Fig.1

Schematic diagram of blade with deviation［11］

Fig.2

Leading edge machining errors［18］

Fig.3

Flow near the leading edge of a nonuniformly coated compressor airfoil where separation occurs with a turbulent reattachment［15］

Fig.4

The reference blade （left） and blade with blunt leading edge （right）［25］ which flow around leading edge

Fig.5

Homogeneous contour error［29］

Fig.6

Performance comparison of uniform profile error blade［29］

Fig.7

Cascade channel Mach number and entropy distribution cloud （inlet angle β1=40°）［29］

Fig.8

Blade torsion error［29］

Fig.9

Relative standard deviation of different resulting flow parameters from MC simulations， each taking into account a different number of PCA modes to describe the geometric variability［18］

Fig.10

Three-dimensional deviation of measured blade to the design intent［47］

Fig.11

Leading edges used in NIPC［63］

Fig.12

Manufacturing deviation from design intent［79］

Fig.13

3D pressure field of one realization［49］

Fig.14

Variation trend of performance with increase of flow channel［49］

Fig.15

Coefficient of influence［79］

Fig.16

Comparison of the experimental and simulated surface errors in 2D for milling of A-side［84］

Fig.17

Comparison between predicted and measured machining errors， the red and blue dots represent interpolation and extrapolation［89］

Fig.18

Zero error compensation method［108］

Fig.19

Block diagram of process for evaluating uncertainty of factors［118］

Fig.20

Online error compensation process for thin-walled parts［125］

Fig.21

Schematic diagram of processing profile waviness［9］

Fig.22

Ripple phenomenon of actual processing blade

Fig.23

Waviness in tolerance range （5 times enlarge） and applied function［128］

Fig.24

Variation of pressure coefficient with Mach number［138］

Fig.25

Variation of position of the maximum pressure with Mach number［138］

Fig.26

Mechanisms leading to entropy increase on wavy surface［128］

Table 1

Waviness characterization［128］

Fig.27

Relative Mach number distribution in S1 plane around leading edge for OP2［128］

Fig.28

Design profile waviness 0 and processing profiles waviness l-3［9］

Fig.29

Generation of material removal process［145］

Fig.30

Adaptive trajectory planning［155］

Fig.31

Comparison of trajectory planning results before and after improvement［156］

Fig.32

Grinding effects at blade leading and trailing edges by different trajectory planning algorithms［156］

Fig.33

Robot path optimization for removing transverse transition lines［160］

Fig.34

Complex geometric shapes on the surface of biological bodies in nature［165］

Fig.35

Tubercles on a humpback whale flipper［175］

Fig.36

Wavy-A Structure of blade［183］

Fig.37

Wavy blade-B Structure［183］

Fig.38

Geometric model of corrugated blade［184］

Fig.39

Streamwise vortex structure induced by corrugated cascades［184］

Fig.40

Schematic diagram of ribbed structure［189］

Fig.41

Simulation of airflow on surfaces with bionic microstructure［190］

Fig.42

Drag reduction performance of various rib geometries［189］

Fig.43

Microscopic structure of bird flight feathers［203］

Fig.44

Effect of angle between 3D herringbone riblets on drag reduction［203］

Fig.45

Schematic drawing of herringbone riblets （or convergent-divergent riblets） and the secondary flow motion they produce in a boundary layer［203］

Fig.46

Microtextured surface fabrication process［205］

Fig.47

Wheel profile geometry and grinding strategy for riblet machining［212］

Fig. 48

Grinding method of TC17 thin wall micro-riblets structure abrasive belt［214］

Fig.49

Schematic representation of surface structuring with new laser-based fabrication process［219］

Fig.50

Schematic representation ofTiAl structuring process （From left to right： halogenation and pre-oxidation for oxidation protection， laser dehalogenation to define the location of the riblets， complete riblet growth during high-temperature exposure in service）［219］

Fig.51

Two main laser AM processes and material types

Fig.52

Principle and equipment of USRP surface texturing［209］