Method and device for detecting internal intensive defects of large castings of thermal power generating units

Abstract

The application relates to the technical field of nondestructive testing, in particular to a method and device for detecting internal intensive defects of large castings of a thermal power unit. The method comprises the following steps: based on an offline three-dimensional simulation model constructed by using metal microstructure attributes of at least one large casting to be detected in the thermal power unit, obtaining a simulated echo beam response law of intensive defects in the offline three-dimensional simulation model; based on the simulated echo beam response law, separating intensive defect signals of the large casting to be detected to obtain a three-dimensional spatial position coordinate distribution diagram of the intensive defects in the large casting to be detected; based on the metal microstructure attributes, calculating the volume of the intensive defects in the at least one large casting to be detected, and determining the extension direction and extension angle of the intensive defects in the large casting to be detected. The application can effectively detect the volume, intensive degree, spatial position distribution, extension path, extension direction and extension angle of the intensive defects in the large casting.

Description

Technical Field

[0001] This application relates to the field of nondestructive testing technology, and in particular to a method and apparatus for detecting dense internal defects in large castings of thermal power units. Background Technology

[0002] Supercritical thermal power units, with their significantly improved thermal efficiency, have become a core technology in recent thermal power generation. Their key pressure-bearing components (such as cylinders, valves, and large castings) operate under harsh environments of extreme high temperatures, high pressures, and complex alternating stresses. Due to the inherent characteristics of the casting process, defects such as porosity, shrinkage cavities, and inclusions inevitably occur within the castings. These defects often appear in a densely distributed manner, with small spacing, numerous components, and complex and varied shapes. Under harsh operating conditions, they can easily become the source of crack initiation and propagation, seriously threatening the safe and stable operation of the unit. Therefore, efficient and accurate non-destructive testing, quantitative evaluation, and risk level indication of dense defects inside large castings are crucial for ensuring the safety of the unit and extending its service life.

[0003] In related technologies, traditional ultrasonic testing methods still focus on acquiring echo signals from these large castings using conventional single-crystal probes or low-channel-number phased arrays, thereby briefly identifying defects within the large castings through these echo signals.

[0004] However, traditional ultrasonic testing methods have significant limitations when dealing with dense defects. When the reflected echoes from dense defects interfere with each other, conventional single-crystal probes or low-channel-number phased arrays are unable to effectively separate and identify adjacent defects, leading to a high risk of misjudgment and missed detection. At the same time, traditional ultrasonic testing methods lack effective means to detect the precise size and spatial distribution of individual defects in dense areas, making it difficult to support accurate safety assessments. When dealing with large castings with complex geometries and dense defects, it is even more necessary to repeatedly adjust the probe position and parameters, resulting in slow testing speed and low efficiency, which cannot meet the needs of large-scale industrial applications and urgently needs to be addressed. Summary of the Invention

[0005] This application provides a method and apparatus for detecting dense internal defects in large castings of thermal power units. This addresses the shortcomings of traditional ultrasonic testing methods, which struggle to effectively separate and identify adjacent defects when the reflected echoes from dense defects interfere with each other, leading to a high risk of misjudgment and missed detection. Furthermore, these methods lack effective means to detect the precise size and spatial distribution of individual defects within dense areas, hindering accurate safety assessments. Moreover, when dealing with large castings with complex geometries and dense defects, repeated adjustments to probe positions and parameters are necessary, resulting in slow detection speeds, low efficiency, and high costs, making it difficult to meet the needs of large-scale industrial applications.

[0006] The first aspect of this application provides a method for detecting dense defects inside large castings of thermal power units, comprising the following steps: based on an offline three-dimensional simulation model of at least one large casting to be inspected, constructed using the metal microstructure properties of at least one large casting to be inspected in a thermal power unit, obtaining the simulated echo beam response law of dense defects in the offline three-dimensional simulation model; based on the simulated echo beam response law, separating the dense defect signals of at least one large casting to be inspected, so as to obtain a three-dimensional spatial position coordinate distribution map of dense defects in at least one large casting to be inspected according to the dense defect signals; based on the metal microstructure properties, inverting and calculating the volume of dense defects in at least one large casting to be inspected, and combining the scanning size of dense defects in at least one large casting to be inspected in multiple sections and the three-dimensional spatial position coordinate distribution map, fitting the extension path of dense defects in at least one large casting to be inspected, and determining the extension direction and extension angle of dense defects in at least one large casting to be inspected according to the extension path.

[0007] Optionally, in one embodiment of this application, obtaining the simulated echo beam response law of dense defects in the offline three-dimensional simulation model includes: setting multiple reflectors with different volumes, positions and densities in the offline three-dimensional simulation model; and generating the simulated echo beam response law based on the beam response law of the reflected echo signals of the multiple reflectors.

[0008] Optionally, in one embodiment of this application, the offline three-dimensional simulation model of at least one large casting to be inspected, constructed based on the metal microstructure properties of at least one large casting to be inspected in a thermal power unit, includes: detecting at least one of the grain size, sound velocity, grain scattering attenuation coefficient, and acoustic impedance value of at least one large casting to be inspected; and constructing the offline three-dimensional simulation model based on at least one of the grain size, sound velocity, grain scattering attenuation coefficient, and acoustic impedance value of at least one large casting to be inspected.

[0009] Optionally, in one embodiment of this application, separating the dense defect signal of at least one of the large castings to be inspected includes: determining at least one signal acquisition frequency band of at least one of the large castings to be inspected based on at least one of the grain size, sound velocity, grain scattering attenuation coefficient, and acoustic impedance value of at least one of the large castings to be inspected; acquiring defect reflection echo signals of at least one of the large castings to be inspected using at least one of the signal acquisition frequency bands based on the target sound beam transmission time and the target sound beam reception delay time; and separating the dense defect signal from the defect reflection echo signal.

[0010] Optionally, in one embodiment of this application, before fitting the extension path of the dense defects in at least one of the large castings under inspection by combining the scanning dimensions of the dense defects in multiple cross sections and the three-dimensional spatial position coordinate distribution map, the method further includes: scanning multiple cross sections of at least one of the large castings under inspection to obtain multi-cross section scanning results of at least one of the large castings under inspection; and obtaining the scanning dimensions of the dense defects in multiple cross sections based on the multi-cross section scanning results.

[0011] Optionally, in one embodiment of this application, the method further includes: obtaining the density and key distribution location of the dense defects based on the three-dimensional spatial coordinate distribution map; predicting the expansion trend of dense defects in at least one of the large castings to be inspected based on the density and key distribution location; and generating a risk distribution map of at least one of the large castings to be inspected by combining the density, the key distribution location, and the expansion trend of dense defects.

[0012] A second aspect of this application provides a device for detecting dense defects inside large castings of thermal power units, comprising: a simulation module, configured to obtain the simulated echo beam response law of dense defects in the offline three-dimensional simulation model based on at least one offline three-dimensional simulation model of the large casting to be inspected, constructed using the metal microstructure properties of at least one large casting to be inspected in the thermal power unit; a separation module, configured to separate the dense defect signal of at least one large casting to be inspected based on the simulated echo beam response law, so as to obtain a three-dimensional spatial position coordinate distribution map of the dense defects in at least one large casting to be inspected based on the dense defect signal; and a detection module, configured to calculate the volume of the dense defects in at least one large casting to be inspected based on the metal microstructure properties, and, in conjunction with the scanning size of the dense defects in at least one large casting to be inspected in multiple sections and the three-dimensional spatial position coordinate distribution map, fit the extension path of the dense defects in at least one large casting to be inspected, and determine the extension direction and extension angle of the dense defects in at least one large casting to be inspected based on the extension path.

[0013] Optionally, in one embodiment of this application, the simulation module includes: a setting unit, configured to set multiple reflectors with different volumes, positions and densities in the offline three-dimensional simulation model; and a generation unit, configured to generate the simulated echo beam response law based on the beam response law of the reflected echo signals of the multiple reflectors.

[0014] Optionally, in one embodiment of this application, the simulation module includes: a detection unit for detecting at least one of the grain size, sound velocity, grain scattering attenuation coefficient, and acoustic impedance value of at least one of the large castings to be inspected; and a construction unit for constructing the offline three-dimensional simulation model based on at least one of the grain size, sound velocity, grain scattering attenuation coefficient, and acoustic impedance value of at least one of the large castings to be inspected.

[0015] Optionally, in one embodiment of this application, the separation module includes: a determining unit, configured to determine at least one signal acquisition frequency band of at least one of the large castings to be inspected based on at least one of the grain size, sound velocity, grain scattering attenuation coefficient, and acoustic impedance value of at least one of the large castings to be inspected; an acquisition unit, configured to acquire defect reflection echo signals of at least one of the large castings to be inspected using at least one of the signal acquisition frequency bands based on the target sound beam emission time and the target sound beam reception delay time; and a separation unit, configured to separate the dense defect signal from the defect reflection echo signal.

[0016] Optionally, in one embodiment of this application, it further includes: a scanning module, configured to scan multiple cross-sections of at least one of the large castings to be inspected before fitting the extension path of the dense defects in at least one of the large castings to be inspected by combining the scanning dimensions of the dense defects in multiple cross-sections and the three-dimensional spatial position coordinate distribution map, so as to obtain the multi-section scanning results of at least one of the large castings to be inspected; and a first acquisition module, configured to acquire the scanning dimensions of the dense defects in multiple cross-sections based on the multi-section scanning results.

[0017] Optionally, in one embodiment of this application, it further includes: a second acquisition module, configured to acquire the density and key distribution location of the dense defects based on the three-dimensional spatial location coordinate distribution map; a prediction module, configured to predict the expansion trend of dense defects in at least one of the large castings to be inspected based on the density and the key distribution location; and a generation module, configured to generate a risk distribution map of at least one of the large castings to be inspected by combining the density, the key distribution location, and the expansion trend of dense defects.

[0018] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for detecting dense internal defects in large castings of thermal power units as described in the above embodiments.

[0019] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for detecting dense internal defects in large castings of thermal power units.

[0020] A fifth aspect of this application provides a computer program product, including a computer program that, when executed, is used to implement the above-described method for detecting dense internal defects in large castings of thermal power units.

[0021] This application's embodiments can effectively detect the volume, density, spatial distribution, extension path, extension direction, and extension angle of dense defects within large castings under inspection. This enables the construction of an offline 3D simulation model through precise testing of the metal microstructure properties of large castings, providing an effective data foundation for the actual detection of dense defects in large castings. By employing multi-dimensional feature coupling modeling and multi-frequency phased array technology, the resolution of dense defects in large castings under inspection is significantly improved, achieving precise quantification of defect 3D location, volume, and extension direction, reducing calculation errors. Combining historical dense defect detection records with the current operating conditions of thermal power units to predict the defect expansion trend of large castings under inspection, and integrating with a safety assessment system, provides a basis for casting maintenance, replacement, and inspection plans, significantly improving the efficiency and accuracy of dense defect detection and safety assessment within large castings. This effectively ensures the safe and stable operation of large castings in supercritical / ultra-supercritical thermal power units, extending the unit's service life. In related technologies, traditional ultrasonic testing methods struggle to effectively separate and identify adjacent defects when the reflected echoes from dense defects interfere with each other, leading to a high risk of misjudgment and missed detection. Furthermore, they lack effective means to detect the precise size and spatial distribution of individual defects within dense areas, making it difficult to support accurate safety assessments. When dealing with large castings with complex geometries and dense defects, repeated adjustments to probe position and parameters are necessary, resulting in slow detection speed, low efficiency, and high cost, making it difficult to meet the needs of large-scale industrial applications.

[0022] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0023] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0024] Figure 1 This is a flowchart illustrating a method for detecting dense internal defects in large castings of thermal power units according to an embodiment of this application.

[0025] Figure 2 This is a schematic diagram of the internal dense defect detection device for large castings of thermal power units provided in the embodiments of this application;

[0026] Figure 3This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application.

[0027] Figure label:

[0028] 10-Detection device for dense internal defects in large castings of thermal power units: 100-Simulation module, 200-Separation module and 300-Detection module; 301-Memory, 302-Processor and 303-Communication interface. Detailed Implementation

[0029] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0030] The following description, with reference to the accompanying drawings, describes a method and apparatus for detecting dense internal defects in large castings of thermal power units according to embodiments of this application. Regarding the related technologies mentioned in the background section, traditional ultrasonic testing methods struggle to effectively separate and identify adjacent defects when the reflected echoes from dense defects interfere with each other, leading to a high risk of misjudgment and missed detection. Furthermore, they lack effective means to detect the precise size and spatial distribution of individual defects within dense areas, making it difficult to support accurate safety assessments. When dealing with large castings with complex geometries and dense defects, repeated adjustments to probe positions and parameters are necessary, resulting in slow detection speed, low efficiency, and high cost, failing to meet the needs of large-scale industrial applications. This application provides a method for detecting dense internal defects in large castings of thermal power units. This method can effectively detect the volume, density, spatial distribution, extension path, extension direction, and extension angle of dense defects within the large casting to be inspected. This approach enables the construction of offline 3D simulation models by accurately testing the microstructural properties of large castings, providing an effective data foundation for the detection of dense defects in large castings under inspection. Through multi-dimensional feature coupling modeling and multi-frequency phased array technology, the resolution of dense defects in the large castings under inspection is significantly improved, achieving precise quantification of defect 3D location, volume, and extension direction, thus reducing calculation errors. By combining historical dense defect detection records with the current operating conditions of the thermal power unit, the defect expansion trend of the large castings under inspection can be predicted. Combined with a safety assessment system, this provides a basis for casting maintenance, replacement, and inspection plans, significantly improving the efficiency and accuracy of dense defect detection and safety assessment within large castings. This effectively ensures the safe and stable operation of large castings in supercritical / ultra-supercritical thermal power units and extends the unit's service life. In related technologies, traditional ultrasonic testing methods struggle to effectively separate and identify adjacent defects when the reflected echoes from dense defects interfere with each other, leading to a high risk of misjudgment and missed detection. Furthermore, they lack effective means to detect the precise size and spatial distribution of individual defects within dense areas, making it difficult to support accurate safety assessments. When dealing with large castings with complex geometries and dense defects, repeated adjustments to probe position and parameters are necessary, resulting in slow detection speed, low efficiency, and high cost, making it difficult to meet the needs of large-scale industrial applications.

[0031] Specifically, Figure 1 This is a flowchart illustrating a method for detecting dense internal defects in large castings of thermal power units, provided as an embodiment of this application.

[0032] like Figure 1 As shown, the method for detecting dense internal defects in large castings of thermal power units includes the following steps:

[0033] In step S101, based on an offline three-dimensional simulation model of at least one large casting to be inspected, constructed using the metal microstructure properties of at least one large casting to be inspected in a thermal power unit, the simulated echo beam response law of dense defects in the offline three-dimensional simulation model is obtained.

[0034] In some embodiments, a thermal power unit may have multiple large castings. Based on this, this application can construct an offline three-dimensional simulation model of at least one large casting to be inspected using the metal microstructure properties of at least one large casting to be inspected in the thermal power unit. That is, a corresponding offline three-dimensional simulation model is constructed using the metal microstructure properties of at least one large casting to be inspected in the thermal power unit. Then, based on the constructed offline three-dimensional simulation model, the simulated echo beam response law of its dense defects is obtained.

[0035] In this context, "large castings to be inspected" refers to large castings whose dense defects need to be detected.

[0036] Furthermore, the offline 3D simulation model can be understood here as a 3D model obtained by simulating the large casting under inspection and its internal dense defects using simulation software combined with the actual metal microstructure properties of the large casting. This allows the offline 3D simulation model to represent the real large casting under inspection to the greatest extent possible. It is important to note that a different offline 3D simulation model should be established for each different large casting under inspection.

[0037] The simulated echo beam response law can be understood here as the variation law of the amplitude, waveform, propagation time, frequency characteristics of the reflected / scattered signal generated by the ultrasonic beam emitting an ultrasonic beam onto the offline three-dimensional simulation model by adapting an appropriate phased array probe and wedge in the simulation software and the ultrasonic beam propagating in the offline three-dimensional simulation model and interacting with the internal dense defects in the offline three-dimensional simulation model.

[0038] After obtaining the simulated echo beam response law of dense defects in the offline three-dimensional simulation model, the embodiments of this application can provide certain parameter basis for the actual inspection process of large castings to be inspected based on the simulated echo beam response law.

[0039] This application embodiment can utilize the metal microstructure properties of at least one large casting to be inspected in a thermal power unit to construct its offline three-dimensional simulation model, thereby enabling the offline three-dimensional simulation model to approximate the real large casting to be inspected as closely as possible. This facilitates the simulation analysis of the large casting to be inspected and its internal dense defects, and further obtains the simulated echo beam response law of dense defects in the offline three-dimensional simulation model, so as to provide a data basis for the actual detection process of dense defects inside the large casting to be inspected.

[0040] Optionally, in one embodiment of this application, an offline three-dimensional simulation model of at least one large casting to be inspected, constructed based on the metal microstructure properties of at least one large casting to be inspected in a thermal power unit, includes: detecting at least one of the grain size, sound velocity, grain scattering attenuation coefficient, and acoustic impedance value of at least one large casting to be inspected; and constructing an offline three-dimensional simulation model based on at least one of the grain size, sound velocity, grain scattering attenuation coefficient, and acoustic impedance value of at least one large casting to be inspected.

[0041] Based on the descriptions of other embodiments, it is understood that the embodiments of this application can construct a corresponding offline three-dimensional simulation model based on the metal microstructure properties of at least one large casting to be inspected.

[0042] In some embodiments, the metal microstructure properties of the large casting to be inspected obtained by this application include, but are not limited to, the metal grain size, sound velocity, grain scattering attenuation coefficient, acoustic impedance value, etc. of the large casting to be inspected. Then, based on these metal microstructure properties, simulation software is used to perform three-dimensional simulation of the large casting to be inspected to obtain a three-dimensional simulation model of the large casting to be inspected.

[0043] In obtaining the metal microstructure properties of the large casting to be inspected, the metal microstructure properties of the entire large casting to be inspected can be characterized by obtaining the metal microstructure properties of a local area of ​​the large casting to be inspected.

[0044] For example, this application may, but is not limited to, select an area of ​​approximately 30mm × 30mm in a non-critical part of the large casting to be inspected, and then use an angle grinder to perform rough grinding, fine grinding, and polishing on this area to remove the oxide layer and processing damage, ensuring surface roughness. Ra≤ 0.8 μm This provides an ideal analytical surface for subsequent testing.

[0045] Then, in this embodiment of the application, cotton can be soaked in a 4% nitric acid alcohol solution to etch the polished area, so that the metal microstructure is clearly visible. The etching time of the polished area can be controlled within 15-30 seconds.

[0046] Next, embodiments of this application may, but are not limited to, use an optical microscope (magnification 200-500x) to observe the corroded area, to determine the microstructure type of the local area, such as pearlite, ferrite, martensite, etc., and to record the micromorphology and distribution characteristics. Then, the metal grain size of the local area is determined according to a certain metal average grain size determination method, and the grain size grade is recorded.

[0047] Furthermore, in this embodiment of the application, an ultrasonic velocity measuring instrument can be used to measure the longitudinal wave velocity and transverse wave velocity multiple times at different locations on the large casting to be inspected (the specific locations can be selected by professionals in this field according to the actual situation and needs; this embodiment of the application is only for illustrative purposes and is not subject to specific limitations), and the average value is taken as the velocity parameter of the casting material of the large casting to be inspected.

[0048] Furthermore, by using an attenuation coefficient measuring device, the embodiments of this application can calculate the grain scattering attenuation coefficient of the large casting under inspection by comparing the acoustic wave attenuation of the defect-free area and the standard defect area; by placing the selected local area sample on the test platform of the acoustic impedance meter, the acoustic impedance value of the large casting under inspection can be measured and recorded.

[0049] Finally, the obtained metal microstructure properties, such as microstructure, grain size, sound velocity, attenuation coefficient, and acoustic impedance, are imported into professional simulation software (such as CIVA). Based on the actual geometric dimensions of the casting (pipe diameter, wall thickness, bending radius, etc.), a high-precision three-dimensional casting data model, i.e., an offline three-dimensional simulation model, can be constructed.

[0050] It should be noted that for each large casting to be inspected, its metal microstructure properties must be detected, and then its corresponding offline three-dimensional simulation model is constructed in order to obtain the simulated echo beam response law for each large casting to be inspected.

[0051] The embodiments of this application can construct an offline three-dimensional simulation model based on the microstructural properties of the metal, such as grain size, sound velocity, grain scattering attenuation coefficient, and acoustic impedance value of the large casting to be inspected. This ensures that the offline three-dimensional simulation model is as close as possible to the real large casting to be inspected, which helps to accurately simulate the inspection process in a virtual environment, thereby improving the reliability and efficiency of actual defect detection of the large casting to be inspected.

[0052] Optionally, in one embodiment of this application, obtaining the simulated echo beam response law of dense defects in an offline three-dimensional simulation model includes: setting multiple reflectors with different volumes, positions and densities in the offline three-dimensional simulation model; and generating simulated echo beam response law based on the beam response law of the reflected echo signals of the multiple reflectors.

[0053] In actual execution, when obtaining the simulated echo beam response law of dense defects in the three-dimensional simulation model, this application may, but is not limited to, set multiple reflectors of different volumes, positions and densities in the offline three-dimensional simulation model, use these reflectors to simulate the morphology of dense internal defects in the large casting to be inspected, and then use the beam response law of the reflected echo signal of these reflectors as the simulated echo beam response law.

[0054] For example, this application may, but is not limited to, within the offline 3D simulation model, set different sizes (diameter 0.5-5mm), spatial distributions (1-10mm), and densities (1-10 defects / dm) according to common casting defect types in large castings (such as porosity, inclusions, shrinkage porosity, etc.). 3 The reflector. The shape of the defect can be approximated by regular geometric shapes such as spheres (simulating pores) and cylinders (simulating inclusions) to ensure that the simulated dense defects formed by these reflectors and shapes can accurately reflect the acoustic characteristics of dense defects in the actual large casting to be inspected.

[0055] Then, in this embodiment of the application, a phased array probe and wedge model with parameters consistent with those used in the actual inspection of large castings can be selected, imported into the simulation software, and an ultrasonic beam is emitted to simulate dense defects. Multi-frequency acoustic wave parameters of 2-10MHz are set to simulate the reflected echo signals of dense defects of different sizes and densities under the illumination of sound waves (ultrasonic beams). This makes the reflector signals clearly identifiable and consistent with the actual data. Then, the amplitude, propagation time, phase, and other acoustic beam response laws of the echo are recorded, and the acoustic beam response laws are used as the simulated echo acoustic beam response laws.

[0056] This application's embodiments can establish a correspondence between defect attributes and signal responses by simulating the echo characteristics of different types, sizes, and distributions of defects under multi-frequency acoustic waves, including amplitude, time, and phase. This provides a theoretical basis for the qualitative (type), quantitative (size / density), and location aspects of dense internal defects in large castings under inspection in actual testing. Furthermore, through simulation matching actual detection parameters, the rationality of phased array probe selection and frequency settings can be verified in advance, which helps to optimize the actual inspection scheme for large castings under inspection, reduce misjudgments and missed detections caused by defect complexity or signal interference, and improve the accuracy and efficiency of actual detection of dense internal defects in large castings under inspection.

[0057] Step S102: Based on the simulated echo beam response law, separate the dense defect signal of at least one large casting to be inspected, so as to obtain the three-dimensional spatial location coordinate distribution map of the dense defects in at least one large casting to be inspected according to the dense defect signal.

[0058] In some embodiments, after obtaining the simulated echo beam response law of dense defects in the offline three-dimensional simulation model, this application can separate the dense defect signal of at least one real large casting to be inspected based on the simulated echo beam response law.

[0059] For example, after obtaining the simulated echo beam response law of the simulated dense defects from the offline three-dimensional simulation model, this application can, but is not limited to, separate the part of the echo signal of the actual large casting under inspection that conforms to the simulated echo beam response law, which is the dense defect signal of the large casting under inspection.

[0060] Furthermore, in this embodiment of the application, a three-dimensional spatial coordinate distribution map of dense defects in the large casting to be inspected can be obtained based on the dense defect signal of the separated large casting to be inspected.

[0061] Specifically, embodiments of this application may, but are not limited to, mechanically connect a phased array probe for inspecting a large casting to an encoder. The encoder's positioning accuracy is recommended to be ±0.1 mm, used to record the axial and circumferential coordinates of the probe on the horizontal plane of the casting. Then, the phased array inspection system is activated, and the phased array probe moves across the surface of the large casting at a step speed of 0.5 mm / step.

[0062] Furthermore, through the phased array vertical section positioning algorithm, this embodiment of the application can calculate the depth position (Z coordinate) of each defect based on the propagation time of the reflected echo signal from the defect in the large casting to be inspected. At the same time, the encoder also acquires the position data of the phased array probe in the horizontal plane in real time (with the X coordinate as the axial distance and the Y coordinate as the arc length corresponding to the circumferential angle).

[0063] By synchronously integrating the depth position (Z) and horizontal plane coordinates (X, Y) through the data acquisition module, the three-dimensional coordinates (spatial position coordinates) of each defect can be obtained, and a three-dimensional coordinate map can be constructed. Each point in the coordinate map represents the spatial position of a defect, thus generating a three-dimensional spatial position coordinate distribution map of dense defects.

[0064] The embodiments of this application can separate the dense defect signals of the corresponding large castings under inspection based on the simulated echo beam response law of dense defects in the offline three-dimensional simulation model, effectively improving the accuracy of the separated dense defect signals. Furthermore, the embodiments of this application can also construct a three-dimensional spatial coordinate distribution map of dense defects in the large castings under inspection based on the dense defect signals, which is convenient for understanding the distribution of dense defects in the large castings under inspection and for performing other data processing and data analysis.

[0065] Optionally, in one embodiment of this application, separating dense defect signals from at least one large casting to be inspected includes: determining at least one signal acquisition frequency band for at least one large casting to be inspected based on at least one of the grain size, sound velocity, grain scattering attenuation coefficient, and acoustic impedance value of at least one large casting to be inspected; acquiring defect reflection echo signals of at least one large casting to be inspected using at least one signal acquisition frequency band based on the target sound beam emission time and the target sound beam reception delay time; and separating dense defect signals from the defect reflection echo signals.

[0066] In some embodiments, when separating dense defect signals of at least one large casting to be inspected, it is necessary to first collect all signals of the large casting to be inspected. In order to better receive and separate the superimposed defect reflection echo signals, this application can set the signal acquisition frequency band to a certain extent.

[0067] Specifically, embodiments of this application may, but are not limited to, determine at least one signal acquisition frequency band for at least one large casting to be inspected based on at least one of the following metal microstructure properties: grain size, sound velocity, grain scattering attenuation coefficient, acoustic impedance value, etc.

[0068] For example, this application may, but is not limited to, select a phased array probe with 64 probe elements, an element spacing of 0.3 mm, supporting dynamic switching of the 2-10 MHz frequency band and having multi-frequency element switching function to emit ultrasonic beams. Based on the measured microstructural properties of the large casting under test, such as grain size, scattering attenuation coefficient, and acoustic impedance, an element switching strategy for the phased array probe can be formulated. For instance, when the grain size of the large casting under test is small (since grain size in metal microstructural properties is proportional to the grain scattering attenuation coefficient, the grain scattering attenuation coefficient can also be used as a data basis), the signal acquisition frequency band is switched to the 8-10 MHz high-frequency band; when the grain scattering attenuation coefficient is large (such as the grain size of 3-4), the signal acquisition frequency band is switched to the 2-5 MHz low-frequency band, etc. Therefore, the embodiments of this application can automatically detect dense defects in large castings without repeated parameter adjustments of multi-frequency probes, in conjunction with offline three-dimensional simulation models. The detection efficiency is 3-5 times higher than that of traditional methods, meeting the needs of large-scale applications.

[0069] It should be noted that the specific signal acquisition frequency bands corresponding to the specific metal microstructure properties can be determined by those skilled in the art based on actual conditions or experiments. The embodiments in this application are only illustrative and do not impose specific limitations.

[0070] Then, the phased array probe is tightly attached to the outer surface of the large casting to be inspected using a coupling agent. Following a preset dynamic focusing rule—specifically, a fan-shaped scanning mode—64 probe elements are excited. The fan-shaped scanning beam deflection angle is 35-75°, the beam step angle is 0.1°, and the segmented depth focusing ranges are 0-20mm, 20-40mm, and 40-60mm. By controlling the transmission and reception delay times of each array element using a phased array instrument, the ultrasonic beam can be dynamically focused at different depths to achieve a comprehensive scan of densely defected areas in the large casting. Finally, the superimposed defect reflection echo signals are received and separated, thus achieving effective separation of dense defect signals.

[0071] The embodiments of this application can determine different signal acquisition frequency bands based on the microscopic properties of the metal structure of the large casting to be inspected, so that the large casting to be inspected can effectively receive ultrasonic beams and reflect echo signals, which facilitates the reception and separation of superimposed defect reflection echo signals, thereby determining the dense defect signals of the large casting to be inspected based on the defect reflection echo signals.

[0072] Step S103: Based on the metal microstructure properties, calculate the volume of dense defects in at least one large casting to be inspected, and combine the scanning size and three-dimensional spatial position coordinate distribution map of dense defects in multiple sections of at least one large casting to be inspected to fit the extension path of dense defects in at least one large casting to be inspected, and determine the extension direction and extension angle of dense defects in at least one large casting to be inspected based on the extension path.

[0073] As one possible approach, after obtaining the metal microstructure properties of the large casting to be inspected and the three-dimensional spatial coordinate distribution map of its dense defects, the embodiments of this application can detect the dense defects in the large casting to be inspected, including but not limited to the density, volume, extension path, extension direction and extension angle of the dense defects.

[0074] For example, in the process of constructing a three-dimensional spatial coordinate distribution map of dense defects in a large casting to be inspected, it is necessary to obtain the reflected echo signal of each defect in the large casting to be inspected to determine the three-dimensional coordinates of each defect. This application can extract the signal parameters of the reflected echo signal of each defect, including but not limited to the echo amplitude (A) and echo width (Δt).

[0075] Then, embodiments of this application can incorporate the material attenuation coefficient of the large casting to be inspected ( αThe volume of dense defects in a large casting under inspection is calculated using a model of the curvature change of the defect reflection interface and the echo energy attenuation. Specifically, in this embodiment, the volume of a single defect is first calculated using the model of the curvature change of the defect reflection interface and the echo energy attenuation, and the spacing between individual defects is calculated based on the three-dimensional coordinates of each individual defect. From this, the number of defects per unit volume can be calculated, thereby inferring the density of the dense defects. Finally, the volume of the dense defects can be derived by combining the boundaries of the dense defects.

[0076] For example, if a spherical defect has a diameter of 50 mm and the distance between it and the nearest defect is 50 mm, then in 1 dm 3 There is only one defect within the volume; a spherical defect has a diameter of 5 mm, and there are 10 defects within a 95 mm radius around it. Therefore, within 1 dm... 3 There are 11 defects within the volume. The volume of the dense defects can be obtained by combining the volumes of these 11 defects.

[0077] It should be noted that the specific boundary of dense defects can be obtained by connecting the three-dimensional coordinates of individual defects. The specific boundary determination rules can be, but are not limited to, determined by those skilled in the art based on the actual situation. For example, the boundary line of a fixed area (such as a unit volume) can be used directly as the boundary of dense defects, or the boundary line obtained by connecting all the outermost individual defects in a defect area can be used as the boundary of dense defects, or the outermost individual defect in a unit area where the number of defects per unit volume is less than a certain value can be used as the boundary, etc. The embodiments in this application are only illustrative and do not impose specific limitations.

[0078] The curvature of the reflective interface (such as the radius of curvature R of the reflector (sphere) or the degree of curvature of the cylinder) directly affects the focusing / diverging characteristics of sound wave reflection. That is, the greater the curvature, the more significant the energy focusing or diverging during sound wave reflection, leading to changes in echo amplitude and echo width. For example, convex defects (such as spherical pores) cause reflected sound waves to diverge, resulting in a lower echo amplitude and a higher width; planar defects, on the other hand, concentrate reflection, resulting in a higher echo amplitude and a narrower width. Based on this, embodiments of this application can, but are not limited to, use a certain geometric optical model (such as the spherical reflection energy distribution formula) to convert the curvature parameter into a correction coefficient k for the echo amplitude and echo width, thereby quantifying the influence of curvature on the echo signal.

[0079] The attenuation of echo energy comprises two parts: first, the attenuation of the casting material itself, i.e., the energy lost by the sound wave during propagation due to grain scattering and absorption, which requires correction of the echo amplitude; second, the energy loss caused by reflection from defect interfaces, which is related to curvature, i.e., changes in curvature alter the effective reflecting area (e.g., the dispersion of the normal direction of a curved surface leads to a reduction in the effective reflecting area). Therefore, it is necessary to calculate the reflection coefficient (the proportion of reflected energy to incident energy) in conjunction with curvature parameters to further correct the echo energy. It should be noted that the specific correction method can be, but is not limited to, determined by those skilled in the art based on the actual situation. The embodiments in this application are merely illustrative and do not constitute specific limitations.

[0080] By coupling the above-mentioned curvature modulation results on echo amplitude and echo width with material attenuation and defect tilt angle, the volume calculation formula for dense defects can be obtained. The formula for calculating volume V can be, but is not limited to, expressed as:

[0081] V = f·k(A, Δt, θ, α)

[0082] Where k is the correction coefficient, A is the echo amplitude, Δt is the -6dB echo width, θ is the defect tilt angle, α is the material attenuation coefficient, and f represents a factor related to the defect geometry or detection conditions.

[0083] Furthermore, by combining the scanning dimensions and three-dimensional spatial coordinate distribution diagrams of dense defects in at least one large casting to be inspected across multiple cross sections, embodiments of this application can fit the extension path of dense defects in at least one large casting to be inspected, and determine the extension direction and extension angle of dense defects in at least one large casting to be inspected based on the extension path.

[0084] Here, the extension direction can be understood as the direction in which the extension path extends; the extension angle can be understood as the angle between the extension direction and the axis of the casting. Additionally, the length of the extension path can also be determined according to the embodiment of this application.

[0085] For example, suppose a multi-slice CT scan of a high-pressure cylinder block is performed, yielding defect data for three key scanning sections (where the axis along the cylinder block is the Z-axis):

[0086] Among them, in the Z=500mm section (XY plane): 3 dense defects were detected, with three-dimensional coordinates of (1200, 800, 500), (1210, 810, 500), and (1205, 805, 500), and the scanning size of each defect was 2-3mm (diameter).

[0087] Cross section Z=1000mm: Two dense defects were detected, with coordinates (1220, 820, 1000) and (1230, 830, 1000), and the scanning size was 2-3mm for both.

[0088] Section Z=1500mm: One defect was detected, with coordinates (1240, 840, 1500) and a scan size of 2.5mm.

[0089] Curve fitting of the above three-dimensional coordinates, using the least squares method (in practical applications, the fitting method can be determined by those skilled in the art based on the actual situation; this embodiment is merely illustrative and not specifically limited), reveals that the points are approximately distributed along a straight line. The fitted path equation can be, but is not limited to, expressed as: X = 0.04Z + 1180, Y = 0.04Z + 780. That is, as the Z-axis (axial direction) increases, the X and Y coordinates increase linearly, forming a diagonally extending path.

[0090] Using the direction vector description, the embodiments of this application may, but are not limited to, take the segment from Z=500 to Z=1500, ΔX=40, ΔY=40, ΔZ=1000, and the direction vector is (40, 40, 1000), indicating that the dense defects are mainly along the axial direction (Z axis), while extending obliquely in the positive X and Y directions.

[0091] At the same time, the angle θ between the extension direction and the Z-axis satisfies , This indicates that the direction of the dense defects is close to the cylinder axis, and the deflection angle is small.

[0092] This application embodiment can calculate the volume of dense defects in the large casting under inspection by combining the metal microstructure properties of the large casting under inspection, and fit the extension path, extension direction and extension angle of the dense defects in the large casting under inspection by combining the scanning size and three-dimensional spatial position coordinate distribution map of the dense defects in multiple cross sections. It can effectively predict the defect expansion trend of the large casting under inspection, and provide a basis for the maintenance, replacement and inspection plan of the large casting under inspection, thereby significantly improving the efficiency and accuracy of the detection and safety assessment of dense defects inside large castings, as well as the safety and reliability of thermal power units, and extending the service life of large castings and thermal power units.

[0093] Optionally, in one embodiment of this application, before fitting the extension path of the dense defects in at least one large casting to be inspected by combining the scanning dimensions and three-dimensional spatial coordinate distribution map of the dense defects in multiple cross sections of at least one large casting to be inspected, the method further includes: scanning multiple cross sections of at least one large casting to be inspected to obtain the multi-cross section scanning results of at least one large casting to be inspected; and obtaining the scanning dimensions of the dense defects in multiple cross sections based on the multi-cross section scanning results.

[0094] Based on the descriptions of other embodiments, it is understood that when fitting the extension path of dense defects in at least one large casting to be inspected, this application needs to combine the scanning size and three-dimensional spatial coordinate distribution map of dense defects in multiple cross sections of the large casting to be inspected.

[0095] In actual implementation, before fitting the extension path of dense defects in at least one large casting to be inspected, this application needs to scan multiple cross-sections of the large casting to be inspected, so as to obtain the scanning size of dense defects in multiple cross-sections based on the multi-cross-section scanning results of the large casting to be inspected obtained by scanning multiple cross-sections.

[0096] For example, embodiments of this application may employ a multi-faceted scanning method, using a phased array probe to scan the casting along the axial direction (the direction along the length of the casting), the circumferential direction (the direction along the circumference of the casting), and the 45° oblique direction, respectively, to obtain multi-faceted scanning results. Then, based on the multi-faceted scanning results, the length, width, and other dimensional parameters (scanning dimensions) of dense defects in different sections are obtained. Combined with the three-dimensional spatial coordinate distribution of the defects, the extension path of the defects is fitted, and the angle between the extension direction and the axial direction of the casting (the direction along the length of the casting) and the extension length are determined.

[0097] This application embodiment can solve the problem of insufficient resolution of dense defects by traditional detection through multi-dimensional feature coupling modeling and multi-frequency phased array technology. It can achieve accurate quantification of the three-dimensional location, volume, extension path, extension direction and extension angle of dense defects. By extending the calculation of individual defects to dense defects, the error in the calculation process can be effectively reduced, thereby providing accurate data for safety assessment.

[0098] Optionally, in one embodiment of this application, the method further includes: obtaining the density and key distribution location of dense defects based on a three-dimensional spatial location coordinate distribution map; predicting the expansion trend of dense defects in at least one large casting to be inspected based on the density and key distribution location; and generating a risk distribution map of at least one large casting to be inspected by combining the density, key distribution location, and expansion trend of dense defects.

[0099] In other embodiments, this application may also generate a color risk distribution map of the large casting to be inspected by combining the density, key distribution location, and expansion trend of dense defects in the large casting to be inspected.

[0100] Based on the three-dimensional spatial coordinate distribution map of dense defects in the large casting to be inspected, the embodiments of this application can intuitively understand and obtain the density of dense defects and their key distribution locations.

[0101] For example, this application can obtain the number of defects and the total area per unit volume of a large casting based on a three-dimensional spatial coordinate distribution map of dense defects in the casting to be inspected. Based on the number of defects per unit volume and the total volume, the density of defects can be assessed, such as <6 defects / dm². 3 6-12 pieces / dm 3 >12 units / dm 3 And so on. Furthermore, the density can be categorized into different levels to assess the safety level of large castings to be inspected. For example, light density (<6 pieces / dm²) 3 Moderate (6-12 pieces / dm) 3 ), severe (>12 / dm) 3 There are three levels, etc.

[0102] Furthermore, based on the three-dimensional spatial coordinate distribution map of dense defects in the large casting to be inspected, the embodiments of this application can also analyze the key distribution locations of these defects in space, such as whether they are concentrated at the abrupt change in the casting cross section, near the surface, etc.; finally, by combining the size, spatial distribution, and density of the defects, as well as the working pressure, temperature, and other working condition parameters of the large casting to be inspected, a quantitative analysis method is used to comprehensively evaluate the safety of the large casting to be inspected.

[0103] For example, professionals in this field can review past inspection records to determine whether the defect size has expanded, whether the density has increased, whether the defect expansion rate has increased, and what the expansion frequency is after the expansion. This allows for a comprehensive evaluation of the safety of the large castings under inspection. Furthermore, by combining operating parameters such as working pressure and temperature of different large castings (e.g., high-pressure steam pipes with a working pressure of 25.83 MPa and a temperature of 573°C, and medium-pressure steam pipes with a working pressure of 4.60 MPa and a temperature of 571°C), they can assess whether the defect expansion rate allows for safe operation until the next maintenance cycle with sufficient safety margin (higher operating pressure and temperature of components result in greater residual stress and faster expansion rates). This comprehensive evaluation of the safe operation of the thermal power unit allows for a comprehensive assessment of its safety.

[0104] If the defects exceed the standard allowable range, the large casting to be inspected is deemed to pose a safety hazard and requires repair or replacement; if the defects are within the allowable range, a reasonable periodic inspection plan is formulated based on the expansion trend of the defects. Therefore, this embodiment of the application establishes a safety evaluation index system based on the obtained information such as the three-dimensional spatial coordinates, spatial distribution, density, volume, and extension direction of the defects, which can effectively assess the current safety status of the large casting to be inspected and the safety status of the thermal power unit.

[0105] Furthermore, based on the density and key distribution location of defects within the large casting to be inspected, the embodiments of this application can also predict the expansion trend of dense defects in the large casting to be inspected.

[0106] For example, this application can record the results of each dense defect detection of a large casting to be inspected. Then, when predicting the expansion trend of dense defects in the large casting to be inspected, it can retrieve the most recent 5 dense defect detection records of the large casting to be inspected from the database, including but not limited to extracting historical data such as defect volume and location coordinates for each dense defect detection. At the same time, it can collect the current operating condition parameters of the thermal power unit, including but not limited to operating pressure (range 10-30MPa), operating temperature (range 300-600℃), cumulative running time, unit load change curve, and number of start-ups and shutdowns.

[0107] Then, based on historical dense defect detection records and operating condition parameters, the rate of change of defect volume over time is calculated. Then, combined with the stress influence coefficient under different operating conditions, the expansion trend of dense defects in the large casting under inspection can be predicted within the next maintenance period (e.g., 12 months). To facilitate understanding of the expansion trend of dense defects, embodiments of this application can also generate a dense defect expansion trend curve based on the expansion trend of dense defects, and indicate the possible volume growth value and positional offset of the defects in the graph.

[0108] Finally, in this embodiment of the application, the obtained data such as the volume of dense defects, the density of dense defects, the spatial distribution, and the expansion trend can be organized into a dataset and input into a trained deep learning model (such as a classification model based on the ResNet architecture) to intelligently classify and quantify the security level of each defect.

[0109] Based on the classification results, embodiments of this application can use 3D visualization software to generate a color 3D risk distribution map. For example, different colors represent risk levels (e.g., green for safe, yellow for warning, and red for danger), sphere size represents defect volume, arrow direction represents expansion trend, and the density of spheres represents concentration. The distribution map can be rotated, scaled, and sectioned to visually display various information about dense defects inside the large casting to be inspected. Dangerous defects are highlighted in the map with flashing warnings, ultimately yielding inspection results including the distribution map and analysis report.

[0110] This application's embodiments can correlate dense defect detection records with the operating conditions of thermal power units to predict the expansion trend of dense defects, and combine them with a safety assessment system to provide a basis for the maintenance, replacement, and inspection plans of large castings to be inspected; the color three-dimensional distribution map can intuitively present the defect information of the large castings to be inspected, making it easier for staff to make quick decisions. It is applicable to large castings including cylinders, valves, and plugs of thermal power units, effectively ensuring the safe and stable operation of large castings in supercritical / ultra-supercritical thermal power units and extending the service life of thermal power units.

[0111] The method for detecting dense defects inside large castings of thermal power units proposed in this application can effectively detect the volume, density, spatial distribution, extension path, extension direction, and extension angle of dense defects inside the large castings to be inspected. This enables the construction of an offline three-dimensional simulation model through precise testing of the microstructural properties of the metal in the large castings to be inspected, providing an effective data foundation for the actual detection of dense defects in the large castings. Through multi-dimensional feature coupling modeling and multi-frequency phased array technology, the resolution of dense defects in the large castings to be inspected is significantly improved, achieving accurate quantification of the three-dimensional location, volume, and extension direction of defects, reducing calculation errors. By combining historical dense defect detection records with the current operating conditions of the thermal power unit to predict the defect expansion trend of the large castings to be inspected, and combining this with a safety assessment system, a basis is provided for casting maintenance, replacement, and inspection plans, significantly improving the efficiency and accuracy of dense defect detection and safety assessment inside large castings, effectively ensuring the safe and stable operation of large castings in supercritical / ultra-supercritical thermal power units, and extending the service life of the units. In related technologies, traditional ultrasonic testing methods struggle to effectively separate and identify adjacent defects when the reflected echoes from dense defects interfere with each other, leading to a high risk of misjudgment and missed detection. Furthermore, they lack effective means to detect the precise size and spatial distribution of individual defects within dense areas, making it difficult to support accurate safety assessments. When dealing with large castings with complex geometries and dense defects, repeated adjustments to probe position and parameters are necessary, resulting in slow detection speed, low efficiency, and high cost, making it difficult to meet the needs of large-scale industrial applications.

[0112] Next, referring to the accompanying drawings, a device for detecting dense internal defects in large castings of thermal power units according to an embodiment of this application is described.

[0113] Figure 2 This is a schematic diagram of the structure of the dense defect detection device inside large castings of thermal power units according to an embodiment of this application.

[0114] like Figure 2 As shown, the device 10 for detecting dense internal defects in large castings of thermal power units includes: a simulation module 100, a separation module 200, and a detection module 300.

[0115] The simulation module 100 is used to obtain the simulated echo beam response law of dense defects in the offline three-dimensional simulation model based on the offline three-dimensional simulation model of at least one large casting to be inspected, which is constructed using the metal microstructure properties of at least one large casting to be inspected in a thermal power unit.

[0116] The separation module 200 is used to separate the dense defect signal of at least one large casting to be inspected based on the simulated echo beam response law, so as to obtain the three-dimensional spatial position coordinate distribution map of the dense defects in at least one large casting to be inspected based on the dense defect signal.

[0117] The detection module 300 is used to calculate the volume of dense defects in at least one large casting to be inspected based on the microstructure properties of the metal, and to fit the extension path of the dense defects in at least one large casting to be inspected by combining the scanning size and three-dimensional spatial position coordinate distribution map of the dense defects in multiple sections of the at least one large casting to be inspected, and to determine the extension direction and extension angle of the dense defects in at least one large casting to be inspected based on the extension path.

[0118] Optionally, in one embodiment of this application, the simulation module 100 includes:

[0119] The setting unit is used to set multiple reflectors with different volumes, positions and densities in a 3D simulation model.

[0120] The generation unit is used to generate a simulated echo beam response law based on the beam response law of the reflected echo signals from multiple reflectors.

[0121] Optionally, in one embodiment of this application, the simulation module 100 includes:

[0122] The detection unit is used to detect at least one of the following: grain size, sound velocity, grain scattering attenuation coefficient, and acoustic impedance value of at least one large casting to be inspected.

[0123] A building unit is used to construct an offline three-dimensional simulation model based on at least one of the grain size, sound velocity, grain scattering attenuation coefficient, and acoustic impedance value of at least one large casting to be inspected.

[0124] Optionally, in one embodiment of this application, the separation module 200 includes:

[0125] The determining unit is used to determine at least one signal acquisition frequency band of at least one large casting to be inspected based on at least one of the following: grain size, sound velocity, grain scattering attenuation coefficient, and acoustic impedance value.

[0126] The acquisition unit is used to acquire defect reflection echo signals of at least one large casting under inspection based on the target acoustic beam transmission time and the target acoustic beam reception delay time, using at least one signal acquisition frequency band.

[0127] The separation unit is used to separate dense defect signals from defect reflection echo signals.

[0128] Optionally, in one embodiment of this application, it further includes:

[0129] The scanning module is used to scan multiple cross-sections of at least one large casting to be inspected before fitting the extension path of the dense defects in at least one large casting to be inspected by combining the scanning size and three-dimensional spatial position coordinate distribution map of the dense defects in multiple cross-sections, so as to obtain the multi-section scanning results of at least one large casting to be inspected.

[0130] The first acquisition module is used to acquire the scanning dimensions of dense defects in multiple cross sections based on the multi-section scanning results.

[0131] Optionally, in one embodiment of this application, it further includes:

[0132] The second acquisition module is used to obtain the density and key distribution location of dense defects based on the three-dimensional spatial coordinate distribution map.

[0133] The prediction module is used to predict the expansion trend of dense defects in at least one large casting to be inspected, based on the density and key distribution location.

[0134] The generation module is used to generate a risk distribution map for at least one large casting to be inspected by combining the density, key distribution location, and the expansion trend of dense defects.

[0135] It should be noted that the explanation of the aforementioned embodiment of the method for detecting dense internal defects in large castings of thermal power units also applies to the device 10 for detecting dense internal defects in large castings of thermal power units in this embodiment, and will not be repeated here.

[0136] The internal dense defect detection device 10 for large castings of thermal power units proposed in this application can effectively detect the volume, density, spatial distribution, extension path, extension direction, and extension angle of dense defects inside the large castings to be inspected. This enables the construction of an offline three-dimensional simulation model through precise testing of the metal microstructure properties of the large castings to be inspected, providing an effective data foundation for the actual detection of dense defects in the large castings. Through multi-dimensional feature coupling modeling and multi-frequency phased array technology, the resolution of dense defects in the large castings to be inspected is significantly improved, achieving accurate quantification of the three-dimensional location, volume, and extension direction of defects, reducing calculation errors. By combining historical dense defect detection records with the current operating conditions of the thermal power unit to predict the defect expansion trend of the large castings to be inspected, and combining this with a safety assessment system, a basis is provided for casting maintenance, replacement, and inspection plans, significantly improving the efficiency and accuracy of internal dense defect detection and safety assessment of large castings, effectively ensuring the safe and stable operation of large castings in supercritical / ultra-supercritical thermal power units, and extending the service life of the units. In related technologies, traditional ultrasonic testing methods struggle to effectively separate and identify adjacent defects when the reflected echoes from dense defects interfere with each other, leading to a high risk of misjudgment and missed detection. Furthermore, they lack effective means to detect the precise size and spatial distribution of individual defects within dense areas, making it difficult to support accurate safety assessments. When dealing with large castings with complex geometries and dense defects, repeated adjustments to probe position and parameters are necessary, resulting in slow detection speed, low efficiency, and high cost, making it difficult to meet the needs of large-scale industrial applications.

[0137] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0138] The memory 301, the processor 302, and the computer program stored on the memory 301 and capable of running on the processor 302.

[0139] When the processor 302 executes the program, it implements the method for detecting dense internal defects in large castings of thermal power units provided in the above embodiments.

[0140] Furthermore, electronic devices also include:

[0141] Communication interface 303 is used for communication between memory 301 and processor 302.

[0142] The memory 301 is used to store computer programs that can run on the processor 302.

[0143] The memory 301 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0144] If the memory 301, processor 302, and communication interface 303 are implemented independently, then the communication interface 303, memory 301, and processor 302 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0145] Optionally, in a specific implementation, if the memory 301, processor 302, and communication interface 303 are integrated on a single chip, then the memory 301, processor 302, and communication interface 303 can communicate with each other through an internal interface.

[0146] Processor 302 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0147] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for detecting dense internal defects in large castings of thermal power units.

[0148] This application also provides a computer program product, including a computer program that can run computer instructions. When the computer instructions are executed by a processor, they implement the method for detecting dense internal defects in large castings of thermal power units provided in this application.

[0149] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0150] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0151] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0152] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0153] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0154] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0155] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0156] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for detecting dense internal defects in large castings of thermal power units, characterized in that, Includes the following steps: Based on at least one offline three-dimensional simulation model of the large casting to be inspected, constructed using the metal microstructure properties of at least one large casting to be inspected in a thermal power unit, the simulated echo beam response law of dense defects in the offline three-dimensional simulation model is obtained. Based on the simulated echo beam response law, at least one dense defect signal of the large casting to be inspected is separated, so as to obtain a three-dimensional spatial position coordinate distribution map of dense defects in at least one large casting to be inspected according to the dense defect signal. Based on the aforementioned metal microstructure properties, the volume of dense defects in at least one of the large castings under inspection is calculated by inversion. Combined with the scanning dimensions of the dense defects in at least one of the large castings under inspection across multiple cross sections and the three-dimensional spatial coordinate distribution map, the extension path of the dense defects in at least one of the large castings under inspection is fitted. Based on the extension path, the extension direction and extension angle of the dense defects in at least one of the large castings under inspection are determined. The step of obtaining the simulated echo beam response law of dense defects in the offline three-dimensional simulation model includes: setting up multiple reflectors with different volumes, positions and densities in the offline three-dimensional simulation model; and generating the simulated echo beam response law based on the beam response law of the reflected echo signals of the multiple reflectors. The step of separating the dense defect signal of at least one of the large castings to be inspected includes: determining at least one signal acquisition frequency band of at least one of the large castings to be inspected based on at least one of the grain size, sound velocity, grain scattering attenuation coefficient, and acoustic impedance value of at least one of the large castings to be inspected; acquiring defect reflection echo signals of at least one of the large castings to be inspected using at least one of the signal acquisition frequency bands based on the target sound beam emission time and the target sound beam reception delay time; and separating the dense defect signal from the defect reflection echo signal.

2. The method for detecting dense internal defects in large castings of thermal power units according to claim 1, characterized in that, The offline three-dimensional simulation model of at least one large casting to be inspected, constructed based on the metal microstructure properties of at least one large casting to be inspected in a thermal power unit, includes: Detect at least one of the following properties of the large casting to be inspected: grain size, sound velocity, grain scattering attenuation coefficient, and acoustic impedance value; The offline three-dimensional simulation model is constructed based on at least one of the grain size, sound velocity, grain scattering attenuation coefficient, and acoustic impedance value of at least one of the large castings to be inspected.

3. The method for detecting dense internal defects in large castings of thermal power units according to claim 1, characterized in that, Before fitting the extension path of the dense defects in at least one of the large castings under inspection by combining the scanned dimensions of the dense defects in multiple cross sections and the three-dimensional spatial coordinate distribution map, the method further includes: Scan at least one of the cross-sections of the large casting to be inspected to obtain the multi-cross-section scanning results of at least one of the large castings to be inspected; Based on the multi-section scanning results, the scanning dimensions of the dense defects in multiple cross sections are obtained.

4. The method for detecting dense internal defects in large castings of thermal power units according to claim 1, characterized in that, Also includes: Based on the three-dimensional spatial coordinate distribution map, the density and key distribution locations of the dense defects are obtained; Based on the density and the location of the key distribution, predict the expansion trend of dense defects in at least one of the large castings to be inspected; By combining the density, the key distribution location, and the expansion trend of the dense defects, at least one risk distribution map of the large casting to be inspected is generated.

5. A device for detecting dense internal defects in large castings of thermal power units, characterized in that, include: The simulation module is used to obtain the simulated echo beam response law of dense defects in the offline three-dimensional simulation model based on at least one offline three-dimensional simulation model of the large casting to be inspected, which is constructed using the metal microstructure properties of at least one large casting to be inspected in the thermal power unit. The separation module is used to separate the dense defect signal of at least one of the large castings to be inspected based on the simulated echo beam response law, so as to obtain a three-dimensional spatial position coordinate distribution map of the dense defects in at least one of the large castings to be inspected based on the dense defect signal. The detection module is used to calculate the volume of dense defects in at least one of the large castings to be inspected based on the microstructure properties of the metal, and to fit the extension path of the dense defects in at least one of the large castings to be inspected by combining the scanning size of the dense defects in multiple sections and the three-dimensional spatial position coordinate distribution map of the dense defects in at least one of the large castings to be inspected, and to determine the extension direction and extension angle of the dense defects in at least one of the large castings to be inspected based on the extension path. The simulation module includes: a setting unit for setting multiple reflectors with different volumes, positions and densities in the offline three-dimensional simulation model; and a generation unit for generating the simulated echo beam response law based on the beam response law of the reflected echo signals of the multiple reflectors. The separation module includes: a determining unit, configured to determine at least one signal acquisition frequency band of at least one of the large castings to be inspected based on at least one of the grain size, sound velocity, grain scattering attenuation coefficient, and acoustic impedance value of at least one of the large castings to be inspected; an acquisition unit, configured to acquire defect reflection echo signals of at least one of the large castings to be inspected using at least one of the signal acquisition frequency bands based on the target sound beam emission time and the target sound beam reception delay time; and a separation unit, configured to separate the dense defect signal from the defect reflection echo signal.

6. An electronic device, characterized in that, include: The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for detecting dense internal defects in large castings of thermal power units as described in any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the method for detecting dense internal defects in large castings of thermal power units as described in any one of claims 1-4.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed, it is used to implement the method for detecting dense internal defects in large castings of thermal power units as described in any one of claims 1-4.

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