Quality Stability of Multi-Station Assembly Process Based on Variation Stream

To analyze the physical structure of assembly process and assure product quality, the quality stability of multi-station assembly process was investigated. First, the assembly process was modeled as a one-dimensional discrete variant system by state space equation based on variation stream. Then, the criterion to judge whether the process is stable or not and the index, stability degree, to show the level of stability were proposed by analyzing the bounded-input bounded-output (BIBO) stability of system. Finally, a simulated example of a sheet metal assembly process with three stations, was provided to verify the effectiveness of the proposed method.

Influence of climate variability and human activities on stream flow variation in the past 50 years in Taoer River, Northeast China

Taoer River Basin, which is located in the west of Northeast China, is an agropastoral ecotone. In recent years, the hydrological cycle and water resources have changed significantly with the deterioration of the environment. Many water problems such as river blanking, wetland shrinking and salinization have occurred in this region. All of these phenomena were directly caused by changes in stream flow under climate variability and human actiities. In light of the situation, the impact of climate variability and human activities on stream flow should be identified immediately to identify the primary driving factors of basin hydrological processes. To achieve this, statistical tests were applied to identify trends in variation and catastrophe points in mean annual stream flow from 1961 to 2011. A runoff sensitive coefficients method and a SIMHYD model were applied to assess the impacts of stream flow variation. The following conclusions were found： 1 ） The years 1985 and 2000 were confirmed to be catastrophe points in the stream flow series. Thus, the study period could be divided into three periods, from 1961 to 1985 （Period I）, 1986 to 2000 （Period II） and 2001 to 2011 （Period III）. 2） Mean annual observed stream flow was 31.54 mm in Period I, then increased to 65.60 mm in Period II and decreased to 2.92 mm in Period III. 3） Using runoff sensitive coefficients, the contribution of climate variability was 41.93% and 43.14% of the increase in stream flow during Periods II and III, suggesting that the contribution of human activities to the increase was 58.07% and 56.86%, respectively. 4） Climate variability accounted for 42.57% and 44.30% of the decrease in stream flow, while human activities accounted for 57.43% and 55.70% of the decrease, according to the SIMHYD model. 5） In comparison of these two methods, the primary driving factors of stream flow variation could be considered to be human activities, which contributed about 15% more than climate variability. It is hoped that these conclusions will .benefit future regional planning and sustainable development.

Machining Error Control by Integrating Multivariate Statistical Process Control and Stream of Variations Methodology

For aircraft manufacturing industries, the analyses and prediction of part machining error during machining process are very important to control and improve part machining quality. In order to effectively control machining error, the method of integrating multivariate statistical process control （MSPC） and stream of variations （SoV） is proposed. Firstly, machining error is modeled by multi-operation approaches for part machining process. SoV is adopted to establish the mathematic model of the relationship between the error of upstream operations and the error of downstream operations. Here error sources not only include the influence of upstream operations but also include many of other error sources. The standard model and the predicted model about SoV are built respectively by whether the operation is done or not to satisfy different requests during part machining process. Secondly, the method of one-step ahead forecast error （OSFE） is used to eliminate autocorrelativity of the sample data from the SoV model, and the T2 control chart in MSPC is built to realize machining error detection according to the data characteristics of the above error model, which can judge whether the operation is out of control or not. If it is, then feedback is sent to the operations. The error model is modified by adjusting the operation out of control, and continually it is used to monitor operations. Finally, a machining instance containing two operations demonstrates the effectiveness of the machining error control method presented in this paper.

Using a pressure controlled vortex design method to control secondary flow losses in a turbine stage

A turbine design method based on pressure controlled vortex design （PCVD） is presented to design a small-size turbine stage. Contrary to the conventional controlled vortex design （CVD） method, the main objective of PCVD is to control the axial velocity and radial pressure in the sta- tor rotor gap. Through controlling axial velocity, the PCVD establishes a direct tie to meridional stream surface. Thus stream surface variation is induced, resulting in a large secondary flow vortex covering the full blade passage in the respective stator and rotor. This secondary flow vortex could be dedicated to control the secondary flow mitigation and migration. Through radial pressure, the PCVD is also associated with the macroscopic driving force of fluid motion. So the better benefit of CVD can be achieved. The core concept behind PCVD is to mainly control the spanwise pressure gradient by altering profile loading at various spanwise locations. Therefore not only the local pro- file lift is affected, but also the resulting throat widths, stage reaction degree, and massflow rate are altered or redistributed respectively. With the PCVD method, the global stage efficiency is increased successfully while the mass flow rate keeps constant. Additionally there is no endwall shape optimization, stacking optimization, or pitch/chord variations, concentrating solely on varying blade profile deflections and stagger.