双联斜齿轮斜齿脱模机构及改进型三板模设计

依据双联斜齿轮和单斜齿轮2个塑件同模注塑的需要,以双联斜齿轮塑件的模腔设计为中心,设计了一副一模两腔点浇口多板模注塑模具。模具中,双联齿轮的上、下斜齿轮斜齿分别使用2个可旋转斜齿轮成型件进行成型;单斜齿轮使用1个可旋转斜齿轮成型件进行成型。3个斜齿成型件设置成转动型成型件,依靠塑件的移动以驱动斜齿成型件转动,达到塑件斜齿脱模的目的;塑件的脱模移动有两种动力来源,一种是模具模板的打开驱动,一种是使用推管顶出驱动。模具为一种改进型三板模结构,定模侧增加了流道板,动模侧增加了垫板,从而可以实现模具的4次开模;4次开模动作中,2次用于流道废料的脱模,1次用于流道镶件的先抽芯驱动,1次用于模腔的打开和双联斜齿轮上斜齿轮的旋转脱模驱动。     

基于误差估值累加开环校正的诱导式欺骗检测方法

诱导式卫星欺骗干扰可诱导航空器逐渐偏离预定航迹，难以被发现，因此及时有效地检测干扰是飞行安全的保障。在现有紧组合导航体制基础上，设计了一种基于误差估值累加开环校正的紧组合导航结构，并证明了其性能与传统闭环校正紧组合导航性能等效。在此结构中，将紧组合导航系统与自适应序贯概率比检测方法结合，提出了一种基于误差估值累加开环校正的诱导式欺骗检测方法，融合紧组合导航信息与其他不受欺骗影响的导航信息，构建欺骗检测统计量进行诱导式欺骗检测。仿真结果表明，开环校正结构可避免随时间累加的惯性导航系统误差所导致的组合导航滤波器发散问题，同时欺骗检测方法可进一步提高算法对“最坏”情形下微小诱导式欺骗的检测效果。     

Manipulation of thick-walled PEEK bushing crystallinity and modulus via instrumented compression molding

An instrumented hot compression molding apparatus was fabricated to allow real-time monitoring and precise temperature control during the compaction and consolidation of large polyether ether ketone (PEEK) products. The objective was to determine the impact of controlled variables on the properties of the molded article. Four different strategies were designed to control the mold thermal profiles. The average crystallinity in a commercial molding process is restricted due to large thermal masses with low thermal conductivity. In contrast, this research was able to reduce the crystallinity range from 33% to 6% by developing a special controlled apparatus and implementing new processing methodologies. In this study, PEEK showed a significant increase in the modulus compared to typical values measured on commercially produced analogs, and a higher degree of property uniformity. In a single commercially molded PEEK billet, compressive modulus variability was 13% at room temperature, and 21% at 225°C. Properties of billets produced using the laboratory apparatus showed a reduction in variability to 2%.     

Bayesian Inference for ARFIMA Models

This article develops practical methods for Bayesian inference in the autoregressive fractionally integrated moving average (ARFIMA) model using the exact likelihood function, any proper prior distribution, and time series that may have thousands of observations. These methods utilize sequentially adaptive Bayesian learning, a sequential Monte Carlo algorithm that can exploit massively parallel desktop computing with graphics processing units (GPUs). The article identifies and solves several problems in the computation of the likelihood function that apparently have not been addressed in the literature. Four applications illustrate the utility of the approach. The most ambitious is an ARFIMA(2,d,2) model for the Campito tree ring time series (length 5405), for which the methods developed in the article provide an essentially uncorrelated sample of size 16,384 from the exact posterior distribution in under four hours. Less ambitious applications take as little as 4 minutes without exploiting GPUs.     

Study on the blackening phenomenon of leaded glass during microgroove molding using nickel phosphorous mold

Precision glass molding (PGM) is a recently developed method to fabricate glass microgroove components. Lead glass is commonly used as an optical material due to its high refractive index and low transition temperature. A nickel-phosphorous (Ni–P) plated mold is traditionally employed in the PGM process for microstructures optics. However, leaded glass is subject to color change and can blacken during the PGM process, reducing the light transmittance of microgrooves. In this paper, an equation for the redox reaction between Ni and Pb is proposed, which is based on the diffusion of inner Ni atoms to the surface of the mold and the standard electrode potential of the Pb ions in leaded glass. A viscoelastic constitutive model of the glass is established to simulate the compression stress distribution during molding. Finally, the effects of molding pressure, molding temperature, and mold material on glass blackening are studied. The results show that the blackening of leaded glass is caused by Pb enriching the surface. The rise in molding stress and temperature increases the deformation of Ni–P plating, which promotes the diffusion of Ni atoms. By adding a titanium incorporated diamond-like carbon (Ti-DLC) coating, the deformation of the Ni–P plating during molding is suppressed, and the diffusion of Ni atoms can be prevented. In this way, the blackening of leaded glass can be prevented.     

Biodegradation of poly(lactic acid)—cassava bagasse composites produced by injection molding

Neat poly (lactic acid) (PLA) and PLA/cassava bagasse (CB) composites were used to produce seedling tubes by extrusion and injection molding. The tubes were buried in simulated soil, and their biodegradation was investigated by weight loss, scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy (FTIR). After 180 days, the composites' biodegradation was higher than neat PLA material, and the higher the CB content, the higher the biodegradation, which caused fissures and voids in the material. The biodegradation of PLA/CB composites increased the phosphorus content in the soil after 180 days. Composites of PLA with CB, an abundant agro-industrial residue in Brazil, are promising because they can reduce the environmental impact due to CB's proper destination, and the composites' costs and biodegradation are faster than pure PLA material. Both the faster biodegradation of the tube and the higher P content are advantageous for seedling tubes.     

高熔体流动速率薄壁注塑聚丙烯专用料的结构与性能分析

采用多种分析测试方法，对3类高熔体流动速率薄壁注塑聚丙烯（PP）专用料的熔融结晶性能、光学性能、分子量分布、力学性能、毛细管流变性能、热收缩性能进行了分析研究。结果表明，K1860与2种市售主流PP1、PP2各项性能相当，其中K1860分子量分布相对PP1、PP2较窄，弯曲性能、拉伸性能更优，可满足大型薄壁注塑制品的生产要求。     

我国中空吹塑行业发展现状及“十四五”期间重点产品、工艺和设备发展方向

从原料、中空成型机和吹塑工艺等方面介绍了我国中空吹塑行业发展现状，并对电子化学品专用超洁净桶、高压储氢四型瓶、全电动中空成型机、微发泡中空成型技术和挤吹聚对苯二甲酸乙二醇酯（PET）容器等在“十四五”期间的重点产品、工艺和设备发展方向进行了展望。     

Vibration analysis and identification of breathing cracks in beams subjected to single or multiple moving mass using online sequential extreme learning machine

In this paper, a novel approach was presented to vibration analysis and identification of breathing cracks in Timoshenko beam under single or multiple moving mass. Dynamic strain energies (DSEs) and translational accelerations in beam structures under moving mass were used as forward problem and application of an emergent learning algorithm called the online sequential extreme learning machine algorithm as inverse problem to predict crack depths and locations. To demonstrate the potential of the proposed vibration analysis over existing ones, two validation studies have been done. To evaluate the proposed method to identify breathing cracks, two examples, namely, clamped–clamped beam and two span continuous beams have been studied. Also, the effect of the discrepancy in stiffness between the finite-element model and the actual tested dynamic system has been investigated. Another examination has been performed in which moving mass with different speeds are utilized. Also, the effect of multi mass passing through the beam has been studied. The obtained results indicated that the proposed method could identify the breathing cracks existence and severity in the beam under moving mass using DSE and accelerations, which may be noisy or noise free.