Provided is a sheet inspection device capable of saving space, reducing member costs, and reducing the number of maintenance steps. A first light source Ls1 and a first inspection part S1 for inspecting a flaw on the front surface of a sheet and a second light source Ls2 and a second inspection part S2 for inspecting a flaw on the back surface of the sheet are disposed in such a positional relationship that when the sheet has a hole, the first inspection part S1 can detect light emitted by the second light source and transmitted through the hole in the sheet.

The invention discloses a low-frequency electromagnetic detection method for large-scale damage of ferromagnetic materials based on broadband excitation. Detection direction of the magnetic field signal of low-frequency electromagnetic sensor is determined according to the size of ferromagnetic member detection defect; the reference signal and detection signal acquisition position are selected, fix the distance between sensor and tested part, excite a Chirp signal as a broadband excitation signal to perform broadband excitation low-frequency electromagnetic detection; the computer processes collected broadband detection signal; use the difference of Euclidean distance between reference signal and defect detection signal as a defect characterization parameter to obtain the Euclidean distance curve of different depth defects on the upper and lower surfaces of ferromagnetic components with the detection position. Through the analysis and processing of the low-frequency electromagnetic broadband detection signal, the Euclidean response signal and reference signal under broadband excitation are used to characterize the change of material damage degree, which can effectively reduce the influence of magnetic field skin effect, and is beneficial to the effective characterization of the upper and lower material surface defects of at different depths.

The invention discloses a low-frequency electromagnetic detection method for large-scale damage of ferromagnetic materials based on broadband excitation. Detection direction of the magnetic field signal of low-frequency electromagnetic sensor is determined according to the size of ferromagnetic member detection defect; the reference signal and detection signal acquisition position are selected, fix the distance between sensor and tested part, excite a Chirp signal as a broadband excitation signal to perform broadband excitation low-frequency electromagnetic detection; the computer processes collected broadband detection signal; use the difference of Euclidean distance between reference signal and defect detection signal as a defect characterization parameter to obtain the Euclidean distance curve of different depth defects on the upper and lower surfaces of ferromagnetic components with the detection position. Through the analysis and processing of the low-frequency electromagnetic broadband detection signal, the Euclidean response signal and reference signal under broadband excitation are used to characterize the change of material damage degree, which can effectively reduce the influence of magnetic field skin effect, and is beneficial to the effective characterization of the upper and lower material surface defects of at different depths.

There is described a probe for non-destructive testing of a curved object, the probe comprising an arrangement of magnets and coils configured for generating shear horizontal guided waves for propagating longitudinally in the object, the probe having a top surface, a bottom surface, and two opposed ends extending between the top surface and the bottom surface, the bottom surface having a non-zero curvature between the two opposed ends and matable with an outer surface of the curved object.

There is described a probe for non-destructive testing of a curved object, the probe comprising an arrangement of magnets and coils configured for generating shear horizontal guided waves for propagating longitudinally in the object, the probe having a top surface, a bottom surface, and two opposed ends extending between the top surface and the bottom surface, the bottom surface having a non-zero curvature between the two opposed ends and matable with an outer surface of the curved object.

A method for evaluating a crack in a metal member comprises a first removal step (S10) and a second removal step (S20). In the first removal step (S10), a step for electrolyzing a metal member having an oxide scale formed on a surface thereof, a step for acquiring an image of the oxide scale as a first image, and a step for determining whether or not a scale crack has occurred are repeated until occurrence of a scale crack is determined. In the second removal step (S20), a step for electrolyzing the metal member having the scale crack, a second image acquisition step for acquiring an image of the oxide scale as a second image, and a second determination step for determining whether or not the scale crack has disappeared are repeated until disappearance of the oxide scale is determined.

A method for evaluating a crack in a metal member comprises a first removal step (S10) and a second removal step (S20). In the first removal step (S10), a step for electrolyzing a metal member having an oxide scale formed on a surface thereof, a step for acquiring an image of the oxide scale as a first image, and a step for determining whether or not a scale crack has occurred are repeated until occurrence of a scale crack is determined. In the second removal step (S20), a step for electrolyzing the metal member having the scale crack, a second image acquisition step for acquiring an image of the oxide scale as a second image, and a second determination step for determining whether or not the scale crack has disappeared are repeated until disappearance of the oxide scale is determined.

A material deterioration evaluation device for evaluating embrittlement of equipment, includes: an operation data obtaining unit that detects and obtains a state of the equipment as operation data; an operation data storage unit that saves the operation data; a temperature evaluation unit that calculates a predetermined evaluation-target site temperature of the equipment based on the operation data; an evaluation-target component material storage unit that stores material data of a material forming the equipment and embrittlement estimation formulas; an embrittlement evaluation unit that calculates an embrittlement quantity of the material forming the equipment based on the evaluation-target site temperature, the material data, and the embrittlement estimation formulas; a risk evaluation unit that calculates a damage risk of the material that forms the equipment based on the embrittlement quantity; and a recommended maintenance time presentation unit that presents a recommended maintenance time of the equipment based on the damage risk.

A material deterioration evaluation device for evaluating embrittlement of equipment, includes: an operation data obtaining unit that detects and obtains a state of the equipment as operation data; an operation data storage unit that saves the operation data; a temperature evaluation unit that calculates a predetermined evaluation-target site temperature of the equipment based on the operation data; an evaluation-target component material storage unit that stores material data of a material forming the equipment and embrittlement estimation formulas; an embrittlement evaluation unit that calculates an embrittlement quantity of the material forming the equipment based on the evaluation-target site temperature, the material data, and the embrittlement estimation formulas; a risk evaluation unit that calculates a damage risk of the material that forms the equipment based on the embrittlement quantity; and a recommended maintenance time presentation unit that presents a recommended maintenance time of the equipment based on the damage risk.

A computer-implemented method is provided for predicting a fatigue response of a material. The method includes receiving a user input specifying one or more surface roughness parameters that characterize a surface of a material for which fatigue life is to be predicted. The method further includes generating at least one realistic virtual surface profile from the specified one or more surface roughness parameters. The method further includes predicting fatigue life of the material in dependence of a stress field applied to the generated virtual surface profile. In accordance with specific embodiments, the prediction of the fatigue life may be carried out using finite element analysis based simulations, machine learning methods, or combinations thereof.