CN113290286B - Disc shear blade service life prediction method based on combination of big data and working conditions - Google Patents

Disc shear blade service life prediction method based on combination of big data and working conditions Download PDF

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CN113290286B
CN113290286B CN202010107255.3A CN202010107255A CN113290286B CN 113290286 B CN113290286 B CN 113290286B CN 202010107255 A CN202010107255 A CN 202010107255A CN 113290286 B CN113290286 B CN 113290286B
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blade
strip steel
service
service life
shear
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CN113290286A (en
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范群
王劲
王辉
吴琼
姚利松
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Baoshan Iron and Steel Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
    • B23D—PLANING; SLOTTING; SHEARING; BROACHING; SAWING; FILING; SCRAPING; LIKE OPERATIONS FOR WORKING METAL BY REMOVING MATERIAL, NOT OTHERWISE PROVIDED FOR
    • B23D33/00—Accessories for shearing machines or shearing devices
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
    • B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
    • B23Q17/00—Arrangements for observing, indicating or measuring on machine tools
    • B23Q17/09—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
    • B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
    • B23Q17/00—Arrangements for observing, indicating or measuring on machine tools
    • B23Q17/09—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool
    • B23Q17/0995—Tool life management

Abstract

The invention discloses a disc shear blade service life prediction method based on combination of big data and working conditions, which comprises the following steps: 1) collecting related production data of the disc shear; 2) measuring and inputting the actual total wear W of the j-th cutter blade off machinesj(ii) a 3) Giving the theoretical total wear W of the insertlj；4)Wsj＝Wlj(ii) a 5) Regressing to obtain values of a, b, c and d; 6) calculating the maximum allowable value W of the blunting degree of the cutting edgemax(ii) a 7) Collecting relevant data of the strip steel to be produced during the service period of the new blade; 8) obtaining a specific expression of the blade passivation degree W; 9) calculating a blade life factor lambda; 10) is λ ≦ 1 If yes, the blade is continuously used, the step 7) is repeated, and if not, an early warning signal is sent out and the blade is replaced. The invention realizes reasonable judgment of the blade passivation degree of the disc shear blade during service, further realizes prediction of the blade service life, provides theoretical support and guidance for field blade replacement and timely changes the blade.

Description

Disc shear blade service life prediction method based on combination of big data and working conditions
Technical Field
The invention relates to a strip steel disc shearing technology, in particular to a disc shear blade service life prediction method based on combination of big data and working conditions.
Background
After the strip steel is rolled, the edge of the strip steel is uneven due to the transverse flow of metal in the strip steel, so that the coiling of the strip steel is influenced, and the subsequent processing is not facilitated. In order to solve the problem, the strip steel needs to be subjected to fixed-width trimming treatment through a circle shear, so that the width requirement of the finished strip steel is met, irregular burrs and defects of the edge of the strip steel are removed, and good edge quality is obtained.
A circle shear is provided with four blades, the left side and the right side of the circle shear are respectively provided with a pair of blades, and each pair of blades is divided into an upper blade and a lower blade. The blade performance can constantly change along with the increase of shearing kilometers, mainly shows wearing and tearing and passivation gradually of cutting edge, and after the cutting edge wearing and tearing and passivation reached certain degree, this blade of reuse was cuted, and belted steel limit portion then can take place to buckle limit, burr, cut constantly scheduling problem, takes place the tipping phenomenon simultaneously easily. At this point, the blade has reached its useful life and should be replaced in a timely manner.
The prior art mainly determines the service life of the blade and replaces the blade according to the shearing kilometer number or the shearing tonnage without corresponding theoretical guidance. If the blade is replaced later, the quality defect of the edge of the strip steel can be generated, and the product is degraded or even scrapped; if the blade is replaced earlier, the blade is not fully utilized within the allowable range of the service life, and unnecessary tool changing time and loss of the effective service life of the blade are increased.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a method for predicting the service life of a disc shear blade based on the combination of big data and working conditions, so that the reasonable judgment on the passivation degree of the blade edge of the disc shear blade during service is realized, the prediction on the service life of the blade is further realized, theoretical support and guidance are provided for field blade replacement, and the aim of replacing the blade in time is fulfilled, so that the shearing quality of the edge of strip steel can be ensured, and the effective service life of the blade can be fully utilized.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for predicting the service life of a disc shear blade based on combination of big data and working conditions comprises the following steps:
1) collecting the relevant production data of the rotary shears, wherein the relevant production data comprises: initial hardness H of bladejThickness h of strip steelijStrength T of strip steelijShear rate VijL number of shear kilometersij，
J is any one-time service serial number, j is more than or equal to 1 and less than or equal to m, and m is the total service times of blades made of the same material in an effective range;
i is any coil of strip steelNumber, i is not less than 1 and not more than nj，njThe total number of the sheared strip steel rolls in the jth blade service period;
2) measuring and inputting the actual total wear W of the j-th cutter blade off machinesj：
Assuming that the radius of the new blade is R (mm), the thickness is D (mm), and the mass is M (g), the density is:
Figure BDA0002388794040000021
respectively measuring the blade mass before and after the operation of the jth blade, and calculating the difference value delta M of the blade mass before and after the operation of the jth bladejThen the total wear rate of the blade in the jth service cycle is:
Figure BDA0002388794040000022
3) giving the theoretical total wear W of the insertljFunctional relationship with related production data:
Figure BDA0002388794040000023
in the formula, xi is a constant related to the characteristics of the blade material, a is a strip steel thickness influence index, b is a strip steel strength influence index, c is a shearing speed influence index, and d is a shearing kilometer number influence coefficient;
4) consider the actual total wear W of the insertsjTheoretical total wear W of bladeljEqual, i.e.:
Wsj＝Wlj；
5) regression is carried out by utilizing all circle shear production data in the total service times to obtain values of a, b, c and d;
6) calculating the maximum allowable value W of the blunting degree of the cutting edgemax：
Figure BDA0002388794040000024
In the formula, betajThe correction coefficient is comprehensively judged according to the actual state of the cutter blade of the jth next time and the shearing quality of the edge of the strip steel in the service cycle (mainly in the later period) of the cutter blade;
7) collecting relevant data of the strip steel to be produced during the service period of the new blade, wherein the relevant data comprises the thickness h of the strip steel to be produced in the front i rollsiStrength T of strip steeliShear rate ViLength L of strip steeliThe initial hardness H of the new blade;
8) obtaining a specific expression of the blade passivation degree W under the current production working condition:
Figure BDA0002388794040000031
9) calculating a blade life factor lambda:
Figure BDA0002388794040000032
10) judging whether the inequality lambda is less than or equal to 1, if so, continuing to use the blade, and repeating the step 7); if the service life of the blade is not up, the service life of the blade is indicated, and a blade service life early warning signal is sent out through the system to remind an operator to replace the blade in time.
In the step 6), the correction coefficient beta is comprehensively judged according to the actual state of the cutter blade of the jth cutter and the shearing quality of the edge of the strip steel in the service period of the cutter bladejComprises the following steps:
βj(ej,fj)＝ε1ej+ε2fj
wherein the j-th cutter blade state score is recorded as ej，0.5<ej<1.5, the better the blade state is, the higher the score is; the shearing quality score of the edge of the strip steel in the service period (mainly the later period) of the blade is recorded as fj，0.5<fj<1, the better the shearing quality of the edge of the strip steel is, the higher the score is;
ε1scoring a weighting factor, 0, for the off-blade machine state<ε1<1；ε2A weighting coefficient of 0 is scored for the edge shearing quality of the strip steel in the service period (mainly the later period) of the blade<ε1<1, and e1+ε2＝1。
In the step 10), if more data samples are needed, repeating the steps 2) -10), and further improving the accuracy of blade life prediction through continuous model optimization.
In the technical scheme, the method for predicting the service life of the disc shear blade based on the combination of big data and the working condition, provided by the invention, realizes accurate prediction of the service life of the blade, so that the aim of timely replacing the blade is fulfilled, the problems of edge buckling, burrs, continuous shearing and the like caused by blade passivation of the edge of strip steel are further avoided, and the effective service life of the blade is fully utilized.
Drawings
FIG. 1 is a flow chart of the prediction method of the present invention.
Detailed Description
The technical scheme of the invention is further explained by combining the drawings and the embodiment.
Referring to fig. 1, a method for predicting the life of a disc shear blade based on combination of big data and working conditions according to the present invention includes the following steps:
1) establishing a disc shear production data automatic acquisition calculation model, acquiring disc shear related production data, wherein the total service times of blades made of the same material in an effective range are 50 times, the service number of any one time is j, and j is more than or equal to 1 and less than or equal to m; the total number of the sheared strip steel rolls in the service period of the jth blade is njWherein the serial number of any coil of strip steel is i, i is more than or equal to 1 and is less than or equal to nj(ii) a Associated production data including initial hardness H of the bladejThickness h of strip steelijStrength T of strip steelijShear rate VijL number of shear kilometersij. Taking the 1 st blade service cycle as an example, the blade produces 456 rolls of strip steel in total, taking the former 10 rolls as an example, and the relevant data are shown in the following table 1:
TABLE 1 Table of parameters related to the production process of disc shear
Figure BDA0002388794040000041
2) Measuring and inputting the actual total wear rate of the j-th blade off machine;
3) giving the theoretical total wear W of the insertljFunctional relationship with related production data:
Figure BDA0002388794040000042
in the formula, xi is a constant related to the characteristics of the blade material, 26, a is a strip steel thickness influence index, b is a strip steel strength influence index, c is a shearing speed influence index, and d is a shearing kilometer number influence coefficient;
4) consider the actual total wear W of the insertsjTheoretical total wear W of bladeljEqual, i.e.:
Wsj＝Wlj；
5) values of a, b, c and d are obtained by regression using all circle shear production data within the total number of service times, as shown in table 2 below:
TABLE 2 data regression results Table
a b c d
0.13 0.05 0.07 0.24
6) Calculating the maximum allowable value W of the blunting degree of the cutting edgemax：
Figure BDA0002388794040000051
Taking the 1 st blade off as an example, the blade off state is better, and e is recorded according to the scoring standard11.2, the edge shearing quality of the strip steel in the service period of the blade is good, and f is recorded according to the grading standardjTaking the cutting blade off-machine state as the main part and the strip steel edge shearing quality as the auxiliary part for comprehensive judgment, and taking epsilon1＝0.8，ε2When the value is 0.2, the following components are present:
β1(e1,f1)＝ε1e1+ε2f1＝1.16
7) collecting relevant data of the strip steel to be produced during the service period of the new blade, including the thickness h of the strip steel to be produced in the previous i rollsiStrength T of strip steeliShear rate ViLength L of strip steeliThe initial hardness H of the new blade;
8) obtaining a specific expression of the blade passivation degree W under the current production working condition:
Figure BDA0002388794040000052
9) calculating a blade life factor lambda:
Figure BDA0002388794040000053
10) determine if the inequality λ ≦ 1? When the front 451 coils of strip steel are cut, if lambda is less than 1, the blade is continuously used, and the step 7) is repeated; when the 452 th strip steel is sheared, if lambda is larger than 1, a blade service life early warning signal is sent out through the system, and an operator is reminded to replace the blade in time.
In conclusion, the invention provides the disc shear blade service life prediction method based on the combination of the big data and the working condition by comprehensively considering the influence of factors such as the strength, the thickness, the shearing speed, the shearing kilometer number and the initial hardness of the blade on the blade passivation aiming at the problem of disc shear blade service life prediction and combining the actual production big data and the working condition.
It should be understood by those skilled in the art that the above embodiments are only for illustrating the present invention and are not to be used as a limitation of the present invention, and that changes and modifications to the above described embodiments are within the scope of the claims of the present invention as long as they are within the spirit and scope of the present invention.

Claims (3)

1. A method for predicting the service life of a disc shear blade based on combination of big data and working conditions is characterized by comprising the following steps:
1) collecting the relevant production data of the rotary shears, wherein the relevant production data comprises: initial hardness H of bladejThickness h of strip steelijStrength T of strip steelijShear rate VijL number of shear kilometersij，
J is any one-time service serial number, j is more than or equal to 1 and less than or equal to m, and m is the total service times of blades made of the same material in an effective range;
i is the serial number of any coil of strip steel, i is more than or equal to 1 and less than or equal to nj，njThe total number of the sheared strip steel rolls in the jth blade service period;
2) measuring and inputting the actual total wear W of the j-th cutter blade off machinesj：
If the radius of the new blade is R, the thickness is D and the mass is M, the density is as follows:
Figure FDA0002388794030000011
respectively measuring the blade mass before and after the operation of the jth blade, and calculating the difference value delta M of the blade mass before and after the operation of the jth bladej，
The total wear rate of the blade in the jth service cycle is:
Figure FDA0002388794030000012
3) giving the theoretical total wear W of the insertljFunctional relationship with related production data:
Figure FDA0002388794030000013
in the formula, xi is a constant related to the characteristics of the blade material, a is a strip steel thickness influence index, b is a strip steel strength influence index, c is a shearing speed influence index, and d is a shearing kilometer number influence coefficient;
4) consider the actual total wear W of the insertsjTheoretical total wear W of bladeljEqual, i.e.:
Wsj＝Wlj；
5) regression is carried out by utilizing all circle shear production data in the total service times to obtain values of a, b, c and d;
6) calculating the maximum allowable value W of the blunting degree of the cutting edgemax：
Figure FDA0002388794030000014
In the formula, betajThe correction coefficient is comprehensively judged according to the actual state of the cutter blade of the next time and the shearing quality of the edge of the strip steel in the service period of the cutter blade;
7) collecting relevant data of the strip steel to be produced during the service period of the new blade, wherein the relevant data comprises the thickness h of the strip steel to be produced in the front i rollsiStrength T of strip steeliShear rate ViLength L of strip steeliThe initial hardness H of the new blade;
8) obtaining an expression of the blade passivation degree W under the current production working condition:
Figure FDA0002388794030000021
9) calculating a blade life factor lambda:
Figure FDA0002388794030000022
10) judging whether the inequality lambda is less than or equal to 1, if so, continuing to use the blade, and repeating the step 7); if the service life of the blade is not up, the service life of the blade is indicated, and a blade service life early warning signal is sent out through the system to remind an operator to replace the blade in time.
2. The method for predicting the life of the disc shear blade based on the combination of big data and working conditions as claimed in claim 1, wherein: in the step 6), the correction coefficient beta is comprehensively judged according to the actual state of the cutter blade of the jth cutter and the shearing quality of the edge of the strip steel in the service period of the cutter bladejComprises the following steps:
βj(ej,fj)＝ε1ej+ε2fj
wherein the j-th cutter blade state score is recorded as ej，0.5<ej<1.5, the better the blade state is, the higher the score is; the shearing quality score of the strip steel edge in the service period of the blade is recorded as fj，0.5<fj<1, the better the shearing quality of the edge of the strip steel is, the higher the score is;
ε1scoring a weighting factor, 0, for the off-blade machine state<ε1<1；ε2A weighting coefficient of 0 is scored for the shearing quality of the edge of the strip steel in the service period of the blade<ε1<1, and e1+ε2＝1。
3. The method for predicting the life of the disc shear blade based on the combination of big data and working conditions as claimed in claim 1, wherein: in step 10), if more data samples are needed, repeating the steps 2) to 10).
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