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Material-technological models based on data obtained directly from a real press-hardening process were examined on two experimental steels, CMnSi TRIP and 42SiCr.
The data for developing the model was measured in a real-world process, with the tool either at room temperature or pre-heated to various temperatures.
Yet another reduction in the cooling rate to 10°C/s led to an increased proportion of ferrite, to even lower mechanical properties, and to no increase in elongation (Fig. 7).
Sequence 42SiCr-05 – press-hardening with Q&P processing – 10°C/s – 200°C – 250°C/600 s, martensite with ferrite and a small amount of bainite, SEM Summary In this experiment, a press-hardening route was tested which had been designed on the basis of data from a real-world forming process.

From the measured data also a concentration - depth profile has been deduced.
(1) The information depth ξ is the average depth for a given specimen of thickness t from which the diffracted X-rays emerge weighed with an absorption factor accounting for the intensity reduction due to absorption: ( ) 00 exp exp 1 exp exp t t z t z dz t t z t z dz τ τ ξ τ τ τ −  −          = = = − −  −  −         ∫ ∫
In this approach it is possible to determine effective XEC's according to ( ) V R 1,2 1,2 1,21 hkl S wS w S = + − , (8) by applying a least squares fit to the data in the f(ψ) plot with the stress and w as fit parameters [3].
The best fit obtained for the measured data points according to the f(ψ) method is achieved for w = 0.0753.

The loading condition used in this study is based on the data at No. 5 stand for roll hot strip finishing roll mill [4, 5].
Unlike the commonly used stress amplitude-mean stress diagram (σa-σm diagram), the σa-σm diagram of sleeve rolls requires considering the fatigue strength reduction due to slip defects [30].
Since Equation (2) is proposed based on data in the range -1≤R≤0, the curve AB is indicted for R=-1~0 in Fig. 8(a).
NSK (1997). https://www.nsk.com/common/data/ctrgPdf/e7005c.pdf
Sano, Reduction of Residual Stress in Bimetallic Work Roll by Tempering, Journal of Japan Society for Technology of Plasticity, 61 (2020) 183-189

The data are taken from the log books of past six years of the plant.
Air has highest resistance to the heat; result in reduction of heat transfer from the mould.
Data collection: Data is collected from continuous casting shop of Bokaro steel plant to train the neural network.
Data at the time of breakout: · Input data = 8×90 matrix, representing 90 samples of 8 elements · Target = 1×90 matrix (target is always 1 or alarm) Data at the time of no breakout: · Input data = 8×90 matrix, representing 90 samples of 8 elements · Target = 1×90 matrix (target is always 0 or no alarm) Total data: · Input data = 8×180 matrix, representing 180 samples of 8 elements · Target = 1×180 matrix (target is either 0 or 1 ) There are lots of variations within the input raw temperature data i.e. 8× 180 matrix.
Along with this the confusion matrix shows 100% accurate result of the test data.

Site measurement was conducted to collect meteorological and pavement surface temperature data.
Easy weather station, thermocouple, and data logger were installed on the site (Figure 2).
The data was taken for twelve days from July 1-12, 2011, includes the total of 1728 data observations with ten minutes intervals.
Other measurement was conducted on Civil Engineering Building, National Central University (NCU), Jhongli, Taiwan to obtain actual data of pavement, wall, and roof surface temperature, also meteorological data of NCU.
Concrete pavement real observation data was used as the base for the comparison.

It is then used to collect data from the manufacturing shop floor.
One of them is that LCA is very data intensive, and lack of data can restrict the conclusions that can be drawn from a specific study.
Such data is analyzed in the methods of PCA mapping using STATBOX.
The score is used to fill the data from each company.
Similarly, the data collection is analyzed and mapped to obtain the axes.

From this study, the average percentage of error between the experimental data with neural network model is about 0.6 %.
The result showed good prediction of neural network model with actual data with average absolute deviation of less than 9%.
The data obtained from this simulator is used as training and validation data for ANN model development.
The model has been validated with experimental data of a CO2 capture pilot miniplant.
Application of artificial neural networks for simulation of experimental CO2 absorption data in a packed column.

The simulation results are compared with the experimental data on sintering of zirconia, alumina, and Cu powder composites.
The data on the densification and surface area evolution kinetics have been utilized for the determination of the constitutive parameters used in a macroscopic description (continuum theory [3]) of sintering.
Damage appears as a result of the neck reduction.

The present work focuses on computation of incubation time tinc for static recrystallization using laboratory hot deformation data and on extrapolation the results to industrial conditions.
Once tinc is computed from Eq. (4) using the laboratory data and the dependences of tinc on T, e, are established, then, for given pass strain and strain rate, Tnr is defined as the temperature, at which the characteristic rolling time (e.g., interpass time tip for given strip dimensions and rolling speed) is equal to the SRX incubation time tinc.
The main reason is that in hot strip rolling mills the interpass times, rolling speeds and pass reductions are interdependent in contrast to laboratory mechanical tests.

According to the geological background and geological exploration data, some representative sections as well as their stability conditions were studied and analyzed based on limit equilibrium method for rigid block and three-dimensional nonlinear FEM in this paper.
At present, the study of this area is still relatively less; therefore, according to the geological background and geological exploration data, some representative sections of Gushubao landslide mass as well as their stability conditions are studied and analyzed based on limit equilibrium method for rigid block and 3-D nonlinear FEM in this paper.
According to the shearing resistance formula (Mohr-Coulomb Formula), the shearing resistance is （5） Where is the normal stress of one point on the sliding surface, and C are the internal frictional angle and cohesive strength; the average strength reduction coefficient when each point reaches the limit equilibrium is defined as the stability safety coefficient of the landslide mass K [6], then K can be represented as Where, is the length of sliding surface and t is the shearing stress along direction.