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Lamb Wave Based Monitoring of Fatigue Crack Growth Using Principal Component Analysis Ye Lu1, a, Mingyu Lu2, b, Lin Ye3, c, Dong Wang4, d, Limin Zhou2, e and Zhongqing Su2, f 1 Department of Civil Engineering Monash University Clayton, VIC 3800, Australia 2 Department of Mechanical Engineering The Hong Kong Polytechnic University Hong Kong SAR People’s Republic of China 3 Laboratory of Smart Materials and Structures (LSMS) Centre for Advanced Materials Technology (CAMT) School of Aerospace, Mechanical and Mechatronic Engineering The University of Sydney, NSW 2006, Australia 4 Beijing Aeronautical Science & Technology Research Institute Beijing 102211, People’s Republic of China a ye.lu@monash.edu (corresponding author), b 08900426r@polyu.edu.hk, c lin.ye@sydney.edu.au, d wangdong1@comac.cc, e mmlmzhou@inet.polyu.edu.hk, f mmsu@polyu.edu.hk Key Words: Fatigue crack growth, Principal component analysis, Lamb waves, Structural health monitoring Abstract
As a result, the crack may be undetectable due to the marginal difference in comparison with the baseline signals, or the characteristics of the received signal, e.g. the damage index, may exhibit nonlinear properties.
Ten circular piezoelectric (PZT) wafers with mechanical properties listed in Table 1 were surface-mounted on the plate, in which wafers P1-P5 functioned as actuators and wafers P6-P10 were corresponding sensors in a pitch-catch configuration, detailed in Figure 2.
Crack growth (a) (b) (c) Figure 4 Distribution of the first two principal components of different crack lengths for sensing paths (a) P1-P6; (b) P3-P8; (c) P5-P10 The main reason for the different properties of the principal components for sensing paths P1-P6 and P3-P8 was the fact that generation and acquisition of Lamb wave signals were completed in the unloading condition.
For sensing path P1-P6, which was the first path affected by crack propagation, the hairline fatigue crack always evolved from closing or semi-contact to full opening, even under the unloading condition.

Develop Heat-Resistant Stalowych in Indoor Heat-resistant Sitall coatings for the protection of nichrome alloys from high-temperature corrosion must have a complex of physical, mechanical and thermomechanical properties, in particular high adhesion strength of the coating with nichrome, as well as resource predetermined by heat and heat resistance.
In this regard, when developing the optimal compositions of these coatings with the listed properties, it was necessary to identify their optimal compositions.
Boric acid and clay Watches-Yarskaya place of birth are introduced to improve the rheological properties of slurries coatings.
As is known, the CTE of coatings is affected primarily on their temperature resistance.
The dependence of thermal resistance and heat resistance of coatings on the composition and technological factors was revealed.

Introduction Carbon nanotubes (CNTs) have attracted tremendous attention due to their unique structural, mechanical, electrical and thermal properties.
However, unsatisfactory electrical properties have been observed for composites prepared by melt mixing.
Therefore, the conditions of melt mixing are crucial to electrical properties of a CNT/polymer composite.
Results and discussion Electrical properties.
Electrical properties of those composites before and after annealing are summarized in Table 1.

In this paper, superplastic properties of 1933 aluminium alloy were evaluated and the effect of hot ECAP on grain refinement and superplasticity was investigated.
Compared with original samples annealed at 400°C, the superplastic elongation of samples processed by ECAP for 4 passes increases by a factor of 130% about, and the range of superplastic temperature varies from 140°C to 210°C.
Moreover, the alloy with fine-grained microstructure usually exhibits excellent physical and mechanical properties.
It is also seen that the microstructure of clamping head (no deformation, only affected by heat) of sample is coarsened than the microstructure (Fig.1 (b)) of original forged block of 1933 alloy remarkably and grains are distinctly more homogeneous (Fig.4 (b)).
Concerned about actual productivity and microstructure, properties of pressed samples, ECAP processing for 4 passes at 300°C is recommended in this paper

The principal properties are shown in table1.
It has many good technical performances, including: (1)The lightweight insulation hollow block has good thermal insulation performance and a certain degree of mechanical property.
To study the fire resistance performance, the grillage shear wall was fired at 2000℃ for 2h at fire protection research laboratory of the ministry of public security in Tianjin, the result showed that there was no obvious damage on the surface and the blocks have good fire-resistance properties [5]
Table 1 The principal properties of the lightweight insulation hollow block No.
The vibrating tube should be placed in right place first in order to prevent affecting the original locations of reinforcing steels

The J/V characteristics were performed soon after preparation of the complete devices to avoid any change in the photoelectric properties caused by age.
It is well known that, the increase in spin coating speed of the sol-gel solution for preparation of ZnO films tends to decrease film thickness and, therefore, tends to change optical, structural and other properties of ZnO film as stated in many literatures [22-24].
Therefore, the observed variation in device performance parameters, as shown in Fig. 7, may be attributed to a variation occurred in film thickness and other its properties with increasing spin coating speed of the ZnO layer.
Mehra, Effect of thickness on structural, electrical, optical and magnetic properties of Co and Al doped ZnO films deposited by sol-gel route, Appl.
Sahdan, Influence of Spinning Speed on the Properties of Sol-Gel Spin Coated ZnO Films, Advanced Materials Research 970 (2014) 115-119

China began studies on NC machining parameter optimization during 1980's, cutting database was established in the same time.However, the application of the cutting database in practice still has many problems, because the current use of cutting database is affected by the differences from production conditions and the limited cutting process data.
Cutting parameters optimization for NC milling based on BP Neural Network Cutting parameter optimization techniques for NC machining have the properties of multi-objective, multi-constraint and multi-variable, so the optimal cutting parameters can't get by traditional optimization algorithm.Artificial neural network with the advantages of parallel computing,distributed associative storage,better fault tolerance and adaptive learning features is in the forward position to deal with complex nonlinear problems and artificial intelligence, which has penetrated into many fields in recent years, and the range of applications is growing, especially the applications in mechanical engineering are extremely broad, such as the researches and applications in error compensation of the machine motion, thermal deformation control, the assessment of process parameters,process parameters optimization, processing error prediction and tool wear estimation have made certain achievements.
According to apecific process issues, the number of network layers, the neurons, fitting error, learning rate and sample data should be required to determine for the application of BP neural network, all these are closely related to the network's prediction accuracy and training time, which will directly affect the network's predictive effect and computation efficiency[5].
BP neural network model of cutting parameters optimization of aluminum alloy shell structure adopts 3 layers, the input layer neurons are the tool type,tool extended lengths,tool diameter,tool diameter ratio,basic dimensions,cutting width,cutting depth,dimensional accuracy and surface finish; the output layer neurons are the spindle speed, feedrate and processing time, for considering the processing time is a value factor of milling efficiency, the neural network structure is 9-36-3, which is shown in Fig.1.
The studies of cutting parameter optimization technique for NC Machining have shown that: taking the neural network technology into the optimization process of cutting parameters is very appropriate, using BP neural network, milling parameters optimization model of aluminum alloy shell structure was built, the prediction and optimization options of cutting parameters of aluminum alloy shell structure was realized , the human factor of craftsmen in the process of cutting parameters selected was avoided, making the choice of cutting parameters with higher reliability, and the optimization effect is obvious.

As the statistical analysis shows, quality level, quality assurance cost, quality failure cost can affect each other and they can change with the positive direction of quality assurance cost and change with the negative direction of quality failure cost[7-9].
The increase and decrease of prevention cost and appraisal cost can affect internal failure cost and external failure cost.
The stable design is for the purpose to make the mean value of product quality characteristics reach the target value as much as possible and minimize the variance of functional characteristics fluctuation caused by various interference factors to minimize quality loss: (6) If it is recorded as, then: (7) If it is defined that Cpm is the second generation of process capability index and Cpmk is the third generation of process capability index, then: (8) It is thus clear that the relation between quality loss function and process capability index is as follows: (9) In case of, namely, , substitute K1 value to obtain the average quality loss of each product as follows: (
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In the latter, the SMG structure is replaced by a coarse grain size by rapid growth of a few grains which drastically reduces the mechanical properties of the material [1].
In addition to a fine grain size in SMG alloys, there are two notable factors affecting the growth of a cell or subgrain within a deformation substructure.

From one hand this is due to the fact that hydrogen in titanium has high mobility and hydrogen accumulation leads to significant changing of mechanical properties of titanium alloys.
Since the samples were hydrogenated up to concentrations varying from 0.9 at.% to 31.5 at.%, the hydrogen accumulation is not the only factor that affects the formation of defects.
Another factor is a phase transformation process.
Since the hydrogenation are carried out in hydrogen at 873 K and was followed by vacuum cooling to room temperature, the processes of defect formation is highly affected, not only by the hydrogen accumulation, but also by the phase transformation processes.