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Metal current scheme at the final stage of hex bolt head reduction: 1-punhe, 2-die, 3-work piece.
The values found for the minimum deformation work Аmin to determine efforts hex head bolts reduction from different materials with different size ratio.
Technological process of bolt stamping: a) core ingot reduction, b) cylindrical head upsetting, c) hex head reduction, d) bolt flange upsetting.
The data obtained proved that the concave shape of the deforming portion of the working area chamber of the punch, unlike the conical deformation, reduces the specific power of deformation by 10-15% and provides a uniform distribution of the metal in the axial sections of the piece, the work piece deformation equalizes and reduces the cross section of the stress state in the central deformation zone [13].
The Practical Significance, Suggestions, Introduction and Conclusions For experimental research technology, based on the data obtained by mathematical modeling in DEFORM-3D a set of tools and work pieces models were made [14].

CrNiMo steels also show a reduction in the fatigue limit, but it is less pronounced compared to CrMnN steels.
In literature a lot of data on corrosion fatigue (CF) behaviour of CrNiMoN steels can be found [[] R.P.
This leads to a shorter crack initiation period and a reduction of fatigue strength.
Figure 1: Fracture surface of CrNiMo-SA, 43 wt% CaCl2 solution, 120 °C, oxides close to initiation point of fracture, fatigue specimen Figure 2: Fracture surface of CrMnN-SA, 43 wt% CaCl2 solution, 120 °C, facetted cleavage, fatigue specimen Figure 3: Crack growth rate curves of CrNiMo and CrMnN steel in glycerine and 43 wt% CaCl2 solution at 120 °C Crack growth rate curves (Figure 3) recorded in glycerine showed that precritical crack growth and crack stopping is present in both materials, as well as a slight reduction of ΔKth with increasing degree of cold working.
From the recorded data it can be derived that the CrMnN steel is susceptible to SCC under testing conditions and SCC susceptibility is increased with increasing degree of cold working [9].

This alloy has high affinity with oxygen which constrains its application at high temperatures due to creep resistance reduction.
This is a constraint factor for the alloy application due to reduction of its mechanical resistance.
For elongation and temperature data collection for the samples was used Antares Software.
The equipment and devices used for creep tests were calibrated ensuring a reliable data collection.
It also has provided higher mechanical resistance with higher values of tp and reduction of steady-state creep rate and consequently longer time to rupture.

The data shows a figure that is very far from the government's target.
The study used data analysis methods, namely logit analysis or logistic regression with multinominal logits.
The collecting data held in February-April in 2023 with google form’s online survey and we share it with facebook and Instagram adsvertisement.
(2) Result and Discussion After the collecting data we get 678 respondens, and from habit’s factor we know that 36,45% respondent who have >60 minutes mobility time in a day choose convensional motorcycle while 60% respondent who have 15-30 minutes mobility time in a day choose electric motorcycle with battery.
Than, we calculate the data with multinomial logit regression.

Fig. 3 Acceleration image when 900Hz 74dB 96dB 120dB Fig.5 Field points' Sound pressure distribution graph of cutter-workpiece system The cutter and the workpiece system Field point Fully reflecting surface Fig. 4 Calculation model for acoustic radiation of cutter-workpiece Analysis of radiated noise Because we adapt the boundary element method to analyze radiation noise, so we should regard the response of surface node of cutter-workpiece system as boundary condition, and the response obtained in the before step is the overall response, then use the data transfer function of the software (Data Transfer Analysis Case) to realize[5].
(b)Test points in XY plane projection (a)Test height Fig.6 Test points height and projection at the XY plane Table 3 Experimental data of noise Number Height (cm) Data(dB) 5 5‘ 1 1‘ 2 2‘ 3 3‘ 4 4‘ Ⅰ 95 88.2 87.6 90.9 87.6 87.5 89.3 89.4 88.3 93.2 92.8 120 90.7 89.7 91.9 90.7 89.4 89.2 88.1 86.7 93.0 92.7 156 92.4 92.7 91.9 89.8 90.3 87.2 93.7 89.6 94.5 92.3 Ⅱ 95 88.9 87.8 86.7 86.2 84.5 84.4 85.2 84.7 88.2 86.5 120 89.0 88.2 86.7 86.3 83.6 84.0 88.0 84.9 89.1 88.8 156 87.5 87.0 88.2 87.1 86.5 85.9 88.0 85.7 87.9 85.4 Ⅲ 95 84.3 83.9 82.7 81.2 83.0 80.9 83.2 81.4 84.7 84.5 120 84.0 84.7 83.0 81.5 82.2 81.5 83.6 85.9 85.2 84.3 156 86.5 85.6 82.7 81.8 83.0 81.5 82.5 82.0 85.8 84.0 Noise measuring instrument used in milling noise experiment is TES-1358 1/1 and 1/3 octave real-time audio analyzer.
The experimental data is shown in table 3.
Experimental data is basically in line with filed points' sound pressure distribution of cutter-workpiece system, which verifies the accuracy of the simulation results of noise field.
Research on Noise Reduction for Vertical Milling Machine[J].

Points cloud data can be obtained quickly in the reverse process to built the 3D model and achieve product‘s rapid design.
Data processing includes point cloud processing, triangulation, and section lines interception.
The point cloud data processing is to import the point cloud into Geomagic software.
Noise reduction and fusion are carried to improve the accuracy of point cloud data.
The 2D section data of second step is input to CNC system.

The BF no.8 expert system has five kinds of mathematical model as follows: (1) Data preprocessing model.
The data preprocessing model runs every 30 seconds
Basic Features of Expert System Models Data Preprocessing Model.
Data preprocessing is consisted of four aspects of content, i.e. 1) the validation of observed data, 2) inducing and calculating all kinds of eigenvalues (indices) for a single type or multi-type of data, 3) statistical analysis of data and 4) calculation of hourly averaged values for operational parameters.
Based on these data, the velocity reduction coefficient of coke and ores at all experimental angles were calculated backwards by using the burden distribution model, and then the regressive equations representing the relationship between the velocity reduction coefficient and the chute angle were established.

Calculated values of the hysteresis characteristics ​​are in good agreement with the experimental data.
Hysteresis characteristics of Co-Au nanoparticles To calculate the hysteresis characteristics, we used the following data: it was assumed that the spontaneous magnetization of gold is Is(Au)=0,2 G [9], cobalt - IsCo=1100 G [10], the anisotropy constant of gold is KAAu=0, cobalt - KACo=4,1∙106erg/cm3 [11].
Conclusion Theoretical study of the dependence of the hysteresis characteristics of Co-Au nanoparticles carried out within our model [7] shows that the reduction in the size of nanoparticles leads to a reduction of the magnetic characteristics as the coercive force, residual saturation magnetization and saturation magnetization.

Temperature programmed reduction was conducted using a 25 mg of catalyst on an apparatus (ChemBET Pulsar & TPR/TPD, Quantachrome).
TPR profiles of the samples exhibited two reduction peaks in the range 244˚C and 297˚C, characteristic for the reduction of CuO.
The occurrence of two reduction peaks indicates the presence of CuO species differently interacting with the ceria and zirconia matrix.
Previous authors have observed similar reduction peaks for copper based catalysts and have attributed the low-temperature (L-T) peak to the reduction of highly dispersed CuO or Cu2+ ions in an octahedral environment, whereas the high-temperature (H-T) peak to the reduction of bulk CuO[14].
The data in Table 1 indicated that the performance of bifunctional catalysts was strongly affected by Ce and Zr molar ratios, i.e. catalysts including simplex ceria or zirconia exhibited low CO2 conversion.

The ordinary Voronoi model Voronoi structures are fundamental geometric data structures that appear naturally in a broad range of scientific areas including biology, statistics and computer science [12-15].
Fig. 4 presents the microstructures of extra low-carbon and high strength bake-hardening steel sheet for different cold rolling reductions [17] and the microstructures generated by the modified Voronoi model for the same reductions, respectively.
It can be seen that the topologies of the microstructures in which the grains are elongated, resemble those of the deform microstructures for different cold rolling reductions.
Fig. 5 shows the grain size distributions for different cold rolling reductions.
The topologies of the microstructures are similar to those of the deform microstructures for different cold rolling reductions, and the grain size distributions follow lognormal distribution.