Precise seamless steel pipe cold drawing process quality online evaluation method

Abstract

The invention provides a precision seamless steel tube cold drawing process quality on-line evaluation method, and relates to the technical field of precision seamless steel tube processing, and the technical scheme is as follows: collecting hydraulic cylinder double-cavity oil pressure and steel tube displacement data in real time, and dynamically calculating five parameters including drawing force, change rate, accumulative impulse, rigidity and tensile stress; and establishing a five-stage progressive anomaly judgment mechanism by combining a drawing force reference objective function constructed by historical qualified data: firstly, capturing a pipe breakage risk based on instantaneous overrun of a drawing force change rate; secondly, the work hardening cumulative effect is recognized through the continuous abnormal frequency; and depth verification is carried out by combining an accumulated impulse boundary, a rigidity theoretical range and a material strength threshold value. The method has the beneficial effects that online monitoring of the whole process of the cold drawing process is achieved, the abnormal response speed is greatly increased, and waste of raw materials is reduced; and meanwhile, the consistency of the yield strength of the product is improved by quantifying the work hardening effect, and a process knowledge base is constructed to drive intelligent manufacturing iterative optimization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of precision seamless steel pipe processing, and particularly relates to a precision seamless steel pipe cold-drawing process quality online evaluation method. BACKGROUND

[0002] Steel pipe drawing is an important metal plastic processing technology. By placing a pre-formed pipe blank on a drawing machine and applying a pulling force to the pipe blank through a drawing die, plastic deformation of the pipe blank occurs through the die hole, thereby achieving precise control of the outer diameter, wall thickness and length of the steel pipe. During the drawing process, the internal structure of the pipe blank changes under the constraint of the die, the grains are refined, and the strength, hardness and surface quality of the steel pipe are improved. This process can produce high-precision and high-performance seamless steel pipes, which are widely used in fields such as petroleum, chemical industry, machinery manufacturing, construction engineering, etc., such as oil drilling pipes, hydraulic support pipes, automobile transmission shaft pipes, etc.

[0003] Currently, steel pipe quality detection is mostly carried out offline after drawing, such as through ultrasonic flaw detection, eddy current detection, etc. to check internal defects and surface quality of the steel pipe. This post-detection method cannot timely discover quality problems in the drawing process, and once a batch of unqualified products appears, it will cause a large amount of waste of raw materials, energy and working hours, and increase production cost. Secondly, in the steel pipe drawing production process, due to the lack of comprehensive recording and effective management of production process data, once a quality problem occurs, it is difficult to trace back to specific production links and influencing factors, which is not conducive to the analysis and improvement of quality problems, and also cannot provide effective data support and decision basis for subsequent production. SUMMARY

[0004] The present application aims to overcome the problems in the background art and provide a precision seamless steel pipe cold-drawing process quality online evaluation method.

[0005] The technical idea of the present application is to collect hydraulic cylinder double-cavity oil pressure and steel pipe displacement data in real time during the cold-drawing process, dynamically calculate five core parameters of drawing force, drawing force change rate, cumulative impulse, steel pipe stiffness and tensile stress, and establish a five-level progressive abnormality judgment mechanism combined with a drawing force reference target function constructed based on historical qualified data. Specifically, the first level is based on instantaneous overrun judgment of the drawing force change rate; the second level captures the cumulative effect through continuous abnormal frequency; the third to fifth levels respectively perform boundary control through cumulative impulse, stiffness theoretical range and material strength threshold. This method realizes online real-time evaluation of the cold-drawing process quality, breaks through the hysteresis of traditional offline detection, effectively prevents the generation of batch defective products through multi-parameter fusion analysis and hierarchical early warning mechanism, and at the same time provides data support for process optimization.

[0006] In order to achieve the above object, the present application adopts the technical scheme and specifically is a precise seamless steel pipe cold-drawing process quality on-line evaluation method, comprising the following steps:

[0007] S1 first screens out the cold-drawing oil pressure data and the steel pipe cold-drawing elongation data in the steel pipe cold-drawing history database, secondly carries out discrete processing on the cold-drawing oil pressure data and the cold-drawing elongation data respectively to obtain the oil pressure data matrix and the elongation data matrix after cold-drawing, and finally converts the oil pressure data matrix into the drawing force data matrix;

[0008] S2 fits the drawing force reference target function according to the drawing force data matrix;

[0009] S3 starts cold-drawing, collects the current left cavity oil pressure value, the current right cavity oil pressure value and the current cold-drawing displacement value at regular time intervals, and according to the current collected data, obtains the current drawing force value, the current drawing force change rate, the current steel pipe cumulative impulse, the current steel pipe stiffness and the current steel pipe tensile stress respectively;

[0010] S4 judges the current cold-drawing state according to the current drawing force change rate;

[0011] S5 judges the current cold-drawing state according to the current drawing force change rate and the cumulative variable;

[0012] S6 judges the current cold-drawing state according to the current steel pipe cumulative impulse;

[0013] S7 judges the current cold-drawing state according to the current tensile stiffness;

[0014] S8 judges the current cold-drawing state according to the current tensile stress;

[0015] S9 repeats steps S3-S9.

[0016] Further, step S1 specifically comprises the following steps:

[0017] S1.1, in the steel pipe cold-drawing history data, screens out the Z groups of left cavity oil pressure data of hydraulic cylinders, the Z groups of right cavity oil pressure data of hydraulic cylinders and the Z groups of steel pipe elongation data after cold-drawing;

[0018] S1.2, selects Q oil pressure data points at equal time intervals for the Z groups of left cavity oil pressure data and the Z groups of right cavity oil pressure data respectively to obtain the left cavity oil pressure data matrix {P left ij} and the right cavity oil pressure data matrix {P right ij}, wherein i=1, 2, 3,..., Z, j=1, 2, 3,..., Q;

[0019] S1.3, obtains the steel pipe elongation data matrix {ΔL i1} after cold-drawing according to the Z groups of steel pipe elongation data after cold-drawing, wherein i=1, 2, 3,..., Z;

[0020] S1.4, according to the conversion formula of drawing force and oil pressure F ij = (P left ij A left -P right ij A right ), the left chamber oil pressure data matrix {P left ij} and the right chamber oil pressure data matrix {P right ij} are converted into the drawing force data matrix {F ij}, i = 1, 2, …, Z, j = 1, 2, …, Q, wherein i is the group index, j is the time index, A left is the piston area of the left chamber of the hydraulic cylinder, and A right is the piston area of the right chamber of the hydraulic cylinder.

[0021] Further, step S2 is specifically: according to the drawing force data matrix {F ij}, a steel pipe cold drawing drawing force reference target function F ref (t) is fitted, that is

[0022]

[0023] Wherein, T is the total drawing time;

[0024]

[0025] Further, step S3 is specifically:

[0026] S3.1, after the steel pipe cold drawing starts, collect once every Δt time, and count the current time as t n = nΔt, n is the current collection number, and each collection respectively obtains the current left chamber oil pressure value P left (t n ), the current right chamber oil pressure value P right (t n ) and the current cold drawing displacement value x(t n );

[0027] S3.2, according to the current left chamber oil pressure value P left (t n ) and the current right chamber oil pressure value P right (t n ) collected, the current drawing force value F(t n ) = P left (t n )A left -P right (t n )A right is obtained, and according to the current drawing force value F(t n ) and the current cold drawing displacement value x(tn ), respectively, to calculate the current drawing force change rate Current steel pipe cumulative impulse Current tensile stiffness And the current tensile stress Where n is the current acquisition number, and A is the cross-sectional area of the steel pipe before cold drawing.

[0028] Further, step S4 is specifically: during the cold drawing process, if the current drawing force change rate k(t n )>α max k max Or k(t n )<α min k min is determined to be currently cold-drawing abnormal, otherwise the next step is executed, wherein k min =min{k1, k2,..., k Q}<0, k max =max{k1, k2,..., k Q}>0, α max is the upper limit coefficient of the maximum and minimum abnormality of the drawing force change rate, and α min is the lower limit coefficient of the maximum and minimum abnormality of the drawing force change rate.

[0029] Further, step S5 is specifically: during the cold drawing process, let N be the cumulative variable, and its initial value is 0, if the current drawing force change rate k(t n )>β max k j Or k(t n )<β min k j , and the cumulative variable N n <β max k j Or k(t n )<β min k j , and the cumulative variable N≥M, then it is determined that the current cold drawing is abnormal; if the current drawing force change rate β min k j k(t n )β max k j is detected, the next step is executed, wherein j=n, β max is the upper limit coefficient of the abnormality of the drawing force change rate, β min is the lower limit coefficient of the abnormality of the drawing force change rate, and M is a preset threshold.

[0030] Further, step S6 is specifically: during the cold drawing process, if the current steel pipe cumulative impulse or , then the current cold-drawing is determined to be abnormal; otherwise, the next step is performed, wherein λ max is an upper limit coefficient of the cumulative impulse abnormality, λ min is a lower limit coefficient of the cumulative impulse abnormality.

[0031] Further, the step S7 is specifically: during the cold-drawing, if the current tensile stiffness K(t) is detected to be greater than K or , then the current cold-drawing is determined to be abnormal; otherwise, the next step is performed, wherein E is the elastic modulus of the current drawing material, A is the cross-sectional area of the steel pipe before the cold-drawing, δ max is an upper limit coefficient of the tensile stiffness abnormality, and δ min is a lower limit coefficient of the tensile stiffness abnormality.

[0032] Further, the step S8 is specifically: during the cold-drawing, if the current tensile stress σ(t) is detected to be greater than σ max , then the current cold-drawing is determined to be abnormal; otherwise, the next step is performed, wherein σ max is the tensile limit strength of the current cold-drawing material.

[0033] Further, the parameters satisfy: the value range of the number of oil pressure groups Z is Z=10-15; the value range of the number of oil pressure points Q is Q=50T-60T, T is a unit of second; the value range of the preset threshold M is M=10-15; the value range of the upper limit coefficient α max of the maximum value of the drawing force change rate is α max =2.5-3; the value range of the lower limit coefficient α min of the maximum value of the drawing force change rate is α min =0.3-0.4; the value range of the abnormality upper limit coefficient β max of the drawing force change rate is β max =1.5-2; the value range of the abnormality lower limit coefficient β min of the drawing force change rate is β min =0.5-0.7; the value range of the upper limit coefficient λ max of the cumulative impulse abnormality is λ max =2-4; the value range of the lower limit coefficient λ min of the cumulative impulse abnormality is λ min =0.3-0.5; the value range of the upper limit coefficient δ max of the tensile stiffness abnormality is δ max =2-2.5; and the value range of the lower limit coefficient δ min of the tensile stiffness abnormality is δ min =0.4-0.5.

[0034] Compared with the prior art, the present application has the following advantages:

[0035] 1. The present application acquires the double-cavity oil pressure and the steel pipe displacement data in real time, dynamically calculates the five core parameters of drawing force, change rate, cumulative impulse, stiffness and tensile stress, constructs a drawing force reference target function combined with historical qualified data, and initiates a five-level progressive abnormality judgment mechanism: the first level captures the pipe breaking risk based on the instantaneous overrun of the drawing force change rate; the second level identifies continuous micro-abnormalities (such as work hardening cumulative effect) through cumulative variables; the third to fifth levels verify the cumulative impulse boundary, the stiffness theoretical range and the material strength threshold value respectively. This method breaks through the traditional offline detection hysteresis, realizes the whole-process online quality monitoring of the cold drawing process, greatly improves the abnormal response speed, reduces the waste of raw materials; at the same time, it quantifies the work hardening effect through the cumulative impulse, accurately feeds back the microstate of the material combined with the dynamic analysis of the stiffness, greatly improves the consistency of the yield strength of the product, and provides data support for process optimization. The whole-process parameters are automatically recorded to form a process knowledge base, drive the drawing force reference function to update iteratively, greatly improve the fault diagnosis efficiency, and build an intelligent manufacturing closed-loop decision system.

[0036] 2. The precision seamless steel pipe cold drawing process quality online evaluation method of the present application can obtain the current cumulative impulse, the current drawing force change rate, the current tensile stress and the current stiffness and other steel pipe cold drawing performance indicators through the conversion of the values collected by the oil pressure sensor and the displacement sensor, and evaluate the current steel pipe quality together with the historical data.

[0037] 3. The precision seamless steel pipe cold drawing process quality online evaluation method of the present application judges the steel pipe quality through the cumulative impulse and the drawing force change rate, and fully considers the nonlinear cumulative effect process of work hardening in the steel pipe cold drawing process.

[0038] 4. Through the precision seamless steel pipe cold drawing process quality online evaluation method of the present application, the values of the sensors are collected in real time, real-time quality evaluation is made to timely issue a warning, and a large amount of pipe waste is avoided, while the production efficiency is greatly improved.

[0039] 5. The precision seamless steel pipe cold drawing process quality online evaluation method of the present application monitors the steel pipe performance indicators in real time throughout the whole cold drawing process, which provides detailed data basis for subsequent product research and development and process improvement. BRIEF DESCRIPTION OF DRAWINGS

[0040] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, illustrate the present application together with the embodiments, and are used to explain the present application, and do not constitute a limitation on the present application.

[0041] Fig. 1 It is a schematic diagram of the principle of the seamless steel pipe hydraulic cold drawing process quality online evaluation method.

[0042] Fig. 2 Cumulative impulse principle diagram for quality line evaluation method of hydraulic cold-drawing process of seamless steel pipe.

[0043] Fig. 3 Flow chart of quality online evaluation method for hydraulic cold-drawing process of seamless steel pipe. DETAILED DESCRIPTION

[0044] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. Of course, the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application.

[0045] Embodiment: As shown in the figure, the present embodiment provides a precision seamless steel pipe cold-drawing process quality online evaluation method, comprising the following steps: Figs. 1-3

[0046] Step one: first, filter out the cold-drawing oil pressure data and the steel pipe cold-drawing elongation data in the steel pipe cold-drawing historical database, second, respectively perform discrete processing on the cold-drawing oil pressure data and the cold-drawing elongation data, to obtain the oil pressure data matrix and the cold-drawing elongation data matrix, and finally, convert the oil pressure data matrix into the drawing force data matrix.

[0047] In the steel pipe cold-drawing historical data, filter out the Z-group hydraulic cylinder left cavity oil pressure data, the Z-group hydraulic cylinder right cavity oil pressure data and the Z-group steel pipe cold-drawing elongation data; select Q oil pressure data points for each time interval for the Z-group left cavity oil pressure data and the Z-group right cavity oil pressure data, to respectively obtain the left cavity oil pressure data matrix {P left ij} and the right cavity oil pressure data matrix {P right ij}, wherein i=1, 2, 3,..., Z, j=1, 2, 3,..., Q; obtain the steel pipe cold-drawing elongation data matrix {ΔL i1} according to the Z-group steel pipe cold-drawing elongation data, i=1, 2, 3,..., Z; convert the left cavity oil pressure data matrix {P ij} and the right cavity oil pressure data matrix {P left ij} into the drawing force data matrix {F left} according to the conversion formula F right ij =(P right A left ij -P right ij A ij}, i=1, 2,..., Z, j=1, 2,..., Q, wherein i is the group index, j is the time index, A left is the hydraulic cylinder left cavity piston area, and A right is the hydraulic cylinder right cavity piston area.

[0048] ​Step two: fitting the drawing force reference target function according to the drawing force data matrix.

[0049] According to the drawing force data matrix {F ij}, the steel pipe cold drawing drawing force reference target function F ref (t) is fitted, that is

[0050]

[0051] Wherein, T is the total drawing time;

[0052]

[0053] Step three: starting cold drawing, collecting the current left cavity oil pressure value, the current right cavity oil pressure value and the current cold drawing displacement value in time, and according to the current collected data, the current drawing force value, the current drawing force change rate, the current steel pipe cumulative impulse, the current steel pipe stiffness and the current steel pipe tensile stress are obtained respectively.

[0054] After the steel pipe cold drawing starts, it is collected every interval Δt time, and the current time is taken as t n =nΔt, n is the current collection times, and the current left cavity oil pressure value P left (t n ), the current right cavity oil pressure value P right (t n ) and the current cold drawing displacement value x(t n ) are obtained respectively every collection.

[0055] According to the current left cavity oil pressure value P left (t n ) and the current right cavity oil pressure value P right (t n ), the current drawing force value F(t n )=P left (t n )A left -P right (t n )A right is obtained, and according to the current drawing force value F(t n ) and the current cold drawing displacement value x(t n ), the current drawing force change rate the current steel pipe cumulative impulse the current tensile stiffness and the current tensile stress are calculated respectively, wherein n is the current collection times, and A is the cross-sectional area before the steel pipe cold drawing.

[0056] Step four: judging the current cold drawing state according to the current drawing force change rate.

[0057] During cold drawing, if the rate of change of the current drawing force k(t) is detected... n )>α max k max or k(t) n )<α min k min If the current cold drawing is abnormal, the machine will automatically stop; otherwise, proceed to the next step, where k min =min{k1,k2,...,k Q}<0,k max =max{k1,k2,...,k Q}>0, α max α is the upper limit coefficient for the anomaly of the rate of change of pulling force. min Limiting coefficient under extreme values ​​of the rate of change of pulling force.

[0058] Step 5: Determine the current cold drawing status based on the current rate of change of drawing force and cumulative variables.

[0059] During the cold drawing process, let N be a cumulative variable with an initial value of 0. If the rate of change of the current drawing force k(t) is detected... n )>β max k j or k(t) n )<β min k j If the cumulative variable N < M, then the cumulative variable is executed once N = N + 1; if the current rate of change of the pulling force k(t) is detected. n )>β max k j or k(t) n )<β min k j If the cumulative variable N ≥ M, then the current cold drawing is judged to be abnormal, and the machine will automatically stop; if the rate of change of the current drawing force β is detected... min k j k(t n )β max k j Then proceed to the next step, where j = n, β max β is the upper limit coefficient for the abnormal rate of change of drawing force. min M is the limiting coefficient under abnormal pull-out force change rate, and M is the preset threshold.

[0060] Step 6: Determine the current cold drawing status based on the current cumulative impact of the steel pipe.

[0061] During the cold drawing process, if the current cumulative impulse of the steel pipe is detected... or If the current cold drawing is abnormal, the machine will automatically stop; otherwise, proceed to the next step, where λ maxThe upper limit coefficient of the accumulated impulse anomaly is λ min The lower limit coefficient of the accumulated impulse anomaly is λ

[0062] Step seven: judging the current cold-drawing state according to the current tensile stiffness.

[0063] During the cold-drawing process, if the current tensile stiffness E(t) is detected to be greater than the preset threshold value E, then the current cold-drawing is determined to be abnormal, and the machine is automatically stopped; otherwise, the next step is executed, wherein E is the elastic modulus of the current cold-drawing material, and A is the cross-sectional area of the steel pipe before cold-drawing. or , then the current cold-drawing is determined to be abnormal, and the machine is automatically stopped; otherwise, the next step is executed, wherein E is the elastic modulus of the current cold-drawing material, and A is the cross-sectional area of the steel pipe before cold-drawing. δ max is the upper limit coefficient of the tensile stiffness anomaly, and δ min is the lower limit coefficient of the tensile stiffness anomaly

[0064] Step eight: judging the current cold-drawing state according to the current tensile stress.

[0065] During the cold-drawing process, if the current tensile stress σ(t) is detected to be greater than the preset threshold value σ max , then the current cold-drawing is determined to be abnormal. If the above condition is not met, the next step is executed, wherein σ max is the tensile limit strength of the current cold-drawing material.

[0066] Step nine: repeating steps three to nine.

[0067] The value ranges of the above-mentioned partial parameters are as follows:

[0068] The value range of the number of oil pressure groups Z is Z=10-15;

[0069] The value range of the number of oil pressure points Q is Q=50T-60T, T is a unit of second;

[0070] The value range of the preset threshold value M is M=10-15;

[0071] The value range of the upper limit coefficient of the maximum value of the drawing force change rate α max is α max =2.5-3, and the value range of the lower limit coefficient of the maximum value of the drawing force change rate α min is α min =0.3-0.4;

[0072] The value range of the upper limit coefficient of the drawing force change rate β max is β max =1.5-2, and the value range of the lower limit coefficient of the drawing force change rate β min is β min =0.5-0.7;

[0073] The value range of the upper limit coefficient of the accumulated impulse anomaly λ max is λmax = 2~4, cumulative impulse abnormal lower limit coefficient λ min = 2~4, cumulative impulse abnormal lower limit coefficient λ min = 0.3~0.5;

[0074] tensile stiffness abnormal upper limit coefficient δ max = 0.3~0.5; max = 2~2.5, tensile stiffness abnormal lower limit coefficient δ min = 0.3~0.5; min = 0.4~0.5.

[0075] The above merely provides the preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for online quality assessment of the cold drawing process of precision seamless steel pipes, characterized in that, Includes the following steps: S1 first filters out cold drawing hydraulic pressure data and cold drawing elongation data from the cold drawing history database of steel pipes. Then, it discretizes the cold drawing hydraulic pressure data and cold drawing elongation data to obtain the hydraulic pressure data matrix and the elongation data matrix after cold drawing. Finally, it converts the hydraulic pressure data matrix into the drawing force data matrix. S2 fits the pull-out force reference objective function based on the pull-out force data matrix; S3 starts cold drawing, and periodically collects the current oil pressure value of the left cavity, the current oil pressure value of the right cavity, and the current cold drawing displacement value. Based on the collected data, the current drawing force value, the current drawing force change rate, the current cumulative impulse of the steel pipe, the current steel pipe stiffness, and the current tensile stress of the steel pipe are obtained respectively. S4 determines the current cold drawing status based on the rate of change of the current drawing force; S5 determines the current cold drawing status based on the current rate of change of drawing force and cumulative variables; S6 determines the current cold drawing status based on the current cumulative impulse of the steel pipe; S7 determines the current cold drawing state based on the current tensile stiffness; S8 determines the current cold drawing state based on the current tensile stress; S9 repeats steps S3-S9.

2. The method for online quality assessment of the cold drawing process of precision seamless steel pipes according to claim 1, characterized in that, Step S1 specifically includes the following steps: S1.

1. From the historical data of cold drawing of steel pipes, filter out the hydraulic pressure data of the left chamber of hydraulic cylinder Z, the hydraulic pressure data of the right chamber of hydraulic cylinder Z, and the elongation data of steel pipes after cold drawing of Z. S1.

2. Select Q oil pressure data points at equal time intervals from the left chamber oil pressure data of group Z and the right chamber oil pressure data of group Z, respectively, to obtain the left chamber oil pressure data matrix {P}. left ij } and right chamber oil pressure data matrix {P right ij }, where i = 1, 2, 3, ..., Z, j = 1, 2, 3, ..., Q; S1.

3. Based on the elongation data of steel pipes after cold drawing in group Z, obtain the elongation data matrix {ΔL} of steel pipes after cold drawing. i1 }, i = 1, 2, 3, ..., Z; S1 .4 According to the conversion formula F between pulling force and hydraulic pressure ij =(P left ij A left -P right ij A right ), and the left chamber oil pressure data matrix {P left ij } and right chamber oil pressure data matrix {P right ij } Converted into a pull-out force data matrix {F ij Let i = 1, 2, ..., Z, j = 1, 2, ..., Q, where i is the group index, j is the time index, and A is the time index. left A is the piston area of ​​the left chamber of the hydraulic cylinder. right This represents the piston area in the right chamber of the hydraulic cylinder.

3. The method for online quality assessment of the cold drawing process of precision seamless steel pipes according to claim 2, characterized in that, Step S2 specifically involves: based on the pull-out force data matrix {F ij The reference objective function F for the cold drawing force of the steel pipe is fitted. ref (t), that is in, T represents the total pull-out time; 4. The method for online quality assessment of the cold drawing process of precision seamless steel pipes according to claim 3, characterized in that, Step S3 is as follows: S3.1 After the cold drawing of the steel pipe begins, data is collected every Δt time interval, and the current time is recorded as t. n =nΔt, where n is the current number of data collections, and each data collection yields the current left cavity oil pressure value P. left (t n Current right chamber oil pressure value P right (t n ) and the current cold-drawn displacement value x(t) n ); S3.2, Based on the collected current left cavity oil pressure value P left (t n Current right chamber oil pressure value P right (t n ), thus obtaining the current pulling force value F(t) n ) = P left (t n A left -P right (t n A right Based on the current pull-out force value F(t) n ) and 3, current cold-drawn displacement value x(t) n The rate of change of the current pull-out force was calculated respectively. Current cumulative impulse of steel pipe Current tensile stiffness and current tensile stress Where n is the current number of data collections, and A is the cross-sectional area of ​​the steel pipe before cold drawing.

5. The method for online quality assessment of the cold drawing process of precision seamless steel pipes according to claim 4, characterized in that, Step S4 specifically involves: during the cold drawing process, if the rate of change of the current drawing force k(t) is detected... n )>α max k max or k(t) n )<α min k min If the condition is met, the current cold-drawing is determined to be abnormal; otherwise, proceed to the next step, where k min =min{k1,k2,...,k Q }<0,k max =max{k1,k2,...,k Q }>0, α max α is the upper limit coefficient for the anomaly of the rate of change of pulling force. min Limiting coefficient under extreme values ​​of the rate of change of pulling force.

6. The method for online quality assessment of the cold drawing process of precision seamless steel pipes according to claim 5, characterized in that, Step S5 specifically involves: During the cold drawing process, let N be the cumulative variable with an initial value of 0. If the rate of change of the current drawing force k(t) is detected... n )>β max k j or k(t) n )<β min k j If the cumulative variable N < M, then the cumulative variable is executed once N = N + 1; if the current rate of change of the pulling force k(t) is detected. n )>β max k j or k(t) n )<β min k j If the cumulative variable N ≥ M, then it is judged as a current cold drawing anomaly; if the current drawing force change rate β is detected... min k j k(t n )β max k j Then proceed to the next step, where j = n, β max β is the upper limit coefficient for the abnormal rate of change of drawing force. min M is the limiting coefficient under abnormal pull-out force change rate, and M is the preset threshold.

7. The method for online quality assessment of the cold drawing process of precision seamless steel pipes according to claim 6, characterized in that, Step S6 specifically involves: during the cold drawing process, if the current cumulative impulse of the steel pipe is detected... or If this occurs, the current cold drawing is determined to be abnormal; Otherwise, proceed to the next step, where λ max λ is the upper limit coefficient for cumulative impulse abnormality. min Cumulative impulse abnormal lower limit coefficient.

8. The method for online quality assessment of the cold drawing process of precision seamless steel pipes according to claim 7, characterized in that, Step S7 specifically involves: during the cold drawing process, if the current tensile stiffness is detected... or If the condition is met, it is determined that the current cold drawing is abnormal; otherwise, proceed to the next step. Here, E is the elastic modulus of the currently drawn material, and A is the cross-sectional area of ​​the steel pipe before cold drawing. δ max The upper limit coefficient of tensile stiffness anomaly and δ min Lower limit coefficient of tensile stiffness anomaly.

9. The method for online quality assessment of the cold drawing process of precision seamless steel pipes according to claim 8, characterized in that, Step S8 specifically involves: during the cold drawing process, if the current tensile stress σ(t) is detected to be greater than σ... max If the condition is met, it is determined that the current cold drawing is abnormal; otherwise, proceed to the next step, where σ max This represents the tensile strength limit of current cold-drawn materials.

10. The method for online quality assessment of the cold drawing process of precision seamless steel pipe according to claim 9, characterized in that, The parameter settings satisfy the following: the value range of the number of hydraulic groups Z is Z = 10 to 15; the value range of the number of hydraulic points Q is Q = 50T to 60T, where T is in seconds; The preset threshold M ranges from M = 10 to 15; the upper limit coefficient α for the extreme value of the pull-out force change rate is... max The range of values ​​for α is max =2.5~3, the coefficient α of the extreme lower limit of the rate of change of pull-out force. min The range of values ​​for α is min =0.3~0.4; Upper limit coefficient of abnormal pull-out force change rate β max The range of values ​​is β max =1.5~2, lower limit coefficient of abnormal pull-out force change rate β min The range of values ​​is β min =0.5~0.7; Cumulative impulse abnormal upper limit coefficient λ max The range of values ​​for λ is max =2~4, cumulative impulse abnormal lower limit coefficient λ min The range of values ​​is λ min =0.3~0.5; Upper limit coefficient of tensile stiffness anomaly δ max The range of values ​​for δ is max =2~2.5, lower limit coefficient of tensile stiffness anomaly δ min The range of values ​​is δ min =0.4~0.5.

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