CN107586942B - A kind of design method of the steel-casting heat treatment process based on multiple regression analysis - Google Patents
A kind of design method of the steel-casting heat treatment process based on multiple regression analysis Download PDFInfo
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Abstract
The design method for the steel-casting heat treatment process based on multiple regression analysis that the invention discloses a kind of, the key step of this method includes: the prediction model established between steel-casting heat treatment heating rate and the impact factor for influencing heating rate, and formulate a large amount of experimental program, regression analysis is carried out to experimental data, the final heating rate calculation formula for determining large-scale steel-casting heat treatment, the selection of large-scale steel-casting heat treatment process parameter is instructed with this calculation formula, avoid the blindness of heat treatment process parameter selection, it ensure that the stability of large-scale steel-casting heat treatment process, also improve the stability of casting quality.
Description
Technical field
The present invention relates to the heat treatment process of large-scale steel-casting, the steel-casting heat treatment specially based on multiple regression analysis
The design method of technique.
Background technique
Suitable heat treatment process is used to large-scale steel-casting, as-cast structure defect can be eliminated, casting stress is reduced, obtains
Obtain good mechanical property.For large-scale steel-casting, when selecting specific heat treatment process, it should guarantee that casting meets standard and wants
The mechanical property asked, guarantee again casting do not crack, flawless, deflection it is small, since casting dimension is big, tonnage is big, structure is multiple
It is miscellaneous, therefore meet above-mentioned technical requirements and to comprehensively consider process of thermal treatment method, parameter designing, process of thermal treatment parameter master
If heating rate, cooling rate, holding temperature and soaking time, wherein heating rate, the selection of cooling rate are critically important, no
Heat treatment cycle and efficiency are only influenced, the structural state of casting and the stress intensity of cast-internal are had an effect on.Heating rate, drop
Warm speed also needs the speed for considering production efficiency and heating rate, cooling rate other than the ability for being limited by heating equipment
Influence to casting stress.Different heating rate, cooling rate, the influence to cast-internal stress is different, thus generates and split
The number of line and the size of deflection are also different.The selection of heating rate and cooling rate for large-scale steel-casting, does not have at present
There are specific specification and clear and simple calculation basis.And the selection of holding temperature and soaking time, have unified and maturation
Calculation method.
At present in industry, there are some softwares that can simulate different heating, cooling speed to the stress influence of cast-internal, but
Software overhead is high, and simulation process is complicated and with duration, and analogue technique is immature, therefore restricted application.
Summary of the invention
The purpose of the present invention is to provide it is a kind of meet actual production, simply and effectively calculate large-scale steel-casting heat treatment
Heating rate and cooling rate, while can prevent casting from occurring the technological deficiency for cracking and deforming during heat treatment, and
And it is conducive to the production of large-scale steel-casting, improves production efficiency, save the cost.
The present invention proposes a kind of steel-casting Design of Heat Treatment Process method based on multiple regression analysis, this method it is specific
Steps are as follows:
Step 1: find out determine steel-casting heating rate and cooling rate factor, mainly have C, Mn, Cr, Mo, V, Ni,
The size of the mass fraction of the chemical components such as Cu, i.e. mass percent and casting, shape, thickest, structure is complicated
Degree, casting adjacent regions thickest than, casting state (such as, if by processing, welding or as cast condition), heating sets
The heat insulating ability of standby ability and power, heating equipment burner hearth and batch etc..
Step 2: finding the substantial influence factor for determining steel-casting heating rate and cooling rate from step 1, and will
It is set as independent variable X1、X2、X3、……、Xi, and the prediction model of the heating rate based on multiple regression analysis is established, it is described
Model is Yi=alpha+beta1X1+β2X2+β3X3+…+βiXi
In formula, YiExpression reaches expected heat treatment performance requirement, and when not occurring cracking, crackle, the defects of deforming
Heating rate；α is the constant term coefficient of prediction model；β1、β2、β3…βiIndicate the coefficient of prediction model independent variable；
Further, the substantial influence factor is carbon equivalent, the thickest of casting, casting adjacent regions thickest
Than.
The mass fraction of the chemical components such as C, Mn, Cr, Mo, V, Ni, Cu in addition to Fe, mass fraction is higher, corresponding
Carbon equivalent carbon equivalent is bigger, and therefore, influence of each component content to heat treatment heating rate and cooling rate can be converted into
Influence of the size of carbon equivalent to heating rate, cooling rate, therefore, material type, that is, carbon equivalent are to influence heating rate, drop
The substantial influence factor of warm speed.
The size of casting and the shape of casting, thickest, complex degree of structure are to heating rate, cooling rate
Impact effect is compared, and influence is significantly smaller.For example, when casting dimension is larger, if wall thickness dimension is smaller, structure not
Complexity, shape are also simple, and heating rate, the setting value of cooling rate are higher；When casting dimension is larger, if wall thickness dimension
It is larger, structure is uncomplicated, shape is also simple, heating rate, the setting value of cooling rate are lower；The size of casting is main
What is influenced is the requirement of corresponding heating equipment size and heating uniformity.
The thickest of casting is thicker, and heating rate, cooling rate will be slower, conversely, then heating rate, cooling rate
Want quicker.Wall thickness is thicker, if risen, temperature Rate, cooling rate are very fast, and cast(ing) surface and the center portion temperature difference will be bigger, casting
Stress will be bigger, easily leads to casting cracking or native defect expands and deformation.
The complex degree of structure of casting is mainly embodied by casting adjacent regions thickest ratio η.The size of η not only shadow
The casting solidification of casting is rung, and influences the substantial influence factor of heat treatment heating rate, cooling rate.η is bigger, heating speed
Degree, cooling rate will be slower, conversely, rising temperature Rate, cooling rate wants quicker.η is bigger, if heating rate, cooling speed
Degree is very fast, and casting heavy wall, thin-walled surface and the center portion temperature difference will be bigger, and heavy wall, thin-walled position center portion between also have the temperature difference,
The relative temperature difference of the two is larger, and the stress of casting will be bigger, easily leads to casting cracking or native defect expands and deformation.
The state ratio of casting is such as whether these mainly influence the residual of heat treating castings by processing, welding either as cast condition
Residue stress size, in general, if these residual stress are larger, casting will be more relatively slow into heating rate after furnace, avoid
Adding thermogenetic stress and residual stress of casting to be superimposed causes cracking or native defect to expand and deform.But pass through reality
Examine verifying, for large-scale steel-casting, process, the as cast condition residual stress after the stress and shake out that welded it is relatively small, and
In the heating up process of casting, i.e., some stress discharges, therefore the state of casting is heating rate, cooling rate
Non-intrinsically safe influence factor.
Power for the ability and equipment (stove) that heat the heating equipment of casting be restrict equipment maximum heating speed,
The external factor of cooling velocity, this quality factor of heating speed, cooling velocity size that non-decision casting itself should use.
The heat insulating ability of burner hearth is better, each position temperature difference of casting is smaller, heating rate, cooling rate can be quicker, conversely,
Heating rate, cooling rate will be more slowly.But furnace installation heat insulating ability used in large-scale steel-casting is preferable, generally will not deviation
It is too big, substantially within≤± 15 DEG C.Influence to heating rate, cooling rate is non-intrinsically safe influence factor.
Batch is bigger, the different parts temperature difference of casting and different casting, the casting temperature of the casting on surface layer and non-surface layer
Difference is larger, and heating rate, cooling rate need slowly, conversely, heating rate, cooling rate can be quicker.But for Big Steel Castings
Steel part, substantially a furnace fill one or furnace dress more than one piece but will not laminated multi-layer between part and part.Therefore batch is to heating speed
The influence of degree, cooling rate is non-intrinsically safe influence factor.
It is final to determine that substantial influence factor is the maximum wall of carbon equivalent, casting by production experience and theoretical foundation
Thick, casting adjacent regions thickest ratio.
Step 3: setting multiple groups different parameters value to independent variable, and a series of different heatings are set to every group of parameter value
Speed and cooling rate are poured casting under conditions of these parameter values, and in given heating rate and cooling rate condition
Under casting is heat-treated, then detect the cracking, crackle, deformation of casting.
Step 4: selection step 3 medium casting meets the parameter value of quality requirement without cracking, flawless, deflection, i.e., it is more
Group independent variable parameter value and heating rate and cooling rate corresponding to it.
Step 5:, using linear regression analysis method, determining prediction model in step 2 according to the data in step 4
Unknowm coefficient；
The heating rate calculation formula specifically obtained is VIt rises=(3.90/ (Ceq* δ * η))+20；
Wherein, VIt risesFor heating rate, unit is DEG C/h；
Ceq is carbon equivalent, and Ceq=C+Mn/6+ (Cr+Mo+V)/5+ (Ni+Cu)/15, each element is mass percent %；
δ is casting thickest, unit inch；
η is casting adjacent regions thickest ratio.
It is heat-treated the calculation formula of cooling rate are as follows: VDrop=VIt rises(10~20), unit be DEG C/h, the heat treatment cooling speed
Degree refers to the cooling velocity of casting in the heat treatment furnace.
The technical effects of the invention are that: the present invention carries out regression analysis by the data to many experiments scheme, determines
The heating rate of large-scale steel-casting heat treatment and the design principle of cooling rate, avoid the blind of heat treatment process parameter selection
Mesh ensure that the stability of large-scale steel-casting heat treatment process, also improve the stability of casting quality.
Specific embodiment
Steel-casting Design of Heat Treatment Process method based on multiple regression analysis, specific step is as follows for this method:
Step 1: find out determine steel-casting heating rate and cooling rate factor, mainly have C, Mn, Cr, Mo, V, Ni,
The size of the mass fraction of the chemical components such as Cu, i.e. mass percent and casting, shape, thickest, structure is complicated
Degree, casting adjacent regions thickest than, casting state (such as, if by processing, welding or as cast condition), heating sets
The heat insulating ability of standby ability and power, heating equipment burner hearth and batch etc..
Step 2: finding the substantial influence factor for determining steel-casting heating rate and cooling rate from step 1, and will
It is set as independent variable X1、X2、X3、……、Xi, and the prediction model of the heating rate based on multiple regression analysis is established, it is described
Model is Yi=alpha+beta1X1+β2X2+β3X3+…+βiXi；
In formula, YiExpression reaches expected heat treatment performance requirement, and when not occurring cracking, crackle, the defects of deforming
Heating rate；α is the constant term coefficient of prediction model；β1、β2、β3…βiIndicate the coefficient of prediction model independent variable.
It is final to determine that substantial influence factor is the maximum wall of carbon equivalent, casting by production experience and theoretical foundation
Thick, casting adjacent regions thickest ratio.
Step 3: set multiple groups different parameters value to independent variable, i.e., it is adjacent to carbon equivalent, the thickest of casting, casting
The position thickest parameter value more different than setting multiple groups, specific see Table 1 for details, and it is a series of different to give every group of parameter value to set
Heating rate and cooling rate, specific see Table 2 for details, according to the scheme of table 1, casting is poured under each group of parameter, and according to table 2
Given heating rate and cooling rate is heat-treated casting, after heat treatment again the cracking of detection statistics casting, split
Line, deformation.
Step 4: casting after the heat treatment of step 3, filters out casting flawless and deflection conforms to quality requirements
Experimental program, be specifically shown in Table 3.
Step 5:, using linear regression analysis method, determining prediction model in step 2 according to the data in step 4
Unknowm coefficient；
The heating rate calculation formula specifically obtained is VIt rises=(3.90/ (Ceq* δ * η))+20；
Wherein, VIt risesFor heating rate, unit is DEG C/h；
Ceq is carbon equivalent, and Ceq=C+Mn/6+ (Cr+Mo+V)/5+ (Ni+Cu)/15, each element is mass percent %；δ
For casting thickest, unit inch；η is casting adjacent regions thickest ratio.
It is heat-treated the calculation formula of cooling rate are as follows: VDrop=VIt rises(10~20), unit be DEG C/h, the heat treatment cooling speed
Degree refers to the cooling velocity of casting in the heat treatment furnace.
Example is formulateeed and implemented according to the calculation formula of heating rate of the present invention and cooling rate, verifies the reliable of the method for the present invention
Property.Table 4 is selected casting parameter, and the heating rate and cooling rate in table 5 are according to the calculation formula in the present invention program
Gained is calculated, and steel-casting is heat-treated by this heat treatment parameter, after heat treatment, whether detection casting has crackle to lack
It falls into, while measuring the size of deflection.
The calculation formula for the heating rate that the present invention finally obtains, be based on live actual production casting collect data into
Row regression analysis obtains, because applicant is engaged in the Steel Castings of many years, has accumulated a large amount of creation data, for save at
This, above-mentioned all experimental programs are all to filter out out from existing creation data, for example, same experimental group in table 1
Casting be all the same model of selection, with the steel-casting of a collection of heat casting in garbled data, it is ensured that casting chemistry at
Split-phase is same, and casting structure is identical, i.e., the thickest of casting, adjacent regions thickest are than identical.For some
Special research and development new product would generally give different heat to the casting of same batch production to obtain optimal cast properties
For treatment process parameter to verify the superiority and inferiority of heat treatment process, therefore in garbled data, it is raw for choosing research and development new product as far as possible
Data are produced, the requirement of table one had not only been can satisfy in this way but also can satisfy the requirement of table 2.
The section Example that above-described embodiment is only chosen within the scope of this invention, not as to the technology of the present invention
Limitation, any on the basis of present inventive concept, the permutation and combination for carrying out technological parameter belongs to the scope that the present invention protects.
Table 1. according to different casting parameter settings experimental group
Table 2. is to each group of setting different heat treatment parameter and verifies desired treatment results.
Table 3. filters out satisfactory data from table 2.
4. specific embodiment of table
The desired treatment results of 5. specific embodiment of table
Claims (1)
1. a kind of design method of the steel-casting heat treatment process based on multiple regression analysis, which is characterized in that the heat treatment
Technique includes the heating rate and cooling rate of heat treatment, specifically includes the following steps:
Step 1: finding out the factor for determining steel-casting heating rate and cooling rate；
Step 2: finding the substantial influence factor for determining steel-casting heat treatment heating rate and cooling rate, institute from step 1
It states substantial influence factor and includes: thickest ratio, the thickest of carbon equivalent, adjacent regions, and set it to independent variable X1、
X2、X3、……、Xi, and the prediction model of the heating rate based on multiple regression analysis is established, the model is Yi=α+β1X1+β2X2+β3X3+…+βiXi
In formula, YiExpression reaches expected heat treatment performance requirement, and heating when not occurring the defects of cracking, crackle, deformation is fast
Degree；α is the constant term coefficient of prediction model；β1、β2、β3…βiIndicate the coefficient of prediction model independent variable；
Step 3: setting multiple groups different parameters value to independent variable, and a series of different heating rates are set to every group of parameter value
And cooling rate, casting is poured under conditions of these parameter values, and right under the conditions of given heating rate and cooling rate
Casting is heat-treated, then detects the cracking, crackle, deformation of casting；
Step 4: selection step 3 medium casting meets the parameter value of quality requirement, i.e. multiple groups without cracking, flawless, deflection certainly
Variable parameter value and heating rate and cooling rate corresponding to it；
Step 5: according to the data in step 4, using linear regression analysis method, the unknown of prediction model in step 2 is determined
Coefficient, the heating rate calculation formula specifically obtained are VIt rises=(3.90/ (Ceq* δ * η))+20, the calculation formula of cooling rate are as follows:
VDrop=VIt rises(10~20), wherein VIt risesFor heating rate, unit is DEG C/h；Ceq is carbon equivalent, Ceq=C+Mn/6+ (Cr+Mo+
V)/5+ (Ni+Cu)/15, each element are mass percent %；δ is casting thickest, unit inch；η is casting adjacent portions
Position thickest ratio.
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