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Additive manufacturing (AM) is one the advanced process for building up a component layer by layer, with one layer of material was bonded to the previously laid layer using a 3D design data.
AM typically uses .stl data of the part to be fabricated as input data.
The CAD model of the part is sliced into 2D layers, and each contoured layer’s data is transferred onto the machine.
The CAD data for the scaffold were exported from CATIA V5 software in stereolithography (.stl) file format and processed via the SLS application software.
The fabricated Polyamide scaffolds possessed well defined pores (refer with Fig. 3) and their structural configurations were also observed to be consistent with the CAD data.

The ultra-pulse velocity (UPV) values of concrete have good relationship with compressive strength with the correlation coefficient of 0.92, 0.87 and 0.70 of 1-day, 3-day and 28-day experiment data, respectively.
When the curing age reach 28 day, the compressive strength of concrete with 10% replacement of SS powder was higher than that of control specimen and further addition of SS powder cause the reduction on compressive strength.
Further addition causes the reduction on compressive strength of concrete.
As for 28-day compressive strength of concrete, the 10% replacement of SS cause the reduction on compressive strength compared with that of control specimen, while the increasing addition of SS make the compressive strength of concrete increase and further addition cause the reduction on compressive strength of concrete.
Further increase in replacement of SS cause reduction on compressive strength of concrete of M40 and M50 of which compressive strength are lower than M10 concrete.

Moreover, neither reliable material data nor standardized testing procedures are available.
The method for systematic mass reduction is well known as automotive lightweighting.
The yield asymmetry was not considered because of missing data at 200°C. 0 50 100 150 200 250 0 0,02 0,04 0,06 nominal stress in MPa Sheet MgAZ31, Rolling Direction Room Temperature, quasi-static nominal strain c) d) 80 40 120 160 true stress in MPa plastic strain Sheet MgAZ31, Rolling Direction 200°C, dϕ/dt = const 0 b) 10 605040 20 20 120 100 80 40 140 stress in MPa Sheet MgAZ31 T = 200°C, dϕ/dt const. 0 0 a) 0,1 0,60,5 0,30,2 50 200 150 100 250 300 350 true stress in MPa plastic strain 230°C 200°C 170°C RT Sheet MgAZ31, Rolling Direction dϕ/dt const. 0 0 0,4 strain in % 30 60 90° 45° 0° 0,1 0,5 0,30,2 0,4 0,001 1/s 0,01 1/s 0,1 1/s 0,6 0 0,01 0,05 0,03 uniaxial compression uniaxial tension 0 50 100 150 200 250 0 0,02 0,04 0,06 nominal stress in MPa Sheet MgAZ31, Rolling Direction Room Temperature, quasi-static nominal strain c) d) 80 40 120 160 true stress in MPa plastic strain Sheet MgAZ31,
The simulation results (Fig. 8) show a very strong thickness reduction and collapse of the elements in these areas.
On this section further variations of input data and material models could be done.

Data were expressed mean ± SD.
Data were expressed mean ± SD.
Data were expressed mean ± SD.
Data were expressed mean ± SD.
In this study, the data showed that the BLA contents of the all RAP-treated groups were significantly lower than that of control group.

However, traditional diagnosis of bottleneck is carried out relying on designer's experiences, theoretical calculation or data analysis after system run.
With the visual results and simulation data based on the evaluation factors, the bottleneck process can be diagnosed.
These databases can integrate other system database by data interface in order to carry out intelligent design of production line.
(3) SDBPL can provide many simulation data.
Using those data, redesigning and reconfiguration of production line in SDBPL are quicker than those of traditional methods.

Sectional loads by three load caculation methods are examined by the analysis results of BO105 and the flight test data of the SA349/2 helicopter.
Lim [3] investigated structural loads data of UH-60A using both 2GCHAS and CAMRAD/JA.
Results and Discussion Two sets of loads data are used to examine the load calculation methods.
The other is the flight test data of SA349/2 Gazelle helicopter [10], which is outfitted with an advanced geometry rotor.
To compare with flight test data, it is necessary for the moments to be rotated to the deformed coordinate system.

However this does not explain the decrease in weld metal hardness as the recorded data infers in clearly identical welding conditions.
However, the microhardness data of the weld profiles shows indicates the relative aluminium hardnesses.
Hardness data collected for laser weld figure 5(f) collates interestingly.
Without measuring weld cooling rates, the hardness data infers that slower cooling results in higher microhardness.
However, as the data indicates, post-welding ageing takes place and the weld metal softens in the majority of cases.

The rationality of proposed formula is validated by comparing analytical results with experimental data.
The rationality of proposed formula is proved from the comparison of the calculated results obtained in this paper and experimental data.
Table 1 Comparison of experimental data and calculation results Specimen No.
Experimental data Theoretical calculation results Experimental data/ calculation results W4 200 6 303.70 418.00 403.97 571.89 0.75 1.37 W5 200 8 374.20 563.00 393.52 487.09 0.95 0.87 W6 200 10 488.50 648.00 521.82 621.38 0.94 0.96 W7 250 6 225.50 403.00 347.59 476.90 0.65 1.18 W8 250 8 419.80 597.00 444.51 616.74 0.94 1.03 W9 250 10 490.00 609.00 474.28 534.97 1.03 0.88 As can be seen from Table 1, the theoretical calculation results of yield bearing capacity are in good agreement with the experimental data, error within 6%, which means that the theoretical formula of yield bearing capacity has high-precision.
The theoretical calculation results and experimental data have certain deviation.

Furtherly, it is demonstrated that the simulated data by this new mode match the experimental data very well.
The main components whose summed probability of the L shell transition exceed 95% are extracted to realize the dimensionality reduction of high-dimensional data as shown in table 2.
Thus, the excitation factor of the L shell is equal to the sum of the excitation factors of the level at the energy of each transition in the data after dimensionality reduction.
In the future, this new method can be used to provide a data basis for artificial intelligence analysis on the material, and also offer a valuable approach to analyze unknown material.
White Morgan, Phototoxic Data Library MCPLlB03: An Update to MCPLlB02 Containing Compton Profiles for Doppler Broadening of Incoherent Scattering.

Some successful manufacturing users have achieved typically 15-45% in quality improvement, costs and time to market reduction.
Data mining is a technology for knowledge discovery in databases.
People can exchange data through a platform built on information and communication technology infrastructure, in addition to this, they can also coordinate activities, share information, emerge private and public sectors, and support globalization commerce [9].
And new product development process management includes user management module, FMEA process data management, product data management and failure data management.
Table 1.Table name in FMEA Knowledge System Table Name Name in Database basic data table pbd_table user information table user_info_table component information table cpt_info_table failure mode table flr_mode_table failure cause table flr_cause_table failure effect table flr_effect_table failure sequence table flr_sequence_table failure improvement table flr_impmt_table The database architecture of this system as Figure 2 showing: System Implementation A prototype system was developed to demonstrate the approaches proposed in this study.