A quality detection method in a functional gradient material preparation process

Abstract

The application discloses a quality detection method in a functional gradient material preparation process, and relates to the technical field of functional gradient materials, and comprises the following steps: collecting the temperature of a blank body in the functional gradient material preparation process to obtain blank body temperature distribution data; performing real-time thickness measurement on the blank body according to the blank body temperature distribution data to obtain a blank body thickness change curve; identifying an abnormal fluctuation section in the blank body thickness change curve, performing phase detection on a physical region where the blank body exists the abnormal fluctuation section to obtain regional phase composition data; performing internal defect scanning on the blank body according to the regional phase composition data to obtain a blank body internal defect atlas; and performing quality evaluation on the blank body based on the blank body internal defect atlas to obtain a quality grade judgment result; the application realizes the guidance of the ultrasonic flaw detection scanning range based on the phase abnormal region, and can perform high-resolution internal defect detection on the high-risk region of the functional gradient material in a targeted manner.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of functionally graded materials, and particularly relates to a quality detection method in a functionally graded material preparation process. BACKGROUND

[0002] Functionally graded materials (FGMs) can effectively alleviate thermal stress and mechanical mismatch between material interfaces due to their spatially continuous composition and performance, and have broad application prospects in the fields of aerospace, biomedical, nuclear energy and electronic devices. With the development of manufacturing technology, processes such as powder metallurgy, plasma spraying, laser cladding and 3D printing are widely used in the preparation process of functionally graded materials. However, due to the multilayer heterogeneous structure constructed layer by layer under high temperature and non-equilibrium conditions, problems such as uneven temperature field, poor interlayer bonding, residual stress concentration and abnormal microstructure evolution are easily generated in the forming process, which seriously affect the reliability and service life of the final product, so it is particularly important to detect the quality in the preparation process in real time and accurately.

[0003] In the prior art, the quality detection of functionally graded materials mainly adopts offline, single-point and post-analysis methods, which mainly include: 1. manual sampling metallographic detection: the sample needs to be cut off after preparation, and the microstructure is observed by microscope after sample preparation, which destroys the blank and cannot provide real-time feedback; 2. fixed point thermocouple temperature measurement: only the temperature of a limited number of points can be obtained, which cannot fully reflect the temperature field distribution of the blank, and it is difficult to correlate the spatial variation of material gradient; 3. independent non-destructive testing: such as using ultrasonic flaw detection for full-area general scanning, or only X-ray sampling inspection on finished products. The above quality detection methods of functionally graded materials have the problems of isolated data and low efficiency, and are not sensitive to early abnormalities (such as small phase deviations) in the preparation process, and cannot realize accurate diagnosis of "abnormality guided detection". Therefore, the existing detection methods have inherent defects such as process and quality disconnection, single data dimension, large detection blindness and insufficient early potential defect warning capability, which are difficult to meet the online monitoring needs of high-quality preparation of functionally graded materials. SUMMARY

[0004] In view of the problems in the prior art, the present application provides a quality detection method in a functionally graded material preparation process, which realizes ultrasonic flaw detection scanning range guided by phase abnormal area, and can detect high-risk areas of functionally graded materials for high-resolution internal defects.

[0005] The technical scheme of the present application is as follows:

[0006] In the first aspect of the present application, a quality detection method in a functionally graded material preparation process is provided, which comprises the following steps:

[0007] Collecting the temperature of the blank in the preparation process of the functionally graded material to obtain blank temperature distribution data;

[0008] According to the blank temperature distribution data, the real-time thickness of the blank is measured to obtain the blank thickness change curve;

[0009] Identify the abnormal fluctuation section in the blank thickness change curve, and perform phase detection on the physical region where the blank has abnormal fluctuation section to obtain regional phase composition data;

[0010] According to the regional phase composition data, the internal defect scanning of the blank is performed to obtain the internal defect atlas of the blank;

[0011] Based on the internal defect atlas of the blank, the quality of the blank is evaluated to obtain the quality grade determination result.

[0012] In some embodiments of the present application, the temperature of the blank is collected by using an infrared thermometer, and the blank temperature distribution data is obtained by the following method:

[0013] The outer surface and cross section of the blank in the preparation process of the functionally graded material are regionally divided by using a grid division tool to obtain a blank temperature measurement grid, and the blank temperature measurement grid is numbered according to the gradient region type to obtain a numbered temperature measurement grid;

[0014] Based on the numbered temperature measurement grid, the fixed-point temperature of the blank is collected by using an infrared thermometer to obtain the blank temperature distribution data.

[0015] In some embodiments of the present application, the real-time thickness of the blank is measured by using a laser thickness gauge, and the blank thickness change curve is obtained by the following method:

[0016] According to the blank temperature distribution data, the temperature gradient analysis of the blank is performed to obtain a temperature gradient graph, and the key region division of the blank is performed according to the temperature gradient graph to obtain a key monitoring region;

[0017] The real-time thickness of the blank in the key monitoring region is measured by using a laser thickness gauge to obtain real-time thickness data, and the real-time thickness data is time-aligned to obtain time-series thickness data;

[0018] The change trend of the time-series thickness data is calculated to obtain the thickness change characteristics, and the blank thickness change curve is generated according to the thickness change characteristics.

[0019] In some embodiments of the present application, the phase detection of the physical region where the blank has abnormal fluctuation section is performed by using an X-ray diffractometer, and the regional phase composition data is obtained by the following method:

[0020] The abnormal fluctuation section corresponding to the blank is marked by a coordinate mapping tool to obtain a to-be-detected region, and the to-be-detected region is sampled to obtain a phase detection sample;

[0021] The phase detection sample is subjected to diffraction scanning by an X-ray diffractometer to obtain a diffraction intensity spectrum, and the characteristic peak position in the diffraction intensity spectrum is identified to obtain a characteristic peak coordinate value;

[0022] The characteristic peak coordinate value is matched with known phase standard diffraction data by a standard card comparison tool to obtain a phase matching result, and the phase matching result is sorted according to the content ratio to obtain regional phase composition data.

[0023] In some embodiments of the present application, the abnormal fluctuation section corresponding to the blank is marked by a coordinate mapping tool to obtain a to-be-detected region, specifically including:

[0024] The abnormal fluctuation section in the blank thickness change curve is subjected to curve segmentation to obtain fluctuation interval data, and the measurement time point in the fluctuation interval data is correspondingly paired with the scanning position of the laser thickness gauge to obtain a fluctuation point position table;

[0025] Based on the fluctuation point position table, the blank is subjected to grid partition marking to obtain a detection grid map, and the region matched with the fluctuation point position table in the detection grid map is subjected to boundary extension to obtain a to-be-detected region.

[0026] In some embodiments of the present application, the phase detection sample is subjected to diffraction scanning by an X-ray diffractometer to obtain a diffraction intensity spectrum, specifically including:

[0027] The phase detection sample is continuously scanned by an X-ray diffractometer to obtain original diffraction data, and the original diffraction data is subjected to background subtraction processing to obtain net diffraction data;

[0028] The net diffraction data is subjected to peak enhancement processing to obtain enhanced diffraction data, and the enhanced diffraction data is subjected to coordinate conversion to obtain a diffraction intensity spectrum.

[0029] In some embodiments of the present application, the enhanced diffraction data is subjected to coordinate conversion to obtain a diffraction intensity spectrum, specifically including:

[0030] The diffraction angle value and the diffraction intensity value in the enhanced diffraction data are extracted in pairs to obtain a diffraction data pair, and the diffraction data pair is set with horizontal axis and vertical axis parameters by a coordinate definition tool to obtain a spectrum coordinate system;

[0031] Based on the spectrum coordinate system, the diffraction data pair is subjected to coordinate point mapping to obtain a diffraction coordinate point, and the diffraction coordinate point is subjected to continuous line processing to obtain a diffraction intensity spectrum.

[0032] In some embodiments of the present application, the internal defect scanning of the blank is performed by using an ultrasonic flaw detector, and the internal defect map is obtained by the following method:

[0033] The phase distribution difference in the phase composition data of the region is analyzed to mark the region with non-compliant phase composition gradient design, obtain the key detection area, and divide the key detection area into grid units to obtain the flaw detection grid;

[0034] The ultrasonic reflection signal of each unit in the flaw detection grid is collected by the ultrasonic flaw detector to obtain the unit reflection signal, and the abnormal reflection wave in the unit reflection signal is identified to obtain the defect signal data;

[0035] The defect signal data is classified according to the defect signal intensity to obtain the classified defect data, and based on the position correspondence between the classified defect data and the flaw detection grid, the internal defect map of the blank containing the defect position, intensity level, etc. is drawn.

[0036] In some embodiments of the present application, the ultrasonic reflection signal of each unit in the flaw detection grid is collected by the ultrasonic flaw detector to obtain the unit reflection signal, specifically including:

[0037] Based on the unit size and arrangement of the flaw detection grid, the probe scanning path of the ultrasonic flaw detector is planned to obtain the grid adaptation path, and the probe is driven to move according to the grid adaptation path to perform the first signal collection on each unit in the flaw detection grid to obtain the initial reflection signal;

[0038] The grid units without collecting effective signals in the initial reflection signal are marked to obtain the blind area unit table, and based on the blind area unit table, the angle and transmission power of the probe are adjusted to perform the second signal collection on the blind area units in the flaw detection grid to obtain the completed reflection signal. The initial reflection signal and the completed reflection signal are correspondingly integrated according to the grid unit number to obtain the unit reflection signal.

[0039] In some embodiments of the present application, the quality level determination result is obtained by the following method:

[0040] The defect position, intensity level, etc. in the internal defect map of the blank are extracted to obtain the defect feature data, and the defect feature data is classified and counted according to the preset defect type to obtain the defect classification statistical result;

[0041] The defect classification statistical result is quantitatively scored by the defect rating standard to obtain the single defect score, and the single defect score is weighted and summed to obtain the comprehensive quality score, and the comprehensive quality score is compared with the preset quality level threshold to obtain the quality level determination result.

[0042] The one or more technical solutions of the present application have the following beneficial effects:

[0043] (1) The quality detection method provided by the present application evaluates the quality of the blank based on the internal defect map of the blank, obtains the quality grade determination result, realizes the guidance of the scanning range of the ultrasonic flaw detection based on the phase abnormal area, can detect the high-risk area with high resolution, obtains the defect map, avoids the inefficiency of full-area scanning, and improves the detection rate and positioning accuracy of micro cracks, pores, delamination and other defects.

[0044] (2) The quality detection method provided by the present application detects the temperature, thickness and phase of the blank in turn, and finally obtains the internal defect map of the blank according to the phase, realizes the quality evaluation of the blank, adopts a multi-level and progressive detection process in the whole detection process, establishes the internal correlation between process parameters and final quality, and when the thickness curve is abnormal, the subsequent phase and defect detection can be automatically triggered, the detection resources can be accurately focused on the high-risk area, the blindness and inefficiency of the existing full-area scanning are overcome, and the transformation from "general inspection" to "precision inspection" is realized.

[0045] (3) The quality detection method provided by the present application realizes that the ultrasonic flaw detector can directly scan the phase abnormal area with high resolution in the detection process by adopting the key area division based on the temperature gradient and the accurate coordinate mapping based on the thickness abnormality. This accurate detection strategy, combined with the grid division and secondary signal completion mechanism in ultrasonic flaw detection, significantly enhances the recognition sensitivity and spatial positioning accuracy of potential defects such as micro cracks, pores and interface delamination.

[0046] (4) The quality detection method provided by the present application establishes the final quality evaluation on the quantitative analysis of the internal defect map, extracts, classifies and statistically scores the defect features, and outputs the objective quality grade determination result, changes the traditional subjective judgment mode relying on manual experience, and provides data-based and standardized decision basis for process optimization and quality traceability. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 The flow chart of the quality detection method in the functional gradient material preparation process of the present application. DETAILED DESCRIPTION

[0048] It should be pointed out that the following detailed description is exemplary and is intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used in the present application have the same meaning as generally understood by those skilled in the art to which the present application belongs.

[0049] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, steps, operations, devices, components and / or combinations thereof, but do not preclude the presence or addition of one or more other features, steps, operations, devices, components and / or combinations thereof.

[0050] Embodiment 1

[0051] In one typical embodiment of the present application, a quality detection method in a functional gradient material preparation process is provided, as shown in the figure, comprising the following steps: Figure 1

[0052] S1, collecting the temperature of the blank in the functional gradient material preparation process to obtain blank temperature distribution data;

[0053] S2, performing real-time thickness measurement on the blank according to the blank temperature distribution data to obtain a blank thickness change curve;

[0054] S3, identifying an abnormal fluctuation section in the blank thickness change curve, performing phase detection on the physical region where the blank has an abnormal fluctuation section to obtain regional phase composition data;

[0055] S4, performing internal defect scanning on the blank according to the regional phase composition data to obtain a blank internal defect map;

[0056] S5, performing quality evaluation on the blank based on the blank internal defect map to obtain a quality grade determination result.

[0057] Each step will be described in detail below.

[0058] S1, collecting the temperature of the blank in the functional gradient material preparation process to obtain blank temperature distribution data.

[0059] ​Specifically, the temperature of the blank is collected by using an infrared thermometer. The specific implementation manner is as follows: in the process of adopting a layer-by-layer forming process such as laser cladding or plasma spraying for the functional gradient material, the infrared thermometer is fixedly installed at a proper position of the forming equipment, so that the temperature measurement field of the infrared thermometer can completely cover the surface area of the blank being deposited at present, and it is ensured that the optical axis of the infrared thermometer is perpendicular to the measured surface of the blank to reduce the measurement error. The infrared thermometer continuously receives the infrared radiation signals emitted by the surface of the blank, and converts the radiation intensity into corresponding temperature values according to the preset material emissivity parameters. The collection frequency needs to be matched with the material deposition rate, so as to ensure that the time resolution of the data is sufficient to reflect the instantaneous change of the temperature. The obtained temperature values of each spatial point are transmitted and recorded in real time by the data collection system, and a two-dimensional or three-dimensional temperature field data, i.e. the blank temperature distribution data, which can reflect the thermal history of the blank in the heating, melting, solidification and cooling stages, is formed.

[0060] For example, in the preparation of a titanium alloy / ceramic functional gradient material, when the interface region of the titanium alloy layer to the ceramic layer transition appears local temperature rise or abnormal temperature drop rate due to the sudden change of the thermal conductivity coefficient, the infrared thermometer can capture the temperature deviation phenomenon of the region, so as to provide accurate timing and spatial basis for subsequent thickness measurement by the laser thickness gauge based on the temperature distribution data, and ensure the continuity and cooperation of the whole detection process.

[0061] The blank temperature distribution data is obtained in the following manner:

[0062] 1. The outer surface and cross section of the blank in the preparation process of the functional gradient material are regionally divided by using a grid division tool, so as to obtain a blank temperature measurement grid, and the blank temperature measurement grid is numbered according to the gradient region type, so as to obtain a numbered temperature measurement grid.

[0063] The specific implementation manner is as follows: in the process of forming the functional gradient material by using a layer-by-layer deposition process such as laser cladding or plasma spraying, firstly, according to the design structure parameters and the material component gradient distribution law of the blank, the grid division tool pre-integrated in the control system is used to perform spatial discretization processing on the geometric model of the blank. According to the actual three-dimensional size information of the blank, the outer surface is divided into a plurality of square or triangular units with the same area or adjusted adaptively according to the curvature. Meanwhile, the cross section perpendicular to the deposition direction is divided into layered regions corresponding to the material gradient layers along the thickness direction, and each layered region corresponds to a specific material component proportion interval.

[0064] For example, from the pure metal layer to the metal-ceramic mixed layer to the pure ceramic layer, a continuous transition region is formed, thereby forming a two-dimensional or three-dimensional grid system covering the overall space structure of the blank, i.e., a blank temperature measurement grid. Then, according to the type of gradient region where each grid cell is located, such as a titanium alloy enrichment area, a transition mixed area, or a ceramic enrichment area, each grid cell is assigned a unique number identification, which includes a region type code and spatial coordinate information, for example, "T1-L3-C2" represents the titanium alloy type first area, the third layer, and the second column of the temperature measurement unit, and finally generates a numbered temperature measurement grid with clear spatial positioning and material attribute labels.

[0065] 2. Based on the numbered temperature measurement grid, use an infrared temperature measurement instrument to collect point temperature of the blank, and obtain the temperature distribution data of the blank.

[0066] The specific implementation is: install the infrared temperature measurement instrument on a programmable motion platform, and align its temperature measurement field of view to the current exposed outer surface area of the blank. The control system maps each cell number in the numbered temperature measurement grid to its corresponding spatial coordinates, and guides the probe of the infrared temperature measurement instrument to move above the center position of each numbered cell in the preset scanning order, keeping vertical alignment and stable focusing. Start temperature measurement collection, the infrared temperature measurement instrument receives the infrared radiation energy emitted by the surface of the unit, and converts it into the corresponding temperature value according to the preset material emissivity correction parameter, while recording the temperature value, the corresponding cell number and the collection time stamp. For temperature collection of cross-sectional area, pause the preparation process after completing deposition of several layers, expose the internal cross section using online cutting or tomographic imaging auxiliary means, and complete rapid temperature measurement of each numbered grid cell on the cross section within a short time. All collected temperature data are classified and arranged according to the cell number, forming a structured data set containing spatial position, material region type and real-time temperature value, i.e., the temperature distribution data of the blank.

[0067] For example, when preparing a titanium alloy / ceramic functional gradient material, when depositing to the third layer transition area, the control system instructs the infrared temperature measurement instrument to preferentially collect the temperature of the mixed region numbered "T1-L3-C2" and "M2-L3-C4" according to the numbered temperature measurement grid. It is found that the temperature is about 80°C higher than that of the adjacent metal region, indicating that there is a local overheating phenomenon in this region. This abnormal data will be used as a key input for subsequent key thickness measurement by the laser thickness gauge.

[0068] S2, real-time thickness measurement of the blank according to the temperature distribution data of the blank, to obtain the thickness change curve of the blank.

[0069] Specifically, the thickness of the blank is measured in real time by using a laser thickness gauge. The specific implementation is as follows: after the infrared thermometer completes the temperature collection of the surface of the blank during the preparation of the functional gradient material and forms continuous blank temperature distribution data, the time stamp and spatial coordinate information of the data are synchronously transmitted to the control system of the laser thickness gauge. The position of the current high-temperature active area or the position of the significant change of the temperature gradient reflected in the temperature distribution data is used as a reference datum to dynamically adjust the measurement starting point and scanning path of the laser thickness gauge, so that the probe of the laser thickness gauge is aligned with the blank area where material deposition or phase change is occurring. The laser thickness gauge emits a focused laser to irradiate the surface of the blank, and simultaneously receives the laser signal reflected by the surface. The distance change between the laser emission point and the reflection point is calculated by the principle of triangulation or interference, and then the thickness value at the measurement point is obtained. As the blank moves on the preparation platform according to the set trajectory, the laser thickness gauge continuously collects the thickness data of multiple positions at a frequency synchronized with the deposition process. These data are integrated in combination with the time sequence for processing, and finally a continuous curve reflecting the thickness evolution trend of the blank during the layer-by-layer accumulation in the forming process is formed, that is, the blank thickness change curve.

[0070] For example, when preparing a titanium alloy / ceramic functional gradient material, when the interface region of the titanium alloy layer to the ceramic layer transitions suddenly thickens or thins due to the difference in material density and melting characteristics, the laser thickness gauge can capture this abnormal fluctuation section in real time, thereby providing a direct basis for subsequent triggering of the X-ray diffractometer to detect the phase of the corresponding region.

[0071] Further, the blank thickness change curve is obtained by the following method:

[0072] 1. According to the blank temperature distribution data, the temperature gradient of the blank is analyzed to obtain a temperature gradient map, and the blank is divided into key regions according to the temperature gradient map to obtain key monitoring regions.

[0073] The specific implementation is as follows: after the infrared thermometer completes the fixed-point temperature collection of the outer surface and cross section of the blank during the preparation of the functional gradient material and generates the blank temperature distribution data containing the temperature values of each temperature measurement grid unit, the data is imported into the temperature analysis module. The temperature difference and spatial distance between adjacent grid units are calculated by using the finite difference method or the interpolation algorithm to obtain the temperature change direction and rate at each position, and a spatial temperature gradient distribution image in the form of a vector arrow or a pseudo-color cloud chart is formed, that is, a temperature gradient map. The high gradient region appears as a color mutation zone or a dense isotherm region. Then, according to the region range where the gradient amplitude in the temperature gradient map exceeds a preset process threshold (such as a temperature difference of more than 50°C per millimeter), and in combination with the design layer sequence structure information of the functional gradient material, the region located at the material component transition interface and having a sharp thermal response is identified.

[0074] For example, in a titanium alloy / ceramic functional gradient material, if a steep temperature gradient is formed by local high-temperature aggregation or rapid cooling at the junction of the third to fifth layers transitioning from the pure titanium layer to the TiC ceramic layer, the system will mark this area as a potential unstable area with continuous spatial coordinates, significant material type changes, and abnormal thermal history, thereby completing the division of the key area of the blank and generating a set of key monitoring targets containing the start and end layer numbers, grid numbers, and three-dimensional coordinates, i.e., the key monitoring area.

[0075] 2. Real-time thickness measurement of the blank in the key monitoring area using a laser thickness gauge to obtain real-time thickness data, and time alignment processing of the real-time thickness data to obtain time-series thickness data.

[0076] The specific implementation is as follows: the control system transmits the spatial coordinate information of the key monitoring area to the motion control unit of the laser thickness gauge, drives the probe of the laser thickness gauge to move along the predetermined path to the surface position above each numbered grid in the area, maintains perpendicular incidence, and emits a focused laser beam at a fixed sampling frequency, receives the light spot signal reflected by the blank surface, calculates the height difference of the current point through the triangular displacement sensing principle, and then deduces the actual deposition thickness at the measurement point, forming a real-time thickness data stream that continuously outputs as the preparation process continues. At the same time, each set of thickness values is attached with a timestamp generated synchronously by the central clock, and is matched with temperature collection time recorded by the infrared thermometer, X-ray diffractometer ready state and other multi-source signals on the time axis, the deviation caused by different equipment responses is eliminated by using linear interpolation or delay compensation algorithm, and all data is ensured to correspond to the same physical state under the same time reference, and finally integrated into a complete data sequence arranged in time sequence and accurately associated with the process timing, i.e., time-series thickness data.

[0077] 3. Change trend calculation of the time-series thickness data to obtain thickness change characteristics, and generation of a blank thickness change curve according to the thickness change characteristics.

[0078] The specific implementation is as follows: the filtered time-series thickness data is input into the trend analysis module, the local growth rate and fluctuation intensity are calculated by using the least squares fitting or moving window standard deviation algorithm, the interval segment where the thickness deviates from the design growth slope by more than ±10% or the sawtooth pattern where more than three consecutive sampling points present non-monotonic increasing is identified as a thickness change characteristic with significant dynamic variability, the system extracts parameters such as start and end time, peak deviation, and duration period accordingly, and draws a continuous broken line graph with time as the horizontal axis and thickness value as the vertical axis, in which the normal deposition trend is represented by a solid line and the abnormal fluctuation segment is marked by a dashed line or highlighted in red, and finally forms a visual curve that can intuitively reflect the geometric morphology evolution law of the key monitoring area in the entire preparation process, i.e., the blank thickness change curve.

[0079] For example, in the preparation of titanium alloy / ceramic functional gradient material, when the system detects that the key monitoring area numbered "M2-L3-C4" has a sharp rise in temperature gradient near the 180th second, the laser thickness gauge is guided to intensively scan this area, and it is found that the time sequence thickness data of this area decreases by 12% in this period. After trend calculation, it is confirmed that it is an abnormal thickness change feature. This result is directly reflected in the billet thickness change curve and becomes the key input condition for triggering the subsequent X-ray diffractometer to detect the phase.

[0080] S3, identifying an abnormal fluctuation section in the billet thickness change curve, detecting the phase of the physical region where the billet has an abnormal fluctuation section, and obtaining the region phase composition data.

[0081] Specifically, the X-ray diffractometer is used to detect the phase of the physical region where the billet has an abnormal fluctuation section. The specific implementation is as follows: after the laser thickness gauge completes the real-time thickness measurement of the billet during the preparation of the functional gradient material and generates a continuous billet thickness change curve, the curve data is input into the analysis system. The analysis system identifies the abnormal fluctuation section that deviates from the normal growth trend by setting a threshold range or using a sliding window algorithm. The abnormal fluctuation section corresponds to a thickness mutation region at a specific time point and a spatial position. Then, the time stamp and spatial coordinate information corresponding to the abnormal fluctuation section are transmitted to the positioning control system of the X-ray diffractometer. The coordinate is used to guide the probe head of the X-ray diffractometer to accurately align the physical region on the surface of the billet corresponding to the abnormal fluctuation section. The angle and scanning range of the incident X-ray are adjusted. The X-ray irradiation is started. The diffraction signal generated by the reflection of the crystal face in the material is received, and the diffraction angle and intensity distribution are recorded. The crystal face spacing is calculated by the Bragg equation. By comparing with the standard PDF card, the crystal phase type and its relative content in the region are identified, so as to obtain the local phase information related to the abnormal thickness change, i.e. the region phase composition data.

[0082] For example, in the preparation of titanium alloy / ceramic functional gradient material, if an abnormal fluctuation section of thickness thinning appears in the transition layer from titanium alloy to ceramic, the X-ray diffractometer will scan the interface region corresponding to this section, and may detect unexpected Ti2AlN phase or unreacted Al3Ti phase, indicating that the region has undergone a non-designed path chemical reaction or insufficient sintering. The region phase composition data obtained thereby will serve as a basis for judging the subsequent start of the ultrasonic flaw detector for defect scanning.

[0083] Further, the region phase composition data is obtained by the following method:

[0084] 1. The billet corresponding to the abnormal fluctuation section is marked by a coordinate mapping tool to obtain a region to be detected, and the region to be detected is sampled to obtain a phase detection sample.

[0085] The specific implementation is that: after the laser thickness gauge completes the real-time thickness measurement of the billet in the preparation process of the functionally graded material and generates the billet thickness change curve, when the system identifies that there is an abnormal fluctuation section in the curve that exceeds the preset fluctuation threshold, the time interval and spatial coordinate information corresponding to the abnormal fluctuation section are input to the coordinate mapping tool, the tool reversely calculates the physical deposition position corresponding to the abnormal fluctuation section, that is, the specific layer number, grid number and geometric range in the three-dimensional structure of the billet, according to the motion trail record of the preparation equipment and the layer thickness accumulation model, and visually marks the region in the form of a highlighted frame or color annotation on the control interface, forming a clear detection area, and then starting the automatic sampling device, which moves to the top of the detection area according to the marked coordinate information, and accurately cuts a block-shaped sample with a size of about 5mmx5mmx2mm from the surface of the billet by micro-drill milling or laser cutting, ensuring that the sampling process does not introduce additional thermal influence or mechanical damage, and the obtained sample is polished and used as a phase detection sample for phase analysis.

[0086] Further, the space position of the billet corresponding to the abnormal fluctuation section is marked by the coordinate mapping tool to obtain the detection area, specifically including:

[0087] (1) The abnormal fluctuation section in the billet thickness change curve is segmented to obtain fluctuation interval data, and the measurement time points in the fluctuation interval data are correspondingly paired with the scanning positions of the laser thickness gauge to obtain a fluctuation point table.

[0088] The specific implementation is that: after generating the billet thickness change curve, the system identifies the abnormal fluctuation section deviating from the normal growth trend in the curve by setting upper and lower threshold bands or using a sliding standard deviation algorithm, cuts the abnormal fluctuation section from the overall curve to form an independent data segment, that is, the fluctuation interval data. The data includes the starting time point, the ending time point and the thickness values of all sampling time points in the interval, then each measurement time point in the fluctuation interval data is time-synchronized and matched with the scanning trail log recorded by the laser thickness gauge during the preparation process, the spatial coordinate position of the laser thickness gauge probe at the same time stamp is found by using the unified clock reference in the central control system, including X, Y, Z three-dimensional coordinates and scanning angle information, forming an ordered data pair set composed of "time point-coordinate value", that is, the fluctuation point table, each row in the table records the collection time of an abnormal measurement point and its corresponding physical position. For example, the thickness decreases at 185 seconds, 188 seconds and 191 seconds, respectively, and the corresponding positions are determined as (X=32.4mm, Y=15.6mm, Z=2.3mm), (X=33.1mm, Y=15.8mm, Z=2.3mm), (X=33.8mm, Y=16.0mm, Z=2.3mm) through matching.

[0089] (2) Based on the fluctuation point table, the green body is grid partition marked to obtain a detection grid map, and the boundary of the region matched with the fluctuation point table in the detection grid map is expanded to obtain a to-be-detected region.

[0090] The specific implementation is as follows: a pre-established three-dimensional geometric model of the green body is called, and regular arranged rectangular cell grids are divided on the outer surface and the deposition layer according to a fixed size (such as 2 mm x 2 mm), each grid cell is given a unique number and records the center coordinates, forming a partition system covering the entire green body structure, that is, a detection grid map; then the coordinate values in the fluctuation point table are matched with the grid cell center coordinates in the detection grid map, the nearest neighbor algorithm or the coordinate range inclusion judgment method is used to determine the grid cell where each fluctuation point is located, and these grid cells are marked as “abnormal grid cells”; then taking each abnormal grid cell as a starting point, one or more grid cells in the adjacent layers in the up, down, left and right directions and the vertical deposition direction are expanded, and the expansion range is set according to the empirical radius of the material heat affected zone. For example, the expansion radius is set to 3 mm, to ensure that the surrounding area potentially affected by the heat disturbance is also included in the detection range, and finally all original abnormal grid cells and their expanded neighbor grid cells are merged into a continuous spatial region, which is the to-be-detected region. For example, when preparing a titanium alloy / ceramic functional gradient material, if the fluctuation point table shows that multiple abnormal points in the range of 185 to 191 seconds appear in the third layer transition zone (X = 32.4-33.8 mm, Y = 15.6-16.0 mm), the system locates the grid cells numbered G3-12, G3-13 and G4-12 as abnormal grid cells in the detection grid map, and then expands a circle of grid cells around them, forming a rectangular region containing nine grid cells G2-11 to G4-14 as the to-be-detected region. The region will be used as the spatial marking result output by the subsequent coordinate mapping tool to guide the sampling device to perform accurate phase detection sample cutting.

[0091] 2. The phase detection sample is scanned by an X-ray diffractometer to obtain a diffraction intensity spectrum, and the characteristic peak position in the diffraction intensity spectrum is identified to obtain a characteristic peak coordinate value.

[0092] The specific implementation is as follows: the phase detection sample is fixed on the sample table of the X-ray diffractometer, the surface thereof is adjusted to be perpendicular to the incident X-ray beam, the scanning angle range is set to 10° to 90° 2θ angle, the Cu-Kα radiation source is used to irradiate in the continuous scanning mode, the detector synchronously records the X-ray intensity signals at different diffraction angles, and the original spectrum with the diffraction angle as the horizontal axis and the intensity value as the vertical axis is generated, that is, the diffraction intensity spectrum, then the peak value identification algorithm is used for smoothing filtering and baseline correction of the spectrum, all local maximum points higher than three times the standard deviation of the background noise are detected, and the diffraction angle values corresponding to the local maximum points are extracted as the characteristic peak coordinate values.

[0093] For example, apparent peak positions appear at 2θ = 36.2°, 42.3°, 61.4°, the characteristic peak coordinate values are matched with known phase standard diffraction data by a standard card matching tool, a phase matching result is obtained, the phase matching result is sorted according to content ratio, and regional phase composition data is obtained. The specific execution mode of the process is: input the extracted characteristic peak coordinate values into the standard card matching tool, the tool calls the standard diffraction data of titanium alloy, ceramic and its compounds in the PDF (Powder Diffraction File) database, adopts Hannawalt retrieval method or FOM value sorting method for one-by-one comparison, finds all candidate phases with a deviation of less than 2θ ± 0.2° from the measured peak position, for example, 36.2° corresponds to the (111) face of TiC, 42.3° corresponds to the (101) face of α-Ti, and 61.4° corresponds to the (002) face of Ti2AlC. The system generates a phase matching result according to the number of matching peaks, intensity coincidence degree and crystal face coverage, and then quantitatively calculates the relative content of each matching phase in the sample by the Rietveld full spectrum fitting method, and finally sorts and outputs from high to low according to the content, such as TiC accounting for 58%, α-Ti accounting for 32%, and Ti2AlC accounting for 10%. The sorting result is the regional phase composition data.

[0094] For example, when preparing a titanium alloy / ceramic functional gradient material, if an abnormal fluctuation segment appears in the third layer transition zone, the above process detects that an unexpected Ti3Al phase appears in the regional phase composition data and accounts for 15%, indicating that a non-equilibrium reaction has occurred in the region to be detected. This data will be used as a direct basis for subsequent ultrasonic flaw detector scanning of internal defects.

[0095] Further, the phase detection test sample is subjected to diffraction scanning by an X-ray diffractometer to obtain a diffraction intensity spectrum, specifically including:

[0096] (1) The phase detection test sample is subjected to continuous scanning by an X-ray diffractometer to obtain original diffraction data, and the original diffraction data is subjected to background subtraction processing to obtain net diffraction data.

[0097] The specific implementation is: the phase detection test sample is fixed on the sample holder of the X-ray diffractometer, the sample table is adjusted to make the detection surface perpendicular to the incident X-ray beam, the scanning mode is set to θ-2θ continuous scanning, the X-ray source uses Cu-Kα radiation (wavelength λ=1.5406 Å), the tube voltage is set to 40 kV, the tube current is set to 30 mA, the detector collects the diffraction signal point by point in the 2θ angle range of 10° to 90° with a step length of 0.02° / step, each step stays for 0.5 seconds, records the X-ray count intensity at the corresponding angle, and forms a group of original data sequence with 2θ angle as the horizontal coordinate and intensity value as the vertical coordinate, that is, the original diffraction data. The data contains diffraction signals from the sample crystal structure and continuous background signals caused by sample support scattering, air scattering and detector background noise. Then the original diffraction data is input into the background subtraction module, and the background trend is modeled by using polynomial fitting or iterative smoothing algorithm. For example, a quadratic or cubic polynomial function is used to fit the aperiodic background distribution, or a Savitzky-Golay filter is used to identify and separate the low-frequency background components. The background curve obtained by fitting is subtracted from the original diffraction data point by point, and only the signal part generated by crystal diffraction is retained to form the net diffraction data without systematic interference.

[0098] (2) Peak enhancement processing is performed on the net diffraction data to obtain enhanced diffraction data, and coordinate conversion is performed on the enhanced diffraction data to obtain a diffraction intensity spectrum.

[0099] The specific implementation is: the net diffraction data is input into the peak enhancement module, gradient operators or high-pass filtering algorithms are used for sharpening processing of the data, the contrast between local maximum values and adjacent data points is enhanced, for example, first derivative zero crossing detection or Laplacian filter is used to highlight the edge features of peaks, and signal-to-noise ratio weighting amplification strategy is applied to amplify the gain of peaks higher than the set threshold (such as three times the standard deviation) and weaken the fluctuations close to the noise level, so as to improve the clarity and distinguishability of obvious diffraction peaks, and output the enhanced data sequence, that is, the enhanced diffraction data. Then, coordinate conversion is performed on the enhanced diffraction data, the data structure originally indexed by instrument step number or original channel number is rearranged according to the 2θ angle value corresponding to each step, converted into a standard coordinate system with accurate diffraction angle (°2θ) as the horizontal axis and calibrated intensity value (counts) as the vertical axis, the data format is converted into a universal XY text or CSV format, the sample number, scanning parameters and time stamp are embedded as meta information, and finally a visual chart file is generated, which can be used for subsequent feature peak identification and standard card comparison, that is, the diffraction intensity spectrum.

[0100] For example, in the preparation of titanium alloy / ceramic functional gradient material, the phase detection sample taken from the G3-12 grid area is scanned to obtain the original diffraction data, and the net diffraction data is obtained after background subtraction, which shows that there are potential peak positions at 2θ=36.2°, 42.3°, 61.4°, and after peak enhancement processing, these peaks are more prominent, and finally through coordinate conversion, the standard format diffraction intensity map is generated, and multiple significant diffraction peaks are clearly presented in the figure, providing a reliable data basis for subsequent identification of TiC, α-Ti and possible intermetallic compounds.

[0101] Further, the enhanced diffraction data is converted to obtain a diffraction intensity map, which specifically includes:

[0102] I. The diffraction angle values and diffraction intensity values in the enhanced diffraction data are extracted in pairs to obtain a diffraction data pair, and the horizontal and vertical axis parameters of the diffraction data pair are set through a coordinate definition tool to obtain a map coordinate system.

[0103] The specific implementation is: after completing the peak enhancement processing of the net diffraction data and generating the enhanced diffraction data, the system performs structured analysis on the data sequence, reads the numerical information in each record line by line, and combines each set of corresponding 2θ angle values and intensity values after enhancement processing in the form of ordered number pairs. For example, the angle 36.2° and its corresponding intensity value 842 counts form (36.2, 842), 42.3° and 1520 counts form (42.3, 1520), and so on, forming an ordered set containing multiple (2θ, I) combinations, i.e. a diffraction data pair. Then the diffraction data pair is input to the coordinate definition tool, which sets the horizontal axis parameter as "diffraction angle (°2θ)" according to the standard expression specification of X-ray diffraction map, the measurement range covers from the starting scan angle 10° to the end angle 90°, the minimum scale unit is 0.5°, the vertical axis parameter is set as "diffraction intensity (counts)", and its range is dynamically adjusted according to the maximum intensity value in the enhanced diffraction data, for example, when the highest intensity is 2000 counts, the upper limit of the vertical axis is set to 2200 counts to leave appropriate margin, and the display attributes such as coordinate axis font, line type and grid line density are uniformly configured by using the preset template. Finally, a two-dimensional rectangular coordinate system conforming to the international general standard, i.e. a map coordinate system, is constructed.

[0104] II. Based on the map coordinate system, the diffraction data pair is mapped to a diffraction coordinate point, and the diffraction coordinate point is continuously connected to obtain a diffraction intensity map.

[0105] The specific implementation is that the 2θ value in each group of diffraction data pairs is mapped to the horizontal axis position of the atlas coordinate system, the intensity value is mapped to the vertical axis position, the corresponding physical display point in the coordinate system is determined, for example, (36.2, 842) is positioned at the intersection position of the horizontal coordinate 36.2 and the vertical coordinate 842, forming discrete graphical marker points, which are diffraction coordinate points; the system uses a vector drawing engine to mark all diffraction coordinate points in the form of small dots or short vertical lines in the atlas coordinate system, and then uses a polyline connection algorithm to sequentially connect adjacent diffraction coordinate points with straight line segments in the order of 2θ angle from small to large, forming a continuous undulating curve. The curve truly reflects the intensity response change trend of the material at different diffraction angles. For the region with obvious peak value, the curve presents sharp protrusions, and for the flat background region, it maintains low amplitude fluctuations, and finally generates a visual image file containing complete coordinate axes, data point connection lines, legend identification and title information, that is, a diffraction intensity atlas.

[0106] For example, when preparing a titanium alloy / ceramic functional gradient material, the phase detection sample taken from the third layer transition zone is analyzed, and the system extracts multiple groups of diffraction data pairs including (36.2, 842), (42.3, 1520), and (61.4, 1180) from the enhanced diffraction data. After establishing the atlas coordinate system with the horizontal axis as “diffraction angle (°2θ)” and the vertical axis as “diffraction intensity (counts)” by the coordinate definition tool, each point is mapped to a diffraction coordinate point, and a clear diffraction intensity atlas is generated through continuous line processing. In the figure, a significant peak shape appears near 36.2°, which provides an intuitive basis for subsequent identification of the TiC phase by standard card comparison.

[0107] S4, according to the regional phase composition data, scanning the internal defects of the blank body to obtain the internal defect atlas of the blank body.

[0108] Specifically, the internal defect scanning of the blank is performed by using an ultrasonic flaw detector. The specific implementation manner is as follows: after the X-ray diffractometer completes the phase detection of the region corresponding to the abnormal fluctuation section in the preparation process of the functionally graded material and obtains the region phase composition data, the abnormal phase distribution position and the spatial coordinate information marked in the data are transmitted to the scanning control system of the ultrasonic flaw detector, the coordinate information is used to determine the internal region of the blank that needs to be detected, the probe position of the ultrasonic flaw detector is adjusted to be opposite to the surface projection position of the region, and the ultrasonic wave emission frequency, pulse period and gain parameters are preset according to the acoustic characteristics of the blank material; the ultrasonic probe is started to emit high-frequency acoustic signals, the acoustic signals are reflected or scattered when encountering the air hole, crack or interface debonding interface with different acoustic impedance in the propagation process in the blank, the return time, amplitude and waveform characteristics of the echo signals received by the probe are recorded; the defect depth is calculated through time domain analysis, the two-dimensional or three-dimensional imaging data are constructed by combining the displacement information of the scanning path, the signal filtering and image reconstruction algorithm processing are performed, and finally the visualized image reflecting the internal defect type, size, position and distribution density of the blank, i.e. the internal defect atlas of the blank, is generated.

[0109] For example, when preparing a titanium alloy / ceramic functionally graded material, if the region phase composition data indicates that there are a large number of brittle intermetallic compounds in the titanium alloy and ceramic transition layer, the ultrasonic flaw detector performs high-resolution scanning on the region, and may identify the microcrack group or local pore zone distributed along the interface, and marks the spatial distribution range of the high-light region in the defect atlas, thereby providing a direct basis for subsequent quality evaluation based on the atlas.

[0110] Further, the internal defect atlas is obtained by the following method:

[0111] 1. The phase distribution difference in the region phase composition data is analyzed to mark the region with non-conforming phase composition to the gradient design, to obtain the key detection area, and the key detection area is divided into grid units to obtain the flaw detection grid.

[0112] The specific implementation is that after the X-ray diffractometer completes the analysis of the phase detection sample in the preparation process of the functionally graded material and generates regional phase composition data, the phase type and content proportion of each detection point in the data are compared with a preset gradient design model layer by layer, and the design model stipulates that the transition region from the titanium alloy layer to the ceramic layer should appear Ti, Ti+TiC mixed phase, and continuous change of TiC as the main phase in turn. The system identifies the region where the actual phase composition deviates from the design path through a numerical comparison algorithm. For example, in the third layer where TiC should be the main phase, if the content of a-Ti exceeds 15% or an unexpected Ti2AlN phase appears, such a region is determined as an abnormal phase region, which is marked in combination with its spatial coordinates on the blank body to form a physical region with a clear boundary range, that is, a key detection area. Subsequently, the key detection area is subjected to local grid processing in the three-dimensional model interface of the control system, the grid unit size is set according to the effective detection diameter of the ultrasonic probe, for example, a square unit of 2 mm x 2 mm is used for division, each unit is given a unique number and records the center coordinates and the layer to which it belongs, and a discrete detection framework covering the entire key detection area, that is, a flaw grid, is formed.

[0113] 2. The ultrasonic flaw detector is used to collect ultrasonic reflection signals of each unit in the flaw grid, to obtain unit reflection signals, and to identify abnormal reflection waves in the unit reflection signals to obtain defect signal data.

[0114] The specific implementation is that the probe of the ultrasonic flaw detector is installed on a programmable motion platform, the control system guides the probe to move to the top of each grid unit in sequence according to the number sequence of the flaw grid, maintains perpendicular incidence and applies coupling agent to ensure that the sound wave is effectively transmitted, sets the pulse frequency to 5 MHz and the pulse period to 1 μs, starts the transmission circuit to generate a high-frequency voltage to excite the piezoelectric crystal, and transmits an ultrasonic beam into the blank body. When the sound wave encounters a discontinuous interface such as a pore, a crack, or an interface debonding in the propagation process, reflection occurs, the probe receives the echo signal and converts it into an electric signal, which is amplified and digitized to form original waveform data containing time, amplitude, and waveform characteristics, that is, unit reflection signals. Subsequently, time domain analysis is performed on each group of unit reflection signals, and additional reflection waves located before the bottom wave and having an amplitude exceeding twice the standard deviation of the baseline noise are identified to determine whether they have crack characteristic waveforms (such as double peaks and sawtooth wave heads) or pore characteristics (wide pulse and low frequency). Reflection waves meeting the defect characteristics are extracted and marked with their occurrence time, position number, and peak intensity to form a structured data set, that is, defect signal data.

[0115] Further, the ultrasonic flaw detector is used to collect ultrasonic reflection signals of each unit in the flaw grid to obtain unit reflection signals, including:

[0116] (1) Based on the unit size and arrangement of the flaw detection grid, the probe scanning path of the ultrasonic flaw detector is planned to obtain a grid adaptive path, and the probe is driven to move according to the grid adaptive path to perform first signal acquisition on each unit in the flaw detection grid to obtain initial reflection signals.

[0117] The specific implementation is as follows: after the division of the key detection area in the preparation process of the functionally graded material and the generation of the flaw detection grid are completed, the system reads the geometric parameters of each grid unit, including the unit size (such as 2 mm x 2 mm), the arrangement order (continuous arrangement by row or column), and the spacing between adjacent units, combines the effective beam coverage diameter (usually 1.8-2.2 mm) of the probe of the ultrasonic flaw detector, and plans a continuous scanning trajectory, i.e., a grid adaptive path, with no repetition, no omission, and the shortest moving distance, by using a path optimization algorithm. The path takes the starting unit as the starting point and passes through the center points of all grid units in turn, ensuring that the probe stays above each unit for a sufficient time to complete signal acquisition. The control system converts the grid adaptive path into motion instructions to drive the ultrasonic probe installed on the multi-axis mechanical arm to move point by point according to the predetermined trajectory. When reaching each grid unit center position, the probe automatically positions and keeps vertical to the surface of the blank, applies an appropriate amount of coupling agent to ensure that the sound wave is effectively transmitted, and then sets the transmission parameters to 5 MHz frequency, 1 μs pulse width, and 100 V excitation voltage, starts the transmission circuit to generate an ultrasonic pulse, and synchronously opens the receiving channel to record the echo signals reflected from the internal interfaces of the material, including the bottom wave, the interface wave, and the possible defect reflection wave, to form a set of time-amplitude waveform data corresponding to the grid unit, i.e., initial reflection signals. The initial reflection signals of all units are stored in order of their numbers and are labeled with position tags.

[0118] (2) The grid units in the initial reflection signals that do not collect effective signals are marked to obtain a blind area unit table, and the angle and transmission power of the probe are adjusted based on the blind area unit table to perform secondary signal acquisition on the blind area units in the flaw detection grid to obtain completed reflection signals. The initial reflection signals and the completed reflection signals are integrated according to the grid unit numbers to obtain unit reflection signals.

[0119] The specific implementation is: after completing the first scanning, the system discriminates the validity of each group of initial reflection signals, and the judgment criteria include whether there is identifiable bottom wave signal, whether the signal-to-noise ratio is greater than a preset threshold (such as 10 dB), whether the waveform is seriously distorted or completely flat without fluctuation, if the signal of a unit does not meet the above conditions, it is determined that no effective signal is collected, and its number is recorded into a structured list, that is, a blind area unit table. For example, G3-13, G4-12 are marked as blind area units, then the system calls the blind area unit table, reconfigures the detection parameters for each unit in the table, adjusts the probe inclination angle (such as from 0° to 5°-10° oblique incidence) to change the sound wave propagation path, avoids the deflection of the sound beam caused by the surface curvature or roughness, and increases the transmission power to 150V to enhance the penetration ability, while prolonging the signal acquisition time window, driving the probe to move to the top of the blind area unit again to perform secondary signal acquisition, and obtaining new echo data, that is, the completed reflection signal. Finally, all the completed reflection signals are matched with their corresponding grid unit numbers, and integrated with the original initial reflection signals, the effective signals collected for the first time are preferentially retained, and only the invalid data segments are replaced with the completed signals to ensure that each group of unit reflection signals contains complete and reliable information, and finally a unified data set covering the entire detection grid without data loss is formed, that is, the unit reflection signal. For example, when preparing titanium alloy / ceramic functional gradient material, when the first scanning found that the G3-13 unit had no effective echo due to serious signal attenuation caused by local surface oxidation layer, the system listed it in the blind area unit table, successfully obtained clear reflection wave after adjusting the probe angle to 7° and increasing the transmission power, and integrated the completed data into complete unit reflection signal, providing reliable input for subsequent defect identification.

[0120] 3. The defect signal data is graded according to the defect signal intensity to obtain graded defect data, and based on the position correspondence relationship between the graded defect data and the detection grid, a blank internal defect map containing defect position, intensity level, etc. is drawn.

[0121] The specific implementation is: the peak intensity value in the defect signal data is divided into three levels according to a preset threshold, for example, 0-30% full scale is level one (slight), 30%-60% is level two (moderate), and 60% or more is level three (severe). The system classifies each defect signal according to its intensity to generate graded defect data containing grid number, defect type, intensity level and depth information. Then call the image generation module to construct a two-dimensional plane map based on the layout of the detection grid, the horizontal and vertical coordinates correspond to the X-Y position of the grid, and different colors or filling patterns are used to represent different intensity levels, for example, green dots represent level one, yellow triangles represent level two, and red squares represent level three. Finally, a visual image is output, which intuitively reflects the defect distribution at each position in the key detection area, that is, the blank internal defect map.

[0122] For example, when preparing a titanium alloy / ceramic functional gradient material, if the regional phase composition data shows that there is an abnormal Ti3Al phase in the third layer transition zone, the system will mark it as a key detection area and divide it into 9 detection grids. After scanning, it is found that there is a strong reflection wave with an amplitude of 75% in the G3-13 unit, which is identified as a penetrating crack signal and classified as a third-level defect. The position of the defect is marked in the internal defect map of the blank with a red square, which provides a direct basis for subsequent quality assessment.

[0123] S5, quality assessment of the blank based on the internal defect map of the blank, and obtaining the quality grade determination result.

[0124] The specific implementation is: after the ultrasonic flaw detector completes the internal defect scanning of the blank during the preparation process of the functional gradient material and generates the internal defect map of the blank, the map data is imported into the quality assessment system. The system segments and extracts the abnormal areas in the map according to the preset defect recognition algorithm, including parameters such as defect area ratio, maximum equivalent size, spatial distribution density, and whether it crosses the key interface layer. At the same time, multi-level threshold values are set according to the design specifications of the functional gradient material. For example, defects with an area ratio of less than 1% and not connected to the interface are classified as minor defects, defects with an area ratio of 1% to 3% or local dense distribution are classified as moderate defects, and defects with an area ratio of more than 3% or penetrating cracks are classified as severe defects. The system compares the extracted defect features with the threshold values of each level, and calculates the overall quality score by weighting the indicators, and maps it to the corresponding quality grade, such as first class (qualified), second class (reduced use) or third class (scrap), and finally outputs a structured report containing defect position, grade classification and determination basis, i.e. quality grade determination result.

[0125] For example, when preparing a titanium alloy / ceramic functional gradient material, if the internal defect map of the blank shows that there are 2.5% of discrete pores in the third to fifth layer interface regions of the titanium alloy to the ceramic transition, and no penetrating cracks, the system determines that it is a moderate defect, which is classified as a second quality grade, indicating that the blank needs to be treated by subsequent hot isostatic pressing before it can be put into use, thereby completing the quality determination process based on the complete detection chain.

[0126] Further, the quality grade determination result is obtained by the following method:

[0127] 1. Extract the defect position and intensity level in the internal defect map of the blank to obtain defect feature data, and classify and count the defect feature data according to the preset defect type to obtain the defect classification and counting result.

[0128] The specific implementation is:

[0129] After the ultrasonic flaw detector completes the internal defect scanning of the key detection area in the preparation process of the functionally graded material and generates a blank internal defect map containing defect position and intensity level information, the system performs image analysis processing on the map, identifies the defect points marked with different colors or symbols in the image, reads the grid number, spatial coordinates (X, Y, Z), depth position, and intensity level (such as first, second, and third levels) of each defect point in the map, and integrates these information into a set of structured data, i.e. defect feature data. Then, according to the pre-set defect type classification rules, the defects are classified into types such as pores, cracks, inclusions, and interface debonding according to their morphological characteristics and distribution rules. The classification basis includes the spatial continuity of defect signals (such as linear distribution for crack determination), the depth concentration of defect signals (such as near-surface dense point defects for pore determination), and whether the defect crosses the material gradient interface (such as interface debonding for defects penetrating the titanium alloy and ceramic layer). The system matches and labels each record in the defect feature data according to the type, and calculates the number and area ratio of each type. For example, there are 3 cracks, the total projection area accounts for 2.1% of the key detection area, and there are 7 pores, the total area accounts for 1.5%. Finally, a summary data table containing the number, distribution density, maximum equivalent size, and proportion information of each type of defect is formed, i.e. the defect classification statistical result.

[0130] 2. Quantitatively score the defect classification statistical result by the defect rating standard to obtain a single defect score, and perform weighted summation calculation based on the single defect score to obtain a comprehensive quality score. Then, compare the comprehensive quality score with the pre-set quality level threshold to obtain the quality level determination result.

[0131] The specific implementation is as follows: call the pre-set defect rating standard table, which specifies the scoring rules for each type of defect, such as 5 points per millimeter for cracks, 2 points per 0.5% for pores, and 8 points for each occurrence of interface debonding. The system calculates the corresponding score according to the parameters in the defect classification statistical result, generates a crack single score, a pore single score, etc., i.e. a single defect score, and then performs weighted summation according to the weight coefficients of each type of defect on the material performance, such as 0.4 for cracks, 0.3 for pores, and 0.3 for interface debonding. The calculation formula is: comprehensive quality score = crack single score × 0.4 + pore single score × 0.3 + interface debonding single score × 0.3. The final score value is obtained, and then the comprehensive quality score is compared with the pre-set quality level threshold. The threshold range is set as follows: score ≥ 90 points for first level (qualified), 80-89 points for second level (downgraded use), and < 80 points for third level (rejected). The system automatically matches the corresponding interval and outputs the corresponding conclusion, i.e. the quality level determination result.

[0132] For example, in the preparation of titanium alloy / ceramic functionally graded materials, if the defect classification statistics result shows that there are 2 microcracks (10 points deducted), 5 pores (6 points deducted), and 1 interface debonding (8 points deducted), the comprehensive quality score is 82 points after weighted calculation, which falls into the interval of 80-89 points, and the system determines that the quality grade is level two, indicating that the blank needs to be repaired for subsequent processing before being put into use.

[0133] The above describes the specific embodiments of the present application in combination with the drawings, but is not a limitation on the protection scope of the present application. It should be understood by those skilled in the art that various modifications or variations made on the basis of the technical solutions of the present application without creative labor are still within the protection scope of the present application.

Claims

1. A method for quality detection in a process for manufacturing a functionally graded material, characterized in that, The method comprises the following steps: Collecting the temperature of the blank during the preparation of the functionally graded material to obtain blank temperature distribution data; According to the blank temperature distribution data, performing temperature gradient analysis on the blank to obtain a temperature gradient graph, and dividing the blank into key monitoring regions according to the temperature gradient graph; using a laser thickness gauge to perform real-time thickness measurement on the blank in the key monitoring regions to obtain real-time thickness data, performing time alignment processing on the real-time thickness data to obtain time-series thickness data; performing change trend calculation on the time-series thickness data to obtain thickness change characteristics, and generating a blank thickness change curve according to the thickness change characteristics; Identifying an abnormal fluctuation section in the blank thickness change curve, performing phase detection on a physical region where the blank has the abnormal fluctuation section to obtain regional phase composition data; According to the regional phase composition data, performing internal defect scanning on the blank to obtain a blank internal defect map; Based on the blank internal defect map, performing quality evaluation on the blank to obtain a quality grade determination result.

2. The method of claim 1, wherein the functional gradient material is a ceramic material. The temperature of the blank is collected by using an infrared thermometer, and the blank temperature distribution data is obtained by the following method: Using a grid division tool to divide the outer surface and cross section of the blank during the preparation of the functionally graded material into regions to obtain a blank temperature measurement grid, and numbering the blank temperature measurement grid according to the gradient region type to obtain a numbered temperature measurement grid; Based on the numbered temperature measurement grid, using an infrared thermometer to collect the temperature of the blank at fixed points to obtain the blank temperature distribution data.

3. The method of claim 1, wherein the functional gradient material is a ceramic material. The physical region where the blank has the abnormal fluctuation section is detected by using an X-ray diffractometer, and the regional phase composition data is obtained by the following method: The space position of the blank corresponding to the abnormal fluctuation section is marked by using a coordinate mapping tool to obtain a to-be-detected region, and the to-be-detected region is sampled to obtain a phase detection sample; The phase detection sample is scanned by using an X-ray diffractometer to obtain a diffraction intensity map, and the characteristic peak position in the diffraction intensity map is identified to obtain characteristic peak coordinate values; The characteristic peak coordinate values are matched with known phase standard diffraction data by using a standard card comparison tool to obtain a phase matching result, and the phase matching result is sorted according to the content ratio to obtain the regional phase composition data.

4. The method of claim 3, wherein the functional gradient material is a ceramic material. The space position of the blank corresponding to the abnormal fluctuation section is marked by using a coordinate mapping tool to obtain a to-be-detected region, which specifically comprises: The abnormal fluctuation section in the blank thickness change curve is segmented to obtain fluctuation interval data, and the measurement time points in the fluctuation interval data are correspondingly paired with the scanning positions of the laser thickness gauge to obtain a fluctuation point position table; Based on the fluctuation point position table, the blank is marked by grid division to obtain a detection grid map, and the regions in the detection grid map that match the fluctuation point position table are boundary expanded to obtain the to-be-detected region.

5. The method of claim 3, wherein the functional gradient material is a ceramic material. The phase detection sample is scanned by using an X-ray diffractometer to obtain a diffraction intensity map, which specifically comprises: The phase detection sample is continuously scanned by using an X-ray diffractometer to obtain original diffraction data, and the original diffraction data is background-subtracted to obtain net diffraction data; The net diffraction data is subjected to peak enhancement processing to obtain enhanced diffraction data, and the enhanced diffraction data is subjected to coordinate conversion to obtain a diffraction intensity spectrum.

6. The method of claim 5, wherein the functional gradient material is a ceramic material. The enhanced diffraction data is subjected to coordinate conversion to obtain a diffraction intensity spectrum, specifically including: The diffraction angle values and diffraction intensity values in the enhanced diffraction data are extracted in pairs to obtain diffraction data pairs, and the horizontal axis and vertical axis parameters of the diffraction data pairs are set through a coordinate definition tool to obtain a spectrum coordinate system; Based on the spectrum coordinate system, the diffraction data pairs are subjected to coordinate point mapping to obtain diffraction coordinate points, and the diffraction coordinate points are subjected to continuous line processing to obtain a diffraction intensity spectrum.

7. The method of claim 1, wherein the functional gradient material is a ceramic material. The green body is subjected to internal defect scanning by an ultrasonic flaw detector, and the internal defect spectrum is obtained by the following method: The phase distribution difference in the regional phase composition data is analyzed to mark the regions where the phase composition does not meet the gradient design, thereby obtaining a key detection area, and the key detection area is subjected to grid cell division to obtain a flaw grid; The ultrasonic reflection signal of each cell in the flaw grid is collected by the ultrasonic flaw detector to obtain a cell reflection signal, and the abnormal reflection wave in the cell reflection signal is identified to obtain defect signal data; The defect signal data is classified according to the defect signal intensity to obtain classified defect data, and based on the positional correspondence between the classified defect data and the flaw grid, a green body internal defect spectrum containing defect position, intensity level, etc. is drawn.

8. The method of claim 7, wherein the functional gradient material is a functionally graded material. The ultrasonic reflection signal of each cell in the flaw grid is collected by the ultrasonic flaw detector, specifically including: Based on the cell size and arrangement of the flaw grid, the probe scanning path of the ultrasonic flaw detector is planned to obtain a grid adaptation path, and the probe is driven to move according to the grid adaptation path to collect the initial reflection signal of each cell in the flaw grid. The grid cells that do not collect effective signals in the initial reflection signal are marked to obtain a blind area cell table, and the angle and transmission power of the probe are adjusted based on the blind area cell table to collect secondary signals from the blind area cells in the flaw grid to obtain completed reflection signals. The initial reflection signal and the completed reflection signal are integrated according to the grid cell number correspondence to obtain the cell reflection signal.

9. The method of claim 1, wherein the functional gradient material is a ceramic material. The quality grade determination result is obtained by the following method: The defect position, intensity level, etc. in the green body internal defect spectrum are extracted to obtain defect feature data, and the defect feature data is classified and counted according to the preset defect type to obtain defect classification statistical results; The defect classification statistical results are quantitatively scored by the defect rating standard to obtain single defect scores, and the single defect scores are weighted and summed to obtain a comprehensive quality score, and the comprehensive quality score is compared with the preset quality grade threshold to obtain the quality grade determination result.

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