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However, the usefulness of this polymer is limited by its price and mechanical properties.
Blending is a commonly used technique in polymer technology that has successfully manipulated both price and properties of a range of commercial polymers [2,3].
Many researchers have tried to improve the mechanical properties of PLLA by developing techniques for blending PLLA with other polymers.
Our particular interest is in observing the detailed effects of polymer structure on these factors; Alpha 4, for example, is isomeric with polycaprolactone (PCL) which has been extensively studied in blends with PLLA [5,6].
This is a first step in the identification of compositional ranges for the necessary melt blending and fabrication work, together with extensive mechanical property and biodegradation studies in which both strong translucent crystalline materials and extensible optically clear materials will be important targets.

Vt—extraction function Access to properties of other characteristics and deepen the feature representation; in order to improve the matching performance.
In order to improve the accuracy of the assembly cost estimation, in the early stages of product design, we should extract the main parameters that affecting product function and structure, and adopt Euclidean distance and gray fuzzy clustering combined algorithm to estimate product assembly cost.
Decision support module which combines a variety of factors, help supporting staff to make the best decisions and output design results[2].
By means of a variety of DFX tools, the work is focused on product development that make designers consider various factors downstream in early development.
The speed and accuracy of assembly cost directly affects on product cost estimation and design quality and cycle.

These properties depend on the structure of metallic glasses, which is meta-stable in nature.
Inoue et al [1] and Kovneristyj et al [2] have reported excellent thermal and mechanical properties of Cu-Zr-Ti alloys.
Among various other factors that affect the value of Tx in a metallic glass, atomic size mismatch is one.
Reduction in free volume & diffusivity, electron to atom ratio, differences in electro-negativities, are few other factors that are responsible for variation in Tx.
Kurosaka, Thermal and mechanical properties of Cu-based Cu-Zr-Ti bulk glassy alloys, Mater.

Institute of Mechanical Engineering, Taizhou Vocational and Technical College, Zhejiang Taizhou, 318000, China 2.
Although there are some mathematical model for describing the relation between fatigue damage and material properties the models lack accuracy.
The population greatly affects the final performance and efficiency of GA, selecting from 30 to 160 according complicated degree of problems.
Code and decode.Every individual in the population is composed of a string of certain sorted genes and different length of string and coding methods greatly affect the solving accuracy and convergence time.
Table.1 The chemical constitution and material properties of modulated Chinese No. 45 steel Chemical constitution（％） C 0.49 Si 0.215 Mn 0.64 P 0.018 S 0.022 Parameters of Mechanical properties [Gpa] 162.2 [Mpa] 668.4 [Mpa] 803.3 (％) 21.60 (％) 65.09 Parameters of fatigue properties （Mpa） （MPa） 2069.3 -0.1608 1.7456 -0.6939 1624.73 0.2092 Table.2 Predicted result by GA-BP, tested result and calculated result by Coffin-mason method results Group No. of sample Class of strain（％） Tested life（Cycle） Predicted life (cycle) Life calculated by Coffin－Manson method Relative error BP-GA Coffin－Manson 1 1.65 738 710 623 0.03794 0.155827 2 1.28 868 960 984 0.10599 0.13364 3 1.07 1289 1243 1385 0.035687 0.07448 4 0.91 1765 1799 1907 0.01926 0.08045 5 0.78 2541 2491 2612 0.019677 0.02794 6 0.58 5286 5408 5027 0.02308 0.048997 Example for multiaxial fatigue.The material used for multiaxial fatigue test is normalized Chinese No. 45 steel

The sheet metal use DC01 material, mechanical properties of material is shown in table 1.
The orthogonal experiment design method has the following two properties: 1.
Fig.1 Automobile reinforced plate Fig.2 Finite element model (a) punch (b) binder (c) blank (d) die Table 1 Main property parameter of DC01 Material Yield strength (MPa) Tensile strength (MPa) Hardening index n Anisotropy coefficient r Elongation (%) DC01 210 270～410 0.22 1.23 ≥28 Orthogonal experiment factors for stamping velocity (factors A), blank holder force (factors B), friction coefficient (factors C), and mold clearance (factors D).
Range represents the size of the influence factors in the orthogonal test table.
Among them the influence factors of blank-holder force is the largest.

Ahankari1,a Atul Suryawanshi2,b and Udayraj Warerkar3,c 1Associate Professor and Head, Mechanical Department, ADCET, Ashta, Maharashtra, India. 2,3UG students Of Mechanical Department, ADCET, Ashta, Maharashtra, India.
Stacking sequence is one of the important processing parameters on which the properties of filament wound components depend to a large extent.
The performance of filament wound (FW) pipes is very much affected by processing parameters such as winding angle, stacking sequence, stacking thickness, volume fraction of fiber, fiber pretension etc.
Apart from the processing variables, certain service factors like lateral load also affect the overall performance of the component.
Hence it is of prime importance to not only focus on process variables but also the factors that a component might encounter during the phase of storage, transportation, on-field installation and service life.

Introduction Austenitic stainless steel and nickel-based alloy have been widely used in structural materials of nuclear power equipment due to good corrosion resistance and mechanical properties, and stress corrosion cracking (SCC) of these materials in the environments of the light water reactors (LWRs) is one of the main problems for the management of the structural safety and service life of nuclear power plants [1].
Stress corrosion cracking of austenitic stainless steel and nickel-based alloys in high temperature water is an electrochemical anodic reaction affected by corrosive environment, stress and material at crack tip.
Particularly, the introduction of the stress intensity factor (KI) as the basic mechanical parameters in last century makes the quantitative prediction of stress corrosion propagation rate of various austenitic stainless steel and nickel-based alloys in high temperature environment possible.
But how to improve the prediction accuracy of stress corrosion crack growth rate is still a serious problem due to the expensive experimental apparatus, long experimental period and many other factors [4].
In order to obtain the mechanical parameters more accurately, the sub-model technology was used in this study.

Information entropy is decided by internal properties of things.
Comprehensive evaluation for disassembly sequences process There are many factors affecting product disassembly sequences, both qualitative and quantitative.
Data of comprehensive evaluation includes project set, factor set and weight set.
It can work out the total property values .and different disassembly sequences are sorted depending on the size of the total property values.
Mechanical Design, 2005, 22(1) , In Chinese [3] Kroll E, Thomas A H.

More recent applications tend to develop MMC in order to improve tensile and fatigue properties and to lighten the material.
The final properties are dependent on the amount, size and distribution of the TiC particles, as well as on the steel matrix microstructure obtained after processing and treatments.
The TiC particle size was determined by Rietveld refinement and estimated to 25 nm after mechanical alloying.
The last factor that may affect the transformation kinetics is the changes in nucleation sites.
We can expect that the main factors that affect the kinetics of ferrite formation are on one side the local composition of the austenite at the TiC/austenite interface that depends on the interactions between the TiC and the matrix during the complete treatment, as well as the increase in nucleation sites.

Amorphous carbon represents a class of materials with a range of optical, mechanical and electrical properties that lie somewhere between those of graphite, polymers and diamond, depending on deposition conditions.
Material analysis has involved a wide range of high-resolution techniques such as NEXAFS, XPS and Raman spectroscopy, to determine sp 3/sp2 structure, XRR and ellipsometry for refractive index and density, nanoindentation and AFM for mechanical and topological properties etc.
The impact of substrate materials, the use of interlayers and dopants on electrical, mechanical and biological properties will then be presented, with particular relevance to device and plasma considerations.
Considering bias and pressure together implies that the energy of ion bombardment is a significant factor, however it is not clear how these parameters affect the dissociation and transport of species to the substrate.
We note a change in properties after the initial stages of growth (< 20nm).