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In this article, we established the three-dimensional solid model by using PROE and then imported WN gear’ three-dimensional model into Ansys software through the data interface of Ansys software and PROE software and conducted a three-dimensional simulation anasys of the impact of dynamic contact.
The dynamic simulation of WN gear transmission system Due to the complexity of WN gear tooth surface, it is more trouble to build a complete gear tooth face by using gear tooth profile equation. we established the three-dimensional solid model by using PROE , imported the WN gear’s three-dimensional model into Ansys software through the data interface of Ansys software and PROE software .Referring to the literature [6],through meshing the model, setting material properties and real constant, creating PART, defining the contact type and applying load, the stress analysis of WN gear was conducted in Ansys software .The analysis model was shown in Figure 2 and the model afer meshing was shown in Figure 3.
Secondly, we established the three-dimensional solid model by using PROE, imported the arc gear three-dimensional model into Ansys software through the data interface of Ansys software and PROE software.
We got clear and intuitive simulation results .The last but not the least , using history-module, the elements’ equivalent stress curves and nodes’ acceleration situation were successfully simulated and the tangential, axial, radial direction vibration situation of WN gear in meshing process were gotten, which provided a theoretical basis for gear parameters design and noise reduction in the gear transmission .

Production planning and scheduling can take out data information from product level, through the operation control level, transmit to management level and complete enterprise information integration by continual flow of information and enhance the core competition of enterprise.
outsourcing collaborative plans and outsourcing orders are created based on the established material requirement plans, which will be formed into Excel table for convenient implementation of the purchasing department. 5)Workshop plans management: according to the established of the MRP, resources should be allocated reasonably for meeting the delivery date demand of manufacturing orders, the function modules include workshop plans creation, static job scheduling, dynamic job scheduling and scheduling algorithm management etc. 6)Production Kanban management: for monitoring execution of workshop plans, the function modules include Kanban creation, statistics of production execution and Gantt charts show etc. 7)Workflow management system: workflow management is the base of enterprise information systems, the function modules include the definition, execution, monitoring, and management of business processes workflow etc. 8)Basic data maintenance: all kinds of basic data information
such as order information, manufacturing data, equipment information, scheduling information and rights information etc which are relevant to rare metal manufacturing are collected and processed.
At present integration methods of information management system mainly include direct access of homogeneous database, API function call of information management systems and middleware technology with a uniform description of data and interface to all[7][8].
Conclusion The level of management has been raised in the following three aspects through the application of PPSS in some rare metal manufacturing enterprises: Firstly, consistency, accuracy and efficiency of production scheduling have been improved effectively; Secondly, some real-time scheduling problems such as rush order and equipment downtime have been solved by scheduling algorithm; At last, there are some obvious improvements on efficiency of enterprise such as: reduction of inventory stock level, reduction of production cost and improvement in the workshop production flexibility of and rapid response level to market.

Impedance spectroscopy was analyzed by Zview software and data analysis was conducted with Origin software [5].
Table 2 AC impedance analysis of Alloy 600 in sodium sulfate solutions under 25℃ C (mg/L) 0.01 0.03 0.05 0.08 0.10 0.15 Rct(Ω) 125.5 58.48 23.37 19.63 16.32 11.75 Ru(kΩ) 35.1 11.0 0.7 2.52 7.42 3.33 Cd(µF) 2.52×10–3 4.07×10–3 7.19×10–3 7.94×10–3 1.01×10–2 8.99×10–3 1: 0.01mol/L 2: 0.03mol/L 3: 0.05mol/L 4: 0.08mol/L 5: 0.10mol/L 6: 0.15mol/L Fig.2 AC impedance diagram of Alloy 600 in sodium sulfate solutions under 25℃ High temperature corrosion data This experiment was carried out with analytical purity Na2SO4 dissolved in deionized water, and concentration was 0.4mol/L with 300℃ temperature for 36 hours; and the corresponding pressure was 7.2MPa.
In the process Tafel experiment, there was air bubbles around Pt electrode, which was caused by cathode reduction hydrogen evolution reaction (2H+ + 2e = H2).
Sala B .et al .Chemistry of Sulfur in High Temperature water Reduction of Sulfates.

From these data, curing rate index (CRI) was calculated by using this formulation (1): Curing Rate Index (CRI) = 100 / (t90-ts2) (1) [7] Fatigue properties.
The calculated data was fatigue life in kilocycles (kc).
Fig. 3 shows the effect of various HNTs/CB hybrids loading towards the cure rate index (CRI) of NR nanocomposites where it can be observed that the reduction of HNTs caused higher CRI which indicates shorter optimum crosslink formation compared to the nanocomposites with more HNTs.
From this figure, it can be seen that the addition of more CB than HNTs caused reduction in fatigue life.

Table 1 Reaction mechanisms and algebraic expressions[9] serial number Reaction mechanism f(x) G(x)(integrated forms of f (x)) R2 A One-dimensional diffusion (1/2)x x2=kt 0.83 B Two-dimensional diffusion 1/[-ln(1-x)] (1-x)ln(1-x)=kt 0.59 C Three-dimensional diffusion (Jander eq.) 3(1-x)2/3/{2[1-(1-x)1/3]} [1-(1-x)1/3]2=kt 0.989 D Three-dimensional diffusion (Ginstein-Brounshtein eq.) (3/2)[(1-x)-1/3-1] 1-2x/3-(1-x)2/3=kt 0.96 E First-order reaction (unimolecular decay law) (1-x) -ln(1-x)=kt 0.998 F Phase-boundary controlled (contracting cylinder) (1-x)1/2 1-(1-x)1/2=kt 0.899 G Phase-boundary controlled (contracting sphere) (1-x)2/3 1-(1-x)1/3=kt 0.981 H Two-dimensional growth of nuclei 2(1-x)[-ln(1-x)]1/2 [-ln(1-x)]1/2=kt 0.951 I Three-dimensional growth of nuclei 3(1-x)[-ln(1-x)]2/3 [-ln(1-x)]2/3=kt 0.99 The data of conversion rate(x) versus time(t) for 773K and 10000ppm was first taken into the G(x) equations for fitting, the G(x)-t curve for each equation is shown in figure
Fig.4 The G(x)-t curves of different mechanism equations Fig.5 The [–ln(1-x)]～t curves at different tempratures (a).10000ppm; (b).5000ppm; (c).2500ppm Equation E was used to deal with all other data for different temperature and H2O concentration.
References [1] F.Ghezzi,W.T.Shmayda,N.Venkataraman, Efficient HTO reduction using a ZrFeMn alloy,Fusion Engineering and Design.28(1995)367-372
[9] K.S.Go,S.R.Son,S.D.Kim,Reaction kinetic of reduction and oxidation of metal oxides for hydrogen production,International Journal of Hydrogen Energy.33(2008),5986-5995.

This in turn leads to a reduction in the friction losses.
This is due to the reduction in pumping head from one side and the increase in the submerged length from the other side.
Fig. 2 Effect of suction pipe diameter on the pumping rate at submergence ratio (0.2) Fig. 3 Effect of suction pipe diameter on the pumping rate at submergence ratio (0.3) Fig. 4 Effect of suction pipe diameter on the pumping rate at submergence ratio (0.4) Fig. 5 Effect of suction pipe diameter on the pumping rate at submergence ratio (0.5) Pumping rate (Kg/s) Ds= 0.063m Ds= 0.048m Ds= 0.033m Ds= 0.027m Ds= 0.021m Fig.6 The relation between pumping rate and submergence ratio for the different suction pipe diameters ----- Experimental data ___ Stenning & Martin model Fig.7 A comparison between the experimental data and Stenning & Martin model References [1] Awari G.

(3) Seen from the operator moving data got with Spaghetti Diagram, there is much unnecessary movement of operations staff in the production process, resulting in waste, as shown in Fig.3.
Fig.5 Spaghetti diagram of optimized elevator controller assembly line The reduction of process time.
Table 1 Compare of elevator controller assembly before and after improvement subject Optimized status Optimized status Reduced ratio Cycle time（day） 6.95 6.09 12.40% Manufacturing time（Min） 121.97 102.33 16.10% Operator number (person) 5 4 20.00% Work time per month（hour） 2405 1953 18.79% Over time per month（hour） 557 105 81.15% Summary By introducing the flexible improvement of elevator controller assembly line, the specific sequence of operations, precautions, methods for analytical tools, methods for data collection and analysis, experience of using analytical tool are described to improve traditional production line with Value Stream Mapping.
Zammori: Application of value stream mapping for lean operations and cycle time reduction: an Indian case study [J].

Material properties data that needed to input into FE program could be represented in Fig. 3.
When adjusting DBRF of drawbead no. 1 and no. 3, the simulation results showed the reduction of cracks area on the part with reducing DBRF as seen in Figs. 9-10.
From the results studied, the suitable process conditions needed to be improved were the new drawbead design, which adjusted two drawbead geometry follow DBRF on die face, had shown the reduction of the cracks zone on the part, when comparing with the original design.
In addition, sincere thanks must be given to Thai Summit Automotive Co., Ltd for useful advice and providing the importance data about the parts.

Techniques which enhance the signal in these situations are therefore highly desirable for early fault detection. [1] In vibration analysis for mechanical fault detection, noise reduction has been mostly done using filters.
The noise reduction methods based on filters can not effectively remove the noise which contains components in all frequency ranges.
The vibration acceleration data of bearings are obtained from the Case Western Reserve University [8].
[8] CWRU, Bearing Data Center, http://www.eecs.case.edu/

Increasing competition has forced manufactures to make considerable reductions in machine design and building cycles.
For the steering system, the secondary reduction gear and Ackerman steering system are worked out just after the input of initial design parameters.
When a new model is required, the stored historical data, similarity algorithms and interference engine help the designers to calculate the degree of similarity of the machine being designed to the one already built, give early suggestion of the trend and work out final results.
Thus in the future work, collecting more empirical knowledge from the experts and learning more rules from the historical data are going to be done.