电弧熔丝增材制造过程传感与控制研究进展及展望

电弧熔丝增材制造(Wire and arc additive manufacturing,WAAM)作为金属增材制造技术的一个重要分支,以电弧为载能束逐层熔化金属丝材,适合中大尺寸复杂金属构件的快速制造,在航空航天、国防领域展现出广阔的应用前景。然而,成形精度低、过程稳定性差、缺陷控制难等问题限制了该技术的高效、高质量发展与应用。为满足高可靠、高自动化与高质量制造的要求,对WAAM全过程实施在线监测与闭环控制已势在必行。分析了WAAM成形质量的特征参量及其主要影响因素,阐述了WAAM过程传感方法的原理与研究现状,总结了WAAM成形质量控制方法,指出了未来WAAM过程传感与控制技术的主要发展方向。

经络信息系统穴位及传导区结构特征的理论模型

目的:现代科学技术实验初步证明了经络的客观存在,但无法得到明确专一的经络生物组织结构支持,经络需要更多的深入研究。本研究从理论上分析经络的特征结构。方法:基于符合物理化学原理和生物遗传变异信息原理的经络信息产生传导方程,及经络通过的生理组织特性进行研究。结果:理论表明经络必须存在穴位和信息传导区,经典经络理论中的感传一体为感传交替过程。经络穴位之间的传导区域大小主要决定于经络物质信息在经络组织侧面渗漏率。相同或相似生理组织区域,经络穴位间距相近。而不同生理组织区域,经络穴位之间的尺度变化明显。经络中的生物信息传感器高密度集中于穴位的近点状区域,而传导线上的密度极低。结论:本研究首次从理论上证明了经络结构的基本特征。同时建立了穴位之间尺度的定量变化规律。证明及修正了传统的经络信息产生传导假说。这也从理论上证明了经络的客观存在。

光纤传感过程分析法评价不同厂家氨茶碱片的体外溶出度

目的:采用光纤传感过程分析法评价不同厂家氨茶碱片的体外溶出度,以考察不同厂家样品的质量差异。方法:采用对照品法,对5个厂家的片剂在4种溶出介质(水、pH1.2盐酸溶液、pH4.5醋酸盐缓冲液和pH6.8磷酸盐缓冲液)中的溶出行为进行测定,并以相似因子法进行比较。结果:氨茶碱片在不同溶剂中的溶出行为不一致,只有B厂样品在4种介质中均具有较好的溶出度;以B厂样品为参比制剂,其他4个厂样品与其比较的相似因子值均小于50%。结论:光纤传感过程分析法能够准确、方便地提供药物的溶出数据、反映药物的溶出行为,为全面考察药物的内在质量提供了良好的检测手段;不同厂家氨茶碱片质量有明显差异。

光纤传感过程分析比较不同厂家缬沙坦胶囊的溶出度

目的:考察不同厂家缬沙坦胶囊体外溶出度曲线,比较不同厂家药品的内在质量,为药品质量控制和临床用药提供参考。方法:用光纤传感过程分析技术监测4个厂家缬沙坦胶囊在pH 6.8,pH 4.5,pH 1.2及水4种溶出介质的溶出过程。采用f2因子法对溶出曲线的相似性进行评价。结果:4个厂家缬沙坦胶囊在pH 6.8磷酸盐缓冲液中30 min时溶出度均大于80%符合药典规定,但溶出曲线差异很大;在其他3种介质中的溶出度偏低且存在较大差异;比较结果说明不同药厂缬沙坦胶囊的质量存在显著性差异。结论:采用FODT法过程分析检测固体制剂的溶出曲线比单点溶出度测定更能直观反映不同厂家制剂工艺、均匀度和药品质量的差异。对不同来源的相同制剂考察不同溶出介质的溶出曲线是非常有必要的。

光纤传感过程分析利巴韦林片的溶出度

目的:建立过程分析利巴韦林片溶出度的方法。方法:参照《美国药典》中利巴韦林片的溶出条件,采用光纤传感药物溶出度实时测定仪建立利巴韦林片的溶出度分析方法(简称FODT法),并与《美国药典》方法进行比较;同时建立可测定3种不同规格(0.02、0.05、0.1 g)利巴韦林片溶出度的FODT法条件(探头规格、检测波长);另外对11批不同厂家产品进行了考察。结果:两种方法所得溶出度结果均符合要求,但采用FODT法可以观察不同厂家样品及同一厂家不同片之间药物实时溶出情况的差异;3种规格样品采用FODT法时的探头规格和检测波长分别为5、2、1 mm和210、228、234 nm。结论:所建立的测定利巴韦林片溶出度的FODT法能够直观有效地监测药物的溶出过程,连续定量地反映了样品的体外溶出特性。

外翻肠囊模型结合光纤传感过程分析蒜氨酸在家兔肠道的吸收

目的:考察大蒜提取物蒜氨酸在家兔肠道内的吸收特性,为新药研究提供实验数据。方法:采用家兔,利用外翻肠囊模型与光纤传感过程分析系统结合,实时原位测定蒜氨酸在家兔肠道内的浓度变化。研究蒜氨酸在家兔十二指肠、空肠、回肠、结肠的4个肠段,高、中、低3个浓度的吸收,每个肠段的每个浓度进行6样本(6只家兔,共计18只家兔)分析,每个肠段接触蒜氨酸的浓度根据半数致死量确定,配制蒜氨酸溶液的质量浓度分别为(700,50,30 mg·L-1),于光纤传感过程分析仪上进行检测。结果:家兔不同肠段对蒜氨酸的吸收能力大小:回肠>空肠>结肠>十二指肠;不同浓度下各肠段的吸收数An值空肠分别为(2.59±0.21,5.40±0.11,3.81±0.27);回肠分别为(5.25±0.07,3.02±0.19,4.09±0.18),各浓度An值均大于1.15,十二指肠分别为(0,0.95±0.16,1.09±0.28);结肠分别为(0.50±0.13,0.90±0.22,1.08±0.24),各浓度An值均<1.15。各浓度在4个肠段的Ke值除十二指肠段外(0,0.007±0.02,0.004±0.09)其余肠段的Ke值都<0.01 cm·min-1。结论:利用光纤传感系统实时测定蒜氨酸原料药在肠道的浓度变化,从而研究其在各肠段的吸收,结果表明蒜氨酸在除十二指肠段外其余肠段的渗透性很好。

核电厚壁管道自动焊在线监测及评估系统研究

为了在保证核电站设备安装焊接质量的同时,进一步提高焊接效率,实现厚壁管道自动化、智能化生产,提出了综合运用焊接过程电参数、焊接熔池表面几何形貌以及焊缝红外温度场信息等工艺,实时对焊接质量进行评估及预测。经过试验验证,该方法在质量监测及在线评估过程中均取得了较好的效果,为后续核电厚壁管道自动焊在线监测及评估系统的进一步应用推广奠定了研究基础。

基于单片机技术测量微弱电流信号系统的研究

随着微电子技术的迅速发展,电子产品已经进入了国民经济的各个领域。展望21世纪,信息高速公路的开通,以计算机为核心的多媒体信息网将进入千家万户,对人类社会的生活方式将再一次进行重大变革,其前途不可限量。同时,对于测量技术也是一样的,当测量电路与计算机接轨以后,以其测量精度高、测量的速度快、以及操作的简便性价比高而得到了广泛的发展和应用。

Fuzzy Neural Network Model of 4-CBA Concentration for Industrial Purified Terephthalic Acid Oxidation Process

A fuzzy neural network (FNN) model is developed to predict the 4-CBA concentration of the oxidation unit in purified terephthalic acid process. Several technologies are used to deal with the process data before modeling.First,a set of preliminary input variables is selected according to prior knowledge and experience. Secondly,a method based on the maximum correlation coefficient is proposed to detect the dead time between the process variables and response variables. Finally, the fuzzy curve method is used to reduce the unimportant input variables.The simulation results based on industrial data show that the relative error range of the FNN model is narrower than that of the American Oil Company (AMOCO) model. Furthermore, the FNN model can predict the trend of the 4-CBA concentration more accurately.

Faulty Sensor Detection and Reconstruction for a PVC Making Process

Based on principal component analysis, this paper presents an application of faulty sensor detection and reconstruction in a batch process, polyvinylchloride (PVC) making process. To deal with inconsistency in process data, it is proposed to use the dynamic time warping technique to make the historical data synchronized first,then build a consistent multi-way principal component analysis model. Fault detection is carried out based on squared prediction error statistical control plot. By defining principal component subspace, residual subspace and sensor validity index, faulty sensor can be reconstructed and identified along the fault direction. Finally, application results are illustrated in detail by use of the real data of an industrial PVC making process.

Stimuli-responsive Membranes： Smart Tools for Controllable Mass-transfer and Separation Processes

As emerging artificial biomimetic membranes, smart or intelligent membranes that are able to respond to environmental stimuli are attracting ever-increasing interests from various fields. Their permeation properties including hydraulic permeability and diffusional permeability can be dramatically controlled or adjusted self-regulatively in response to small chemical and/or physical stimuli in their environments. Such environmental stimuli-responsive smart membranes could find myriad applications in numerous fields ranging from controlled release to separations. Here the trans-membrane mass-transfer and membrane separation is introduced as the beginning to initiate the requirement of smart membranes, and then bio-inspired design of environmental stimuli-responsive smart membranes and four essential elements for smart membranes are introduced and discussed. Next, smart membrane types and their applications as smart tools for controllable mass-transfer in controlled release and separations are reviewed. The research tooics in the near future are also suggested.

Soft Sensor for Inputs and Parameters Using Nonlinear Singular State Observer in Chemical Processes

Chemical processes are usually nonlinear singular systems.In this study,a soft sensor using nonlinear singular state observer is established for unknown inputs and uncertain model parameters in chemical processes,which are augmented as state variables.Based on the observability of the singular system,this paper presents a simplified observability criterion under certain conditions for unknown inputs and uncertain model parameters.When the observability is satisfied,the unknown inputs and the uncertain model parameters are estimated online by the soft sensor using augmented nonlinear singular state observer.The riser reactor of fluid catalytic cracking unit is used as an example for analysis and simulation.With the catalyst circulation rate as the only unknown input without model error,one temperature sensor at the riser reactor outlet will ensure the correct estimation for the catalyst circulation rate.However,when uncertain model parameters also exist,additional temperature sensors must be used to ensure correct estimation for unknown inputs and uncertain model parameters of chemical processes.

Dynamic soft sensor development based on Gaussian mixture regression for fermentation processes

The dynamic soft sensor based on a single Gaussian process regression(GPR) model has been developed in fermentation processes.However,limitations of single regression models,for multiphase/multimode fermentation processes,may result in large prediction errors and complexity of the soft sensor.Therefore,a dynamic soft sensor based on Gaussian mixture regression(GMR) was proposed to overcome the problems.Two structure parameters,the number of Gaussian components and the order of the model,are crucial to the soft sensor model.To achieve a simple and effective soft sensor,an iterative strategy was proposed to optimize the two structure parameters synchronously.For the aim of comparisons,the proposed dynamic GMR soft sensor and the existing dynamic GPR soft sensor were both investigated to estimate biomass concentration in a Penicillin simulation process and an industrial Erythromycin fermentation process.Results show that the proposed dynamic GMR soft sensor has higher prediction accuracy and is more suitable for dynamic multiphase/multimode fermentation processes.

Outlining a stepwise,multi-parameter debris flow monitoring and warning system:an example of application in Aizi Valley,China

In recent years, the increasing frequency of debris flow demands enhanced effectiveness and efficiency of warning systems. Effective warning systems are essential not only from an economic point of view but are also considered as a frontline approach to alleviate hazards. Currently, the key issues are the imbalance between the limited lifespan of equipment, the relatively long period between the recurrences of such hazards, and the wide range of critical rainfall that trigger these disasters. This paper attempts to provide a stepwise multi-parameter debris flow warning system after taking into account the shortcomings observed in other warning systems. The whole system is divided into five stages. Differentwarning levels can be issued based on the critical rainfall thresholds. Monitoring starts when early warning is issued and it continues with debris flow near warning, triggering warning, movement warning and hazard warning stages. For early warning, historical archives of earthquake and drought are used to choose a debris flow-susceptible site for further monitoring. Secondly, weather forecasts provide an alert of possible near warning. Hazardous precipitation, model calculation and debris flow initiation tests, pore pressure sensors and water content sensors are combined to check the critical rainfall and to publically announce a triggering warning. In the final two stages, equipment such as rainfall gauges, flow stage sensors, vibration sensors, low sound sensors and infrasound meters are used to assess movement processes and issue hazardwarnings. In addition to these warnings, communitybased knowledge and information is also obtained and discussed in detail. The proposed stepwise, multiparameter debris flow monitoring and warning system has been applied in Aizi valley China which continuously monitors the debris flow activities.

Data-driven intelligent monitoring system for key variables in wastewater treatment process

In wastewater treatment process(WWTP), the accurate and real-time monitoring values of key variables are crucial for the operational strategies. However, most of the existing methods have difficulty in obtaining the real-time values of some key variables in the process. In order to handle this issue, a data-driven intelligent monitoring system, using the soft sensor technique and data distribution service, is developed to monitor the concentrations of effluent total phosphorous(TP) and ammonia nitrogen(NH_4-N). In this intelligent monitoring system, a fuzzy neural network(FNN) is applied for designing the soft sensor model, and a principal component analysis(PCA) method is used to select the input variables of the soft sensor model. Moreover, data transfer software is exploited to insert the soft sensor technique to the supervisory control and data acquisition(SCADA) system. Finally, this proposed intelligent monitoring system is tested in several real plants to demonstrate the reliability and effectiveness of the monitoring performance.

On-line estimation of concentration parameters in fermentation processes

It has long been thought that bioprocess, with their inherent measurement difficulties and complex dynamics, posed almost insurmountable problems to engineers. A novel software sensor is proposed to make more effective use of those meas- urements that are already available, which enable improvement in fermentation process control. The proposed method is based on mixtures of Gaussian processes (GP) with expectation maximization (EM) algorithm employed for parameter estimation of mixture of models. The mixture model can alleviate computational complexity of GP and also accord with changes of operating condition in fermentation processes, i.e., it would certainly be able to examine what types of process-knowledge would be most relevant for local models’ specific operating points of the process and then combine them into a global one. Demonstrated by on-line estimate of yeast concentration in fermentation industry as an example, it is shown that soft sensor based state estimation is a powerful technique for both enhancing automatic control performance of biological systems and implementing on-line moni- toring and optimization.

Measurement of Forces and Torques during Non-Homogeneous Material Drilling Operation

The purpose of this study is to measure the forces and torques produced in the drilling process of a non-homogenous material （bone）. An automated 5 DoF CataLyst-5 robot is used during the drilling process and it is integrated to a 6 DoF force-torque sensor. A force-torque controller which is built in the Matlab Simulink environment is employed to control the drilling process of the bone. Different feed rate is used during the experimental process of the bone drilling operation. The sensor is calibrated to measure the tri-axial direction of the resultant forces and torques. The profiles of the forces and torques obtained are non-linear due to the diversity of the bone density. The profiles generated also indicated fluctuation in the interface layers of the bone.

Cooperative Sensing and Distributed Control of a Diffusion Process Using Centroidal Voronoi Tessellations

This paper considers how to use a group of robots to sense and control a diffusion process.The diffusion process is modeled by a partial differential equation (PDE),which is a both spatially and temporally variant system.The robots can serve as mobile sensors,actuators,or both.Centroidal Voronoi Tessellations based coverage control algorithm is proposed for the cooperative sensing task.For the diffusion control problem,this paper considers spraying control via a group of networked mobile robots equipped with chemical neutralizers,known as smart mobile sprayers or actuators,in a domain of interest having static mesh sensor network for concentration sensing.This paper also introduces the information sharing and consensus strategy when using centroidal Voronoi tessellations algorithm to control a diffusion process.The information is shared not only on where to spray but also on how much to spray among the mobile actuators.Benefits from using CVT and information consensus seeking for sensing and control of a diffusion process are demonstrated in simulation results.

Design and Implementation of Image Acquisition System Based on STM32

In order to solve the difficulty of reading inconvenience in the measurement process of digital track gauge, a kind of image data acquisition system with low cost and stable performance is designed. The system uses the Cortex-M4 as the core of the STM32F407ZGT6 as the control core, the use of OV2640 as an image sensor to collect images, and the collection of image files stored in the SD card for subsequent image processing to achieve the goal of rail adjustment to lay the foundation. The experimental results show that the image acquisition is stable and refiable and the collected images are clear and meet the design requirements.

Technology of fiber-optic temperature sensing and its application in ＆ temperature measuring of gob area

Based on advantages of technology of distributive fiber-optic temperature sensing and specific to its applications in monitoring mine conflagration, the corresponding Processes such as connection arrangement, signal transmission and monitoring were illustrated. As applied in Sitai Coal Mine of Datong Coal Mine Group Co., this method is effective and accurate and could provide reliable gist for monitoring spontaneous combustion in gob area of mines.