CN112692521A - Fine processing method for connecting node based on assembly type steel structure - Google Patents
Fine processing method for connecting node based on assembly type steel structure Download PDFInfo
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Abstract
The invention discloses a fine processing method of a connecting node based on an assembled steel structure, which comprises the following steps: step 1, heating a steel structure to be assembled in a program-controlled manner, keeping the temperature for 20-24 hours at a constant temperature after heating, and then cooling a steel structure product; step 2, assembling steel structure products to be assembled while the steel structure products are hot, welding girth welds of areas outside the high-strength bolt connection structure through high-strength bolt connection, continuously cooling the welded steel structure products, and keeping the temperature for 18-24 hours at constant temperature after cooling; step 3, after the high-strength bolt connecting structure is dismantled, welding the seam of the area until all the seams are welded; step 4, polishing the chamfer angle, the edge and the edge of the weld bead of the welded steel structure product, then hanging the steel structure in a degreasing solution, and finally washing and drying the steel structure by using clear water; step 5, placing the treated steel structural member in a solution containing caustic soda and sodium nitrate or sodium nitrite, and drying at normal temperature to form an oxide film on the surface of the steel structural member; and 6, spraying composite protective coating on the processed steel structural part, and finishing fine processing of the connection node of the assembly type steel structure after drying.
Description
Technical Field
The invention relates to the technical field of quality control of connection nodes of assembled steel structures, in particular to a fine machining method of connection nodes based on an assembled steel structure.
Background
The steel structure is natural assembled structure, and for the assembled concrete building, the assembled steel structure building has following advantage:
1) no on-site cast-in-place node exists, the installation speed is higher, and the construction quality is easier to ensure;
2) the steel structure is a ductile material, so that the steel structure has better anti-seismic performance;
3) compared with a concrete structure, the steel structure has lighter self weight and lower foundation cost;
4) the steel structure is a recyclable material, so that the environment is more green and environment-friendly;
5) the well-designed steel structure assembly type building has better economical efficiency than an assembly type concrete building.
6) The beam column has smaller section, and can obtain more usable area.
At present, beam-column joints of fabricated steel structures are mainly connected by bolting welding, but construction quality cannot be easily controlled, the steel structures of on-site welding seams are easy to corrode, and the corrosion resistance of the steel structures is reduced.
Disclosure of Invention
The invention designs and develops a fine processing method of a connecting node based on an assembly type steel structure, and aims to solve the problem of fine processing of the connecting node of the assembly type steel structure and have good corrosion resistance.
The technical scheme provided by the invention is as follows:
a fine machining method for a connecting node based on an assembly type steel structure comprises the following steps:
step 1, heating a steel structure to be assembled to a first temperature range by program control heating, keeping the temperature for 20-24 hours at a constant temperature after the temperature is raised to the highest temperature of the first temperature range, and then cooling a steel structure product to a second temperature range;
step 2, assembling the steel structure products to be assembled while the steel structure products are hot, connecting the steel structure products through high-strength bolts, carrying out girth welding on the areas outside the high-strength bolt connection structure by using argon arc welding, continuously cooling the welded steel structure products to a third temperature range, and carrying out constant temperature heat preservation for 18-24 hours when the temperature is reduced to the lowest point;
step 3, after the high-strength bolt connecting structure is dismantled, welding the seam of the area until all the seams are welded;
step 4, polishing the chamfer angle, the edge and the edge of the weld bead of the welded steel structure product, then suspending the steel structure member in a degreasing solution, allowing the degreasing solution to freely flow on the surface of the steel structure member in a circulating manner, and finally washing and drying the steel structure member by using clear water;
step 5, placing the steel structural part treated in the step 4 into a solution containing caustic soda and sodium nitrate or active sodium nitrite, and drying at normal temperature to form an oxide film on the surface of the steel structural part;
and 6, spraying composite protective coating on the steel structural part processed in the step 5, and drying to finish fine processing of the connection node of the assembly type steel structure.
Preferably, in the step 1, the first temperature range is 400-600 ℃, and the temperature rise range of the steel structure to the first temperature range is 1.0-2.5 ℃/min; and
and the second temperature interval is 40-100 ℃, and the cooling range of the steel structure from the first temperature interval to the second temperature interval is 0.5-1.5 ℃/min.
Preferably, in the step 2, the third temperature interval is 5 to 10 ℃, and the cooling range of the steel structure from the second temperature interval to the third temperature interval is 0.1 to 0.5 ℃/min.
Preferably, in the step 2, the control of the girth welding based on the BP neural network includes:
step 2.1, measuring a first temperature T of the steel structure when the temperature is raised to a first temperature interval in program control heating through a sensor according to a sampling periodaDetermining the temperature rise amplitude AaMeasuring the second temperature T of the steel structure from the first temperature interval to the second temperature interval through a sensorbDetermining the cooling amplitude Ab；
Step 2.2, normalizing the parameters in sequence to determine the output of the three-layer BP neural networkInpayer vector x ═ x1,x2,x3,x4}; wherein x is1Is a first temperature coefficient, x2Is a temperature rise amplitude coefficient, x3Is the second temperature coefficient, x4Is a cooling amplitude coefficient;
step 2.3, mapping the input layer vector to a middle layer, wherein the middle layer vector y is { y ═ y1,y2,…,ymM is the number of intermediate layer nodes;
step 2.4, obtaining an output layer vector o ═ { o ═ o1,o2,o3,o4}；o1Welding current regulation factor for girth welding, o2Arc voltage regulation factor for girth welding, o3Adjustment factor of welding speed for girth welding, o4A weld line energy adjustment factor for girth welding;
step 2.5, controlling the welding current, the arc voltage, the welding speed and the welding line energy of the girth welding to ensure that
Wherein the content of the first and second substances,
respectively outputting the first four parameters of the layer vector, I, for the ith sampling periodmax、Umax、Vmax、QmaxRespectively the maximum welding current set during girth weldingMaximum arc voltage, maximum welding speed and maximum welding line energy, Ii+1、Ui+1、Vi+1、Qi+1Respectively the welding current, the arc voltage, the welding speed and the welding line energy set in the (i + 1) th sampling period.
Preferably, the number m of the intermediate layer nodes satisfies:
wherein n is the number of nodes of the input layer, and p is the number of nodes of the output layer.
Preferably, in step 2.2, the first temperature and the second temperature are normalized by the formula:
wherein, Ta_maxIs the maximum temperature, T, of the first temperature intervala_minIs the minimum temperature, T, of the first temperature intervalb_maxIs the maximum temperature, T, of the second temperature intervalb_minIs the minimum temperature of the second temperature interval.
Preferably, in step 2.2, the formula for normalizing the temperature rise amplitude is as follows:
wherein the content of the first and second substances,
in the formula, Ta_maxIs the maximum temperature, T, of the first temperature intervala_minIs the minimum temperature of the first temperature interval, Aa_maxTo maximize the temperature rise, Aa_minAt a minimum temperature rise amplitude, Aa_0For normalizing comparison of heating amplitudes, λ1Normalizing the first empirical regulation coefficient for the temperature rise amplitude, wherein the value range is 0.87-0.93 and lambda2And normalizing the second empirical regulation coefficient for the temperature rise amplitude, wherein the value range is 0.28-0.35.
Preferably, in step 2.2, the formula for normalizing the cooling amplitude is as follows:
wherein the content of the first and second substances,
in the formula, Tb_maxIs the maximum temperature, T, of the second temperature intervalb_minIs the minimum temperature of the second temperature interval, Ab_maxTo the maximum extent of cooling, Ab_minTo a minimum cooling amplitude, Ab_0For normalized comparison of cooling amplitude, gamma1Normalizing the first experimental adjustment coefficient for the cooling amplitude, wherein the value range is 0.93-1.01 and gamma is2And normalizing the second empirical regulation coefficient for the cooling amplitude, wherein the value range is 0.88-0.96.
Preferably, in step 2.2, the initial operating state, the welding current, the arc voltage, the welding speed and the welding line energy of the control girth welding satisfy empirical values:
I0＝0.85Imax；
U0＝0.87Umax；
V0＝0.75Vmax；
Q0＝0.94Qmax；
wherein, I0、U0、V0、Q0Respectively, initial welding current, initial arc voltage, initial welding speed and initial welding line energy in the girth welding process, Imax、Umax、Vmax、QmaxMaximum welding current and maximum arc current set in the girth welding processPressure, maximum welding speed, and maximum weld line energy.
Preferably, λ1A value of 0.9, λ2Value of 0.3, gamma1The value is 0.97, gamma2The value is 0.94.
Compared with the prior art, the invention has the following beneficial effects: according to the invention, after preheating and cooling treatment of the assembled steel structure, the welding process of the connecting node is finely processed through the control of the BP neural network, and meanwhile, the connecting node is subjected to anticorrosion treatment.
Detailed Description
The present invention is described in further detail below to enable those skilled in the art to practice the invention with reference to the description.
The invention provides a fine processing method of a connecting node based on an assembled steel structure, which comprises the following steps:
step 1, heating a steel structure to be assembled to a first temperature range by program control heating, keeping the temperature for 20-24 hours at a constant temperature after the temperature is raised to the highest temperature of the first temperature range, and then cooling a steel structure product to a second temperature range;
step 2, assembling the steel structure products to be assembled while the steel structure products are hot, connecting the steel structure products through high-strength bolts, welding girth welds of areas outside the high-strength bolt connection structure through argon arc welding, continuously cooling the welded steel structure products to a third temperature range, and keeping the temperature for 18-24 hours at constant temperature when the temperature is reduced to the lowest point;
step 3, after the high-strength bolt connecting structure is dismantled, welding the seam of the area until all the seams are welded;
step 4, polishing the chamfer angle, the edge and the edge of the weld bead of the welded steel structure product, then suspending the steel structure member in a degreasing solution, allowing the degreasing solution to freely flow on the surface of the steel structure member in a circulating manner, and finally washing and drying the steel structure member by using clear water;
step 5, placing the steel structural part treated in the step 4 into a solution containing caustic soda and sodium nitrate or active sodium nitrite, and drying at normal temperature to form an oxide film on the surface of the steel structural part;
and 6, spraying composite protective paint on the steel structural part processed in the step 5, forming a protective layer outside the oxide film layer after drying, and finishing fine processing of the connection node of the assembly type steel structure.
In another embodiment, the first temperature range is 400-600 ℃, the temperature rising range of the steel structure from the first temperature range to the first temperature range is 1.0-2.5 ℃/min, the second temperature range is 40-100 ℃, the temperature falling range of the steel structure from the first temperature range to the second temperature range is 0.5-1.5 ℃/min, the third temperature range is 5-10 ℃, and the temperature falling range of the steel structure from the second temperature range to the third temperature range is 0.1-0.5 ℃/min.
In another embodiment, in the welding process, the steel structure product is welded by argon arc welding, the number of welding seams in a single welding pass is controlled to be 25-45 cm/layer, meanwhile, 2 or 3 welding seams are prevented from being vertically crossed, the thickness deviation of the welding seams is controlled to be 0.2-0.5 cm, and the number of welding passes in a single welding layer is controlled to be 4-9/layer.
In another embodiment, in the welding process, the welding current is 600-650A, the arc voltage is 30-40V, the welding speed is 28-32cm/min, the welding line energy is 40-60KJ/cm, the preheating temperature is 90-110 ℃, the interchannel temperature is 130-200 ℃, and the post-welding hydrogen elimination conditions are 290-310 ℃ and 2.0-3.0 h.
The invention adopts BP neural network to control girth welding, comprising the following steps:
and 3.1, establishing a BP neural network model.
The BP network system structure adopted by the invention is composed of three layers, wherein the first layer is an input layer, n nodes are provided in total, n detection signals representing the working state of the equipment are correspondingly provided, and the signal parameters are provided by a data preprocessing module. The second layer is a hidden layer, and has m nodes, and is determined by the training process of the network in a self-adaptive mode. The third layer is an output layer, p nodes are provided in total, and the output is determined by the response actually needed by the system.
The mathematical model of the network is:
inputting a vector: x ═ x1,x2,...,xn)T
Intermediate layer vector: y ═ y1,y2,...,ym)T
Outputting a vector: o ═ O1,o2,...,op)T
In the invention, the number of nodes of the input layer is n equal to 4, and the number of nodes of the output layer is p equal to 4. The number m of hidden layer nodes is estimated by the following formula:
the input signal has 4 parameters expressed as: x is the number of1Is a first temperature coefficient, x2Is a temperature rise amplitude coefficient, x3Is the second temperature coefficient, x4Is the cooling amplitude coefficient.
The data acquired by the sensors belong to different physical quantities, and the dimensions of the data are different. Therefore, the data needs to be normalized to a number between 0-1 before it is input into the artificial neural network.
Specifically, the method is used for measuring the first temperature T of the steel structure when the temperature of the steel structure is raised to the first temperature interval by program control heating by using a temperature sensoraNormalized to obtain a first temperature coefficient x1：
Wherein, Ta_maxIs the maximum temperature, T, of the first temperature intervala_minIs the minimum temperature of the first temperature interval.
Temperature rise amplitude AaAfter normalization, a temperature rise amplitude coefficient x is obtained2：
Wherein the content of the first and second substances,
in the formula, Ta_maxIs the maximum temperature, T, of the first temperature intervala_minIs the minimum temperature of the first temperature interval, Aa_maxTo maximize the temperature rise, Aa_minAt a minimum temperature rise amplitude, Aa_0For normalizing comparison of heating amplitudes, λ1Normalizing the first empirical regulation coefficient for the temperature rise amplitude, wherein the value range is 0.87-0.93 and lambda2And normalizing the second empirical regulation coefficient for the temperature rise amplitude, wherein the value range is 0.28-0.35.
For measuring a second temperature T for a temperature drop from a first temperature interval to a second temperature interval using a temperature sensorbNormalized to obtain a second temperature coefficient x3：
Wherein, Tb_maxIs the maximum temperature, T, of the second temperature intervalb_minIs the minimum temperature of the second temperature interval.
Extent of temperature reduction AbAfter normalization, obtaining a cooling amplitude coefficient x4：
Wherein the content of the first and second substances,
in the formula, Tb_maxIs the maximum temperature, T, of the second temperature intervalb_minIs the minimum temperature of the second temperature interval, Ab_maxTo the maximum extent of cooling, Ab_minTo a minimum cooling amplitude, Ab_0For normalized comparison of cooling amplitude, gamma1Normalizing the first experimental adjustment coefficient for the cooling amplitude, wherein the value range is 0.93-1.01 and gamma is2The second empirical adjustment factor is normalized for the magnitude of the temperature decrease,the value range is 0.88-0.96.
In another embodiment, λ1A value of 0.9, λ2Value of 0.3, gamma1The value is 0.97, gamma2The value is 0.94.
The 4 parameters of the output signal are respectively expressed as: o1Welding current regulation factor for girth welding, o2Arc voltage regulation factor for girth welding, o3Adjustment factor of welding speed for girth welding, o4The adjustment coefficient of the welding line energy for girth welding.
Welding current regulation factor o of girth welding1Is expressed as the ratio of the welding current in the next sampling period to the set maximum welding current in the current sampling period, i.e. in the ith sampling period, the collected welding current is IiOutputting the welding current regulation coefficient of the ith sampling period through a BP neural network
Then, the welding current in the (I + 1) th sampling period is controlled to be Ii+1To make it satisfy
Arc voltage regulation factor o of girth welding2Expressed as the ratio of the arc voltage in the next sampling period to the set maximum arc voltage in the current sampling period, i.e. in the ith sampling period, the collected arc voltage is UiOutputting the arc voltage regulation coefficient of the ith sampling period through a BP neural network
Then, controlling the arc voltage in the (i + 1) th sampling period to be Ui+1To make it satisfy
Girth weldingWelding speed adjustment coefficient o3Expressed as the ratio of the welding speed in the next sampling period to the set maximum welding speed in the current sampling period, i.e. in the ith sampling period, the welding speed is acquired as ViOutputting the welding speed regulating coefficient of the ith sampling period through a BP neural network
Then, the welding speed in the (i + 1) th sampling period is controlled to be Vi+1To make it satisfy
Coefficient of adjustment o of weld line energy for girth welding4Expressed as the ratio of the welding line energy in the next sampling period to the set maximum welding line energy in the current sampling period, i.e. in the ith sampling period, the welding line energy collected is QiOutputting the welding line energy regulating coefficient of the ith sampling period through a BP neural network
Then, the welding line energy in the (i + 1) th sampling period is controlled to be Qi+1To make it satisfy
And 2.2, training the BP neural network.
After the BP neural network node model is established, the training of the BP neural network can be carried out. Obtaining a training sample according to historical experience data of the product, and giving a connection weight w between an input node i and a hidden layer node jijConnection weight w between hidden layer node j and output layer node kjkThreshold value theta of hidden layer node jjThreshold value theta of output layer node kk、wij、wjk、θj、θkAre all random numbers between-1 and 1.
Continuously correcting w in the training processijAnd wjkUntil the system error is less than or equal to the expected error, the training process of the neural network is completed.
As shown in table 1, a set of training samples is given, along with the values of the nodes in the training process.
TABLE 1 training Process node values
And 2.3, inputting the operation parameters into a neural network to obtain a regulation and control coefficient in the process of collecting girth welding.
After the intelligent hardware is powered on and started, the welding current, the arc voltage, the welding speed and the welding line energy are all maximum values, and the intelligent hardware starts to operate, namely when in an initial operation state, the initial welding current is I0＝0.85ImaxInitial arc voltage of U0＝0.87UmaxInitial welding speed is V0＝0.75VmaxInitial weld line energy of Q0＝0.94Qmax。
Simultaneously determining an initial first temperature Ta0Initial temperature rise amplitude Aa0Initial second temperature Tb0And an initial cooling amplitude Ab0Normalizing the parameters to obtain an initial input vector of the BP neural network
Obtaining an initial output vector through operation of a BP neural network
And 2.4, controlling the welding current, the arc voltage, the welding speed and the welding line energy of girth welding.
Obtaining an initial output vector
Then, the adjustment and control of the circular seam can be carried outThe welding current, the arc voltage, the welding speed and the welding line energy of the welding are respectively as follows, wherein the welding current, the arc voltage, the welding speed and the welding line energy of the girth welding are controlled in the next sampling period:
obtaining the first temperature T of the ith sampling periodaiTemperature rising amplitude AaiA second temperature TbiAnd a temperature reduction range AbiObtaining the input vector of the ith sampling period by formatting
Obtaining an output vector to the ith sampling period through the operation of a BP neural network
And then controlling the welding current, the arc voltage, the welding speed and the welding line energy of the girth welding, so that the welding current, the arc voltage, the welding speed and the welding line energy of the girth welding in the (i + 1) th sampling period are respectively as follows:
through the arrangement, the welding state in the girth welding process is detected in real time, and the welding current, the arc voltage, the welding speed and the welding line energy are regulated and controlled by adopting a BP neural network algorithm, so that the girth welding process reaches the nearest running state, and the fine processing of the connecting node is realized.
While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the generic concept as defined by the claims and their equivalents.
Claims (10)
1. A fine machining method for a connecting node based on an assembly type steel structure is characterized by comprising the following steps:
step 1, heating a steel structure to be assembled to a first temperature range by program control heating, keeping the temperature for 20-24 hours at a constant temperature after the temperature is raised to the highest temperature of the first temperature range, and then cooling a steel structure product to a second temperature range;
step 2, assembling the steel structure products to be assembled while the steel structure products are hot, connecting the steel structure products through high-strength bolts, carrying out girth welding on the areas outside the high-strength bolt connection structure by using argon arc welding, continuously cooling the welded steel structure products to a third temperature range, and carrying out constant temperature heat preservation for 18-24 hours when the temperature is reduced to the lowest point;
step 3, after the high-strength bolt connecting structure is dismantled, welding the seam of the area until all the seams are welded;
step 4, polishing the chamfer angle, the edge and the edge of the weld bead of the welded steel structure product, then suspending the steel structure member in a degreasing solution, allowing the degreasing solution to freely flow on the surface of the steel structure member in a circulating manner, and finally washing and drying the steel structure member by using clear water;
step 5, placing the steel structural part treated in the step 4 into a solution containing caustic soda and sodium nitrate or active sodium nitrite, and drying at normal temperature to form an oxide film on the surface of the steel structural part;
and 6, spraying composite protective coating on the steel structural part processed in the step 5, and drying to finish fine processing of the connection node of the assembly type steel structure.
2. The method for finely processing the connection node based on the fabricated steel structure according to claim 1, wherein in the step 1, the first temperature interval is 400-600 ℃, and the temperature rise range of the steel structure until the first temperature interval is 1.0-2.5 ℃/min; and
and the second temperature interval is 40-100 ℃, and the cooling range of the steel structure from the first temperature interval to the second temperature interval is 0.5-1.5 ℃/min.
3. The method for finely processing the connection node based on the fabricated steel structure as claimed in claim 2, wherein in the step 2, the third temperature interval is 5-10 ℃, and the cooling range of the steel structure from the second temperature interval to the third temperature interval is 0.1-0.5 ℃/min.
4. The method for finely machining a connection node based on an assembled steel structure according to claim 3, wherein in the step 2, the control of the girth welding based on the BP neural network comprises the following steps:
step 2.1, measuring the temperature of the steel structure in the interval of program-controlled heating to the first temperature through a sensor according to a sampling periodFirst temperature TaDetermining the temperature rise amplitude AaMeasuring the second temperature T of the steel structure from the first temperature interval to the second temperature interval through a sensorbDetermining the cooling amplitude Ab；
Step 2.2, normalizing the parameters in sequence, and determining an input layer vector x ═ x of the three-layer BP neural network1,x2,x3,x4}; wherein x is1Is a first temperature coefficient, x2Is a temperature rise amplitude coefficient, x3Is the second temperature coefficient, x4Is a cooling amplitude coefficient;
step 2.3, mapping the input layer vector to a middle layer, wherein the middle layer vector y is { y ═ y1,y2,…,ymM is the number of intermediate layer nodes;
step 2.4, obtaining an output layer vector o ═ { o ═ o1,o2,o3,o4}；o1Welding current regulation factor for girth welding, o2Arc voltage regulation factor for girth welding, o3Adjustment factor of welding speed for girth welding, o4A weld line energy adjustment factor for girth welding;
step 2.5, controlling the welding current, the arc voltage, the welding speed and the welding line energy of the girth welding to ensure that
Wherein the content of the first and second substances,
respectively outputting the first four parameters of the layer vector, I, for the ith sampling periodmax、Umax、Vmax、QmaxRespectively the maximum welding current, the maximum arc voltage, the maximum welding speed and the maximum welding line energy set in the girth welding process, Ii+1、Ui+1、Vi+1、Qi+1Respectively the welding current, the arc voltage, the welding speed and the welding line energy set in the (i + 1) th sampling period.
5. The fine processing method of the connecting node based on the assembly type steel structure as claimed in claim 3, wherein the number m of the intermediate layer nodes satisfies the following conditions:
wherein n is the number of nodes of the input layer, and p is the number of nodes of the output layer.
6. A method for refining a connection node based on an assembled steel structure according to claim 4, wherein in step 2.2, the formula for normalizing the first temperature and the second temperature is:
wherein, Ta_maxIs the maximum temperature, T, of the first temperature intervala_minIs the minimum temperature, T, of the first temperature intervalb_maxIs the maximum temperature, T, of the second temperature intervalb_minIs the minimum temperature of the second temperature intervalAnd (4) degree.
7. A method for refining a connection node based on an assembled steel structure according to claim 4, wherein in step 2.2, the formula for normalizing the temperature rise amplitude is as follows:
wherein the content of the first and second substances,
in the formula, Ta_maxIs the maximum temperature, T, of the first temperature intervala_minIs the minimum temperature of the first temperature interval, Aa_maxTo maximize the temperature rise, Aa_minAt a minimum temperature rise amplitude, Aa_0For normalizing comparison of heating amplitudes, λ1Normalizing the first empirical regulation coefficient for the temperature rise amplitude, wherein the value range is 0.87-0.93 and lambda2And normalizing the second empirical regulation coefficient for the temperature rise amplitude, wherein the value range is 0.28-0.35.
8. A method for refining a connection node based on an assembled steel structure according to claim 4, wherein in step 2.2, the formula for normalizing the cooling amplitude is as follows:
wherein the content of the first and second substances,
in the formula, Tb_maxIs the maximum temperature, T, of the second temperature intervalb_minIs the minimum temperature of the second temperature interval, Ab_maxTo the maximum extent of cooling, Ab_minTo a minimum cooling amplitude, Ab_0For normalized comparison of cooling amplitude, gamma1Normalizing the first experimental adjustment coefficient for the cooling amplitude, wherein the value range is 0.93-1.01 and gamma is2And normalizing the second empirical regulation coefficient for the cooling amplitude, wherein the value range is 0.88-0.96.
9. A method for refining a connection node based on an assembled steel structure according to claim 4, characterized in that in step 2.2, the initial operating state, the welding current, the arc voltage, the welding speed and the welding line energy of the control girth welding satisfy empirical values:
I0＝0.85Imax；
U0＝0.87Umax；
V0＝0.75Vmax；
Q0＝0.94Qmax；
wherein, I0、U0、V0、Q0Respectively, initial welding current, initial arc voltage, initial welding speed and initial welding line energy in the girth welding process, Imax、Umax、Vmax、QmaxThe maximum welding current, the maximum arc voltage, the maximum welding speed and the maximum welding line energy which are set in the girth welding process are respectively set.
10. The method for finely machining a connection node based on an assembled steel structure according to claim 3, wherein λ1A value of 0.9, λ2Value of 0.3, gamma1The value is 0.97, gamma2The value is 0.94.
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