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As far as milling is concerned, a reduction in the volume of β grains was observed under the machined surface when the cutting speed increased [11].
However, a reduction in microhardness is observed from 10-12 µm, distributed over varying depth according to the different studies [8,11,12].
They were determined from the machine’s capacity, the resistance of the cutters and Airbus data.
Increase in the cutting speed leads to a reduction in residual stresses (E2/E4 and E3/E5).
A reduction in residual stresses was seen with the increase in cutting speed and depth of pass.

K [1], the usage of aluminium in automotive applications has been linked to weight reductions of up to 50%, significantly improving the overall performance of vehicles.
Several studies, including those by [3], have demonstrated that even small reductions in vehicle weight can lead to substantial improvements in fuel efficiency.
This reduction is likely due to the improved ductility of the 6082-T0 alloy, which facilitates easier deformation during the joining process.
The fatigue load range-number of cycles to failure life (ΔL-N) curves were constructed on a single logarithmic scale, utilizing the test data and the least squares method (LSM) for fitting.
Tennis, ‘Lightweight material for weight reductions in an automotive suspension part lower link’, Materials Today: Proceedings, p.

In PTS-GEP, the research conducts experiment over the data from previously reported research and compares the result to two other algorithms namely simple GEP, UC-GEP.
For simple coding, fast convergence rate and strong data modeling capability for unknown data, GEP has become an international research focus.
In addition, new GEP research results have been presented as follows:applying GEP in time sequence analysis, the realization of intelligent model base system based on GEP[5], GEP function mining algorithm based on grid[6], attributes reduction and classification algorithm based on GEP and neural network [7], stock prediction based on IP and GEP algorithm[8], and so on.
The experiment data come from the reference[5], fitness function and model accuracy evaluation function are the same as the reference[5].Each algorithm run 20 times independently, By comparing the fitness value and the model accuracy to test the performance of algorithm.The experiment results are shown in table 2.

In addition, the strain fatigue parameters of the AZ91 alloy were determined through analyzing the corresponding strain fatigue life data.
Magnesium components equal in strength are 40% lighter than steel and 20% lighter than their aluminium counterparts, which enables car manufacturers to meet regulatory requirements for lighter weight vehicles and a corresponding reduction in emissions.
However, few low-cycle fatigue data are available for this alloy [7].
The fatigue life data (total strain amplitude, 2/tε∆ , versus number of cycles to failure, Nf) of the AZ91 alloy produced by different cast methods are shown in Fig. 3.
It is obvious that for both alloys, the elastic strain amplitude and fatigue life data can be correlated by Basquin law.

Third, GRNN can handle linear and nonlinear data.
, have a forecasting effect when be short of sample data.
The input layer merely serves as an input data buffer and does not perform any processing.
Hence, there are I pattern neurons running in parallel if the training data set consists of a total of i = 1, 2, … , I samples.
This paper choose 11 parameters illustrated as Table 1 according to the really data.

Data gathering is time-consuming and very expensive since individual metering devices have to be used for each consumer.
Hybrid Load Signature Identification algorithm VDFT VDFT Data Acquisition Feature Extraction Data Base Disaggregation Algorithm SID code LSI OUTPUT HLSI OUTPUT 80-90% Disaggregation 100% Disaggregation In Fig. 3, the block diagram of the disaggregation algorithm is presented.
The algorithm first acquires continuous data segments of voltage and current which will be used in the next step, to determine the overall values of active (P) and reactive (Q) powers.
Staake, Leveraging smart meter data to recognize home appliances, 2012 IEEE International Conference on Pervasive Computing and Communications (PerCom) (2012) 190-197
Greitzer, Smart meter data analysis, 2012 IEEE PES Transmission and Distribution Conference and Exposition (T&D) (2012) 1-6

The modern mechanism for organizing procurement on the basis of the Unified Information System allows collecting data for monitoring the bids and final proposals of procurement participants.
Based on these data, we have conducted a comprehensive analysis of quantitative and qualitative indicators of state and municipal procurements of goods, works and services.
The average price reduction for competitive procurement was 8.2%.
From the analysis of data on canceled contracts, it follows that, both in 2015 and in 2016, termination of contracts was mainly carried out by the parties' agreement.
Table 1 presents the data characterizing the volume and share of procurement for contract work in the Bryansk region in terms of procedures types, to provide for government and municipal needs in 2017 (refer with: Fig. 3).

This clearly means that some effect of Al substitution, different from the mere increase in interband scattering, must be taken into account to explain the data.
Lines are the best fit of the data within the Eliashberg theory, calculated with a dominant spin-flip scattering in the σ bands plus smaller contributions from either the π-π (solid lines) or the σ-π (dashed lines) channels.
Actually, a fitting of the data within the two-band Eliashberg theory is destined to fail since, in a wide range of Tc values, both gaps are smaller than the BCS value (dotted line in Fig.4), while a BCS-like gap ratio is recovered at Tc = 8.5 K, when ∆≈ 1 meV.
The best fit of the data, although unsatisfactory in the intermediate Tc range, requires a substantial reduction in the σ-band DOS (solid lines in Fig.4) that is indeed experimentally observed by NMR [22].
Lines: fit of the experimental data within the Eliashberg theory.

The disorientation data indicated little dependence of the grain boundary disorientation distribution on the ECAP process route.
Tensile data.
In Fig.7 the tensile data are summarized as a function of the number of passes.
The disorientation data confirmed that repetitive pressing results in a progressive increase in the fraction of high-angle grain boundaries (Fig.5).
A comparison of the simulated evolution of the dislocation cell size and yield strength with experimental data showed a good capability of the model to account for major features of the deformation behaviour of Al under ECAP.

This assumption is supported by the literature data on oxygen precipitation kinetics [13], where it was found that oxygen precipitate dissolution is diffusion limited rather than reaction limited process.
Thickness of the layers before and after the dissolution was measured by a spectroscopic ellipsometer. 49 data points with 5 mm edge exclusion were taken for each wafer.
Few samples were analyzed by XTEM and XRR (X-ray reflection) to confirm ellipsometry data.
The data fit very well to the straight line with the slope of 45%, which is the ratio of specific volumes of Si and SiO2, as predicted by Eq.7.
For each annealing condition the same value of B, but different A were used to fit edge and center data.