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Online since: December 2009
Authors: Thanapong Sareein, Sasiporn Prasertpalichat, Supon Ananta, Muangjai Unruan, Jirapa Tangsritrakul, Athipong Ngamjarurojana, Rattikorn Yimnirun
Let us now consider the controlling factor in each limiting case.
The P-E hysteresis loop of BT-1Fe-1.5Nb under the stress of (a) 0 MPa (b) 185 MPa To explain the experimental results on the decrease of the ferroelectric properties with increasing compressive stress, as shown in Figs. 1, 2, and 3, one can interpret the changes by exploiting the domain-reorientation processes.
However, it is also noticed that the aging rate seems to be affected slightly by the compressive stress.
The mechanism of this enhanced aging is not clearly known, but could be due to lowered activation energy for defect-dipole motion with external mechanical load.
In addition, the aging rate is not strongly affected by the applied stress in donor-dominant hybrid-doped BaTiO3 ceramics, as seen in Figs. 2 and 3.
Online since: June 2010
Authors: Danuta Kotnarowska
Effect of Erosive Particle Velocity on the Intensity of Polymeric Coating Wear Danuta Kotnarowska Faculty of Mechanical Engineering, Technical University of Radom al.
Nature of the erosive wear process considerably depends both on physico-chemical coating properties and such factors as: material kind, size and shape of an erosive particle, its angle of incidence as well as a velocity with which particle strikes a surface [2-5].
Erosive wear intensity of coatings depends also on environmental factors that indirectly affect coating properties as well as mechanical factors that determine erosive wear resistance [9-13].
Kotnarowska: Influence of ambient factors on operational parameters of the protective epoxy coatings for technical equipment.
Wojtyniak: Influence of Ageing on Mechanical Properties of Epoxy Coatings.
Online since: July 2020
Authors: Poppy Puspitasari, Ardianto Prasetiyo, Aloon Eko Widiono, Sukarni Sukarni, Retno Wulandari
The powder of TiO2 with a particle size of <25 nm was blended with T.Chuii through the mechanical mixing method.
Therefore, both the activation energy and pre-exponential factor were defined.
In accordance with Figure 4, then the activation energy and pre-exponential factor were calculated, and the overall results were presented in Table 2.
Lo, Leucaena biochar produced by microwave torrefaction : Fuel properties and energy efficiency, Appl.
Wardana, Potential and properties of marine microalgae Nannochloropsis oculata as biomass fuel feedstock, Int.
Online since: June 2020
Authors: Gui Xin Wang, Wen Ping Zhang, Xiao-Bo LI, Wasim M. K. Helal
The main causes of the failure of con-rods are fatigue and other factors according to the previous investigations [9-16].
Materials and Methods 2.1Materials Although there are a number of materials available in the market that can be used for manufacturing con-rods, the material used should be carefully selected and it should have good mechanical properties [32]. 42CrMoA has a good combination of strength and toughness in the quenched and tempered conditions [24].
The safety factors of other parts of con-rod are shown in Table 5.
Table 5 Safety factors of con-rod parts.
China Mechanical Engineering, 15, 365-369
Online since: June 2012
Authors: Sung Woon Cha, Young Ho Kim, Jeonghun Ahn
However, recently, research and industrial applications, using the specific material properties obtained from pore morphology, such as optics, acoustics, etc., are actively performed.
Recently, there has also been research regarding the changes of the electrical properties.
Batch process is used often in research experiments, since it is easy to control the process factors.
The factors of the saturation process that affect the resultant porosity are the blowing agent, saturation pressure, saturation time, and saturation temperature, while the foaming process factors are the foaming temperature and foaming time.
To control the foaming ratio, this experiment only varied the foaming temperature, while keeping all other factors constant.
Online since: December 2010
Authors: R.J. Song, J.L. Niu, Dong Hai Chen
The factors influencing machining accuracy were pointed out from the viewpoint of system.
The NAM-820 ultra-precision CNC lathes uses the ultra precision aerostatic spindle which wins the first prize in National Science and Technology Progress Award with independent intellectual property rights to ensure the spindle rotation accuracy <0.05µm.
Factors effects machining accuracy of ultra-precision machine tool Precision machine tools mainly composed by the mechanical system, control system and management system which influence each other to ensure the accuracy of precision machine tool processing.
The mechanical system is the body structure of ultra-precision machine tool and its performance is the fundamental guarantee of the processing precision.
CHINA MECHANICAL ENGINEERING, 1999,10(5), p. 570-576
Online since: October 2015
Authors: Andrey N. Chibisov
Thus, the nanocrystals morphology is the main factor affecting the stability of the phase state of nanocrystalline ZrO2.
Therefore, the aim of this work was to study the equilibrium morphology shape of ZrO2 nanocrystals for a specified size, and their energy properties.
The energy properties of nanocrystals.
Music, Factors Influencing the Stability of Low Temperature Tetragonal ZrO2, Croat.
Scheffler, Density-functional theory calculations for poly-atomic systems: electronic structure, static and elastic properties and ab initio molecular dynamics, Comp.
Online since: March 2012
Authors: Jin Yao, Jin Ming Li, Can Jun Xiao
At the same time, the impact-factors need to be considered for an accurate simulation, then a lot of simulation factors need to be considered into the simulation model.
Each agency has its own properties.
Job includes the properties as quantity, current state (processing, waiting), product type, in time and out time.
All the factors lead to the real equipment is inconstant.
The simulation model will be more accurate including these factors.
Online since: November 2006
Authors: Hélio Lucena Lira, J.Marcos Sasak, Lisiane Navarro de Lima Santana, Gelmires Araújo Neves, Maria Isabel Brasileiro, D.H.S. Oliveira, A.P. Novaes
The aim of this study is to obtain mullite from the residue produced by kaolin industry, alumina and ball-clay and to characterize by physical and mechanical properties of the produced mullite.
The composition II present amount of mullite inferior compared to the others and this is due to the small content of silica from raw materials that is not enough to reach with residual alumina to form mullite and this affect the physical and mechanical properties of the specimens.
On the other hand, the compositions with greater amount of silica (I, III and IV) present high content of mullite and also better physical and mechanical properties, as can be seen in Table 9.
Figure 5 show the mechanical behavior of the specimens and the compositions I, III and IV present superior properties when compared with composition II.
According to Ebadzadeh [2] the factors that affect the mechanical resistance is the high porosity and the big size of the mullite grain.
Online since: November 2012
Authors: Guo Xiang Lin, Yong Hui Tang, Wei Min Zhou
There are many factors affecting the security of fully mechanized mining face.
These factors can be divided into three major factors of man, machine, and the environment from the man - machine - environment systems engineering perspective.
After analyzing of man - machine - environment impact factors of the mechanized mining face, the comprehensive evaluation index system can be established, as shown in Fig.1.
rate reliability service life failure property temperature illumination environment humidity gas According to the equation (4) in step 2.1, we can calculate the membership degree of the evaluation factors, as shown in Table 2.
Table 2 Membership of the evaluation factors Table 3 ,and Entropy of the evaluation factors evaluation factors best good normal bad worst evaluation factors AHP method entropy method entropy weight age E1 0.8409 0.0974 0.0000 0.0000 0.0000 age E1 0.1394 0.0730 0.1004 seniority E2 0.0000 0.0008 0.9727 0.0044 0.0000 seniority E2 0.0422 0.0902 0.3780 training time E3 0.7349 0.2917 0.0005 0.0000 0.0000 training time E3 0.0767 0.0676 0.0512 good rate E4 1.0000 0.0625 0.0131 0.0005 0.0000 good rate E4 0.0283 0.0753 0.0207 repair rate E5 0.0000 0.9969 0.1550 0.0008 0.0000 repair rate E5 0.0159 0.0691 0.0108 reliability E6 0.0020 1.0000 0.1459 0.0000 0.0000 reliability E6 0.1637 0.0701 0.1132 service life E7 0.0625 1.0000 0.0625 0.0000 0.0020 service life E7 0.0511 0.0669 0.0335 comfortable E8 0.6889 0.2571 0.0003 0.0008 0.0000 comfortable E8 0.0094 0.0571 0.0049 failure E9 0.0625 1.0000 0.0625 0.0020 0.0020 failure E9 0.0924 0.0505 0.0463 property E10 0.1059 0.9727 0.0092 0.0044
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