CN107537866A - A kind of forecasting procedure of Stand Mill wet jetting piles unit work roll surface roughness - Google Patents
A kind of forecasting procedure of Stand Mill wet jetting piles unit work roll surface roughness Download PDFInfo
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Abstract
The present invention relates to a kind of forecasting procedure of Stand Mill wet jetting piles unit work roll surface roughness, mainly solve existing Stand Mill wet jetting piles unit work roll surface roughness can not accurate online forecasting technical problem.The inventive method, including：(a) the Stand Mill wet jetting piles unit work roll surface roughness forecasting model that unit is directed to different steel grade production technologies is calculated；(b) lower limit Ra of the working roll in rolling cycle inside surface roughness is givenr,min；(c) give coil of strip parameter n and initialize n=1；(d) working roll initial surface roughness Ra when parameter (e) forecast of the n-th coiled strip steel of rolling produces the (n+1)th coiled strip steel in the collection work roller rolling cycler,p+1；(f) Ra is judgedr,p+1≤Rar,minWhether set upIf it is not, then p=p+1 is made, Rar0=Rar,p+1, it is transferred to step (c)；If it is, making p=p 1, step (g) is transferred to；(g) p is exported, illustrates that after producing pth coiled strip steel roll change should be shut down, the forecast of work roll surface roughness terminates, into next roll change cycle.
Description
Technical field
The present invention relates to the working roll of Stand Mill wet jetting piles unit, more particularly to a kind of Stand Mill wet jetting piles unit working roll
The forecasting procedure of surface roughness, cold-rolled steel sheet wet jetting piles rolling technical field.
Background technology
One of surface roughness characteristic important as smooth finished product cold-rolled steel sheet, when it not only influences cold-rolling steel-plate punch
Deformational behavior and coating after outward appearance looks, and the corrosion resistance of material can be changed.In production high value added product such as vapour
Sweep, appliance plate, when wheel and air bottle steel, very strict is required to strip surface quality, so the research of surface quality is more next
More it is valued by people.For the wet jetting piles operation of rolling, belt steel surface roughness depends mainly on the size of working roll
Surface roughness.
It is, in general, that working roll can wear when in use, work roll surface roughness is work in the operation of rolling
Microscopic form after roller abrasion.
Chinese patent literature CN201310013209.7, a kind of belt steel surface roughness suitable for Two-stand Temper Mill group
Control method, mainly it is to provide a kind of belt steel surface roughness control method that belt steel surface roughness can be avoided overproof.It is logical
The distribution to two frame roll-forces is crossed, using outlet band roughness as object function, breaking elongation, plate shape are met that contract will
Ask as constraints, at utmost control belt steel surface roughness to meet target roughness requirements.The patent is concerned with into
Product belt steel surface roughness control problem rather than the work roll surface roughness forecasting problem suitable for wet jetting piles unit.
Chinese patent literature CN201510069718.0, a kind of roughness prediction of electric spark texturing working roll production strip
And control method, mainly pass through electric spark texturing working roll initial surface roughness, strip parameter, rolling work before the upper machine of record
Minimum oil film thickness, interfacial contact pressure and relative strip contact surface with working roll with respect to position between skill parametric solution interface
Move, on the basis of electric spark abrasion mechanism is analyzed, obtain electric spark texturing working roller abrasion depth and surface roughness.Should
Patent is the work roll surface roughness forecasting model that the mechanism based on electric spark texturing is established, while the patent is more paid close attention to
It is that belt steel surface roughness is forecast and controlled.
In wet jetting piles field, there is not pertinent literature to be related to the work roll surface roughness forecast suitable for being skin pass mill group
The foundation of model, at this stage, the roll change cycle for working roll are mainly the degree of wear that working roll is judged by field experience
And then the roll change cycle of experience property is formulated, so the roll change cycle formulated only according to field experience is excessively inflexible, add
Unit unnecessary time cost and technical costs.
The content of the invention
It is an object of the invention to provide a kind of forecasting procedure of Stand Mill wet jetting piles unit work roll surface roughness, mainly
Solve existing Stand Mill wet jetting piles unit work roll surface roughness can not accurate online forecasting technical problem, realize Stand Mill
The refinement maintenance of wet jetting piles unit working roll, reduce the cost of cold-rolled steel sheet wet jetting piles, it is ensured that the surface roughness of steel plate.
The purpose of the present invention is achieved through the following technical solutions：A kind of Stand Mill wet jetting piles unit work roll surface is thick
The forecasting procedure of rugosity, comprises the following steps：
(a) the Stand Mill wet jetting piles unit work roll surface roughness for calculating unit for different steel grade production technologies is forecast
Model；
(b) lower limit Ra of the working roll in rolling cycle inside surface roughness is givenr,min；
(c) give coil of strip parameter n and initialize n=1；
(d) parameter of the n-th coiled strip steel is rolled in the collection work roller rolling cycle, it is coarse mainly to include working roll initial surface
Spend Rar0, draught pressure pn, working roll and belt steel surface difference of hardness Kd,n, rolling milimeter number Ln, nonextruding fluidized bed thickness ξ, with
And optimal coefficient { ηy,αPy,αKy,αLy}；
(e) working roll initial surface roughness Ra during the (n+1)th coiled strip steel of forecast productionr,p+1, its expression formula is
(f) Ra is judgedr,p+1≤Rar,minWhether set upIf it is not, then p=p+1 is made, Rar0=Rar,p+1, it is transferred to step
(c)；If it is, making p=p-1, step (g) is transferred to；
(g) p is exported, illustrates that after producing pth coiled strip steel roll change, online Stand Mill wet jetting piles unit working roll table should be shut down
Surface roughness forecast terminates, into next roll change cycle.
Described step (a) includes：
(a1) parameter collection, collection work roller initial surface roughness Rar0,i(i=1,2 ..., n), draught pressure pi(i
=1,2 ..., n), strip and work roll surface difference of hardness Kd,i(i=1,2 ..., n), nonextruding fluidized bed thickness ξi(i=1,
2 ..., n), rolling milimeter number Li,j(i=1,2 ..., n, j=1,2 ..., m), survey work under the conditions of difference rolling milimeter numbers
Make roll surface roughness Ra 'ri,j(i=1,2 ..., n, j=1,2 ..., m)；
(a2) sets target function initial value G0, defining unit equipment characteristic influences coefficient and initializes ηmax, ηmin, and seek
Excellent step-length △ η, setting unit equipment characteristic influence coefficient optimizing pilot process parameter k1, and make k1=0；
(a3) η=η is mademax-k1△η；
(a4) defining draught pressure influences coefficient and initializes αPmax, αPminAnd optimizing step-length △ αP, set draught pressure shadow
Ring coefficient optimizing pilot process parameter k2, and make k2=0；
(a5) α is madeP=αPmax-k2△αP；
(a6) defining working roll influences coefficient with belt steel surface difference of hardness and initializes αKmax, αKminAnd optimizing step-length △ αK,
Setting working roll and belt steel surface difference of hardness influences coefficient optimizing pilot process parameter k3, and make k3=0；
(a7) α is madeK=αKmax-k3△αK；
(a8) define working roll roughness attenuation coefficient and initialize αLmax, αLminAnd optimizing step-length △ αL, set working roll
Roughness attenuation coefficient optimizing pilot process parameter k4, and make k4=0；
(a9) α is madeL=αLmax-k4△αL；
(a10) evaluation work roll surface roughness Rari,j, its expression formula is
(a11) control function formula F is calculatedi,j(X)=| Rar′i,j-Rari,j|；
(a12) calculating target function formula
(a13) inequality G is judged<G0Whether set upIf set up, G is made0=G, optimal ηy=η, αPy=αP, αKy=
αK, αLy=αL, step (a14) is transferred to, otherwise, is directly transferred to step (a14)；
(a14) inequality is judgedWhether set upIf inequality is set up, k is made4=k4+ 1, turn
Enter step (a9), be otherwise transferred to step (a15)；
(a15) inequality is judgedWhether set upIf inequality is set up, k is made3=k3+ 1, turn
Enter step (a7), be otherwise transferred to step (a16)；
(a16) inequality is judgedWhether set upIf inequality is set up, k is made2=k2+ 1, turn
Enter step (a5), be otherwise transferred to step (a17)；
(a17) inequality is judgedWhether set upIf inequality is set up, k is made1=k1+ 1, it is transferred to
Step (a3), otherwise it is transferred to step (a18)；
(a18) exporting optimal unit equipment characteristic influences coefficient ηy, optimal draught pressure influence factor alphaPy, optimal working roll
Factor alpha is influenceed with belt steel surface difference of hardnessKy, optimal working roll roughness attenuation coefficient αLy。
The inventive method passes through real-time collecting work rolling according to Stand Mill wet jetting piles unit work roll surface abrasion mechanism
Related process parameters have returned out accurate work roll surface roughness forecasting model in cycle processed, accurate by the forecasting model
The numerical value of the different rolling milimeter number bottom working roll surface roughnesses of true forecast.
The inventive method studies discovery for many years based on applicant：For Stand Mill wet jetting piles unit, draught pressure, working roll
It is the principal element that causes working roll to be worn in the operation of rolling with belt steel surface difference of hardness, original work roll surface roughness；
Nonextruding fluidized bed " filling up " directly reduces working roll table in working roll microcosmic surface trench caused by lubricating process system
Surface roughness.
The present invention has following good effect compared with prior art：1st, the inventive method realizes Stand Mill wet jetting piles unit
Work roll surface roughness real-time prediction so that factory can be thick according to the Stand Mill wet jetting piles unit work roll surface established
Rugosity forecasting model determines the roll change cycle of rational Stand Mill wet jetting piles unit working roll, makes working roll utilization benefit maximum
Change.2nd, the forecast precision of the Stand Mill wet jetting piles unit work roll surface roughness of the inventive method is high, realizes that Stand Mill is wet flat
The refinement maintenance of complete machine group working roll, reduce the cost of cold-rolled steel sheet wet jetting piles, it is ensured that the surface roughness of steel plate.
Brief description of the drawings
Fig. 1 is the forecasting procedure schematic flow sheet of Stand Mill wet jetting piles unit work roll surface roughness of the present invention
Fig. 2 is that the flow of the forecasting procedure step (a) of Stand Mill wet jetting piles unit work roll surface roughness of the present invention is shown
It is intended to
Embodiment
Referring to Figures 1 and 2, a kind of forecasting procedure of Stand Mill wet jetting piles unit work roll surface roughness, including it is following
Step：
(a) the Stand Mill wet jetting piles unit work roll surface roughness for calculating unit for different steel grade production technologies is forecast
Model；
(a1) parameter collection, collection work roller initial surface roughness Rar0,i=1.63 μm, and 1.63 μm, 1.63 μm,
1.63 μm }, draught pressure pi={ 6978kN, 6987kN, 6955kN, 3196kN }, strip and work roll surface difference of hardness Kd,i=
{ 35HS, 42HS, 35HS, 52HS }, nonextruding fluidized bed thickness ξi={ 0.06 μm, 0.06 μm, 0.06 μm, 0.06 μm }, rolling
Milimeter number Li,j={ 5.1km, 10.2km, 14.9km, 20.1km, 24.9km；4.9km, 9.9km, 14.8km, 20.7km,
25.4km；4.8km, 10.6km, 15.6km, 21.1km, 25.3km；4.8km, 10km, 15.1km, 20.1km, 24.7km }, no
With surveyed under the conditions of rolling milimeter number work roll surface roughness Ra 'ri,j=1.592 μm, and 1.563 μm, 1.514 μm, 1.478 μ
M, 1.457 μm,；1.614 μm, 1.586 μm, 1.528 μm, 1.492 μm, 1.477 μm；1.596 μm, 1.584 μm, 1.565 μm,
1.527 μm, 1.496 μm；1.587 μm, 1.565 μm, 1.556 μm, 1.541 μm, 1.511 μm }；
(a2) sets target function initial value G0=1.0 × 1020, defining unit equipment characteristic influences coefficient and initializes
ηmax=2, ηmin=1 and optimizing step-length △ η=0.1, setting unit equipment characteristic influence coefficient optimizing pilot process parameter k1, and
Make k1=0；
(a3) η=η is mademax-k1△ η=2；
(a4) defining draught pressure influences coefficient and initializes αPmax=-0.001, αPmin=-1 and optimizing step-length △ αP=
0.001, setting draught pressure influences coefficient optimizing pilot process parameter k2, and make k2=0；
(a5) α is madeP=αPmax-k2△αP=-0.001；
(a6) defining working roll influences coefficient with belt steel surface difference of hardness and initializes αKmax=0.1, αKmin=0.02 and seek
Excellent step-length △ αK=0.01, setting working roll influences coefficient optimizing pilot process parameter k with belt steel surface difference of hardness3, and make k3=
0；
(a7) α is madeK=αKmax-k3△αK=0.1；
(a8) define working roll roughness attenuation coefficient and initialize αLmax=-0.001, αLmin=-1 and optimizing step-length △
αL=0.001, setting working roll roughness attenuation coefficient optimizing pilot process parameter k4, and make k4=0；
(a9) α is madeL=αLmax-k4△αL=-0.001；
(a10) evaluation work roll surface roughness Rari,j, as a result as shown in table 1,
Work roll surface roughness calculated value in the case of the different rolling milimeter numbers of table 1
(a11) control function formula F is calculatedi,j(X), as a result as shown in table 1：
(a12) calculating target function formula G (X)=3.29；
(a13) inequality G=3.29 is judged<G0=1.0 × 1020Whether set upFrom step (a12) result of calculation,
Inequality result is obviously set up, then makes G0=G=3.29, optimal ηy=2, αPy=-0.001, αKy=0.1, αLy=-0.001, turn
Enter step (a14)；
(a14) inequality k is judged4Whether ＜ 999 sets upIf inequality is set up, k is made4=k4+ 1, it is transferred to step
(a9), otherwise it is transferred to step (a15)；
(a15) inequality k is judged3Whether ＜ 8 sets upIf inequality is set up, k is made3=k3+ 1, step (a7) is transferred to,
Otherwise it is transferred to step (a16)；
(a16) inequality k is judged2Whether ＜ 999 sets upIf inequality is set up, k is made2=k2+ 1, it is transferred to step
(a5), otherwise it is transferred to step (a17)；
(a17) inequality k is judged1Whether ＜ 10 sets upIf inequality is set up, k is made1=k1+ 1, it is transferred to step
(a3), otherwise it is transferred to step (a18)；
(a18) exporting optimal unit equipment characteristic influences coefficient ηy=1.2, optimal draught pressure influences factor alphaPy=-
0.021, optimal working roll influences factor alpha with belt steel surface difference of hardnessKy=0.06, optimal working roll roughness attenuation coefficient αLy
=-0.025.
(b) lower limit Ra of the working roll in rolling cycle inside surface roughness is givenr,min=0.8 μm；
(c) give coil of strip parameter n and initialize n=1；
(d) parameter of the n-th coiled strip steel is rolled in the collection work roller rolling cycle, it is coarse mainly to include working roll initial surface
Spend Rar0=1.63 μm, draught pressure pn=6878kN, working roll and belt steel surface difference of hardness Kd,n=42HS, rolling milimeter number Ln
=30km, nonextruding fluidized bed thickness ξ=0.06 μm, and optimal coefficient { 1.2, -0.021,0.06, -0.025 }；
(e) working roll initial surface roughness Ra during the (n+1)th coiled strip steel of forecast productionr,p+1=1.56 μm；
(f) Ra is judgedr,p+1=1.56≤Rar,minWhether=0.8 set upFrom step (e) result of calculation, inequality
Obviously it is invalid.Then make p=2, Rar0=1.56, it is transferred to step (c)；
(g) by calculating, when working roll rolls 54 coiled strip steel, work roll surface roughness reaches lower limit, now defeated
Go out p=54, illustrate that after producing the 54th coiled strip steel roll change, online Stand Mill wet jetting piles unit work roll surface roughness should be shut down
Forecast terminates, into next roll change cycle.
Stand Mill wet jetting piles unit work roll surface roughness result is such as in production cycle, after the production of the strips of different steel grades
Shown in table 2.
Stand Mill wet jetting piles unit work roll surface roughness value in the production cycle of table 2
The present invention is enabled an operator to by online Stand Mill wet jetting piles unit work roll surface roughness accurate forecast
Enough DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM values and roll change process system, to wet jetting piles unit working roll refinement maintenance, reduce cold-rolled steel sheet and table occur
The risk of face quality problems, while avoid and put into unnecessary time cost and technical costs, bring benefit to enterprise.
Above-mentioned embodiment is only illustrative of the invention and is not intended to limit the scope of the invention.
Claims (2)
1. a kind of forecasting procedure of Stand Mill wet jetting piles unit work roll surface roughness, it is characterized in that, comprise the following steps：
(a) Stand Mill wet jetting piles unit work roll surface roughness forecast mould of the unit for different steel grade production technologies is calculated
Type；
(b) lower limit Ra of the working roll in rolling cycle inside surface roughness is givenr,min；
(c) give coil of strip parameter n and initialize n=1；
(d) parameter of the n-th coiled strip steel is rolled in the collection work roller rolling cycle, mainly includes working roll initial surface roughness
Rar0, draught pressure pn, working roll and belt steel surface difference of hardness Kd,n, rolling milimeter number Ln, nonextruding fluidized bed thickness ξ, and
Optimal coefficient { ηy,αPy,αKy,αLy}；
(e) working roll initial surface roughness Ra during the (n+1)th coiled strip steel of forecast productionr,p+1, its expression formula is
(f) Ra is judgedr,p+1≤Rar,minWhether set upIf it is not, then p=p+1 is made, Rar0=Rar,p+1, it is transferred to step (c)；
If it is, making p=p-1, step (g) is transferred to；
(g) p is exported, illustrates that after producing pth coiled strip steel roll change should be shut down, online Stand Mill wet jetting piles unit work roll surface is thick
Rugosity forecast terminates, into next roll change cycle.
2. a kind of forecasting procedure of Stand Mill wet jetting piles unit work roll surface roughness according to claim 1, it is special
Sign is that described step (a) includes：
(a1) parameter collection, collection work roller initial surface roughness Rar0,i(i=1,2 ..., n), draught pressure pi(i=1,
2 ..., n), strip and work roll surface difference of hardness Kd,i(i=1,2 ..., n), nonextruding fluidized bed thickness ξi(i=1,
2 ..., n), rolling milimeter number Li,j(i=1,2 ..., n, j=1,2 ..., m), survey work under the conditions of difference rolling milimeter numbers
Make roll surface roughness Ra 'ri,j(i=1,2 ..., n, j=1,2 ..., m)；
(a2) sets target function initial value G0, defining unit equipment characteristic influences coefficient and initializes ηmax, ηmin, and optimizing step
Long Δ η, setting unit equipment characteristic influence coefficient optimizing pilot process parameter k1, and make k1=0；
(a3) η=η is mademax-k1Δη；
(a4) defining draught pressure influences coefficient and initializes αPmax, αPminAnd optimizing step delta αP, setting draught pressure influence system
Number optimizing pilot process parameter k2, and make k2=0；
(a5) α is madeP=αPmax-k2ΔαP；
(a6) defining working roll influences coefficient with belt steel surface difference of hardness and initializes αKmax, αKminAnd optimizing step delta αK, setting
Working roll influences coefficient optimizing pilot process parameter k with belt steel surface difference of hardness3, and make k3=0；
(a7) α is madeK=αKmax-k3ΔαK；
(a8) define working roll roughness attenuation coefficient and initialize αLmax, αLminAnd optimizing step delta αL, it is coarse to set working roll
Spend attenuation coefficient optimizing pilot process parameter k4, and make k4=0；
(a9) α is madeL=αLmax-k4ΔαL；
(a10) evaluation work roll surface roughness RaRi, j, its expression formula is
(a11) control function formula F is calculatedI, j(X)=| Ra 'Ri, j-RaRi, j|；
(a12) calculating target function formula
(a13) inequality G ＜ G are judged0Whether set upIf set up, G is made0=G, optimal ηy=η, αPy=αP, αKy=αK, αLy
=αL, step (a14) is transferred to, otherwise, is directly transferred to step (a14)；
(a14) inequality is judgedWhether set upIf inequality is set up, k is made4=k4+ 1, it is transferred to step
(a9), otherwise it is transferred to step (a15)；
(a15) inequality is judgedWhether set upIf inequality is set up, k is made3=k3+ 1, it is transferred to step
Suddenly (a7), otherwise it is transferred to step (a16)；
(a16) inequality is judgedWhether set upIf inequality is set up, k is made2=k2+ 1, it is transferred to step
Suddenly (a5), otherwise it is transferred to step (a17)；
(a17) inequality is judgedWhether set upIf inequality is set up, k is made1=k1+ 1, it is transferred to step
(a3), otherwise it is transferred to step (a18)；
(a18) exporting optimal unit equipment characteristic influences coefficient ηy, optimal draught pressure influence factor alphaPy, optimal working roll and band
Steel surface difference of hardness influences factor alphaKy, optimal working roll roughness attenuation coefficient αLy。
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| CN108580558A (en) * | 2018-04-10 | 2018-09-28 | 燕山大学 | Roller technology parameter optimization setting method under the conditions of secondary cold-rolling unit small deformation |
| CN110624957A (en) * | 2019-09-30 | 2019-12-31 | 江苏九天光电科技有限公司 | Method for controlling process lubrication system of wet temper mill set by taking roughness as target |
| CN112139255A (en) * | 2019-06-27 | 2020-12-29 | 上海梅山钢铁股份有限公司 | Elongation rate control method for double-frame wet temper mill |
| CN114589207A (en) * | 2020-12-07 | 2022-06-07 | 上海梅山钢铁股份有限公司 | Surface roughness control method of tinned substrate |
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| CN110624957A (en) * | 2019-09-30 | 2019-12-31 | 江苏九天光电科技有限公司 | Method for controlling process lubrication system of wet temper mill set by taking roughness as target |
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| CN114589207B (en) * | 2020-12-07 | 2023-05-09 | 上海梅山钢铁股份有限公司 | Surface roughness control method of tin-plated substrate |
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| CN107537866B (en) | 2019-04-19 |
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