The breakage prediction for hydromechanical deep drawing based on local bifurcation theory

A criterion of sheet metal localized necking under plane stress was established based on the bifurcation theory and the characteristics theory of differential equation. In order to be capable to incorporate the directional dependence of the plastic strain rate on stress rate, Ito-Goya’s constitutive equation which gave a one to one relationship between stress rate component and plastic strain rate component was employed. The hydromechanical deep drawing process of a cylindrical cup part was simulated using the commercial software ABAQUS IMPLICIT. The onset of breakage of the part during the forming process was predicted by combining the simulation results with the local necking criterion. The proposed method is applied to the hydro-mechanical deep drawing process for A2219 aluminum alloy sheet metal to predict the breakage of the cylindrical cup part. The proposed method can be applied to the prediction of breakage in the forming of the automotive bodies

Approximate Closest Community Search in Networks

Recently, there has been significant interest in the study of the community search problem in social and information networks: given one or more query nodes, find densely connected communities containing the query nodes. However, most existing studies do not address the "free rider" issue, that is, nodes far away from query nodes and irrelevant to them are included in the detected community. Some state-of-the-art models have attempted to address this issue, but not only are their formulated problems NP-hard, they do not admit any approximations without restrictive assumptions, which may not always hold in practice. In this paper, given an undirected graph G and a set of query nodes Q, we study community search using the k-truss based community model. We formulate our problem of finding a closest truss community (CTC), as finding a connected k-truss subgraph with the largest k that contains Q, and has the minimum diameter among such subgraphs. We prove this problem is NP-hard. Furthermore, it is NP-hard to approximate the problem within a factor $(2-\varepsilon)$, for any $\varepsilon >0$. However, we develop a greedy algorithmic framework, which first finds a CTC containing Q, and then iteratively removes the furthest nodes from Q, from the graph. The method achieves 2-approximation to the optimal solution. To further improve the efficiency, we make use of a compact truss index and develop efficient algorithms for k-truss identification and maintenance as nodes get eliminated. In addition, using bulk deletion optimization and local exploration strategies, we propose two more efficient algorithms. One of them trades some approximation quality for efficiency while the other is a very efficient heuristic. Extensive experiments on 6 real-world networks show the effectiveness and efficiency of our community model and search algorithms

A Component-Driven Model for Regime Switching and Its Empirical Evidence

In this paper we propose a general component-driven model to analyze economic data with different characteristics (or regimes) in different time periods. Motivated by empirical data characteristics, our discussion focuses on a simple model driven by a random walk component and a stationary ARMA component that are governed by a Markovian state variable. The proposed model is capable of describing both stationary and non-stationary behaviors of data and allows its random innovations to have both permanent and transitory effects. This model also permits a deterministic trend with or without breaks and hence constitutes intermediate cases between the trend-stationary model and a random walk with drift. We investigate properties of the proposed model and derive an estimation algorithm. A simulation-based test is also proposed to distinguish between the proposed model and an ARIMA model. In empirical application, we apply this model to U.S. quarterly real GDP and find that unit-root nonstationarity is likely to be the prevailing dynamic pattern in more than 80 percent of the sample periods. As nonstationarity (stationarity) periods match the NBER dating of expansions (recessions) closely, our result suggests that the innovations in expansions (recessions) are more likely to have a permanent (transitory) effect.component-driven model, Markov trend, permanent shock, regime switching, transitory shock, trend stationarity, unit root