Method for detecting oxygen content distribution state of neodymium-iron-boron rapidly quenched thin strip
Patent Information
- Application Number
- CN202611378842.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-09-07
- Publication Date
- 2026-10-30
AI Technical Summary
[0003]钕铁硼快淬薄带氧含量检测技术通常仅采用各项传统的检测技术对氧含量进行检测，而在检测过程中，还存在各种其他因素对检测结果存在影响，但现有的钕铁硼快淬薄带氧含量检测技术难以剔除这类因素的影响，导致检测结果与实际的氧含量存在较大差异，现有的钕铁硼快淬薄带氧含量检测技术还存在难以消除其他因素对含氧量检测的影响，导致检测结果与实际含氧量存在较大差异的问题
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Abstract
Description
Technical Field
[0001] This invention relates to the field of oxygen content detection technology in NdFeB rapid quenching thin strips, specifically a method for detecting the oxygen content distribution state in NdFeB rapid quenching thin strips. Background Technology
[0002] The oxygen content detection technology for NdFeB rapid quenching thin strips refers to the technical method for qualitative or quantitative analysis of the spatial distribution of oxygen content in thin strips made of NdFeB alloy through rapid solidification.
[0003] The oxygen content detection technology for NdFeB rapid quenching thin strips typically only uses traditional detection techniques to measure oxygen content. However, various other factors can affect the detection results during the process, and existing NdFeB rapid quenching thin strip oxygen content detection technologies struggle to eliminate the influence of these factors. This leads to a significant discrepancy between the detected results and the actual oxygen content. Furthermore, the existing NdFeB rapid quenching thin strip oxygen content detection technology still suffers from the problem of not being able to eliminate the influence of other factors on oxygen content detection, resulting in a large difference between the detected results and the actual oxygen content. Summary of the Invention
[0004] This invention aims to at least partially solve one of the technical problems in the prior art. It involves focusing a pulsed laser and momentarily irradiating the surface of a NdFeB rapidly quenched thin strip with the focused laser to obtain plasma. The plasma is then subjected to spectral analysis to obtain the oxygen and iron spectral intensities. Based on these intensities, the ferro-oxygen spectral characteristics are calculated. Experimental samples are prepared, and their ferro-oxygen spectral characteristics are analyzed. Furthermore, a standard curve comparing the ferro-oxygen spectral characteristics with oxygen content is analyzed. Before laser irradiation, the roughness of the irradiation point and the ambient humidity are detected, and influencing parameters are calculated. The estimated oxygen concentration of the NdFeB rapidly quenched thin strip is then calculated based on the standard curve and calibrated according to the influencing parameters. Finally, distributed detection is performed on the surface of the NdFeB rapidly quenched thin strip to obtain the distribution of oxygen content. This invention addresses the problem in existing NdFeB rapidly quenched thin strip oxygen content detection technologies, which struggle to eliminate the influence of other factors on oxygen content detection, leading to significant discrepancies between the detection results and the actual oxygen content.
[0005] To achieve the above objectives, in a first aspect, this application provides a method for detecting the oxygen content distribution in rapidly quenched NdFeB thin strips, comprising the following steps:
[0006] A plasma is obtained by focusing a pulsed laser and then using the focused laser to momentarily irradiate the surface of a rapidly quenched NdFeB strip.
[0007] The plasma was subjected to spectral analysis to obtain the oxygen and iron spectral intensities, and the ferro-oxygen spectral characteristics were calculated based on the oxygen and iron spectral intensities.
[0008] Experimental samples were prepared, the ferro-oxygen spectral characteristics of the experimental samples were analyzed, and the standard curve of ferro-oxygen spectral characteristics versus oxygen content was further analyzed.
[0009] Before laser irradiation, the roughness of the irradiation point and the indoor ambient humidity are detected and the influencing parameters are calculated.
[0010] The oxygen concentration of NdFeB rapid quenching thin strips was calculated and calibrated based on the standard curve and influencing parameters, and then the distribution of oxygen content in the NdFeB rapid quenching thin strips was analyzed.
[0011] Furthermore, the step of focusing the pulsed laser and momentarily irradiating the surface of the NdFeB rapid-quenched thin strip with the focused laser to obtain plasma also includes the following sub-steps:
[0012] A focusing lens is used to focus the pulsed laser emitted by the pulsed laser into a focused laser.
[0013] The surface of the neodymium iron boron rapid quenching thin strip is irradiated momentarily with a focused laser. After irradiation, the pulsed laser is turned off, and plasma will be generated.
[0014] Furthermore, the step of performing spectral analysis on the plasma and obtaining the oxygen and iron spectral intensities, and calculating the ferro-oxygen spectral characteristics based on the oxygen and iron spectral intensities, further includes the following sub-steps:
[0015] The plasma was analyzed using spectral analysis techniques to extract the oxygen and iron spectral intensities, which were denoted as OS and IS, respectively.
[0016] Calculate OS / IS, and the result is called the ferrite spectral characteristic and denoted as OIF.
[0017] Furthermore, the steps of preparing experimental samples, analyzing the ferro-oxygen spectral characteristics of the experimental samples, and further analyzing the standard curve of ferro-oxygen spectral characteristics versus oxygen content also include the following sub-steps:
[0018] Prepare experimental samples with known oxygen content and constant oxygen content gradient, analyze the ferro-oxygen spectral characteristics of the experimental samples, and define them as sample characteristics;
[0019] The oxygen content of the experimental sample is obtained and defined as the sample oxygen concentration. The functional relationship between the sample characteristics and the sample oxygen concentration is analyzed to obtain a standard curve.
[0020] Furthermore, the step of detecting the roughness of the irradiation point and the indoor ambient humidity and calculating the influencing parameters before laser irradiation also includes the following sub-steps:
[0021] Before detecting the characteristics of the sample, the roughness of the irradiation point and the indoor ambient humidity are detected simultaneously. The roughness and ambient humidity are grouped according to different sample oxygen concentrations, which is called sample grouping. The ambient humidity is grouped according to different roughness, which is called humidity grouping. The roughness is grouped according to different ambient humidity, which is called roughness grouping.
[0022] The humidity influence curve and the roughness influence curve are analyzed based on humidity grouping and coarseness grouping, and the influence parameters are further calculated.
[0023] The advantage lies in the fact that, among the various factors affecting the detection results, roughness is difficult to control. If the distribution of oxygen content is to be detected, it is impossible to target a specific roughness irradiation point for detection. Adjusting the ambient humidity requires the construction of a special sealed space, which is costly and cannot be adapted to different manufacturers. The other factors can be controlled to a certain extent, and under normal circumstances, the other factors have the same impact on the intensity of oxygen and iron spectra. The difference in the calculated ferro-oxygen spectral characteristics is very small and can be ignored. Therefore, this embodiment mainly calibrates the detection results based on roughness and ambient humidity. However, the influence of roughness and ambient humidity on the detection results is different. If a parameter is directly calculated, the influence weights of the two will be different, and the calculated influence parameter will not be representative. Therefore, this embodiment assigns a weight to roughness by analyzing the influence of the two on the detection results, and then calculates the influence parameter so that the influence of the two on the detection results is the same, thereby improving the accuracy and rationality of oxygen content detection in NdFeB rapid quenching thin strips.
[0024] Furthermore, the step of calculating and calibrating the detected oxygen concentration of the NdFeB rapid quenching thin strip based on the standard curve and influence parameters, and then analyzing the oxygen content distribution of the NdFeB rapid quenching thin strip, further includes the following sub-steps:
[0025] The estimated oxygen concentration of NdFeB rapid quenching thin strip was calculated based on the standard curve, and the estimated oxygen concentration was calibrated based on the influencing parameters.
[0026] Distributed detection was performed on the surface of NdFeB rapid quenching thin strips to obtain the distribution of oxygen content in the NdFeB rapid quenching thin strips.
[0027] Furthermore, the step of calculating the estimated oxygen concentration of the NdFeB rapid quenching thin strip based on the standard curve and calibrating the estimated oxygen concentration according to the influencing parameters also includes the following sub-steps:
[0028] The functional relationship between the influencing parameters and sample characteristics is analyzed and defined as the fluctuation calibration curve;
[0029] Obtain the standard curve, define the coordinate points next to the standard curve as concentration analysis points, obtain the concentration analysis point corresponding to the minimum influence parameter and define it as the minimum offset point, obtain the concentration analysis point corresponding to the maximum influence parameter and define it as the maximum offset point;
[0030] The ferrooxy spectral characteristics of NdFeB rapid quenching thin strips were calibrated based on the minimum and maximum offset points, and the predicted oxygen concentration was analyzed.
[0031] The advantage lies in the fact that traditional detection methods directly calculate oxygen content based on a standard curve, while this embodiment calibrates the ferrite spectral characteristics calculated based on influencing parameters before calculating oxygen content. This eliminates the offset of ferrite spectral characteristics caused by different roughness and environmental humidity, thus improving the accuracy and effectiveness of oxygen content detection in NdFeB rapid quenching thin strips.
[0032] Furthermore, the step of performing distributed detection on the surface of the NdFeB rapid quenching thin strip to obtain the oxygen content distribution state of the NdFeB rapid quenching thin strip also includes the following sub-steps:
[0033] An irradiation point is set at a preset length interval on the surface of the NdFeB rapid quenching thin strip, and the estimated oxygen concentration at each irradiation point is analyzed to obtain the distribution of oxygen content.
[0034] The estimated oxygen concentration at adjacent irradiation points is considered to change at a constant gradient, and the distribution of oxygen content in the NdFeB rapid quenching strip is visualized by using gradient colors.
[0035] In a second aspect, this application provides an electronic device, the electronic device including a memory and one or more processors; the memory is coupled to the processors; wherein the memory stores computer program code, the computer program code including computer instructions, which, when executed by the processor, cause the electronic device to perform the steps of the method in the first aspect.
[0036] Thirdly, this application provides a storage medium storing computer instructions thereon, characterized in that, when the computer instructions are executed by a processor, the steps of the method as described in the first aspect are performed. Attached Figure Description
[0037] The following describes the accompanying drawings used in this application.
[0038] Figure 1 This is a flowchart of the steps of the method of the present invention;
[0039] Figure 2 This is a schematic diagram of the standard curve of the present invention;
[0040] Figure 3This is a schematic diagram illustrating the relationship between the sample analysis group, humidity analysis group, humidity analysis features, coarseness analysis group, and coarseness analysis features of the present invention.
[0041] Figure 4 This is a schematic diagram of the humidity effect curve of the present invention;
[0042] Figure 5 This is a schematic diagram of the fluctuation calibration curve of the present invention. Detailed Implementation
[0043] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described below with reference to the accompanying drawings.
[0044] The terms "first" and "second," etc., used in the specification, claims, and drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.
[0045] The term "embodiment" as used herein means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.
[0046] Common techniques for detecting oxygen content in rapidly quenched NdFeB thin strips include infrared absorption methods or scanning Kelvin probes. However, these methods typically have various limitations, a common one being the difficulty in eliminating the influence of other factors on the test results. To address these issues, this application provides a method for detecting the oxygen content distribution in rapidly quenched NdFeB thin strips. Please refer to [link to relevant documentation]. Figure 1 As shown, it includes the following steps:
[0047] Step S1 involves focusing a pulsed laser and using the focused laser to momentarily irradiate the surface of a NdFeB rapid-quenched thin strip to obtain plasma. Step S1 includes the following sub-steps:
[0048] Step S101: Use a focusing lens to focus the pulsed laser emitted by the pulsed laser into a focused laser;
[0049] Step S102: The surface of the neodymium iron boron rapid quenching thin strip is momentarily irradiated with a focused laser. After the irradiation is completed, the pulsed laser is turned off, and plasma will be generated at this time.
[0050] In practice, the pulsed laser is focused from a millimeter-level spot to a μm-level spot, minimizing damage to the NdFeB rapid quenching strip. Since the NdFeB rapid quenching strip needs to be crushed in subsequent processing steps, this damage can be ignored. The focused laser has a very high power density, which can transform the surface material of the NdFeB rapid quenching strip into plasma. Therefore, the surface of the NdFeB rapid quenching strip is irradiated with a focused laser for a moment, forming a high-temperature plasma after irradiation.
[0051] Step S2 involves performing spectral analysis on the plasma and obtaining the oxygen and iron spectral intensities. The ferro-oxygen spectral characteristics are then calculated based on these intensities. Step S2 includes the following sub-steps:
[0052] Step S201: Perform spectral analysis on the plasma using spectral analysis technology to extract the oxygen spectral intensity and iron spectral intensity in the plasma, denoted as OS and IS, respectively;
[0053] Step S202: Calculate OS / IS, and refer to the calculation result as the ferrite spectral characteristic and denote it as OIF;
[0054] In practice, since the plasma is formed from the surface material of NdFeB rapid quenching thin strips, its internal oxygen and iron content can be compared with the oxygen and iron content on the surface of the NdFeB rapid quenching thin strips. Therefore, the oxygen and iron spectral intensities of the plasma can be directly represented by the oxygen and iron spectral intensities of the NdFeB rapid quenching thin strip surface. Furthermore, the accuracy of the spectral intensity obtained from plasma spectral analysis is higher than that obtained by directly analyzing the surface of the NdFeB rapid quenching thin strip. Therefore, existing spectral analysis techniques are used to extract the oxygen spectral intensity OS and iron spectral intensity IS from the plasma. The OS / IS ratio is calculated because the energy of each laser pulse is not exactly the same. When the energy is high, the ablation is greater, the plasma temperature is higher, and the spectra of all elements become stronger. When the energy is low, all spectra become weaker. Iron is the most important element in the NdFeB matrix, and its content is basically fixed. Therefore, the ratio of oxygen spectral intensity to iron spectral intensity is used as a reference to obtain the ferro-oxygen spectral characteristics. For example, when the detected oxygen spectral intensity OS is 1220 and the iron spectral intensity IS is 15800, the ferro-oxygen spectral characteristics OIF of the current irradiation point can be calculated as 0.078205, rounded to six decimal places.
[0055] Step S3 involves preparing experimental samples, analyzing the ferro-oxygen spectral characteristics of the samples, and further analyzing the standard curve of ferro-oxygen spectral characteristics versus oxygen content. Step S3 includes the following sub-steps:
[0056] Step S301: Prepare an experimental sample with known oxygen content and an equal gradient of oxygen content, and analyze the ferro-oxygen spectral characteristics of the experimental sample, which are defined as sample characteristics;
[0057] Please see Figure 2 As shown, in step S302, the oxygen content of the experimental sample is obtained, defined as the sample oxygen concentration, and the functional relationship between the sample characteristics and the sample oxygen concentration is analyzed to obtain a standard curve.
[0058] In practice, the preparation of experimental samples is the same as that of other existing detection technologies. Therefore, this embodiment will not describe the preparation of experimental samples in detail. For example, in this embodiment, five sets of experimental samples were obtained, corresponding to oxygen concentrations of 0 wt%, 0.05 wt%, 0.1 wt%, 0.15 wt%, and 0.2 wt%, respectively. When analyzing the sample characteristics, the surface of the experimental samples was tested multiple times under different ambient humidity. A new irradiation point was set for each test, resulting in a dataset of sample oxygen concentration and sample characteristics, defined as the sample dataset. Partial data of the sample dataset is shown in Table 1 below:
[0059] Table 1. Partial data from the sample dataset.
[0060]
[0061] The environmental humidity and roughness are derived from step S401. The analysis of the standard curve involves constructing a coordinate system with sample characteristics as the X-axis and sample oxygen concentration as the Y-axis, and then fitting a function to it. The resulting curve is the standard curve. Figure 2 As shown, Figure 2 The curve in the figure is the standard curve.
[0062] Step S4: Before laser irradiation, detect the roughness of the irradiation point and the indoor ambient humidity, and calculate the influencing parameters; Step S4 includes the following sub-steps:
[0063] Step S401: Before detecting the characteristics of the sample, the roughness of the irradiation point and the ambient humidity in the room are detected simultaneously. The roughness and ambient humidity are grouped according to different sample oxygen concentrations, which is called sample grouping. The ambient humidity is grouped according to different roughness, which is called humidity grouping. The roughness is grouped according to different ambient humidity, which is called roughness grouping.
[0064] In practice, for example, the first and second data points with the same oxygen concentration in Table 1 are the same sample group. Similarly, the third and fourth data points are also a sample group. In this sample group, the roughness of the third and fourth data points is the same. Therefore, the third and fourth data points also form a humidity group. The same applies to the roughness group. This will not be explained further here.
[0065] Step S402: Analyze the humidity influence curve and the roughness influence curve based on humidity grouping and coarseness grouping, and further calculate the influence parameters;
[0066] Please see Figure 3 As shown, in step S403, when analyzing any humidity group, it is named a humidity analysis group, and the sample group to which the humidity analysis group belongs is named a sample analysis group. The sample characteristics detected by the sample analysis group in each environmental humidity in the humidity analysis group are obtained and defined as humidity analysis characteristics. The environmental humidity is sorted in ascending order and represented by the symbol H. n At the same time, H n The corresponding humidity analysis characteristic is represented by the symbol HF. n , where n is a positive integer and n is a subscript;
[0067] In specific implementation, steps S402 is supplemented in detail through steps S403 to S409, and the relationship between the sample analysis group, humidity analysis group, humidity analysis characteristics, coarse analysis group, and coarse analysis characteristics is as follows: Figure 3 As shown, steps S403 and S406 are both for obtaining humidity analysis features and coarseness analysis features, which will not be described in detail in this embodiment.
[0068] Step S404: Define the minimum ambient humidity as the minimum humidity. Keep H1 of each humidity group within each sample group at the minimum humidity level. Calculate H... n / H1, to get H n The humidity ratio, expressed as the symbol HP n Simultaneously calculate HF n / HF1, get H n The characteristic fluctuation is represented by the symbol FF. n ;
[0069] Please see Figure 4 As shown, in step S405, the functional relationship between humidity ratio and characteristic fluctuation is analyzed and defined as the humidity influence curve. The maximum value of humidity ratio and the characteristic fluctuation at the median value on the humidity influence curve are obtained and represented by the symbols MAXF and CENF, respectively.
[0070] In practice, the minimum humidity was determined to be 10%. During the experimental phase, an environmental humidity test at 10% was conducted for each humidity group, resulting in H1 of 10% for each group. HP was then further calculated. n and FF nFor example, when H2 is 15%, HP2 is calculated to be 1.5, HF1 to be 0.042868, and HF2 to be 0.045029, resulting in FF2 of 1.050411. This means that when the ambient humidity increases by 0.5 times compared to 10%, the humidity analysis characteristic will increase by 0.050411 times. Analyzing the humidity influence curve essentially involves constructing a coordinate system using the humidity ratio and characteristic fluctuations as the X and Y axes and fitting a function. The curve corresponding to the fitted function in the coordinate system is the humidity influence curve. The humidity influence curve obtained from the analysis is shown below. Figure 4 As shown, Figure 4 The curve in the curve is the humidity influence curve. Within the range of humidity ratio values in the humidity influence curve, the maximum humidity ratio is 9 and the median is 5. The characteristic fluctuations corresponding to X-axis values of 1, 9 and 5 are obtained, and the MAXF and CENF values are 1.82 and 1.31, respectively.
[0071] Step S406: When analyzing any roughness group, name it the roughness analysis group, and name the sample group to which the roughness analysis group belongs the sample analysis group. Obtain the sample features detected by the sample analysis group in each roughness of the roughness analysis group, define them as roughness analysis features, and sort the roughness in ascending order, denoted by the symbol R. m At the same time, R m The corresponding coarse analysis feature is represented by the symbol RF. m , where m is a positive integer and m is a subscript;
[0072] Step S407: Define the minimum roughness as the minimum roughness. R1 for each roughness group within each sample group remains at the minimum roughness. Calculate R... m / R1, get H n The roughness ratio is represented by the symbol RP. m Simultaneously calculate RF m / RF1, get R m The characteristic fluctuation is represented by the symbol GF. m ;
[0073] Step S408: Analyze the functional relationship between the roughness ratio and the characteristic fluctuation, which is defined as the roughness influence curve. Obtain the maximum value of the roughness ratio and the characteristic fluctuation at the median value on the roughness influence curve, which are represented by the symbols MAXG and CENG, respectively.
[0074] In practice, the process of analyzing MAXG and CENG is the same as that of analyzing MAXF and CENF, and will not be explained further here.
[0075] Step S409, Calculate The weighting coefficients of the roughness ratio are obtained, and α is the weighting coefficient. Calculation The parameters showing the influence of surface roughness on ambient humidity can then be obtained;
[0076] In practice, because roughness and environmental humidity have different effects on the detected ferrite spectral characteristics, therefore... A weighting coefficient is added to the roughness ratio so that its calculated influence on ferrite spectral characteristics is consistent with that of ambient humidity. For example, the analysis yields MAXG and CENG values of 0.86 and 0.94 respectively, resulting in an calculated α of 5.51. The result, rounded to two decimal places, indicates that the influence of ambient humidity on ferrite spectral characteristics is 5.51 times that of roughness. Roughness is inversely proportional to ferrite spectral characteristics, while ambient humidity is directly proportional. Therefore, using... As an influencing parameter, for example, in a certain data point, the environmental ratio is 1.5 and the coarseness ratio is 2, corresponding to HP n RP is 1.5. n The value is 2, and the calculated influence parameter is 0.1361. The calculation result is rounded to four decimal places.
[0077] Step S5 involves calculating and calibrating the oxygen concentration of the NdFeB rapid quenching thin strip based on the standard curve and influencing parameters, and then analyzing the oxygen content distribution of the NdFeB rapid quenching thin strip. Step S5 includes the following sub-steps:
[0078] Step S501: Calculate the estimated oxygen concentration of the NdFeB rapid quenching thin strip according to the standard curve, and calibrate the estimated oxygen concentration according to the influencing parameters.
[0079] Step S501 includes the following sub-steps:
[0080] Please see Figure 5 As shown, in step S5011, the functional relationship between the influencing parameters and sample characteristics is analyzed and defined as the fluctuation calibration curve;
[0081] Step S5012: Obtain the standard curve, define the coordinate points next to the standard curve as concentration analysis points, obtain the concentration analysis point corresponding to the minimum influence parameter and define it as the minimum offset point, and obtain the concentration analysis point corresponding to the maximum influence parameter and define it as the maximum offset point.
[0082] In practice, analyzing the fluctuation calibration curve involves constructing a coordinate system using the influencing parameters and sample characteristics as the X and Y axes, respectively, and then fitting a function. The curve corresponding to the fitted function is the fluctuation calibration curve. Figure 5 As shown, Figure 5 The curve shown is the fluctuation calibration curve. Figure 2The coordinate points in the diagram are the concentration analysis points. In this embodiment, the minimum influence parameter is 0.0454. The concentration analysis point with an influence parameter of 0.0454 is obtained, which is the minimum offset point. Meanwhile, in this embodiment, the maximum influence parameter is 1.8993. The concentration analysis point with an influence parameter of 1.8993 is obtained, which is the maximum offset point.
[0083] Step S5013: The ferro-oxygen spectral characteristics of the NdFeB rapid quenching thin strip are calibrated based on the minimum and maximum offset points, and the predicted oxygen concentration is analyzed.
[0084] Step S5014: Obtain the ferro-oxygen spectral features corresponding to the minimum offset point and the maximum offset point, and define them as the minimum spectral feature and the maximum spectral feature, respectively. At the same time, obtain the coordinate points on the standard curve that have the same oxygen content as the minimum spectral feature or the maximum spectral feature, and define them as the standard points of the curve.
[0085] Step S5015: The minimum spectral feature is represented by the symbol MSF, the maximum spectral feature is represented by the symbol MSC, the ferrite spectral feature of the curve standard point corresponding to MSF or MSC is defined as the reference spectral feature, represented by the symbol RS, and the difference between RS-MSF and MSC-RS is calculated, which are defined as the minimum feature offset and the maximum feature offset, respectively.
[0086] Step S5016: Calculate the average value of the minimum feature offset, defined as the minimum mean offset, denoted by the symbol MIND; calculate the average value of the maximum feature offset, defined as the maximum mean offset, denoted by the symbol MAXD.
[0087] In specific implementation, steps S5014 to S5019 are detailed supplements to step S5013. For example, a certain minimum offset point is located at... Figure 2 The minimum spectral characteristic (MSF) was 0.059332, corresponding to an oxygen concentration of 0.05 wt%. Figure 2 The coordinate point on the standard curve where the sample oxygen concentration is 0.05 wt% is the standard point of the curve. The ferro-oxygen spectral feature (sample feature) corresponding to the standard point of the curve is obtained as 0.072846, which is the reference spectral feature RS. The minimum feature offset is calculated to be 0.013514. Similarly, the maximum feature offset is calculated. Then, the minimum mean offset MIND is calculated to be 0.014279 and the maximum mean offset MAXD is calculated to be 0.018562. This means that when the influencing parameter is minimized, the detected ferro-oxygen spectral feature decreases by an average of 0.014279, and when the influencing parameter is maximized, the detected ferro-oxygen spectral feature increases by an average of 0.018562.
[0088] Step S5017: Obtain the ferrite spectral characteristics and influencing parameters obtained from actual detection, and define them as the spectral characteristics to be calculated and the parameters to be calculated, respectively. The spectral characteristics to be calculated are represented by the symbol SV.
[0089] Step S5018: Substitute the parameters to be calculated into the fluctuation calibration curve to obtain the fluctuation reference value, denoted as LB; obtain the sample characteristics corresponding to the minimum and maximum of the influencing parameters, denoted as MINQ and MAXQ respectively.
[0090] Step S5019, Solve the equation The calibration characteristics of the irradiation point are obtained, where K is the calibration characteristic. Substituting the calibration characteristics into the standard curve, the predicted oxygen concentration of the irradiation point is obtained.
[0091] In specific implementation, for example, in a certain actual test, the spectral feature SV and the parameter to be calculated were obtained as 0.092644 and 0.8688, respectively. The sample feature corresponding to the coordinate point on the fluctuation calibration curve where the influence parameter is 0.8688 was obtained, yielding a fluctuation reference value LB of 1.273865. Simultaneously, MINQ was obtained as 1.1342 and MAXQ as 1.8428. Further calculation yielded a calibration feature K of 0.100450. The calculation result was rounded to six decimal places. Then, the sample oxygen concentration corresponding to the coordinate point on the standard curve where the X-axis is 0.100450 was obtained, resulting in an estimated oxygen concentration of 0.0782 wt% at the irradiation point. In the equation for calculating the calibration feature, This represents the position of the current influencing parameter relative to its minimum and maximum values in the fluctuation calibration curve. The calibration feature K also varies between the minimum and maximum actual offsets, and its position relative to these values is approximately... Assuming that the actual detectable ferrite spectral feature after eliminating the influence of environmental humidity and roughness is K, K+MAXD and K-MIND represent the maximum and minimum shifts in the ferrite spectral feature caused by the affected parameters at K. During detection, the ferrite spectral feature will fluctuate between these values. The actual detected spectral feature SV is 0.092644, representing that K is based on... The proportional shift changed to 0.092644, therefore a relationship exists.
[0092]
[0093] Solving for K yields the actual ferrite spectral characteristics that should be detected after removing the influence of environmental humidity and roughness. Then, the predicted oxygen concentration is obtained based on the actual ferrite spectral characteristics.
[0094] Step S502: Distributed detection is performed on the surface of the NdFeB rapid quenching thin strip to obtain the distribution state of oxygen content in the NdFeB rapid quenching thin strip;
[0095] Step S502 includes the following sub-steps:
[0096] Step S5021: Set an irradiation point at a preset length interval on the surface of the NdFeB rapid quenching thin strip, analyze the estimated oxygen concentration at each irradiation point, and obtain the distribution state of oxygen content.
[0097] Step S5022: The estimated oxygen concentration of adjacent irradiation points is regarded as a uniform gradient change, and the distribution of oxygen content in the NdFeB rapid quenching thin strip is visualized by gradient color.
[0098] In practice, since the NdFeB rapid quenching thin strip needs to be crushed during subsequent processing, minimally invasive testing can be performed on its surface at will. An array of irradiation points can be set up to sequentially test the surface of the NdFeB rapid quenching thin strip, thereby obtaining the oxygen content at different points on the surface of the NdFeB rapid quenching thin strip. The undetected portion between adjacent irradiation points is considered to have an equal gradient change. For example, if the estimated oxygen concentrations of two adjacent irradiation points are 0.0867wt% and 0.0842wt% respectively, then the oxygen content on the surface between them is considered to have a linear change from 0.0867wt% to 0.0842wt% based on the distance between them. The gradient visualization is similar to the gradient color in a thermographic image, representing a gradient change in temperature. This embodiment will not be described in detail.
[0099] This application also provides an electronic device, which may include one or more processors, memory and communication interfaces.
[0100] Memory, communication interfaces, and processor are coupled together. For example, memory, communication interfaces, and processor can be coupled together via a bus.
[0101] The communication interface is used for data transmission with other devices. Computer program code is stored in the memory. The computer program code includes computer instructions, which, when executed by a processor, cause the electronic device to perform the relevant method steps described in this application.
[0102] A processor can be a processor or controller, such as a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with this disclosure. A processor can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.
[0103] The bus can be a peripheral component interconnect (PCI) bus, an extended industry standard architecture (EISA) bus, etc. These buses can be categorized as address buses, data buses, control buses, etc.
[0104] This application also provides a server, which includes a processor, a memory, and a communication interface; the memory is used to store a computer-executable program; when the computer-executable program is invoked by the processor, the processor is used to execute the relevant method steps in the above method embodiments through the communication interface.
[0105] This application also provides a communication device, which includes a memory and one or more processors; the memory is coupled to the processors; wherein the memory stores computer program code, which includes computer instructions, and when the computer instructions are executed by the processor, the communication device performs the relevant method steps in the above method embodiments.
[0106] This application also provides a computer-readable storage medium storing computer program code. When the processor executes the computer program code, the electronic device executes the relevant method steps in the above method embodiments.
[0107] This application also provides a computer program product containing instructions that, when executed on a computer or processor, cause the computer or processor to perform the relevant method steps as described in the above method embodiments.
[0108] The electronic devices, computer storage media, or computer program products provided in this application are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods provided above, and will not be repeated here.
[0109] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.
[0110] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
Claims
1. A method for detecting the oxygen content distribution in rapidly quenched NdFeB thin strips, characterized in that, include: A plasma is obtained by focusing a pulsed laser and then using the focused laser to momentarily irradiate the surface of a rapidly quenched NdFeB strip. The plasma was subjected to spectral analysis to obtain the oxygen and iron spectral intensities, and the ferro-oxygen spectral characteristics were calculated based on the oxygen and iron spectral intensities. Experimental samples were prepared, the ferro-oxygen spectral characteristics of the experimental samples were analyzed, and the standard curve of ferro-oxygen spectral characteristics versus oxygen content was further analyzed. Before laser irradiation, the roughness of the irradiation point and the indoor ambient humidity are detected and the influencing parameters are calculated. The oxygen concentration of NdFeB rapid quenching thin strips was calculated and calibrated based on the standard curve and influencing parameters, and then the distribution of oxygen content in the NdFeB rapid quenching thin strips was analyzed.
2. The method according to claim 1, characterized in that, The step of focusing a pulsed laser and instantaneously irradiating the surface of a neodymium iron boron rapid-quenched thin strip with the focused laser to obtain plasma further includes the following sub-steps: A focusing lens is used to focus the pulsed laser emitted by a pulsed laser into a focused laser. The surface of the neodymium iron boron rapid quenching thin strip is irradiated momentarily with a focused laser. After irradiation, the pulsed laser is turned off, and plasma will be generated.
3. The method according to claim 2, characterized in that, The step of performing spectral analysis on the plasma and obtaining the oxygen and iron spectral intensities, and calculating the ferro-oxygen spectral characteristics based on the oxygen and iron spectral intensities, further includes the following sub-steps: The plasma was analyzed using spectral analysis techniques to extract the oxygen and iron spectral intensities, which were denoted as OS and IS, respectively. Calculate OS / IS, and the result is called the ferrite spectral characteristic and denoted as OIF.
4. The method according to claim 3, characterized in that, The steps of preparing experimental samples, analyzing the ferro-oxygen spectral characteristics of the experimental samples, and further analyzing the standard curve of ferro-oxygen spectral characteristics versus oxygen content also include the following sub-steps: Prepare experimental samples with known oxygen content and constant oxygen content gradient, analyze the ferro-oxygen spectral characteristics of the experimental samples, and define them as sample characteristics; The oxygen content of the experimental sample is obtained and defined as the sample oxygen concentration. The functional relationship between the sample characteristics and the sample oxygen concentration is analyzed to obtain a standard curve.
5. The method according to claim 4, characterized in that, The step of detecting the roughness of the irradiation point and the indoor ambient humidity and calculating the influencing parameters before laser irradiation also includes the following sub-steps: Before detecting the characteristics of the sample, the roughness of the irradiation point and the indoor ambient humidity are detected simultaneously. The roughness and ambient humidity are grouped according to different sample oxygen concentrations, which is called sample grouping. The ambient humidity is grouped according to different roughness, which is called humidity grouping. The roughness is grouped according to different ambient humidity, which is called roughness grouping. The humidity influence curve and the roughness influence curve are analyzed based on humidity grouping and roughness grouping, and the influence parameters are further calculated.
6. The method according to claim 5, characterized in that, The step of calculating and calibrating the oxygen concentration of the NdFeB rapid quenching thin strip based on the standard curve and influence parameters, and then analyzing the distribution of oxygen content in the NdFeB rapid quenching thin strip, further includes the following sub-steps: The estimated oxygen concentration of NdFeB rapid quenching thin strip was calculated based on the standard curve, and the estimated oxygen concentration was calibrated based on the influencing parameters. Distributed detection was performed on the surface of NdFeB rapid quenching thin strips to obtain the distribution of oxygen content in the NdFeB rapid quenching thin strips.
7. The method according to claim 6, characterized in that, The step of calculating the estimated oxygen concentration of NdFeB rapid quenching thin strip based on the standard curve and calibrating the estimated oxygen concentration based on the influencing parameters further includes the following sub-steps: The functional relationship between the influencing parameters and sample characteristics is analyzed and defined as the fluctuation calibration curve; Obtain the standard curve, define the coordinate points next to the standard curve as concentration analysis points, obtain the concentration analysis point corresponding to the minimum influence parameter and define it as the minimum offset point, obtain the concentration analysis point corresponding to the maximum influence parameter and define it as the maximum offset point; The ferrooxy spectral characteristics of NdFeB rapid quenching thin strips were calibrated based on the minimum and maximum offset points, and the predicted oxygen concentration was analyzed.
8. The method according to claim 7, characterized in that, The step of performing distributed detection on the surface of the NdFeB rapid quenching thin strip to obtain the oxygen content distribution state of the NdFeB rapid quenching thin strip further includes the following sub-steps: An irradiation point is set at a preset length interval on the surface of the NdFeB rapid quenching thin strip, and the estimated oxygen concentration at each irradiation point is analyzed to obtain the distribution of oxygen content. The estimated oxygen concentration at adjacent irradiation points is considered to change at a constant gradient, and the distribution of oxygen content in the NdFeB rapid quenching strip is visualized by using gradient colors.
9. An electronic device, characterized in that, The electronic device includes a memory and one or more processors; the memory is coupled to the processors; wherein the memory stores computer program code, the computer program code including computer instructions, which, when executed by the processor, cause the electronic device to perform the steps of the method as described in any one of claims 1-8.
10. A storage medium storing computer instructions thereon, characterized in that, When the computer instructions are executed by the processor, the steps of the method as described in any one of claims 1-8 are performed.