JP3510437B2 - Evaluation method for thin steel sheet products - Google Patents

Description

Description: BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a technical field of manufacturing a thin steel sheet product, and an object of the present invention is to prevent a defect caused by inclusions in the product. In addition to efficiently predicting the occurrence of defects in products by evaluating non-metallic inclusions in samples taken from product plates, and by changing the shipping schedule of products predicted to be insufficient in quality, And a method for preventing the occurrence of defects. [0002] Non-metallic inclusions contained in steel cause quality defects such as surface defects in products or crack defects during processing. Therefore, the reduction technology is important for smelting technology. In the steelmaking process, considerable costs are being spent on measures to reduce inclusions. The demands for workability and surface properties required for recent steel sheet products have become increasingly severe.
Decreasing the inclusions in steel according to the demands imposes a great burden on manufacturing costs. [0003] In the case of manufacturing a strict product with an intermediate product,
In order to prevent users from having inclusion defects,
It is necessary to detect defects (inclusions) in the steel in the state of the rolled thin steel sheet, and to judge whether or not it can be shipped as strict quality material based on the amount of detected defects. More specifically, a method of inspecting the amount of defects in a thin steel sheet by means such as a magnetic particle flaw detection method, an ultrasonic flaw detection method, a leakage magnetic flux type defect detection method, and the like to judge whether or not shipping is possible is generally performed. I have. However, in these methods, not only nonmetallic inclusions in steel, but also surface defects and flaws such as roll flaws and linear flaws are detected in substantially the same manner, and the detection signal causes a discrepancy between them and inclusions. No distinction is practically possible. [0005] For this reason, the evaluation by these flaw detection methods has a large detection error in some cases, and there is a problem in performing accurate defect occurrence prediction. This is because in these methods
The problem is that there is no way to clearly distinguish inclusions from other defects. As a method for directly evaluating inclusions in a product plate, T [O] analysis, EB method and the like are well known.
Since all of these evaluation methods evaluate the total amount of relatively small inclusions, it is possible to evaluate the cleanliness of steel, which is the root cause of defects, but the inclusions having relatively large particle diameters can be evaluated. At present, the correspondence with the actual occurrence of defects in products is not always high. As a method for evaluating inclusions on a product plate,
As a method for evaluating inclusions having a large particle size, in the case of bearing steel, JP-A-5-25587 and JP-A-6-20
As disclosed in Japanese Unexamined Patent Publication No. 73-73, there is known a method for evaluating the maximum diameter of inclusions in a material because the maximum diameter of inclusions contained in steel defines the life. However, in the case of a thin steel sheet, the mechanism of fracture is different from that of fatigue failure, and not only inclusions having the largest grain size cause defects, and the same handling as bearing steel cannot be performed. [0009] As described above, it is considered that the source of inclusion defect in a thin steel product is a relatively large inclusion. It is an object of the present invention to provide an index for selectively and efficiently evaluating a large large inclusion. [0010] In order to solve the above-mentioned problems, the gist of the present invention is to evaluate the inclusions in a thin steel sheet product and to determine the defects caused by non-metallic inclusions in the product. In the method of predicting the occurrence of pitting, based on the correspondence between the maximum particle size of inclusions and the quality of the product specified in advance for each steel type, the statistics of the inclusion size of a small sample are used to easily calculate extreme value statistics. Thin steel sheet characterized by estimating the maximum grain size of inclusions contained in steel, and predicting the frequency of detection of product defects based on the estimated value and the correspondence between the maximum grain size of inclusions and product quality described above. This is the product evaluation method. DETAILED DESCRIPTION OF THE INVENTION The present invention will be described below in detail. The present inventors have evaluated various inclusions in steel to establish an inclusion evaluation index in steel that can accurately predict the occurrence of internal defects due to inclusions in a product sheet of a thin steel sheet. The evaluation of inclusion-induced defects was repeated. When the frequency of occurrence of defects in the product plate is organized by the conventionally used molten steel cleanliness evaluation index, the dispersion in the low cleanness area is particularly large, and the correlation between cleanliness and defect occurrence is not necessarily not high. Generally speaking, minute inclusions of a few μm alone existing in a steel sheet rarely directly cause defects. When the particle size distribution of the inclusions in the steel sheet is examined in detail, the particle size distribution of the whole inclusions, as shown in FIG. . For this reason, the ratio of large inclusions to the total amount of inclusions is low, and the size of the inclusions does not directly reflect the size of large inclusions that cause defects even when the total amount of inclusions is evaluated. It is for the above-mentioned reason that the cleanliness evaluation index, which has conventionally been performed with an emphasis on the evaluation of the total amount of inclusions, has poor correspondence with product defects. However, in many cases, when the amount of inclusions in steel is large (even if it is mainly composed of minute inclusions),
Due to the mutual coalescence of these inclusions, there is a certain size of inclusions with a large particle size.Therefore, when comparing the inclusions with a large amount of inclusions with those with a small amount of inclusions, the incidence of defects is large for those with a large amount of inclusions. It becomes. However, in a situation where the total amount of inclusions in the steel has been remarkably reduced as in recent years, and as a result, the incidence of defects caused by inclusions has become extremely small, the total amount of inclusions to be evaluated originally is small. An evaluation technique is needed to directly evaluate the amount of large inclusions in a small number. It is generally practiced to estimate the size of inclusions that caused a defect from the size of the inclusion defect detected in the product. As a result of investigating the correspondence between inclusions in steel and product defects,
In addition to confirming that most of the large inclusions having a particle size of μm or more occupy, as shown in FIG. 2, the size of the largest defect found in the product (the maximum particle size of the inclusion) and the defect of the product And the number of detected particles (the number of inclusions having a size of 100 μm or more). As described above, the defects caused by inclusions in a thin steel sheet product are not caused by inclusions having the maximum grain size. Can cause. For this reason, in order to predict the defects caused by inclusions, it is strictly necessary to evaluate the number of all inclusions exceeding the critical grain size. Instead of evaluating the number of particle inclusions, if the particle size of the largest inclusion is known, information corresponding to the number of large particle inclusions can be obtained, and the frequency of occurrence of defects can be predicted. As a method for evaluating the maximum particle size of inclusions in steel, a method of actually measuring the particle size of inclusions in steel and evaluating the maximum diameter can be considered, but it is desired to evaluate it as a practical problem. It is impossible to measure the particle size of all inclusions in steel. Therefore, as a method of estimating the maximum diameter of the inclusions present in the steel, the maximum inclusion diameter in a limited amount of sample extracted from the steel is evaluated to determine the maximum diameter of the inclusions in the steel. The application of the extreme value statistical method to estimate the maximum particle size of inclusions was investigated. The method of measuring the maximum inclusion diameter in steel by the extreme value statistics is described in “Papers A of the Japan Society of Mechanical Engineers, 55 (1989) 509,
p. As shown in "58", the maximum particle size of inclusions observed in a plurality of samples taken from steel material is measured, and the maximum particle size of inclusions contained in the base steel material is estimated therefrom. It is. By using this method, it is possible to estimate the maximum value of the characteristic value in the population without necessarily investigating all evaluation targets. As a result of the study by the inventors, even when the result of estimating the maximum particle size of inclusions using such an extreme value statistical method is adopted, there is a correlation between the estimated maximum particle size of inclusions and product quality. Confirmed that there is a relationship. As a method of evaluating the particle size of each inclusion, a method of adopting the maximum particle size when viewed in a specific direction in the observation visual field, the maximum particle size of the inclusion regardless of the direction Various methods are conceivable, such as a method of adopting a diameter, a method of evaluating the area of inclusions, and a method of evaluating the diameter of inclusions as the diameter of a circle having the same area. The absolute value of the obtained inclusion particle size changes depending on which method is used as the particle size evaluation method.
If the evaluation method is unified to any of the methods, since the relative relationship between the particle sizes does not change much, the correlation between the maximum particle size of the target inclusion and the number of large particle inclusions is maintained. In addition, the purpose of using the maximum grain size as an index for determining the frequency of occurrence of defects can be achieved. Incidentally, the correlation between the frequency of occurrence of defects and the amount of large-size inclusions in steel actually differs greatly depending on the usage of the steel material to be evaluated. For example, a material that is used as a flat plate with little processing cannot be a defect even if large-sized inclusions are present in steel unless it is exposed on the surface. On the other hand, in the case of a steel material which is subjected to a strong working such as a tensile working or an ironing work, 20 to 30
Defects such as breakage of the processed part of the steel sheet occur even with inclusions of μm. Therefore, it is natural that the correspondence between the maximum inclusion diameter and the quality defect as described above differs depending on the use of the steel material. Therefore, for these correspondences, judgment criteria should be created by accumulating data of the correspondences for each steel type. An embodiment of the present invention will be described. (Example 1) A sample of a tin plate product plate processed for a beverage can was sampled, and the particle size of inclusions in the sample was evaluated under a microscope. Here, the measurement was made not on the particle diameter of all the inclusions in the visual field but on the particle diameter of the largest inclusion observed in the visual field. The measurement magnification of the microscopic observation was 400 times, and the size of one visual field was 100 vertically and horizontally from the center of the visual field of the microscope.
40000 μm ² in μm. The measurement of 100 visual fields was taken as one set, and the particle diameter of the largest inclusion in the 100 visual fields was taken as the representative maximum diameter of the 100 visual fields. The same evaluation was repeated 40 times, and when 40 representative maximum diameters were obtained, these representative maximum diameters were plotted on extreme value statistical probability paper. The total measured area is 1
It was 60 mm ² . In evaluating the maximum diameter of the inclusions contained in the base material by the extreme value statistics, it was estimated as the maximum diameter of the inclusions expected to be present per 1 kg of the product. FIG. 3 shows the correspondence between the estimated maximum inclusion particle diameters of the materials of various production lots measured in this way and the internal defect occurrence rates of the lots. It can be seen that the defect occurrence rate rises sharply with an increase in the estimated maximum diameter. Using this, the maximum particle size of inclusions is determined from a sample collected from a product, and applied to a correlation diagram as shown in FIG. 3, it is possible to accurately predict the occurrence of defects. FIG. 4 shows the correspondence between the T [O] analysis value of the same sample and the internal defect occurrence rate. Although it seems that there is a large correlation as a whole, the variation is large, and it can be seen that the prediction accuracy of the defect occurrence based on the T [O] value is not high in estimation accuracy. (Example 2) A sample of a product plate of a tinplate for general processing was collected, and the particle size of inclusions in the sample was evaluated under a microscope. The evaluation method is the same as the method described in the first embodiment. Estimated maximum inclusion size for materials of various production lots,
FIG. 5 shows the correspondence between the lot and the internal defect occurrence rate. It can be seen that there is a critical inclusion maximum diameter at which the internal defect occurrence rate increases as in Example 1. However,
The absolute value of the critical maximum inclusion particle size is larger than that of Example 1. This is considered to be because the amount of processing is smaller and defects are less likely to occur in a general processing tin compared to the beverage can material shown in Example 1. As described above, according to the method of the present invention,
Based on the relationship between the maximum particle size of inclusions and product quality previously defined for each steel type, the maximum value of inclusions contained in steel can be easily determined by extreme value statistics from the results of measurement of the particle size of inclusions of a small sample of product. Since the particle size can be estimated, and the estimated value and the corresponding relationship between the above-described maximum inclusion particle size and product quality can be used to predict the product defect detection frequency of the product plate, the shipping schedule of the product plate can be estimated. It can be changed to an application that does not cause quality defects due to inclusions, and it is possible to prevent inclusion troubles at customers.

BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a diagram showing a particle size distribution of inclusions in steel. FIG. 2 is a diagram showing a relationship between a defect size and a detection frequency. FIG. 3 is an extreme value of a steel material subjected to heavy working. Diagram showing the relationship between the maximum inclusion particle size estimated by statistics and the internal defect occurrence rate. FIG. 4 shows the relationship between the T [O] analysis value and the internal defect occurrence rate. Diagram showing the relationship between the maximum inclusion particle size estimated by extreme value statistics and the internal defect occurrence rate

Claims (1)

(57) [Claims] [Claim 1] In a method of estimating inclusions in a thin steel sheet product and predicting the occurrence of defects due to non-metallic inclusions in the product, a method is specified in advance for each steel type. Based on the corresponding relationship between inclusion maximum particle size and product quality, the maximum particle size of inclusions contained in steel was easily estimated from extreme value statistics from the results of inclusion particle size measurement of a small sample, A method for evaluating a thin steel sheet product, comprising predicting a product defect detection frequency based on the estimated value and the correspondence between the above-described maximum inclusion particle size and product quality.