been obtained at large strain. The foam viscoelasticity at large strain has been characterized by stress relaxation and strain recovery tests, relevant for foam applications. Reduced packing time and pressure have been shown to lead to the presence of undesired large cavities. The morphological defects appear to have a negligible impact on the macroscopic mechanical behavior of the foams at infinitesimal strain, but lead to critical inconsistency at large strain. Furthermore, the mechanical behavior of the tested polyether block amide foams is controlled first by hard versus soft segments ratio, and second by the microstructure fineness.     

3D MRI of explanted sheep hearts with submillimeter isotropic spatial resolution: comparison between diffusion tensor and structure tensor imaging

Magnetic Resonance Materials in Physics, Biology and Medicine - The aim of the study is to compare structure tensor imaging (STI) with diffusion tensor imaging (DTI) of the sheep heart...     

基于介电泳效应的高速抛光电极分布仿真研究与实验验证

目的解决传统平面环抛过程中存在的两种问题:(1)抛光液受抛光盘和工件旋转离心力作用而抛光液在加工区域分布不均,导致加工工件高平面度差;(2)抛光液受到的离心力作用限制了抛光盘转速,导致抛光效率低。方法提出一种基于介电泳效应的平面抛光方法(DEPP),在抛光区域增加一个非均匀电场,利用中性粒子在非均匀电场中极化后受介电泳力的作用,使其具有向电极和抛光区域中心运动的现象,降低旋转离心力对抛光液的甩出作用,实现对平面工件的高速、高精度抛光。采用有限元分析软件数值模拟极化后磨粒所受介电泳力对离心力的抑制作用,优化产生非均匀电场的不同电极宽度,得到最优非均匀电场电极分布参数,实际测量优化电极后抛光液所受介电泳力的大小和方向,最后搭建试验平台验证介电泳效应高速抛光平面工件的有效性。结果提高抛光盘转速,进行抛光磨砂玻璃对比实验,加工1 h以后,采用介电泳效应抛光能完全去除玻璃磨砂层,工件平整度好,最终RMS值为0.276λ;无介电泳效应抛光后,工件中心部分磨砂层仍有存在,工件平整度相对较差,最终RMS值为0.694λ。通过测量加工去除量,介电泳效应抛光比无介电泳效应抛光的去除率提升了18%结论通过仿真模拟和实验验证,证明了调整电极布置形式以及优化电极分布参数后,介电泳效应高速平面抛光的方法能够有效提升抛光效率和抛光后工件表面平面度。     

Micro cam system driven by electrostatic comb-drive actuators based on SOI-MEMS technology

Microsystem Technologies - This paper reports a design and fabrication process of a micro cam system (MCS) with a flat-faced translating follower. The cam rim with cover diameter of 2.4 mm...     

A printed circuit board capacitive sensor for air bubble inside fluidic flow detection

This paper presents a three-electrode capacitive fluidic sensor for detecting an air bubble inside a fluidic channel such as blood vessels, oil or medical liquid channels. The capacitor is designed and fabricated based on a printed circuit board (PCB). The electrodes are fabricated by using copper via structure through top to bottom surface of the PCB. A plastic pipe is layout through the capacitive sensor and perpendicular to the PCB surface. Capacitance of sensor changes when an air bubble inside fluidic flow cross the sensor. The capacitance change can be monitored by using a differential capacitive amplifier, a lock-in amplifier, filter and an NI acquisition card. Signal is processed and calculated on a computer. Air bubble inside the liquid flow are detected by monitor the unbalance signal between the three electrode potential voltages. Output voltage depends on the volume of the air bubble due to dielectric change between capacitor’s electrodes. Output voltage is up to 53 mV when an 2.28 mm³ air bubble crosses the sensing channel. Air bubble velocity can be estimated based on the output pulse signal. This proposed fluidic sensor can be used for void fraction detection in medical devices and systems; fluidic characterization; and water–gas, oil–water and oil–water–gas multiphase flows in petroleum technology. That structure also can apply to the micro-size for detecting in microfluidic to monitor and control changes in microfluidic channels.     

Constructing online switching barriers: examining the effects of switching costs and alternative attractiveness on e-store loyalty in online pure-play retailers

Developing switching barriers to retain customers has become a critical marketing strategy for online retailers. However, research on the role of switching barriers in e-retailing is still limited. Recent trends show that when competitors are just one click away, it is questionable if customer loyalty can be achieved at all in online environments. This leads to the research question on whether switching barriers have any impact on e-loyalty in pure-play retailers. The paper examines the influence of switching barriers on customer retention (i.e., e-store loyalty) and further investigates the moderating effects of switching costs and alternative attractiveness. Data were gathered via a survey of 590 shoppers of online pure-play retailers in the UK. Findings show that customer satisfaction and the two dimensions of switching barriers (perceived switching costs and perceived attractiveness of alternatives) significantly influence customer loyalty. Contrary to findings in earlier studies, it was found that switching costs did not moderate the relationships between satisfaction and loyalty nor between perceived attractiveness of alternatives and loyalty. The paper makes imperative recommendations to develop switching barriers and to foster loyalty along with suggestions for future research.     

High quality voice conversion using prosodic and high-resolution spectral features

Voice conversion methods have advanced rapidly over the last decade. Studies have shown that speaker characteristics are captured by spectral feature as well as various prosodic features. Most existing conversion methods focus on the spectral feature as it directly represents the timbre characteristics, while some conversion methods have focused only on the prosodic feature represented by the fundamental frequency. In this paper, a comprehensive framework using deep neural networks to convert both timbre and prosodic features is proposed. The timbre feature is represented by a high-resolution spectral feature. The prosodic features include F0, intensity and duration. It is well known that DNN is useful as a tool to model high-dimensional features. In this work, we show that DNN initialized by our proposed autoencoder pretraining yields good quality DNN conversion models. This pretraining is tailor-made for voice conversion and leverages on autoencoder to capture the generic spectral shape of source speech. Additionally, our framework uses segmental DNN models to capture the evolution of the prosodic features over time. To reconstruct the converted speech, the spectral feature produced by the DNN model is combined with the three prosodic features produced by the DNN segmental models. Our experimental results show that the application of both prosodic and high-resolution spectral features leads to quality converted speech as measured by objective evaluation and subjective listening tests.     

Factors influencing the decision to crowdsource: A systematic literature review

Crowdsourcing is currently attracting much attention from organisations for its competitive advantages over traditional work structures regarding how to utilise skills and labour and especially to harvest expertise and innovation. Prior research suggests that the decision to crowdsource cannot simply be based on perceived advantages; rather multiple factors should be considered. However, a structured account and integration of the most important decision factors is still lacking. This research fills the gap by providing a systematic literature review of the decision to crowdsource. Our results identify nine factors and sixteen sub-factors influencing this decision. These factors are structured into a decision framework concerning task, people, management, and environmental factors. Based on this framework, we give several recommendations for managers making the crowdsourcing decision.