Predicting the damage development in epoxy resins using an anisotropic damage model

For fiber-reinforced plastics, the strain-rate dependent response is governed by the matrix behavior. In this work, the Goldberg model is considered for the epoxy matrix constitutive material model. Moreover, the strain-rate dependency is achieved by direct influence on the elastic modulus, the inelastic strain, and the material strain to failure. In addition, an anisotropic damage response is implemented and extended through a strain-rate dependent definition. Since the constitutive model relies on nonphysical parameters, a parameter study is further performed. Additional numerical investigations using a micro-mechanical model are performed. Tension and shear loading conditions are evaluated and the influence of different strain rates is explored. Furthermore, the implemented anisotropic damage model is compared and discussed against an isotropic damage model.     

Process-induced variability modeling of subthreshold leakage power considering device stacking

The dominance of leakage currents in circuit design has been impelled by steady downscaling of MOSFET into nanometer regime, and has become a significant component of total IC power dissipation. The issue is further aggravated with the inability to gauge the tolerance of process parameters around their nominal value. Consequently, the drive to improve the static power prediction has enticed accurate and reliable modeling of leakage current, specifically for ultralow power applications. In contrast to gate- and band-to-band-tunneling leakages, subthreshold leakage exhibits high susceptibility to process variations and hence has been considered for variability modeling. Fluctuations in the device electrical and geometry parameters result in a wider distribution of subthreshold leakage current. Hence, taking into account stacking effect, an analytical variability model to estimate subthreshold leakage power in subthreshold circuits, in the presence of threshold voltage variations is proposed. Further, the impact of threshold voltage variability on subthreshold leakage power is modeled in conjunction with simultaneous variations in gate length and width. The leakage power variability is characterized by model-generated distributions obtained using Monte Carlo analysis and validated against SPICE simulations. The proposed model is about 700 computationally faster than SPICE simulations with mean error being less than 0.19%.     

深埋矿体移动带三维圈定方法及应用

针对深埋矿山开采移动带二维圈定方法工作量大、精度低且难以满足日益增长的经济效益需求的问题，提出了一种深埋复杂矿体移动范围三维确定方法。结合沙岭金矿千米深井工程实例，在移动范围确定过程中，借助先进的3D建模技术获得三维矿山模型。该模型可以实现各类地质体的可视化，直观地展示其形态与产状，清晰地反映各构成因素间的相互关系。在3D模型中截取的特征剖面与经典移动带圈定岩移参数获取方法相结合，能轻松获得各特征剖面上的岩移参数值。通过给定的岩移参数值进而确定各特征剖面上的地表移动边界点，将各移动边界点连接即可实现矿山移动范围的圈定。研究结果表明，基于三维地质建模技术的移动带圈定方法合理可行，确定的纱岭金矿地表移动带能够兼顾安全生产与经济效益，为矿山生产开发奠定了有益基础，为类似矿山提供了有益借鉴。     

基于最近邻与神经网络融合模型的软测量建模方法

软测量建模能够有效地解决生产过程中在线分析仪表测量滞后大、价格昂贵、维护保养复杂等问题。目前，基于数据驱动的神经网络是软测量建模的主要工具之一。而在建模数据的采集过程中，主导变量的采集相对辅助变量要困难得多，由此产生了大量缺失标签的数据。但传统的软测量建模方法却忽视了这些无标签数据，只利用少量的有标签数据建模，从而影响了模型的预测精度。为了解决标签缺失的问题，采用最近邻算法对无标签数据进行伪标记，同时设计了由卷积操作与门限循环单元神经网络（GRU）结合的网络结构来进一步利用无标签数据，提取不同时刻数据中的动态特征，提高神经网络的预测精度。最后将该方法应用于丙烯精馏塔塔顶丙烷浓度的预测，实验结果表明该模型能有效处理非线性动态系统的标签缺失问题，具有更高的预测精度。     

A review on spindle thermal error compensation in machine tools

Thermal error caused by the thermal deformation is one of the most significant factors influencing the accuracy of the machine tool. Among all the heat sources which lead to the thermal distortions, the spindle is the main one. This paper presents an overview of the research about the compensation of the spindle thermal error. Thermal error compensation is considered as a more convenient, effective and cost-efficient way to reduce the thermal error compared with other thermal error control and reduction methods. Based on the analytical calculation, numerical analysis and experimental tests of the spindle thermal error, the thermal error models are established and then applied for implementing the thermal error compensation. Different kinds of methods adopted in testing, modeling and compensating are listed and discussed. In addition, because the thermal key points are vital to the temperature testing, thermal error modeling, and even influence the effectiveness of compensation, various approaches of selecting thermal key points are introduced as well. This paper aims to give a basic introduction of the whole process of the spindle thermal error compensation and presents a summary of methods applied on different topics of it.     

Adaptive force–environment estimator for manipulators based on adaptive wavelet neural network

This study focuses on the accurate tracking control and sensorless estimation of external force disturbances on robot manipulators. The proposed approach is based on an adaptive Wavelet Neural Network (WNN), named Adaptive Force-Environment Estimator (WNN-AFEE). Unlike disturbance observers, WNN_AFEE does not require the inverse of the Jacobian transpose for computing the force, thus, it has no computational problem near singular points. In this scheme, WNN estimates the external force disturbance to attenuate its effects on the control system performance by estimating the environment model. A Lyapunov based design is presented to determine adaptive laws for tuning WNN parameters. Another advantage of the proposed approach is that it can estimate the force even when there are some parametric uncertainties in the robot model, because an additional adaptive law is designed to estimate the robot parameters. In a theorem, the stability of the closed loop system is proved and a general condition is presented for identifying the force and robot parameters. Some suggestions are provided for improving the estimation and control performance. Then, a WNN-AFEE is designed for a planar manipulator as an example, and some simulations are performed for different conditions. WNN_AFEE results are compared attentively with the results of an adaptive force estimator and a disturbance estimator. These comparisons show the efficiency of the proposed controller in dealing with different conditions.     

Automating data exchange in process choreographies

Communication between organizations is formalized as process choreographies in daily business. While the correct ordering of exchanged messages can be modeled and enacted with current choreography techniques, no approach exists to describe and automate the exchange of data between processes in a choreography using messages. This paper describes an entirely model-driven approach for BPMN introducing a few concepts that suffice to model data retrieval, data transformation, message exchange, and correlation – four aspects of data exchange. For automation, this work utilizes a recent concept to enact data dependencies in internal processes. We present a modeling guideline to derive local process models from a given choreography; their operational semantics allows to correctly enact the entire choreography from the derived models only including the exchange of data. Targeting on successful interactions, we discuss means to ensure correct process choreography modeling. Finally, we implemented our approach by extending the camunda BPM platform with our approach and show its feasibility by realizing all service interaction patterns using only model-based concepts.     

Modeling surface water-groundwater interaction in arid and semi-arid regions with intensive agriculture

In semi-arid and arid areas with intensive agriculture, surface water-groundwater (SW-GW) interaction and agricultural water use are two critical and closely interrelated hydrological processes. However, the impact of agricultural water use on the hydrologic cycle has been rarely explored by integrated SW-GW modeling, especially in large basins. This study coupled the Storm Water Management Model (SWMM), which is able to simulate highly engineered flow systems, with the Coupled Ground-Water and Surface-Water Flow Model (GSFLOW). The new model was applied to study the hydrologic cycle of the Zhangye Basin, northwest China, a typical arid to semi-arid area with significant irrigation. After the successful calibration, the model produced a holistic view of the hydrological cycle impact by the agricultural water use, and generated insights into the spatial and temporal patterns of the SW-GW interaction in the study area. Different water resources management scenarios were also evaluated via the modeling. The results showed that if the irrigation demand continuous to increase, the current management strategy would lead to acceleration of the groundwater depletion, and therefore introduce ecological problems to this basin. Overall, this study demonstrated the applicability of the new model and its value to the water resources management in arid and semi-arid areas.     

A new verification technique for large processes based on identification of relevant tasks

Verification recently has become a challenging topic for business process languages. Verification techniques like model checking allow to ensure that a process complies with domain-specific requirements, prior to the execution. To execute full-state verification techniques like model checking, the state space of the process needs to be constructed. This tends to increase exponentially with the size of the process schema, or it can even be infinite. We address this issue by means of requirements-specific reduction techniques, i.e., reducing the size of the state space without changing the result of the verification. We present an approach that, for a given requirement the system must fulfill, identifies the tasks relevant for the verification. Our approach then uses these relevant tasks for a reduction that confines the process to regions of interest for the verification. To evaluate our new technique, we use real-world industrial processes and requirements. Mainly because these processes make heavy use of parallelization, full-state-search verification algorithms are not able to verify them. With our reduction in turn, even complex processes with many parallel branches can be verified in less than 10 s.