Prediction system for predicting maximum size of DS inclusions in steel through multiple metallographic method

Abstract

The invention provides a prediction system for predicting the maximum size of DS inclusions in steel through a multiple metallographic method, and the system comprises a data input unit which is used for taking the size data of the DS inclusions obtained through a metallographic inspection method after a sample is polished and inspected as input data; the data verification unit is used for carrying out quantity statistics on the input data and verifying discrete abnormal data in the input data; the data sorting unit is used for sorting the size data of the inclusions in an ascending order, and the cumulative probability calculation unit is used for detecting the occurrence probability of the size data of the inclusions; the distribution function calculation unit is used for speculating the maximum value of the probability of the inclusion size measurement group; the maximum length prediction unit is used for calculating the average value of the lengths of the inclusions under different probabilities and predicting the maximum size of the inclusions; and the visual statistical unit is used for drawing an inclusion maximum length analysis chart and a distribution chart.

Description

Technical Field

[0001] The present invention relates to the technical field of iron and steel metallurgy, and particularly to a prediction system for predicting the maximum size of DS inclusions in steel by the multiple metallographic method. Background Art

[0002] With the improvement of domestic smelting process level, the metallurgical quality level of bearing steel has been further improved. It is more difficult to detect large-size inclusions in steel by traditional rating methods. When evaluating inclusions, the inclusion level data obtained from each factory or each furnace tend to be the same, and it is impossible to further evaluate the control degree of inclusions in each furnace.

[0003] At present, the traditional inclusion rating method is mainly used in the field of iron and steel metallurgy to evaluate inclusions in steel, such as metallographic method, non-destructive testing methods (ultrasonic testing and X-ray flaw detection, etc.), inclusion aggregation detection methods (cold crucible remelting and electron beam remelting, etc.) and fatigue methods. The metallographic method and the inclusion aggregation method can only detect small-volume specimens and are rated according to standards such as GB / T18254-2016 and GB / T10561. Due to the small number of samples, it is difficult to detect large-size inclusions in steel, and it is impossible to evaluate the quality of steel only through partial inspection data for large-volume steel parts; non-destructive testing methods can detect large-volume steel parts, but it is difficult to detect inclusions with a size less than 100μm (the size of inclusions in steel is generally less than 100μm). The fatigue method determines the inclusion size from the fatigue fracture surface, which is accurate but time-consuming and costly. The grade and the length of non-metallic inclusions increase exponentially and are continuous data, while the detected non-metallic inclusions are highly accidental and the data is discrete. In actual inspection and analysis, it is impossible to inspect all steel materials to obtain detailed inclusion information, and it is difficult to detect the maximum size inclusions in steel by special methods. Therefore, it is necessary to evaluate the maximum non-metallic inclusions in steel by a specific method and deduce unknown data from known data. Summary of the Invention

[0004] In view of the above-mentioned technical problems, a prediction system for predicting the maximum size of DS inclusions in steel by the multiple metallographic method is provided. The present invention mainly uses a data input unit, a data verification unit, a data sorting unit, a cumulative probability calculation unit, a distribution function calculation unit, a maximum length prediction unit and a visualization statistics unit to realize the prediction of the maximum size inclusions in metal.

[0005] The technical means adopted by the present invention are as follows:

[0006] A prediction system for predicting the maximum size of DS inclusions in steel by the multiple metallographic method, comprising: a data input unit, a data verification unit, a data sorting unit, a cumulative probability calculation unit, a distribution function calculation unit, a maximum length prediction unit and a visualization statistics unit, wherein:

[0007] The data input unit is used to take the size data of DS-type inclusions obtained by the metallographic inspection method after the specimen is polished and inspected as input data and input it into the prediction system;

[0008] The data verification unit is used to perform a quantity statistics on the input data in the data input unit, calculate the maximum value, minimum value, average value, and standard deviation in the input data, and verify the discrete abnormal data in the input data;

[0009] The data sorting unit is used to sort the verified inclusion size data in ascending order and store it in the sorting area;

[0010] The cumulative probability calculation unit is used to check the occurrence probability of the inclusion size data;

[0011] The distribution function calculation unit is used to calculate the maximum distribution parameters δ mom and λ mom and speculate to obtain the maximum value of the probability of the inclusion size measurement group;

[0012] The maximum length prediction unit is used to calculate the average value of the inclusion length under different probabilities according to the speculation result of the distribution function calculation unit, determine the 95% confidence interval of each data point, and predict the maximum size of the inclusions;

[0013] The visualization statistics unit is used to plot the inclusion maximum length analysis chart and distribution chart for the data calculated and predicted in the maximum length prediction unit.

[0014] Further, the data input unit randomly cuts an inclusion specimen at the position of half of the radius of the rolled material, performs heat treatment on the specimen, then performs annealing, quenching, and low-temperature tempering, checks multiple metallographic polished surfaces for each specimen, and records the inclusions with the maximum size for each specimen.

[0015] Further, the standard limit value in the data verification unit uses the Grubbs test method to take the critical value k in the embedded database, performs data verification according to the amount of test data. If both the maximum limit value and the minimum limit value are less than the standard limit value, there is no discrete abnormal data in the test data value.

[0016] Further, use the cumulative probability calculation unit to calculate the cumulative probability:

[0017]

[0018] Among them, P i represents the cumulative probability, F(y i ) and each data point X iCorrespond to each other; N represents the number of inclusions inspected, and the reduced variable Red.Var = -ln(-lnP).

[0019] Furthermore, the distribution function calculation unit uses the average inclusion length value and the standard deviation calculated in the data verification unit to calculate the maximum distribution parameters δmom and λmom of the maximum value as the initial calculation values, and uses the planning summation function SOLVER to calculate the positioning parameter δ ML and the scale parameter λ ML .

[0020] Furthermore, in the maximum length prediction unit:

[0021] Calculate each data point on the average line:

[0022] x i = δ ML (Red.Var) + λ ML = δ ML yi + λ ML

[0023] where x i represents the length of each inclusion detection; δ ML is the positioning parameter, λ ML is the scale parameter, and Red.Var is the reduced variable;

[0024] Determine the 95% confidence interval points of each data point:

[0025]

[0026] 95% CL = ±2SE(x)

[0027]

[0028] where x low represents the predicted minimum inclusion length, and x high represents the predicted maximum inclusion length, and the maximum distribution state of the inclusions is obtained. Calculate the predicted maximum size of the inclusions under different cumulative probabilities.

[0029] Compared with the prior art, the present invention has the following advantages:

[0030] The prediction system for predicting the maximum size of DS inclusions in steel by the multiple metallographic method provided by the present invention uses a data input unit, a data verification unit, a data sorting unit, a cumulative probability calculation unit, a distribution function calculation unit, a maximum length prediction unit, and a visualization statistics unit to realize the prediction of the maximum size inclusions in metals, and intuitively displays the prediction results with analysis diagrams and distribution diagrams.

[0031] Based on the above reasons, the present invention can be widely promoted in the field of iron and steel metallurgy technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0033] Figure 1 It is a framework diagram of a prediction system for predicting the maximum size of DS inclusions in steel by the multiple metallographic method in the present invention.

[0034] Figure 2 It is an analysis diagram of the maximum inclusion length in the embodiment of the present invention.

[0035] Figure 3 It is a distribution diagram of the maximum inclusion length in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0036] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. The following will describe the present invention in detail with reference to the drawings and in combination with the embodiments.

[0037] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, rather than all, of the embodiments of the present invention. The following description of at least one exemplary embodiment is actually only illustrative and in no way limits the present invention and its application or use. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0038] It should be noted that the terms used here are only for describing the specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or their combinations.

[0039] Unless otherwise specifically stated, the relative arrangements of components and steps, numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present invention. At the same time, it should be clear that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn in accordance with actual proportional relationships. Technologies, methods, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, such technologies, methods, and devices should be regarded as part of the authorized specification. In all the examples shown and discussed here, any specific values should be construed as merely exemplary and not as a limitation. Therefore, other examples of the exemplary embodiments may have different values. It should be noted that like reference numerals and letters denote like items in the following drawings, and thus, once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0040] In the description of the present invention, it should be understood that the orientation or positional relationships indicated by orientation words such as "front, rear, upper, lower, left, right", "lateral, vertical, perpendicular, horizontal", and "top, bottom", etc. are generally based on the orientation or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description. Without contrary instructions, these orientation words do not indicate and imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and thus cannot be construed as limiting the protection scope of the present invention: the orientation words "inner, outer" refer to the inside and outside relative to the contour of each component itself.

[0041] For ease of description, spatial relative terms such as "above", "over", "on the upper surface", "above-mentioned", etc. may be used here to describe the spatial positional relationship of one device or feature shown in the drawings with other devices or features. It should be understood that the spatial relative terms are intended to encompass different orientations in use or operation in addition to the orientation described in the drawings for the device. For example, if the device in the drawing is inverted, the device described as "above other devices or structures" or "over other devices or structures" will then be positioned "below other devices or structures" or "under other devices or structures". Thus, the exemplary term "above" can include both the orientations of "above" and "below". The device can also be positioned in other different ways (rotated 90 degrees or in other orientations), and corresponding interpretations should be made for the spatial relative descriptions used here.

[0042] In addition, it should be noted that the use of words such as "first", "second", etc. to limit components is only for the convenience of distinguishing the corresponding components. Without additional statements, the above words have no special meanings, and thus cannot be construed as limiting the protection scope of the present invention.

[0043] As Figure 1As shown in the figure, the present invention provides a prediction system for predicting the maximum size of DS inclusions in steel by the multiple metallographic method, including: a data input unit, a data verification unit, a data sorting unit, a cumulative probability calculation unit, a distribution function calculation unit, a maximum length prediction unit, and a visualization and statistics unit, where:

[0044] The data input unit is used to take the size data of DS inclusions obtained by the metallographic inspection method after the specimen is polished and inspected as input data and input it into the prediction system;

[0045] The data verification unit is used to count the quantity of the input data in the data input unit, and calculate the maximum value, minimum value, average value, and standard deviation in the input data, and verify the discrete abnormal data in the input data;

[0046] The data sorting unit is used to sort the verified inclusion size data in ascending order and store it in the sorting area;

[0047] The cumulative probability calculation unit is used to check the occurrence probability of the inclusion size data;

[0048] The distribution function calculation unit is used to calculate the maximum value distribution parameters δ mom and λ mom using the average inclusion length value and the standard deviation, and infer the maximum value of the probability of the inclusion size measurement group;

[0049] The maximum length prediction unit is used to calculate the average value of the inclusion length under different probabilities according to the inference result of the distribution function calculation unit, and determine the 95% confidence interval of each data point to predict the maximum size of the inclusion;

[0050] The visualization and statistics unit is used to draw an inclusion maximum length analysis chart and a distribution chart for the data calculated and predicted in the maximum length prediction unit.

[0051] In specific implementation, as a preferred implementation manner of the present invention, the data input unit randomly cuts an inclusion specimen at a position of half of the radius of the rolled material. After heat treatment of the specimen, annealing, quenching, and low-temperature tempering are carried out. Multiple metallographic polished surfaces of each specimen are inspected, and the maximum size inclusions of each specimen are recorded.

[0052] In specific implementation, as a preferred implementation manner of the present invention, the standard limit value in the data verification unit uses the Grubbs test method to take the critical value k in the embedded database, and data verification is carried out according to the quantity of the test data. If both the maximum limit value and the minimum limit value are less than the standard limit value, there is no discrete abnormal data in the test data value.

[0053] In specific implementation, as a preferred implementation manner of the present invention, the cumulative probability is calculated using the cumulative probability calculation unit:

[0054]

[0055] Among them, P i represents the cumulative probability, and F(y i ) corresponds to each data point X i one by one; N represents the number of inclusion inspections, and the reduction variable Red.Var = -ln(-lnP).

[0056] In specific implementation, as a preferred implementation manner of the present invention, the distribution function calculation unit calculates the maximum distribution parameters δmom and λmom of the average inclusion length value and the standard deviation calculated in the data verification unit as the initial calculation values, and uses the planning summation function SOLVER to calculate the positioning parameter δ ML and the scale parameter λ ML .

[0057] In specific implementation, as a preferred implementation manner of the present invention, in the maximum length prediction unit:

[0058] Calculate each data point on the average line:

[0059] x i = δ ML (Red.Var)+ λ ML = δ ML yi + λ ML

[0060] Among them, x i represents the length of each inclusion detection; δ ML is the positioning parameter, λ ML is the scale parameter, and Red.Var is the reduction variable;

[0061] Measure the 95% confidence interval points of each data point:

[0062]

[0063] 95% CL = ±2SE(x)

[0064]

[0065] Among them, x low represents the predicted minimum inclusion length, x high represents the predicted maximum inclusion length, and the maximum distribution state of the inclusions is obtained. Calculate the predicted maximum size of the inclusions under different cumulative probabilities.

[0066] Embodiment

[0067] Such as Figure 1As shown in the figure, the present invention provides a prediction system for predicting the maximum size of DS inclusions in steel by the multiple metallographic method. Inclusion specimens are randomly cut at the position of half of the radius of the rolled material. The size of the specimen is 10mm×20mm (rolling direction), and the inspected area is 200mm 2 , and the specimens are heat-treated, annealed, quenched, and tempered at low temperature in accordance with the provisions of GB / T 18254-2016. Quenching heating temperature: 820°C to 840°C. Quenching heating time: Insulate for 1.5 minutes per 1mm of the specimen diameter. Coolant: Oil cooling. Tempering temperature: 150°C ± 10°C. Tempering time: 1 hour to 2 hours.

[0068] The metallographic polished surface is parallel to the rolling direction. After each specimen polished surface is inspected, 0.3 - 0.5mm is ground off, then ground and polished for inspection. Each specimen is inspected on 3 - 4 polished surfaces in total, and the inclusion with the largest size in each specimen is found. A total of 18 or 24 data are input into the data input unit. The specific data are shown in Table 1.

[0069] Input data in the data input unit in Table 1

[0070]

[0071] The verification data in the data verification unit are shown in Table 2:

[0072] Verification data in the data verification unit in Table 2

[0073]

[0074]

[0075] The sorting results in the data sorting unit are shown in Table 3:

[0076] Sorting results in the data sorting unit in Table 3

[0077]

[0078] The calculation results in the cumulative probability calculation unit are shown in Table 4:

[0079] Calculation results of the cumulative probability calculation unit in Table 4

[0080]

[0081]

[0082] The calculation results of the distribution function calculation unit are shown in Table 5:

[0083] Calculation results of the distribution function calculation unit in Table 5

[0084]

[0085] The calculation results of the maximum length prediction unit are shown in Table 6 as follows:

[0086]

[0087]

[0088] The maximum size of the inclusions finally predicted is shown in Table 7 as follows:

[0089] Table 7 The maximum size of the predicted inclusions

[0090]

[0091] As Figure 2 and Figure 3 shown, the visualization statistical unit visually displays the maximum length of the predicted inclusions with analysis diagrams and distribution diagrams.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A prediction system for predicting the maximum size of DS inclusions in steel using multiple metallographic methods, characterized in that: include: Data input unit, data verification unit, data sorting unit, cumulative probability calculation unit, distribution function calculation unit, maximum length prediction unit and visualization statistics unit, wherein: The data input unit is used to input the size data of DS inclusions obtained by metallographic inspection after the sample is polished and inspected as input data into the prediction system; The data verification unit is used to perform quantitative statistics on the input data in the data input unit, and calculate the maximum value, minimum value, average value, and standard deviation of the input data, and verify discrete abnormal data in the input data; The data sorting unit is used to sort the verified inclusion size data in ascending order and store them in the sorting area; The cumulative probability calculation unit is used to check the occurrence probability of inclusion size data; The distribution function calculation unit is used to calculate the maximum value distribution parameter δ using the average length value and standard deviation of the inclusions. mom and λ mom , it is inferred that the maximum value of the probability of obtaining the inclusion size measurement group; The maximum length prediction unit is used to calculate the average value of the inclusion length under different probabilities according to the inference result of the distribution function calculation unit, and to determine the 95% confidence interval of each data point to predict the maximum size of the inclusion; The visualization statistics unit is used to draw an inclusion maximum length analysis diagram and a distribution diagram based on the data calculated and predicted in the maximum length prediction unit.

2. The prediction system for predicting the maximum size of DS inclusions in steel by multiple metallographic method according to claim 1 is characterized in that: The data input unit randomly cuts inclusion samples at a position half the radius of the rolled material, performs heat treatment on the samples, and then performs annealing, quenching and low-temperature tempering. Each sample is inspected for multiple metallographic polishing surfaces, and the maximum size of inclusions in each sample is recorded.

3. The prediction system for predicting the maximum size of DS inclusions in steel by multiple metallographic method according to claim 1, characterized in that: The standard limit value in the data verification unit uses the Grubbs test method to take the critical value k in the embedded database, and performs data verification according to the amount of test data. If the maximum limit value and the minimum limit value are both smaller than the standard limit value, there is no discrete abnormal data in the test data value.

4. The prediction system for predicting the maximum size of DS inclusions in steel by multiple metallographic method according to claim 1, characterized in that: The cumulative probability is calculated using the cumulative probability calculation unit: Among them, P i represents the cumulative probability, F(y i ) and each data point X i Corresponding to each other; N represents the number of inclusion inspections, reducing the variable Red.Var = -ln(-lnP).

5. The prediction system for predicting the maximum size of DS inclusions in steel by multiple metallographic method according to claim 1, characterized in that: The distribution function calculation unit calculates the maximum distribution parameters δmom and λmom calculated by the average length value and standard deviation of the inclusions calculated in the data verification unit as initial calculation values, and calculates the positioning parameter δmom by using the planning summation function SOLVER. ML and scale parameter λ ML .

6. The prediction system for predicting the maximum size of DS inclusions in steel by multiple metallographic method according to claim 1, characterized in that: In the maximum length prediction unit: Calculate the individual data points on the average line: x i =d ML (Red.Var)+λ ML =d ML yi+l ML Among them, x i Indicates the length of each inclusion detection; δ ML is the positioning parameter, λ ML is the scale parameter, Red.Var is the reduced variable; Determine the 95% confidence interval points for each data point: 95% CL = ± 2 SE (x) Among them, x low represents the predicted minimum inclusion length, x high It indicates the predicted maximum inclusion length and obtains the maximum distribution state of inclusions; it calculates the predicted maximum size of inclusions under different cumulative probabilities.

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