CN105488336A - Method for measuring hardness nonuniformity of 9Cr ferrite heat-resistant steel - Google Patents
Method for measuring hardness nonuniformity of 9Cr ferrite heat-resistant steel Download PDFInfo
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Abstract
The invention discloses a method for measuring the hardness nonuniformity of 9Cr ferrite heat-resistant steel. The method comprises the following steps: (1) carrying out equal-interval measurement on steel microhardness; (2) independently making the normalization ascending order and descending order distribution curves of microhardness data to reflect the low segment, the middle segment and the high segment of the microhardness; (3) taking the lowest hardness as a starting point to gradually increase a slope from a data point to an equation of linear regression to achieve a minimum value which conforms to a condition so as to obtain a microhardness range which takes ferrite as a principal segment, adopting a descending order curve of the hardness, and taking the highest hardness as the starting point to gradually increase the slope from the data point to the equation of linear regression to achieve the minimum value which conforms to the condition so as to obtain the microhardness range which takes martensite as the principal segment; and (4) taking a score value corresponding to boundary hardness data which takes the obtained ferrite as the principal segment, the score value corresponding to boundary hardness data which takes the ferrite plus the martensite as a mixed segment and the score value corresponding to boundary hardness data which takes the martensite as the principal segment to obtain the proportion of each typical segment. The method realizes the identification and the quantization of the tissue and the hardness of the hardness nonuniformity of the 9Cr ferrite heat-resistant steel.
Description
Technical field
The present invention relates to the mensuration of 9Cr jessop hardness unevenness, disclose a kind of method measuring 9Cr jessop hardness unevenness, specifically, relate to that to measure ferrite in the tested region of steel sample be principal piece, the method for ferrite adds martensite to be principal piece and martensite be these 3 typical segment proportions of principal piece.
Background technology
Be no more than 250HB according to the hardness of the requirement P91 material pipeline of ASMESA335-SA335M, but in the checkout procedure of P91 material, no matter be imported materials and items or home made materials, its lower hardness has been a very general problem.Clearly propose in the DL/T438-2009 " fuel-burning power plant alloying technology supervision code " that power industry is newly promulgated, the hardness of P91 material must not lower than the requirement of 180HB.But still there is the phenomenon of a large amount of softs at the P91 pipeline run and header, this runs to the long-term stability of unit and brings serious potential safety hazard.It is extremely important for carrying out metallographic examination to soft P91 pipe.When there is multiple typical organization in material, adopt the ratio shared by traditional metallographic method these typical organizations not energetic.How utilizing the equidistant measurement data of microhardness to calculate borderline hardness data between each representative region, thus determine each representative region proportion, is a key of the present invention.
Summary of the invention
In order to overcome above-mentioned defect, the object of the present invention is to provide a kind of method measuring 9Cr jessop hardness unevenness, the method is easy, accurate, is easy to realize.
To achieve these goals, the technical solution used in the present invention is: a kind of method measuring 9Cr jessop hardness unevenness, comprises the steps:
1. the equidistant micro-hardness measurement data of relevant steel sample are obtained;
2. corresponding micro-hardness data normalization ascending order and descending distribution curve is made respectively according to equidistant micro-hardness measurement data;
3. in micro-hardness data normalization ascending order distribution curve, be that starting point increases data point to the slope of equation of linear regression gradually and reaches qualified minimal value with lowest hardness, condition refers in slope distribution curve, the left side adjacent with minimum point has at least continuous 3 data points to successively decrease, and the right adjacent with minimal value has at least continuous 3 data points to increase progressively, thus obtain the coboundary hardness data that ferrite is principal piece;
4. in micro-hardness data normalization descending distribution curve, be that starting point increases data point to the slope of equation of linear regression gradually and reaches qualified minimal value with maximum hardness, condition refers in slope distribution curve, the left side adjacent with minimum point has at least continuous 3 data points to successively decrease, and the right adjacent with minimum point has at least continuous 3 data points to increase progressively, thus obtain the lower boundary hardness data that martensite is principal piece;
5. the lower boundary hardness data being principal piece with the obtained ferrite coboundary hardness data that are principal piece and martensite to calculate in the tested region of steel sample ferrite for principal piece, and it be principal piece and martensite is these 3 typical segment proportions of principal piece that ferrite adds martensite.
Described step 1. and step 2. between also have following steps: remove exceptional data point.
Step 1. in, the preparation process of steel sample is: metallographic sample preparation method is by the polishing of tested sample sightingpiston, polishing, erosion routinely.
Step 5. in, be principal piece proportion F% according to the coboundary hardness data determination ferrite that ferrite is principal piece, be principal piece proportion M% according to the lower boundary hardness data determination martensite that martensite is principal piece, finally calculate ferrite and add the ratio of martensite shared by principal piece (F+M) %=100%-F%-M%.
Step 3. in, in micro-hardness data normalization ascending order distribution curve, be that starting point increases data point gradually and carries out linear regression y=a with lowest hardness
i+ b
if
s, a
ifor constant, y is n the micro-hardness data arranged in order, obtains the slope b that each group data following are corresponding
i, slope b
icomprise b
i3, b
i4, b
i5 ... b
in, fs1, fs2, fs3... are horizontal ordinate 1/n, 2/n, 3/n of micro-hardness data normalization ascending order distribution curve ... y1, y2, y3... are the ordinate that in micro-hardness data normalization ascending order distribution curve, horizontal ordinate fs1, fs2, fs3... are corresponding respectively, and concrete operations are as follows:
Again by tried to achieve slope value b
i3, b
i4, b
i5 ... b
in makes slope distribution curve I, and horizontal ordinate is y3, y4, y5 ... yn, ordinate corresponds to b
i3, b
i4, b
i5 ... b
in, slope distribution curve I finds first qualified minimal value b from left to right
ii (left side has at least continuous 3 data points to successively decrease, and the right has at least continuous 3 data points to increase progressively), b
ii is b
i3, b
i4, b
i5 ... b
ione in n, now b
ithe yi that i is corresponding is the coboundary hardness data that ferrite is principal piece.
Step 4. in, in micro-hardness data normalization descending distribution curve, be that starting point increases data point gradually and carries out linear regression y=a with maximum hardness
iI+ b
iIf
s, a
iIfor constant, y is n the micro-hardness data arranged in order, obtains the slope b that each group data is corresponding
iI, slope b
iIcomprise b
iI(n-2), b
iI(n-3) ... b
iI1, fsn, fs (n-1), fs (n-2) ... be horizontal ordinate n/n, (n-1)/n, (the n-2)/n of micro-hardness data normalization descending distribution curve ... yn, y (n-1), y (n-2) ... be respectively horizontal ordinate fsn, fs (n-1), fs (n-2) in micro-hardness data normalization descending distribution curve ... corresponding ordinate, concrete operations are as follows:
Again by tried to achieve slope value b
iI1, b
iI2 ... b
iI(n-3), b
iI(n-2) make slope distribution curve II, horizontal ordinate is y1, y2 ... y (n-3), y (n-2), ordinate corresponds to b
iI1, b
iI2 ... b
iI(n-3), b
iI(n-2), slope distribution curve II finds first the qualified minimal value b that to turn left from the right side
iIj (left side has at least continuous 3 data points to successively decrease, and the right has at least continuous 3 data points to increase progressively), b
iIj is b
iI1, b
iI2 ... b
iI(n-3), b
iI(n-2) one in, now b
iIthe yj that j is corresponding is the lower boundary hardness data that martensite is principal piece.
Compared with prior art, the beneficial effect that the present invention has is:
1. the present invention utilizes traditional metallographic method not identify and quantizes 9Cr Gang Zhongsange typical organization this present situation of section ratio, proposes the borderline hardness data utilizing equidistant micro-hardness measurement data to calculate each representative region, thus determines each representative region proportion.
2. the present invention calculate process simple, fast, result accurately, reliable;
3. results of measuring of the present invention can instruct test, decreases in metal lographic examination the blindness judging microstructure;
4. the present invention is except being applied to 9Cr jessop, also can be used in other steel and the measuring and calculating of each typical organization proportion of alloy, has broad application prospect.
In a word, computation process of the present invention is simple, quick, can be widely used in 9Cr jessop hardness unevenness and measure.
Accompanying drawing explanation
Fig. 1 is micro-hardness measurement mesh lines;
Fig. 2 is micro-hardness data normalization ascending order distribution curve;
Fig. 3 is micro-hardness data normalization descending distribution curve;
Fig. 4 is slope distribution curve 1;
Fig. 5 is slope distribution curve 2;
Fig. 6 is the micro-hardness data normalization ascending order curve finally obtained.
Embodiment
Below in conjunction with specific embodiment and Figure of description, the present invention is further illustrated.
Method of the present invention, comprises the following steps:
1. data are obtained
First the present invention needs to obtain hardness data by micro-hardness experiments.10mm × 10mm region is selected in sample to be tested metallographic observation face, equidistantly carries out micro-hardness testing (HV0.1) by mesh lines shown in Fig. 1.
2. exceptional data point is removed, the micro-hardness data normalization ascending order of making respectively according to obtained data and descending distribution curve.This step is to reject the data point departing from the excessive beginning of whole piece curve and ending, if do not reject, then can affect result of calculation, is specially the point removing spacing comparatively large (such as remove spacing and be more than or equal to 8).
In micro-hardness data normalization ascending order distribution curve, horizontal ordinate (fs) is followed successively by 1/n, 2/n, 3/n ... (n-1)/n, n/n, corresponding ordinate y is followed successively by this n micro-hardness data by ascending order arrangement.
In micro-hardness data normalization descending distribution curve, horizontal ordinate (fs) is followed successively by 1/n, 2/n, 3/n ... (n-1)/n, n/n, corresponding ordinate y is followed successively by this n micro-hardness data by descending sort.
Wherein, horizontal ordinate (fs) represents the count frequency after by the normalization of each pipe sample measuring point sum, is namely less than or greater than the ratio that the measuring point number of certain y value is shared in measuring point sum.
3. ferrite is the calculating of the coboundary hardness data of principal piece
In micro-hardness data normalization ascending order distribution curve, be that starting point increases data point gradually and carries out linear regression (y=a with lowest hardness
i+ b
if
s), a
ifor constant term, be also the intercept of equation in Y-axis, b
ifor regression coefficient, be also linear regression equation y=a
i+ b
if
sslope in coordinate axis, y is n the micro-hardness data arranged in order, obtains the slope b that each group data following are corresponding
i(comprise b
i3, b
i4, b
i5 ... b
in), fs1, fs2, fs3... are horizontal ordinate 1/n, 2/n, 3/n of micro-hardness data normalization ascending order distribution curve ... y1, y2, y3... are the ordinate that in micro-hardness data normalization ascending order distribution curve, fs1, fs2, fs3... are corresponding respectively, and concrete operations are as follows:
Again by tried to achieve slope value (b
i3, b
i4, b
i5 ... b
in) make slope distribution curve I, horizontal ordinate (fs) is (y3, y4, y5 ... yn), ordinate corresponds to (b
i3, b
i4, b
i5 ... b
in), slope distribution curve I finds first qualified minimal value b from left to right
ii (left side has at least continuous 3 data points to successively decrease, and the right has at least continuous 3 data points to increase progressively), b
ii is b
i3, b
i4, b
i5 ... b
ione in n, now b
ithe yi that i is corresponding is the coboundary hardness data that ferrite is principal piece.
4. martensite is the calculating of the lower boundary hardness data of principal piece
In micro-hardness data normalization descending distribution curve, be that starting point increases data point gradually and carries out linear regression (y=a with maximum hardness
iI+ b
iIf
s), a
iIfor constant term, be also the intercept of equation in Y-axis, b
iIfor regression coefficient, be also linear regression equation y=a
iI+ b
iIf
sslope in coordinate axis, y is n the micro-hardness data arranged in order, obtains the slope b that each group data is corresponding
iI(comprise b
iI(n-2), b
iI(n-3) ... b
iI1), fsn, fs (n-1), fs (n-2) ... be horizontal ordinate n/n, (n-1)/n, (the n-2)/n of micro-hardness data normalization descending distribution curve ... yn, y (n-1), y (n-2) ... be respectively horizontal ordinate fsn, fs (n-1), fs (n-2) in micro-hardness data normalization descending distribution curve ... corresponding ordinate, concrete operations are as follows:
Again by tried to achieve slope value (b
iI1, b
iI2 ... b
iI(n-3), b
iI(n-2)) make slope distribution curve II, horizontal ordinate (fs) is (y1, y2 ... y (n-3), y (n-2)), ordinate corresponds to (b
iI1, b
iI2 ... b
iI(n-3), b
iI(n-2)), slope distribution curve II finds first the qualified minimal value b that to turn left from the right side
iIj (left side has at least continuous 3 data points to successively decrease, and the right has at least continuous 3 data points to increase progressively), b
iIj is b
iI1, b
iI2 ... b
iI(n-3), b
iI(n-2) one in, now b
iIthe yj that j is corresponding is the lower boundary hardness data that martensite is principal piece.
5. the ratio shared by each typical segment is calculated.
Ferrite is principal piece proportion M%=i/n
Martensite is principal piece proportion F%=j/n
It is principal piece proportion (F+M) %=1-i/n-j/n that ferrite adds martensite
Embodiment 1: the points hardness's non-uniform areas processing metallographic specimen choosing " main steam line concentric reducer " pipe fitting, 10mm × 10mm region is selected in metallographic observation face, equidistantly carries out micro-hardness testing (HV0.1) by mesh lines shown in Fig. 1.Microhardness numerical value is in table 1.
Table 1: equidistantly micro-hardness testing result (HV0.1)
First, the data point in consolidated statement 1, removes exceptional data point (144HV0.1,164HV0.1), now n=98, the micro-hardness data normalization ascending order of making respectively and descending distribution curve.
3. be that starting point increases data point gradually and carries out linear regression (y=a with lowest hardness according to step
i+ b
if
s), obtain the slope that each group data is corresponding, then tried to achieve slope value is made slope distribution curve I, curve finds first qualified minimal value b from left to right
i(left side has at least continuous 3 data points to successively decrease to i, and the right has at least continuous 3 data points to increase progressively), the hardness of its correspondence is 188HV0.1 (now i=36, n=98), i.e. the coboundary hardness data of 188HV0.1 to be ferrite be principal piece.
4. be that starting point increases data point gradually and carries out linear regression (y=a with maximum hardness according to step
iI+ b
iIf
s), obtain slope corresponding to each group data, then tried to achieve slope value is made slope distribution curve II, curve finds first the qualified minimal value b that to turn left from the right side
iI(left side has at least continuous 3 data points to successively decrease to j, and the right has at least continuous 3 data points to increase progressively), the hardness of its correspondence is 220HV0.1 (now j=16, n=98), i.e. the lower boundary hardness data of 220HV0.1 to be martensite be principal piece.
Finally, comprehensive the above results, obtains the microhardness sectional curve of main steam line concentric reducer sample as Fig. 6, wherein:
172 ~ 188HV0.1, ferrite is principal piece, proportion F%=36/98=36.7%;
221 ~ 230HV0.1, martensite is principal piece, proportion M%=16/98=16.3%;
191 ~ 220HV0.1, it is principal piece that ferrite adds martensite, proportion (F+M) %=100%-36.7%-16.3%=47.0%.
The ferrite that the inventive method is determined is principal piece, and ferrite adds martensite mixing section, and martensite is that the microhardness of principal piece is with to contrast metallograph microhardness measured result anastomose property better.
Above-described embodiment of the present invention, does not form limiting the scope of the present invention.Any amendment done within the spirit and principles in the present invention, equivalent replacement and improvement etc., all should be included within claims of the present invention.
Claims (6)
1. measure a method for 9Cr jessop hardness unevenness, comprise the steps:
1. the equidistant micro-hardness measurement data of relevant steel sample are obtained;
2. corresponding micro-hardness data normalization ascending order and descending distribution curve is made respectively according to equidistant micro-hardness measurement data;
3. in micro-hardness data normalization ascending order distribution curve, be that starting point increases data point to the slope of equation of linear regression gradually and reaches qualified minimal value with lowest hardness, condition refers in slope distribution curve, the left side adjacent with minimum point has at least continuous 3 data points to successively decrease, and the right adjacent with minimal value has at least continuous 3 data points to increase progressively, thus obtain the coboundary hardness data that ferrite is principal piece;
4. in micro-hardness data normalization descending distribution curve, be that starting point increases data point to the slope of equation of linear regression gradually and reaches qualified minimal value with maximum hardness, condition refers in slope distribution curve, the left side adjacent with minimum point has at least continuous 3 data points to successively decrease, and the right adjacent with minimum point has at least continuous 3 data points to increase progressively, thus obtain the lower boundary hardness data that martensite is principal piece;
5. the lower boundary hardness data being principal piece with the obtained ferrite coboundary hardness data that are principal piece and martensite to calculate in the tested region of steel sample ferrite for principal piece, and it be principal piece and martensite is these 3 typical segment proportions of principal piece that ferrite adds martensite.
2. a kind of method measuring 9Cr jessop hardness unevenness according to claim 1, is characterized in that, described step 1. and step 2. between also have following steps: remove exceptional data point.
3. a kind of method measuring 9Cr jessop hardness unevenness according to claim 1, is characterized in that, step 1. in, the preparation process of steel sample is: metallographic sample preparation method is by the polishing of tested sample sightingpiston, polishing, erosion routinely.
4. a kind of method measuring 9Cr jessop hardness unevenness according to claim 1, it is characterized in that, step 5. in, be principal piece proportion F% according to the coboundary hardness data determination ferrite that ferrite is principal piece, be principal piece proportion M% according to the lower boundary hardness data determination martensite that martensite is principal piece, finally calculate ferrite and add the ratio of martensite shared by principal piece (F+M) %=100%-F%-M%.
5. a kind of method measuring 9Cr jessop hardness unevenness according to claim 1, it is characterized in that, step 3. in, in micro-hardness data normalization ascending order distribution curve, be that starting point increases data point gradually and carries out linear regression y=a with lowest hardness
i+ b
if
s, a
ifor constant, y is n the micro-hardness data by ascending order arrangement, obtains the slope b that each group data following are corresponding
i, slope b
icomprise b
i3, b
i4, b
i5 ... b
in, fs1, fs2, fs3... are horizontal ordinate 1/n, 2/n, 3/n of micro-hardness data normalization ascending order distribution curve ... y1, y2, y3... are the ordinate that in micro-hardness data normalization ascending order distribution curve, fs1, fs2, fs3... are corresponding respectively, and concrete operations are as follows:
(fs1, y1), (fs2, y2), (fs3, y3) y=a
i+ b
if
sslope: b
i3
(fs1, y1), (fs2, y2), (fs3, y3), (fs4, y4) y=a
i+ b
if
sslope: b
i4
(fs1, y1), (fs2, y2), (fs3, y3), (fs4, y4), (fs5, y5) y=a
i+ b
if
sslope: b
i5
··
··
··
(fs1, y1), (fs2, y2), (fs3, y3), (fs4, y4), (fs5, y5) ... (fsn, yn) y=a
i+ b
if
sslope: b
in is again by tried to achieve slope value b
i3, b
i4, b
i5 ... b
in makes slope distribution curve I, and horizontal ordinate is y3, y4, y5 ... yn, ordinate corresponds to b
i3, b
i4, b
i5 ... b
in, curve finds first qualified minimal value b from left to right
ii, b
ii is b
i3, b
i4, b
i5 ... b
ione in n, now b
ithe yi that i is corresponding is the coboundary hardness data that ferrite is principal piece.
6. a kind of method measuring 9Cr jessop hardness unevenness according to claim 1, it is characterized in that, step 4. in, in micro-hardness data normalization descending distribution curve, be that starting point increases data point gradually and carries out linear regression y=a with maximum hardness
iI+ b
iIf
s, a
iIfor constant, y is n the micro-hardness data by descending sort, obtains the slope b that each group data is corresponding
iI, slope b
iIcomprise b
iI(n-2), b
iI(n-3) ... b
iI1, fsn, fs (n-1), fs (n-2) ... be horizontal ordinate n/n, (n-1)/n, (the n-2)/n of micro-hardness data normalization descending distribution curve ... yn, y (n-1), y (n-2) ... be respectively horizontal ordinate fsn, fs (n-1), fs (n-2) in micro-hardness data normalization descending distribution curve ... corresponding ordinate, concrete operations are as follows:
(fsn, yn), (fs (n-1), y (n-1)), (fs (n-2), y (n-2)) y=a
iI+ b
iIf
sslope: b
iI(n-2)
(fsn, yn), (fs (n-1), y (n-1)), (fs (n-2), y (n-2)), (fs (n-3), y (n-3)) y=a
iI+ b
iIf
sslope: b
iI(n-3)
··
··
··
(fsn, yn), (fs (n-1), y (n-1)), (fs (n-2), y (n-2)), (fs (n-3), y (n-3)) ... (fs1, y1) y=a
iI+ b
iIf
sslope: b
iI1 again by tried to achieve slope value b
iI1, b
iI2 ... b
iI(n-3), b
iI(n-2) make slope distribution curve II, horizontal ordinate is y1, y2 ... y (n-3), y (n-2), ordinate corresponds to b
iI1, b
iI2 ... b
iI(n-3), b
iI(n-2), curve finds first the qualified minimal value b that to turn left from the right side
iIj, b
iIj is b
iI1, b
iI2 ... b
iI(n-3), b
iI(n-2) one in, now b
iIthe yj that j is corresponding is the lower boundary hardness data that martensite is principal piece.
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