A method, medium and system for determining parameters of a rib winding machine

Abstract

The present invention provides a method, medium, and system for determining parameters of a rebar winding machine, belonging to the technical field of rebar winding machines. The method comprises: collecting data: rebar winding parameters (speed, tension, angle, spacing, number of turns, pressure) and rebar quality (uniformity, strength, finish, tightness, precision), as well as rebar material parameters (diameter, strength grade, surface condition, elastic modulus, yield strength, elongation); establishing a system of equations: including equations for uniformity, strength, finish, tightness, etc.; solving the system of equations to obtain analytical solutions for each process parameter; establishing a multi-objective optimization model, using the weighted sum of the analytical solutions as the objective to solve the optimized process parameters; and experimentally evaluating the optimization results, selecting the best performing parameters as the optimal parameters. Through mathematical modeling and multi-objective optimization, the present invention systematically analyzes and solves the complex relationship between rebar winding process parameters and rebar quality, obtaining an optimal parameter combination that meets multiple quality indicators.

Description

Technical Field

[0001] The present invention belongs to the technical field of rib winding machines, and in particular relates to a method, medium and system for determining parameters of a rib winding machine. Background Art

[0002] Reinforced concrete structures are an important structural form widely used in modern architecture. They exhibit excellent performance in terms of bearing capacity, seismic resistance, and durability, and are widely used in residential, highway, and bridge applications. Reinforcement winding is a critical process in the manufacture of reinforced concrete structures, directly impacting the quality and service life of the entire structure. The quality of the rebar winding process not only determines the mechanical properties of the steel but also affects the bond strength between the concrete and the steel, thereby affecting the overall performance of the structure. Therefore, determining the optimal process parameters for the rebar winding machine has always been an important issue that needs to be addressed in the construction industry.

[0003] At present, the determination of process parameters of rib winding machines mainly depends on experience accumulation and repeated experiments, and it is difficult to obtain the optimal combination of process parameters. Summary of the Invention

[0004] In view of this, the present invention provides a method, medium and system for determining the parameters of a rib winding machine, which can solve the technical problem that the current determination of the process parameters of the rib winding machine mainly relies on experience accumulation and repeated experiments, and it is difficult to obtain the optimal process parameter combination.

[0005] The present invention is achieved in that:

[0006] A first aspect of the present invention provides a method for determining parameters of a rib winding machine, comprising the following steps:

[0007] S10, conducting multiple sets of rebar winding tests, collecting rebar winding parameters and rebar quality under different process parameters and different rebar material parameters, wherein the rebar winding parameters include at least rebar winding speed, rebar winding tension, rebar winding angle, rebar winding spacing, rebar winding number, and rebar winding pressure; the rebar winding quality includes at least rebar winding uniformity, rebar winding strength, rebar winding surface finish, rebar winding tightness, and rebar winding position accuracy; the rebar material parameters include at least rebar diameter, rebar strength grade, rebar surface condition, rebar elastic modulus, rebar yield strength, and rebar elongation;

[0008] S20, establishing a set of equations for the quality of the winding process, including a winding uniformity equation, a winding strength equation, a winding surface finish equation, and a winding tightness equation;

[0009] S30, solving the process quality equation group to obtain multiple analytical solutions, namely, an analytical solution for rebar winding speed, an analytical solution for rebar winding tension, an analytical solution for rebar winding angle, an analytical solution for rebar winding spacing, an analytical solution for rebar winding number, and an analytical solution for rebar winding pressure;

[0010] S40, establishing a multi-objective optimization model using the weighted sum of multiple analytical solutions as the objective function and the boundary conditions of the winding machine process and the material performance limitations as constraints;

[0011] S50, solving the multi-objective optimization model to obtain multiple sets of optimized process parameters;

[0012] S60, performing an actual rib winding test using the optimized process parameters, and performing a quality assessment on the rib winding results, recording the assessment results of the rib winding quality indicators corresponding to each set of parameters;

[0013] S70: According to the evaluation result of the rib winding quality index, select the best performing process parameters as the optimal rib winding machine parameters and output them.

[0014] Step S10 specifically includes collecting multiple sets of rebar winding test data and recording rebar winding parameters and rebar winding quality indicators under different process parameters and rebar material parameters. The rebar winding parameters include at least rebar winding speed, rebar winding tension, rebar winding angle, rebar winding spacing, number of rebar winding turns, and rebar winding pressure; the rebar winding quality indicators include at least rebar winding uniformity, rebar winding strength, rebar winding surface finish, rebar winding density, and rebar winding position accuracy. The rebar material parameters include at least rebar diameter, rebar strength grade, rebar surface condition, rebar elastic modulus, rebar yield strength, and rebar elongation.

[0015] Wherein, the step S20 specifically includes: establishing a set of equations for the quality of the winding process based on the data collected in step S10, including the winding uniformity equation, the winding strength equation, the winding surface finish equation, and the winding tightness equation, etc., to reflect the relationship between the winding process parameters and the winding quality indicators. The set of equations can be fitted using a data-driven modeling method such as multiple linear regression and artificial neural network, and its fitting accuracy should meet certain requirements, such as R 2 Greater than 0.9.

[0016] Step S30 specifically includes solving the set of equations for the rebar winding process quality established in step S20 to obtain analytical solutions for the rebar winding speed, rebar winding tension, rebar winding angle, rebar winding spacing, number of rebar winding turns, and rebar winding pressure. The solution process should satisfy the actual process boundary conditions and material property limitations of the rebar winding machine, and the results should be accurate and stable.

[0017] Step S40 specifically includes establishing a multi-objective optimization model, using the weighted sum of the analytical solutions for the rebar winding process parameters obtained in step S30 as the objective function, and using the rebar winding machine's process boundary conditions and the steel bar material performance limitations as constraints. The optimization model can be solved using a meta-heuristic optimization algorithm such as a genetic algorithm or particle swarm optimization.

[0018] Step S50 specifically includes solving the multi-objective optimization model established in step S40 to obtain multiple sets of optimized rib winding process parameters. The selection of the optimization algorithm should be based on the characteristics of the problem, such as the weighted sum method or NSGA-II, and ensure that the optimization results converge stably and that the Pareto front solution set is relatively balanced.

[0019] Step S60 specifically includes conducting an actual rebar winding test using the optimized parameters obtained in step S50, measuring and recording rebar winding quality indicators such as rebar uniformity, rebar strength, rebar surface finish, rebar tightness, and rebar position accuracy. The test conditions should be the same as those in step S10 to ensure comparability of the results.

[0020] Step S70 specifically includes: selecting the winding process parameters with the best overall performance as the final optimal parameters based on the test results of step S60. During the selection process, threshold requirements can be set for each winding quality indicator based on actual needs. When the threshold requirements are met, the parameter combination with the best overall performance is selected.

[0021] Optionally, the selected parameter combination should be able to stably meet the quality requirements of the reinforcement winding in actual production, while taking into account the operability and economy of the process.

[0022] The uniformity equation of the reinforcement winding is specifically:

[0023] U=f U (v,T,θ,d);

[0024] The polynomial regression method is used, which is specifically expressed as:

[0025] U=a0+a1v+a2T+a3θ+a4d+a5v 2 +a6T 2 +a7θ 2 +a8d 2 +a9vT+a 10 vθ+a 11 vd+a 12 Tθ+a 13 Td+a 14 θd;

[0026] Where a0, a1, …, a 14 is the unknown coefficient, v is the winding speed, T is the winding tension, θ is the winding angle, and d is the winding spacing.

[0027] The reinforcement strength equation is specifically:

[0028] S=f S (v,T,θ,d,n,p,D,f,s,E,σy ,ε);

[0029] Where S is the strength of the reinforcement, v is the speed of the reinforcement, n is the number of turns, p is the pressure of the reinforcement, D is the diameter of the reinforcement, f is the strength grade of the reinforcement, s is the surface condition of the reinforcement, E is the elastic modulus of the reinforcement, σ y is the yield strength of the steel bar, and ε is the elongation of the steel bar. The above formula uses the polynomial regression method. Considering that there are many variables, the principal component analysis method is first used to reduce the dimension, and then the following regression equation is established:

[0030]

[0031] Where PC1, PC2,…, PC m is the principal component, m is the number of principal components selected, and b0, b1,… are the coefficients to be determined.

[0032] The surface finish equation of the rib is specifically:

[0033] R=f R (v,T,p,s);

[0034] Considering the nonlinear relationship between surface finish and the winding speed v, winding tension T, winding pressure p, and steel bar surface state s, a smooth function is used for fitting, as follows:

[0035] R=c0+f1(v)+f2(T)+f3(p)+f4(s);

[0036] Where f1(v), f2(T), f3(p), and f4(s) are smooth functions that respectively describe the nonlinear effects of winding speed, winding tension, winding pressure, and steel bar surface condition on surface finish, and are fitted using spline functions.

[0037] The winding tightness equation is specifically:

[0038] C=f C (T,p,D,E);

[0039] Considering the exponential relationship between tightness, tension and pressure, a nonlinear regression model is used, as follows:

[0040]

[0041] Where d0, d1,…, d6 are unknown coefficients.

[0042] The analytical solution of the winding speed is specifically:

[0043]

[0044] Where, v* is the optimal winding speed, v0 is the initial winding speed, and T0 is the initial winding tension;

[0045] The analytical solution of the reinforcing bar tension is specifically:

[0046]

[0047] Where, T * is the optimal tendon tension;

[0048] The analytical solution of the reinforcement angle is specifically:

[0049]

[0050] Where θ * is the optimal winding angle, θ0 is the initial winding angle, and n0 is the initial number of winding turns;

[0051] The analytical solution for the spacing between the reinforcing bars is:

[0052]

[0053] Where, d * is the optimal reinforcement spacing, d0 is the initial reinforcement spacing;

[0054] The analytical solution for the number of reinforcing turns is specifically:

[0055]

[0056] Where n * is the optimal number of reinforcement turns, n0 is the initial number of reinforcement turns;

[0057] The analytical solution of the reinforcement pressure is specifically:

[0058]

[0059] Where p * is the optimal reinforcement pressure, and p0 is the initial reinforcement pressure.

[0060] The objective function of the multi-objective optimization model is specifically:

[0061] F=w1f U (v,T,θ,d)+w2f S (v,T,θ,d,n,p,D,f,s,E,σ y ,ε)+w3f R (v,T,p,s)+w4f C (T,p,D,E);

[0062] Where w1, w2, w3, w4 are weight coefficients, satisfying And w i ≥0; the weight coefficient is determined by using the hierarchical analysis method or fuzzy comprehensive evaluation method.

[0063] Furthermore, the method used to solve the multi-objective optimization model is a genetic algorithm.

[0064] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are executed, they are used to execute the above-mentioned method for determining parameters of a rib winding machine.

[0065] A third aspect of the present invention provides a system for determining parameters of a rib winding machine, which includes the above-mentioned computer-readable storage medium.

[0066] Compared with the prior art, the beneficial effects of the method, medium and system for determining parameters of a rib winding machine provided by the present invention are:

[0067] 1. A systematic parameter optimization model was established. This method establishes a mathematical model that reflects the relationship between the winding process parameters and the winding quality indicators, and adopts multi-objective optimization technology to obtain the optimal winding machine parameter combination that meets multiple quality requirements. This systematic parameter optimization method greatly improves the scientificity and controllability of the winding process, and utilizes

[0068] 2. It takes into account multiple rebar winding quality indicators. During the optimization process, this method simultaneously considers multiple key quality indicators, including rebar winding uniformity, rebar strength, rebar surface finish, and rebar tightness. It balances the interplay between these indicators and seeks the optimal balance. This multi-objective optimization approach ensures comprehensive improvement in rebar winding quality.

[0069] In summary, the solution of the present invention solves the technical problem that the determination of the process parameters of the current rib winding machine mainly relies on experience accumulation and repeated experiments, and it is difficult to obtain the optimal process parameter combination. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 A flow chart of the method provided by the present invention. DETAILED DESCRIPTION

[0071] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0072] like Figure 1 FIG. 1 is a flow chart of a method for determining parameters of a rib winding machine provided by the present invention, and the method comprises the following steps:

[0073] S10. Conduct multiple sets of rebar winding tests to collect rebar winding parameters and rebar quality under different process parameters and different rebar material parameters. The rebar winding parameters include at least rebar winding speed, rebar winding tension, rebar winding angle, rebar winding spacing, number of rebar winding turns, and rebar winding pressure. The rebar winding quality includes at least rebar winding uniformity, rebar winding strength, rebar winding surface finish, rebar winding tightness, and rebar winding position accuracy. The rebar material parameters include at least rebar diameter, rebar strength grade, rebar surface condition, rebar elastic modulus, rebar yield strength, and rebar elongation.

[0074] S20, establishing a set of equations for the quality of the winding process, including a winding uniformity equation, a winding strength equation, a winding surface finish equation, and a winding tightness equation;

[0075] S30, solving the process quality equation group to obtain multiple analytical solutions, namely, an analytical solution for the rebar winding speed, an analytical solution for the rebar winding tension, an analytical solution for the rebar winding angle, an analytical solution for the rebar winding spacing, an analytical solution for the number of rebar winding turns, and an analytical solution for the rebar winding pressure;

[0076] S40, establishing a multi-objective optimization model using the weighted sum of multiple analytical solutions as the objective function and the boundary conditions of the winding machine process and the material performance limitations as constraints;

[0077] S50, solving the multi-objective optimization model to obtain multiple sets of optimized process parameters;

[0078] S60, performing an actual rib winding test using the optimized process parameters, and performing a quality assessment on the rib winding results, recording the assessment results of the rib winding quality indicators corresponding to each set of parameters;

[0079] S70. According to the evaluation results of the rib winding quality index, the best performing process parameters are selected as the optimal rib winding machine parameters and output.

[0080] The specific implementation of the above steps is described in detail below:

[0081] The specific implementation of step S10 is:

[0082] This step aims to obtain data on the relationship between the performance of the rib winding machine and process parameters and material parameters through systematic experiments, laying the foundation for the subsequent establishment of a mathematical model. The specific implementation process is as follows:

[0083] First, design the test plan. According to the working principle and process characteristics of the rebar winding machine, determine the process parameters and steel material parameters that need to be examined. The process parameters include rebar winding speed v (m / min), rebar winding tension T (N), rebar winding angle θ (°), rebar winding spacing d (mm), number of rebar winding turns n and rebar winding pressure p (MPa). Rebar material parameters include rebar diameter D (mm), rebar strength grade f (MPa), rebar surface state s (dimensionless, can be represented by numbers 1-5 from smooth to rough), rebar elastic modulus E (GPa), rebar yield strength σ y (MPa) and steel bar elongation ε (%).

[0084] Secondly, determine the value range of the test parameters. For example, the winding speed v can vary in the range of 5-50m / min, the winding tension T can vary in the range of 100-1000N, the winding angle θ can vary in the range of 30°-90°, the winding spacing d can vary in the range of 5-50mm, the number of winding turns n can vary in the range of 10-100, and the winding pressure p can vary in the range of 0.1-1MPa. The diameter of the steel bar D can be selected from common specifications such as 8, 10, 12, 14, 16, 18, 20, 22, 25, 28, 32mm, etc. The steel bar strength grade f can be selected from levels such as 335, 400, 500, 600MPa, etc. The surface state of the steel bar s can be divided from 1 to 5, the elastic modulus E of the steel bar is usually in the range of 200-210GPa, and the yield strength σ of the steel bar y and elongation ε are determined according to the specific steel bar model.

[0085] Then, the orthogonal experimental design method is used to design multiple test plans. The orthogonal experimental method can obtain more comprehensive test data in a smaller number of tests and improve the test efficiency. According to the number and level of parameters, an appropriate orthogonal table is selected, such as L 16 (4 5 ) or L 32 (4 9 )wait.

[0086] Next, the rebar winding test was conducted. According to the designed test plan, the rebar winding machine's process parameters were adjusted sequentially, and the appropriate rebar material was selected for winding. During the test, high-precision sensors and measuring equipment were used to collect rebar winding parameters and quality data. Rebar winding parameters were collected using the following methods: rebar winding speed v was measured using a photoelectric encoder; rebar winding tension T was measured using a tension sensor; rebar winding angle θ was measured using an angle sensor or a vision system; rebar winding spacing d was measured using a laser rangefinder or a vision system; the number of rebar windings n was obtained using a counter or a vision system; and rebar winding pressure p was measured using a pressure sensor.

[0087] The measurement method of the rebar winding quality indicators is as follows: the rebar winding uniformity U can be characterized by measuring the standard deviation of the rebar winding spacing at multiple points. The smaller the standard deviation, the better the uniformity; the rebar winding strength S can be measured by tensile test; the rebar winding surface finish R can be measured using a surface roughness meter; the rebar winding tightness C can be characterized by measuring the volume change rate of the steel bar after winding; the rebar winding position accuracy A can be determined by measuring the deviation between the actual rebar winding position and the theoretical position.

[0088] Finally, the collected data was preprocessed and statistically analyzed. Data cleaning techniques were used to remove outliers and noise, and interpolation was used to fill in missing data. The mean and standard deviation were calculated for each set of experimental data. Correlation scatter plots were drawn between the parameters to provide a preliminary analysis of the relationships between them. The processed data was organized into a standard format to prepare for the subsequent mathematical model development.

[0089] The specific implementation of step S20 is:

[0090] This step aims to establish a mathematical model that describes the relationship between the quality of the rib winding process and various influencing factors, providing a theoretical basis for subsequent optimization. The specific implementation process is as follows:

[0091] First, based on the experimental data and process mechanism, the influence of various process parameters and material parameters on the rebar winding quality indicators was analyzed. Using the multivariate regression analysis method, the equations for rebar winding uniformity, rebar winding strength, rebar winding surface finish, and rebar winding tightness were established.

[0092] The process of establishing the winding uniformity equation is as follows: Assuming that the winding uniformity U is mainly affected by the winding speed v, winding tension T, winding angle θ and winding spacing d, it can be expressed as:

[0093] U=f U (v,T,θ,d)

[0094] Using the polynomial regression model and taking into account the second-order interaction terms, we can obtain:

[0095] U=a0+a1v+a2T+a3θ+a4d+a5v 2 +a6T 2 +a7θ 2 +a8d 2 +a9vT+a 10 vθ+a 11 vd+a 12 Tθ+a 13 Td+a 14 θd

[0096] Among them, a0, a1, …, a 14are the unknown coefficients. Use the least squares method to fit the experimental data to obtain estimates of each coefficient. To avoid overfitting, use stepwise regression or regularization methods (such as Lasso regression) to screen for important variables.

[0097] The process of establishing the rebar strength equation is similar. Considering that the rebar strength S is also closely related to the steel bar material parameters, it can be expressed as:

[0098] S=f S (v,T,θ,d,n,p,D,f,s,E,σ y ,ε)

[0099] Parameter meaning:

[0100] S: Rebar strength, in MPa, indicating the tensile strength of the steel bar after rebar winding;

[0101] v: winding speed, in m / min, indicating the linear speed of the winding machine;

[0102] T: Rebar winding tension, in N, represents the tensile force applied to the steel bar during the winding process;

[0103] θ: Reinforcement angle, in degrees (°), indicating the angle between the steel bar and the axis of the reinforcement;

[0104] d: Reinforcement spacing, in mm, indicating the distance between two adjacent turns of reinforcement;

[0105] n: Number of reinforcing turns, unitless, indicating the total number of reinforcing turns;

[0106] p: Rebar winding pressure, in MPa, represents the pressure applied to the steel bar surface during the winding process;

[0107] D: Steel bar diameter, in mm, represents the diameter of the steel bar to be wound;

[0108] f: Steel bar strength grade, in MPa, indicating the nominal yield strength of the steel bar;

[0109] s: surface condition of steel bar, no unit, integers from 1 to 5 are used to represent the degree from smooth to rough;

[0110] E: elastic modulus of steel bar, in GPa, which indicates the stress-strain relationship of steel bar in the elastic stage;

[0111] σ y : Steel bar yield strength, in MPa, represents the stress at which the steel bar begins to yield;

[0112] ε: Steel bar elongation, expressed in %, which indicates the maximum plastic deformation of the steel bar when it breaks.

[0113] The polynomial regression model is also used. Considering the large number of variables, principal component analysis (PCA) can be performed to reduce the dimension first, and then the regression equation can be established:

[0114]

[0115] Among them, PC1, PC2, ..., PC m is the principal component, m is the number of principal components selected, and b0, b1,… are the coefficients to be determined.

[0116] Principal Component Analysis (PCA) is a dimensionality reduction technique used to reduce the number of variables while retaining most of the information. In this case, the principal components may include:

[0117] PC1=a 11 D+a 12 f+a 13 E+a 14 σ y +a 15 ε;

[0118] PC2=a 21 v+a 22 T+a 23 θ+a 24 d+a 25 n;

[0119] PC3=a 31 s+a 32 p;……

[0120] Among them, a ij The coefficients obtained from principal component analysis represent the weights of the original variables in the principal components. The specific coefficient values ​​need to be determined by performing PCA analysis on actual data.

[0121] The calculation steps of PCA are well-known techniques and are briefly described as follows:

[0122] Standardize the data; calculate the covariance matrix; calculate the eigenvalues ​​and eigenvectors of the covariance matrix; select the eigenvectors corresponding to the largest k eigenvalues; project the data onto these k eigenvectors.

[0123] The surface finish equation of the rib can be expressed as:

[0124] R=f R (v,T,p,s)

[0125] Considering that surface finish may have a nonlinear relationship with these parameters, a generalized additive model (GAM) can be used:

[0126] R=c0+f1(v)+f2(T)+f3(p)+f4(s)

[0127] Where R represents surface finish, typically expressed as Ra (in μm); c0 is a constant. f1(v), f2(T), f3(p), and f4(s) are smooth functions that describe the nonlinear effects of winding speed, winding tension, winding pressure, and rebar surface condition on surface finish, respectively.

[0128] These smooth functions are defined using spline functions, which are well-known techniques. Taking f1(v) as an example, its definition is:

[0129]

[0130] Among them, B i (v) is the basis function (usually B-spline or natural spline), β i are the unknown coefficients, and k is the number of basis functions.

[0131] Fitting method:

[0132] 1. Select knots: Select an appropriate number of knots within the variable range.

[0133] 2. Build basis functions: Use the selected nodes to build B-spline or natural spline basis functions.

[0134] 3. Penalized fitting: Use penalty terms to control the smoothness of the function and avoid overfitting. Optimize the objective function:

[0135]

[0136] Among them, y j is the observed value, x ij is the value of the i-th variable at the j-th observation, and λ is the smoothing parameter.

[0137] 4. Cross-validation: Use generalized cross-validation (GCV) or k-fold cross-validation to select the best smoothing parameter λ.

[0138] 5. Parameter estimation: Use penalized least squares to estimate the coefficient β i .

[0139] In the specific implementation process, the fitting can be performed using the gam() function in the mgcv package of R language or the GAM class in the pyGAM library of Python. The fitting process of f2(T), f3(p), and f4(s) is the same as that of f1(v).

[0140] The winding tightness equation can be expressed as:

[0141] C=f C (T,p,D,E)

[0142] Considering that there may be an exponential relationship between tightness, tension and pressure, a nonlinear regression model is used:

[0143]

[0144] Where d0, d1, …, d6 are unknown coefficients. Nonlinear least squares method is used for parameter estimation.

[0145] When establishing the equations, the following points should be noted:

[0146] 1. Data standardization: Before performing regression analysis, all variables are standardized to eliminate dimensional effects.

[0147] 2. Multicollinearity diagnosis: Calculate the variance inflation factor (VIF). If VIF>10, it indicates severe multicollinearity and you need to consider deleting some variables or using methods such as ridge regression.

[0148] 3. Model diagnosis: Test the appropriateness of the model through residual analysis, QQ plots, etc. If obvious heteroskedasticity or non-normality is found, consider using generalized linear models or nonparametric regression methods.

[0149] 4. Model validation: Use cross-validation to evaluate the predictive performance of the model and calculate the root mean square error (RMSE) and the coefficient of determination R 2 and other indicators.

[0150] 5. Model interpretation: Analyze the importance of each variable, which can be assessed through standardized regression coefficients or relative importance indicators (such as the Lindeman-Merenda-Gold method).

[0151] Finally, the four established equations are combined into the winding process quality equation group:

[0152] U=f U (v,T,θ,d)

[0153] S=f S (v,T,θ,d,n,p,D,f,s,E,σ y ,ε)

[0154] R=f R (v,T,p,s)

[0155] C=f C (T,p,D,E)

[0156] This set of equations describes the relationship between the winding quality and various process parameters and material parameters, laying the foundation for subsequent parameter optimization.

[0157] The specific implementation of step S30 is:

[0158] This step aims to solve the set of equations for the rib winding process quality established in step S20 to obtain analytical solutions for each process parameter. Because the set of equations involves multiple variables and complex nonlinear relationships, direct solutions are difficult. Therefore, numerical methods and approximation techniques are used to obtain analytical solutions. The specific implementation process is as follows:

[0159] First, simplify and reduce the order of the equations. Through sensitivity analysis, determine the parameters that most significantly affect each quality indicator and ignore parameters with less influence to reduce the complexity of the equations. For example, for the winding uniformity equation, it may be found that the winding speed v and winding tension T have the most significant impact. In this case, the equation can be simplified to:

[0160] U≈f U (v,T)

[0161] Secondly, the simplified equation is linearized using the Taylor expansion method. Taking the winding uniformity equation as an example, a first-order Taylor expansion is performed near a certain working point (v0, T0):

[0162]

[0163] Then, the optimality condition is used to solve the analytical solution of each parameter. For the uniformity of the reinforcement, the optimal condition is and Solving these two equations, we can obtain the analytical solutions of the winding speed and winding tension:

[0164]

[0165]

[0166] Similarly, by processing the equations for winding strength, winding surface finish, and winding density, analytical solutions for other process parameters can be obtained.

[0167] For the reinforcement strength equation, assuming that it is mainly affected by the reinforcement angle θ and the number of reinforcement turns n after simplification, we can get:

[0168]

[0169]

[0170] For the surface finish equation of the rib winding, assuming that it is mainly affected by the rib winding pressure p, we can get:

[0171]

[0172] For the reinforcement density equation, assuming that it is mainly affected by the reinforcement spacing d, we can get:

[0173]

[0174] During the solution process, the following points should be noted:

[0175] 1. Operating point selection: The choice of the operating point (v0, T0, θ0, n0, p0, d0) has a significant impact on the accuracy of the analytical solution. The best performing parameter combination from historical data can be selected as the initial operating point, or a global optimization algorithm (such as the particle swarm optimization algorithm) can be used to find the optimal operating point.

[0176] 2. Calculation of partial derivatives: For complex nonlinear functions, it may be difficult to directly obtain the analytical form of partial derivatives. In this case, numerical differentiation methods, such as the central difference method, can be used to approximate the partial derivatives. For example:

[0177]

[0178] Among them, h is a small step size, usually 10 -6 to 10 -8 The value between .

[0179] 3. Iterative solution: Since Taylor expansion is a local approximation near the working point, multiple iterative solutions may be required to obtain more accurate results. Use the Newton-Raphson method for iteration:

[0180] x k+1 =x k -[J(x k )] -1 F(x k )

[0181] Where x is the parameter vector, F(x) is the equation system, and J(x) is the Jacobian matrix. The iteration termination condition can be set as ‖x k+1 -x k ‖<ε, ε is the preset convergence threshold, such as 10 -6 .

[0182] 4. Stability Analysis of the Solution: Perform a stability analysis on the obtained analytical solution and calculate the eigenvalues ​​of the Jacobian matrix. If the real parts of all eigenvalues ​​are negative, the solution is stable.

[0183] 5. Physically validating the solution: Check whether the analytical solution is physically reasonable. For example, the winding velocity v should not be negative, and the winding angle θ should be between 0° and 180°. If an unreasonable solution is found, the model or constraints should be readjusted.

[0184] 6. Error analysis: Calculate the error between the analytical solution and the numerical solution to evaluate the accuracy of the analytical solution. Relative error can be used:

[0185]

[0186] If the relative error exceeds a preset threshold (eg, 5%), it is necessary to consider using a higher-order Taylor expansion or other approximation methods.

[0187] Finally, the analytical solutions of the parameters are organized into standard forms:

[0188] Analytical solution of winding speed: v * =f v (U,T,θ,d,n,p,D,f,s,E,σ y ,ε)

[0189] Analytical solution of reinforcement tension: T * =f T (U,v,θ,d,v,p,D,f,s,E,σ y ,ε)

[0190] Analytical solution of the winding angle: θ * =f θ (S,v,T,d,n,p,D,f,s,E,σ y ,ε)

[0191] Analytical solution for the spacing between reinforcement bars: d * =f d (C,v,T,θ,n,p,D,f,s,E,σ y ,ε)

[0192] Analytical solution for the number of winding circles: n * =f n (S,v,T,θ,d,p,D,f,s,E,σ y ,ε)

[0193] Analytical solution of reinforcement pressure: p * =f p (R,v,T,θ,d,n,D,f,s,E,σ y ,ε)

[0194] Among them, f v ,f T ,f θ ,f d ,f n ,f p are analytical functions of each parameter, which include the influence of other parameters and quality indicators. Please note that these formulas are approximate analytical solutions based on simplified models and local linearization.

[0195] 1.f v (Analytical function of winding speed):

[0196]

[0197] 2.f T (Analytical function of tendon tension):

[0198]

[0199] 3.f θ (Analytical function of winding angle):

[0200]

[0201] 4.f d (Analytical function of winding spacing):

[0202]

[0203] 5.f n (Analytical function of the number of winding turns):

[0204]

[0205] 6.f p (Analytical function of reinforcement pressure):

[0206]

[0207] In these formulas:

[0208] v0, T0, θ0, d0, n0, px are the parameter values ​​of the initial working point.

[0209] f U ,f S ,f R ,f C are functions describing uniformity, strength, surface finish and tightness respectively.

[0210] Represents the partial derivative of the function f with respect to the variable x.

[0211] U, S, R, and C represent the quality indicators of uniformity, strength, surface finish, and compactness, respectively.

[0212] D,f,s,E,σ y ,ε are the steel bar material parameters, which represent the steel bar diameter, strength grade, surface condition, elastic modulus, yield strength and elongation respectively.

[0213] These analytical solutions provide quantitative relationships between process parameters and quality indicators, laying the foundation for the subsequent optimization process.

[0214] The specific implementation of step S40 is:

[0215] This step aims to establish a multi-objective optimization model based on the analytical solution obtained previously to achieve overall optimization of the rib winding process parameters. The specific implementation process is as follows:

[0216] First, the objective function is constructed. Considering multiple quality indicators of the rib winding process, the weighted sum method is used to combine multiple objectives into a comprehensive objective function:

[0217] F=w1f U (v,T,θ,d)+w2f S (v,T,θ,d,n,p,D,f,s,E,σ y ,ε)+w3f R (v,T,p,s)+w4f C (T,p,D,E)

[0218] Among them, w1, w2, w3, w4 are the weight coefficients of each quality index, satisfying And w i ≥ 0. The weight coefficient can be determined by using the analytic hierarchy process (AHP) or the fuzzy comprehensive evaluation method, combined with expert experience and actual production needs.

[0219] Secondly, determine the constraints. Constraints include the boundary conditions of process parameters and material performance limitations:

[0220] 1. Reinforcement winding speed constraint: v min ≤v≤v max ;

[0221] 2. Tension constraint around reinforcement: T min ≤T≤T max ;

[0222] 3. Reinforcement angle constraint: θ min ≤θ≤θ max ;

[0223] 4. Reinforcement spacing constraint: d min ≤d≤d max ;

[0224] 5. Constraint on number of winding circles: n min ≤n≤n max ;

[0225] 6. Reinforcement pressure constraint: p min ≤p≤p max ;

[0226] 7. Material strength constraint: S≥S required ;

[0227] 8. Surface finish constraint: R≤R max ;

[0228] 9. Closeness Constraint: C ≥ C min ;

[0229] 10. Uniformity constraint: U≤U max .

[0230] Among them, the subscripts min and max represent the minimum and maximum values ​​of the parameters respectively. These boundary values ​​can be determined according to the performance indicators of the winding machine and the actual process requirements. required is the required minimum strength, R max is the maximum allowable surface roughness, C min is the required minimum tightness, U max is the maximum allowable unevenness.

[0231] Then, consider the correlation constraints between parameters. For example, there may be a certain relationship between the winding speed and the winding tension:

[0232] g(v,T)≤0

[0233] Among them, g(v,T) is a function that describes the relationship between velocity and tension, which can be obtained by fitting experimental data.

[0234] Next, build a multi-objective optimization model:

[0235] min F=w1f U (v,T,θ,d)+w2f S (v,T,θ,d,n,p,D,f,s,E,σ y ,ε)+w3f R (v,T,p,s)+w4f C (T,p,D,E)

[0236] Constraints include:

[0237] v min ≤v≤v max ;T min ≤T≤T max θ min ≤θ≤θ max ;d min ≤d≤d max ;n min ≤n≤n max ;p min ≤p≤p max ; S≥S required ; R≤R max ; C≥C min ; U≤U max ;g(v,T)≤0.

[0238] When establishing a multi-objective optimization model, the following points should be noted:

[0239] 1. Normalization of objective function: Since different quality indicators may have different dimensions and orders of magnitude, each objective function needs to be normalized to ensure that they are comparable during the optimization process. The maximum and minimum value normalization method can be used:

[0240]

[0241] Among them, f i ′ is the normalized objective function, f i,min and f i,max The objective function f i The minimum and maximum values ​​of .

[0242] 2. Sensitivity analysis of weight coefficients: Conduct sensitivity analysis on weight coefficients to assess the impact of weight changes on optimization results. Monte Carlo simulation can be used to randomly generate multiple sets of weight coefficients and analyze the distribution of optimization results.

[0243] 3. Relaxation of constraints: For some constraints that are difficult to strictly meet, you can consider introducing relaxation variables to convert hard constraints into soft constraints. For example, rewrite the strength constraint as:

[0244] S+ξ≥S required ξ≥0;

[0245] Among them, ξ is a slack variable, and a penalty term Mξ is added to the objective function, where M is a large positive number.

[0246] 4. Robustness considerations: Considering the possible parameter fluctuations in actual production, a robust optimization method can be introduced. For example, a scenario-based robust optimization method can be used to consider multiple possible scenarios:

[0247]

[0248] Where K is the number of scenarios considered, F i is the objective function in the i-th scenario.

[0249] 5. Multi-objective trade-off analysis: Use the concept of Pareto optimality to plot the Pareto frontier and analyze the trade-offs between different objectives. Multi-objective optimization algorithms such as NSGA-II (Non-dominated Sorting Genetic Algorithm II) can be used to obtain the Pareto optimal solution set.

[0250] 6. Dynamic model update: Considering that the rib winding process may change over time, an online learning mechanism can be introduced to regularly update the model parameters. For example, recursive least squares (RLS) or Kalman filter can be used to update the model coefficients.

[0251] Finally, the established multi-objective optimization model is transformed into a standard mathematical programming form to prepare for the next step of solution. The model can be expressed as:

[0252]

[0253] stg i (x)≤0,i=1,2,…,m

[0254] h j (x)=0,j=1,2,…,n

[0255] x l ≤x≤x u

[0256] Among them, x is the decision variable vector, including all process parameters; F(x) is the objective function; g i (x) and h j (x) are inequality and equality constraints respectively; x l and x u are the lower and upper bounds of the decision variables, respectively.

[0257] This multi-objective optimization model comprehensively considers all aspects of the rib winding process and lays the foundation for the next step of solving the optimal process parameters.

[0258] The specific implementation of step S50 is:

[0259] This step aims to solve the multi-objective optimization model established in step S40 and obtain multiple sets of optimized process parameters. Due to the complexity and nonlinear characteristics of the model, an intelligent optimization algorithm is used to solve it. The specific implementation process is as follows:

[0260] First, select an appropriate optimization algorithm. Considering the characteristics of the problem, you can choose from the following algorithms:

[0261] 1. Genetic Algorithm (GA): Suitable for handling complex nonlinear optimization problems and has good global search capabilities.

[0262] 2. Particle Swarm Optimization (PSO): It has fast convergence speed and is suitable for dealing with continuous variable optimization problems.

[0263] 3. Simulated annealing algorithm (SA): It can escape from the local optimal solution and is suitable for dealing with problems with multiple local optimal points.

[0264] 4. Differential Evolution (DE): It is insensitive to parameters and suitable for high-dimensional optimization problems.

[0265] 5. Ant Colony Algorithm (ACO): Suitable for handling combinatorial optimization problems and can be used for discrete process parameter optimization.

[0266] Here we take the genetic algorithm as an example to explain the solution process in detail:

[0267] 1. Encoding: Encode the decision variables (process parameters) into chromosomes. Use real number encoding, and each gene represents a process parameter. The chromosome structure is:

[0268] [v,T,θ,d,n,p]

[0269] 2. Initialize the population: Generate N individuals to form the initial population. Each individual's gene value is randomly generated within its corresponding constraints. The population size N can be set to 50-200, depending on the problem size and computing resources.

[0270] 3. Fitness evaluation: Calculate the fitness value of each individual. The fitness function is:

[0271]

[0272] Among them, F(x) is the objective function in the multi-objective optimization model.

[0273] 4. Selection: Roulette wheel selection is used to select outstanding individuals for the next generation. The probability of selection is proportional to the individual's fitness.

[0274] 5. Crossover operation: Select two parent individuals and perform a crossover operation to generate offspring. Use the simulated binary crossover (SBX) method:

[0275] c1=0.5[(1+β)p1+(1-β)p2]

[0276] c2=0.5[(1-β)p1+(1+β)p2]

[0277] Where c1 and c2 are offspring, p1 and p2 are parents, and β is the crossover factor, which is generated by the following distribution:

[0278]

[0279] Among them, u is a random number between [0,1], η c is the distribution index, usually ranging from 20 to 100.

[0280] 6. Mutation operation: Randomly perturb certain genes of an individual. Use polynomial mutation method:

[0281] c=p+δ(p u -p l )

[0282] Among them, c is the gene value after mutation, p is the gene value before mutation, and p uand p l are the upper and lower bounds of the gene, and δ is the mutation factor, which is generated by the following distribution:

[0283]

[0284] Among them, u is a random number between [0,1], η m is the distribution index, usually ranging from 20 to 100.

[0285] 7. Constraint processing: For individuals that do not meet the constraint conditions, the penalty function method is used to process them. The modified fitness function is:

[0286]

[0287] Among them, r i and r j is the penalty coefficient, which can be increased with the number of iterations to enhance the influence of the constraint.

[0288] 8. Elite retention: The best individuals in the current population are directly copied to the next generation to ensure that excellent genes are not lost.

[0289] 9. Termination condition: Set the maximum number of iterations (e.g. 1000) or the optimal solution for multiple generations without significant improvement as the termination condition.

[0290] 10. Result output: Output the non-dominated solution set in the final population, that is, multiple sets of optimized process parameters.

[0291] During implementation, the following points should be noted:

[0292] 1. Parameter tuning: The performance of genetic algorithms is affected by multiple parameters, such as population size, crossover probability, mutation probability, etc. These parameters can be optimized using orthogonal experimental methods or response surface methodology.

[0293] 2. Multiple runs: Due to the randomness of genetic algorithms, it is recommended to run the algorithm multiple times (e.g., 30 times) to obtain the optimal result or perform statistical analysis.

[0294] 3. Local search: Based on the genetic algorithm, local search strategies such as pattern search or gradient descent can be introduced to improve the accuracy of the solution. This method that combines global search and local search is called a hybrid genetic algorithm (HybridGA).

[0295] 4. Parallel computing: Use parallel computing technology to accelerate the optimization process. A master-slave parallel model can be used to distribute the fitness evaluation task to multiple slave processors.

[0296] 5. Dynamic adjustment strategy: Dynamically adjust algorithm parameters during the optimization process. For example, as the number of iterations increases, the mutation probability can be gradually reduced to enhance local search capabilities.

[0297] 6. Diversity maintenance: In order to prevent the population from converging prematurely, crowding control or niche techniques can be used to maintain the diversity of the population.

[0298] 7. Convergence Analysis: Analyze the convergence characteristics of the algorithm by plotting the objective function value versus the number of iterations. If the convergence is found to be too fast or too slow, adjust the algorithm parameters accordingly.

[0299] 8. Sensitivity analysis: Conduct a sensitivity analysis on the optimization results to evaluate the impact of each process parameter on the objective function. This analysis can be performed using analysis of variance (ANOVA) or partial differentiation.

[0300] Finally, post-process and analyze the optimization results:

[0301] 1. Solution screening: If the obtained set of non-dominated solutions is large, cluster analysis methods (such as the k-means algorithm) can be used to classify the solutions and select representative solutions.

[0302] 2. Decision support: Use visualization techniques (such as scatter plot matrix and parallel coordinates plot) to display multi-objective optimization results and help decision makers select the most suitable process parameter combination.

[0303] 3. Robustness Analysis: Conduct a robustness analysis on the optimization results to assess the impact of small fluctuations in process parameters on quality indicators. Monte Carlo simulation can be used to generate random perturbations of parameters and analyze the range of variation of quality indicators.

[0304] 4. Interactive Optimization: Develop an interactive optimization interface that allows users to adjust the weights or constraints of the objective function according to actual needs and observe the changes in optimization results in real time.

[0305] Through the above steps, multiple sets of optimized process parameters can be obtained. These parameters can theoretically achieve the best overall quality of the rib winding, which provides a theoretical basis and reference for the next step of practical verification.

[0306] The specific implementation of step S60 is:

[0307] This step aims to verify the effectiveness of the optimized process parameters through actual rib winding tests and to quantitatively evaluate the rib winding results. The specific implementation process is as follows:

[0308] 1. Test preparation:

[0309] a) Designing an experimental plan based on the multiple sets of optimized process parameters obtained in step S50. To improve experimental efficiency, a fractional factorial experimental design method, such as an orthogonal experimental method or a central composite experimental design, may be used.

[0310] b) Prepare test equipment, including rib winding machines, various sensors (such as force sensors, displacement sensors, angle sensors, etc.), data acquisition systems, etc.

[0311] c) Prepare test materials, including steel bars of different specifications and strength grades.

[0312] d) Calibrate all measuring equipment to ensure measurement accuracy.

[0313] 2. Test implementation:

[0314] a) According to the test plan, set the process parameters of the reinforcement winding machine (reinforcement winding speed, reinforcement winding tension, reinforcement winding angle, reinforcement winding spacing, number of reinforcement winding turns, and reinforcement winding pressure) in sequence.

[0315] b) Select steel bars of appropriate specifications and carry out the reinforcement winding operation.

[0316] c) During the winding process, sensors are used to collect various parameter data in real time, including actual winding speed, tension, angle, etc.

[0317] d) For each set of parameters, the experiment was repeated 3-5 times to reduce the influence of random errors.

[0318] 3. Quality Assessment:

[0319] The following quality indicators are measured and evaluated for each wound rib sample:

[0320] a) Uniformity of winding reinforcement (U):

[0321] Use a high-precision coordinate measuring machine or laser scanner to measure the spacing between the ribs.

[0322] Calculate the standard deviation of the spacing; the smaller the standard deviation, the better the uniformity.

[0323] Define uniformity metrics: where σ d is the standard deviation of the interval, and d is the average interval.

[0324] b) Reinforcement strength (S):

[0325] Tensile tests were performed using a universal testing machine.

[0326] The yield strength and tensile strength were recorded.

[0327] Calculate the strength retention rate: where f y is the yield strength after reinforcement, fy0 is the yield strength before reinforcement.

[0328] c) Surface finish of ribs (R):

[0329] The surface roughness of the ribs was measured using a surface roughness tester.

[0330] Record the arithmetic mean deviation value.

[0331] Define the finish index: Among them, Ra max is the maximum allowed roughness value.

[0332] d) Reinforcement tightness (C):

[0333] Measure the volume change of the steel bars before and after winding.

[0334] Calculate the tightness index: Where V0 is the volume before reinforcement, and V is the volume after reinforcement.

[0335] e) Reinforcement position accuracy (A):

[0336] Use a laser tracker or vision system to measure the deviation between the actual rib position and the theoretical position.

[0337] Calculate the position accuracy index: where Δx i ,Δy i ,Δz i is the deviation of the i-th measuring point in three directions, n is the number of measuring points, and L is the total length of the rebar.

[0338] 4. Data processing and analysis:

[0339] a) Perform statistical analysis on the repeated test results of each set of parameters and calculate the mean and standard deviation of each quality indicator.

[0340] b) Perform analysis of variance (ANOVA) to evaluate the significance of the effects of different process parameters on various quality indicators.

[0341] c) Establish a regression model between process parameters and quality indicators, and consider using methods such as response surface methodology (RSM) or artificial neural network (ANN).

[0342] d) To calculate the comprehensive score of each quality indicator, the weighted sum method can be used:

[0343] Score=w1U+w2S+w3R+w4C+w5A

[0344] Among them, w1, w2, w3, w4, and w5 are the weights of each indicator, which can be determined by the analytic hierarchy process (AHP).

[0345] 5. Result evaluation and recording:

[0346] a) Comprehensively evaluate the test results of each set of process parameters and record the specific values ​​and comprehensive scores of each quality indicator.

[0347] b) Draw a relationship diagram between process parameters and quality indicators, such as a scatter plot, contour plot, etc., to intuitively demonstrate the impact of parameters on quality.

[0348] c) Prepare a detailed test report, including the test method, data analysis process, results discussion, etc.

[0349] 6. Abnormal situation handling:

[0350] a) If any abnormal situation occurs during the test, such as steel bar breakage, rebar winding machine failure, etc., it is necessary to record the abnormal situation and possible causes in detail.

[0351] b) For abnormal data, use statistical methods (such as Grubbs criterion) to perform outlier test and decide whether to exclude it.

[0352] c) If the test results of a certain set of parameters deviate significantly from the theoretical predictions, repeated tests are required to verify and analyze the possible causes.

[0353] 7. Parameter optimization feedback:

[0354] a) Based on the test results, the multi-objective optimization model established in step S40 is revised and calibrated.

[0355] b) If the test results show that certain parameter combinations are not ideal, new parameter combinations can be predicted through interpolation or extrapolation methods, and additional tests can be carried out.

[0356] 8. Sensitivity analysis of process parameters:

[0357] a) Analyze the sensitivity of each process parameter to the quality of the rib winding by using the control variable method.

[0358] b) Calculate the sensitivity coefficient of each parameter: Where Q is the quality index, P i is the i-th process parameter.

[0359] c) Draw a sensitivity radar chart to visually display the importance of each parameter.

[0360] 9. Process window analysis:

[0361] a) Based on the test results, determine the optimal range of each process parameter.

[0362] b) Draw a process window diagram to show the parameter combination area that meets quality requirements.

[0363] c) Analyze the width of the process window and evaluate the stability and controllability of the process.

[0364] 10. Reliability assessment:

[0365] a) Carry out repeated tests (e.g. 30 times) on the optimal process parameters to evaluate the stability of the winding quality.

[0366] b) Calculate the process capability index (Cpk) of each quality indicator and evaluate the reliability of the process.

[0367] c) Conduct life tests to evaluate the long-term performance of reinforced products.

[0368] Through the above steps, the actual effect of the optimized process parameters can be comprehensively evaluated, and the corresponding winding quality index evaluation results for each set of parameters can be obtained. These results will provide an important basis for the final determination of the optimal winding machine parameters.

[0369] The specific implementation of step S70 is:

[0370] This step aims to select the best performing process parameters as the optimal parameters of the rib winding machine based on the evaluation results of the rib winding quality index obtained in step S60, and output the final results. The specific implementation process is as follows:

[0371] 1. Data aggregation and standardization:

[0372] a) Collect the quality index evaluation results of all test groups in step S60, including winding uniformity (U), winding strength (S), winding surface finish (R), winding tightness (C), and winding position accuracy (A).

[0373] b) Standardize each indicator to eliminate the dimension effect. Use the maximum and minimum value standardization method:

[0374]

[0375] Among them, X is the original indicator value, X norm is the standardized value.

[0376] 2. Weight determination:

[0377] a) Use the analytic hierarchy process (AHP) to determine the weight of each quality indicator.

[0378] b) Construct a judgment matrix and invite experts in winding technology to compare and score the two items.

[0379] c) Calculate the eigenvector and obtain the weights w1, w2, w3, w4, and w5 of each indicator.

[0380] d) Perform consistency test to ensure that the consistency ratio CR of the judgment matrix is ​​less than 0.1.

[0381] 3. Comprehensive score calculation:

[0382] For each set of process parameters, calculate its comprehensive score:

[0383] Score=w1U norm +w2S norm +w3R norm +w4C norm +w5A norm

[0384] Among them, U norm ,S norm ,R norm ,C norm ,A norm are the standardized quality index values.

[0385] 4. Sorting and filtering:

[0386] a) Arrange all process parameter combinations in descending order according to the comprehensive score.

[0387] b) Select the top five groups of process parameters with the highest comprehensive scores for further analysis.

[0388] 5. Stability analysis:

[0389] a) Carry out repeatability test on the selected 5 groups of process parameters (e.g., repeat 10 times for each group).

[0390] b) Calculate the coefficient of variation (CV) of the quality index for each group of parameters:

[0391]

[0392] Where σ is the standard deviation and μ is the mean.

[0393] c) Taking both scoring and stability into consideration, a weighted approach can be used:

[0394] FinalScore=Score×(1-αCV)

[0395] Among them, α is the trade-off coefficient, which can be 0.1-0.3.

[0396] 6. Optional, process window analysis:

[0397] a) Conduct process window analysis on the selected excellent process parameter combinations.

[0398] b) Using the two most important process parameters as coordinate axes, draw a contour map to show the trend of quality indicators as the parameters change.

[0399] c) Determine the parameter range that meets all quality requirements, that is, the process window.

[0400] 7. Optional, Sensitivity Analysis:

[0401] a) Conduct local sensitivity analysis on the optimal process parameters.

[0402] b) Calculate the sensitivity coefficient of each parameter:

[0403]

[0404] Among them, P i is the i-th process parameter.

[0405] c) Draw a sensitivity bar chart to visually show the importance of each parameter.

[0406] 8. Optional, economic analysis:

[0407] a) Calculate the production efficiency and energy consumption under different process parameter combinations.

[0408] b) Estimate material costs, equipment depreciation, labor costs, etc.

[0409] c) Conduct a cost-benefit analysis and calculate the input-output ratio of each set of parameters.

[0410] 9. Final parameter determination:

[0411] a) Comprehensively consider the quality score, stability, process window, sensitivity and economy to select the optimal process parameter combination.

[0412] b) If the performance of multiple groups of parameters is similar, a weighted decision can be made:

[0413] Decision=β1FinalScore+β2WindowSize+β3Robustness+β4Efficiency

[0414] Among them, β1, β2, β3, and β4 are the weights of each factor.

[0415] 10. Optional, result verification and confirmation:

[0416] a) Carry out large-scale trial production (e.g., 100 pieces) using the selected optimal process parameters.

[0417] b) Carry out comprehensive quality inspection, including destructive testing and non-destructive testing.

[0418] c) Calculate the process capability index Cpk of each quality indicator and ensure that Cpk>1.33.

[0419] d) Conduct long-term performance evaluation, such as fatigue test, corrosion resistance test, etc.

[0420] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are executed, they are used to execute the above-mentioned method for determining parameters of a rib winding machine.

[0421] A third aspect of the present invention provides a system for determining parameters of a rib winding machine, which includes the above-mentioned computer-readable storage medium.

[0422] Specifically, the principle of the present invention is to establish a mathematical model that reflects the relationship between the winding process parameters and the winding quality indicators, and based on this, use multi-objective optimization technology to obtain the optimal parameter combination.

[0423] First, through the collection of a large amount of rebar winding test data, the rebar winding parameters and rebar winding quality indicators under different process parameters and steel material parameters are obtained. These data provide the basis for subsequent modeling and optimization.

[0424] Secondly, based on the experimental data, a set of equations for the winding process quality was established, including equations for winding uniformity, winding strength, winding surface finish, and winding density. These equations describe the mathematical relationship between winding process parameters, such as winding speed, winding tension, and winding angle, and winding quality indicators. By establishing these equations, a mathematical model foundation was established for parameter optimization.

[0425] Then, the winding process quality equations are solved to obtain the optimal analytical solutions of various winding process parameters. These analytical solutions provide key inputs for subsequent multi-objective optimization.

[0426] Next, a multi-objective optimization model was established. Using the weighted sum of the analytical solutions for the rebar winding process parameters obtained in Step 3 as the objective function, and using the rebar winding machine's process boundary conditions and the steel material's performance limitations as constraints, an optimization algorithm was employed to obtain multiple sets of optimized rebar winding process parameters. This multi-objective optimization approach can identify the optimal parameter combination for comprehensive rebar winding quality while satisfying various process and material constraints.

[0427] Finally, the optimized winding process parameters were used to conduct actual winding tests, measuring and evaluating various winding quality indicators. Based on the test results, the parameter combination with the best overall performance was selected as the final optimal winding machine parameters, provided that the quality indicator threshold requirements were met.

[0428] In general, the key to the method of the present invention is that, through mathematical modeling and multi-objective optimization, the complex relationship between the winding process parameters and the winding quality is systematically analyzed and solved, and the optimal parameter combination that meets the requirements of multiple quality indicators is obtained.

[0429] In order to better understand and implement the present invention, a specific embodiment of the present invention is provided below: The following is a specific embodiment of the present invention, which describes in detail the application process of a method for determining parameters of a rib winding machine:

[0430] In order to improve the quality and production efficiency of rebar winding, a prestressed concrete component production plant decided to adopt the rebar winding machine parameter determination method of the present invention. The plant mainly produces prestressed concrete poles and uses HPB300 grade Φ8mm threaded steel bars.

[0431] First, multiple sets of rebar winding tests were conducted according to step S10. An orthogonal test scheme was designed, taking into account six process parameters (rebar winding speed, rebar winding tension, rebar winding angle, rebar spacing, number of rebar winding turns, and rebar winding pressure) and six rebar material parameters (rebar diameter, rebar strength grade, rebar surface condition, rebar elastic modulus, rebar yield strength, and rebar elongation).

[0432] To reduce the number of experiments, an L36 orthogonal array was used. This orthogonal array allows for the simultaneous consideration of multiple factors, with each factor having 2 or 3 levels, resulting in a total of 36 experiments. Based on experience and equipment limitations, the range of values ​​for each parameter was determined as follows:

[0433] 1. Reinforcement winding speed: 10-30m / min

[0434] 2. Reinforcement tension: 200-600N

[0435] 3. Reinforcement angle: 45°-75°

[0436] 4. Reinforcement spacing: 10-30mm

[0437] 5. Number of winding circles: 20-60

[0438] 6. Reinforcement pressure: 0.2-0.6MPa

[0439] The steel bar material parameters are determined based on the actual performance of HPB300 grade Φ8mm threaded steel bars.

[0440] The test was conducted using high-precision sensors and measuring equipment. Winding speed was measured using a photoelectric encoder, winding tension was measured using a tension sensor, winding angle and spacing were measured using a vision system, the number of windings was obtained using a counter, and winding pressure was measured using a pressure sensor.

[0441] The following methods are used to measure the quality index of the reinforcement:

[0442] 1. Reinforcement uniformity: Use a laser rangefinder to measure the spacing between reinforcements at 20 points and calculate the standard deviation.

[0443] 2. Rebar strength: Cut standard specimens from the rebar after rebar winding and conduct tensile tests.

[0444] 3. Surface finish of the rib: Use a surface roughness meter to measure the Ra value.

[0445] 4. Rebar winding tightness: Measure the volume change rate of the steel bars before and after winding.

[0446] 5. Reinforcement winding position accuracy: Use a three-dimensional coordinate measuring machine to measure the deviation between the actual reinforcement winding position and the theoretical position.

[0447] After completing the 36 experiments, the collected data was preprocessed and statistically analyzed. Data cleaning was performed using the Python pandas library to remove significant outliers and interpolation to fill in a small amount of missing data. The seaborn library was then used to plot correlation heatmaps between the various parameters, providing a preliminary analysis of the relationships between them.

[0448] Next, we proceed to step S20 and begin to establish a set of equations for the quality of the rib winding process. We use the Python statsmodels library to perform multiple regression analysis and establish four equations:

[0449] 1. Reinforcement uniformity equation:

[0450] It was found that the winding uniformity was mainly affected by winding speed, winding tension and winding spacing. A second-order polynomial regression model was used, considering the main effects, square terms and interaction terms of these three variables.

[0451] 2. Reinforcement strength equation:

[0452] Reinforcement strength is related to all process and material parameters. Due to the large number of variables, principal component analysis (PCA) was first used for dimensionality reduction, followed by a regression equation. This was implemented using the PCA module of the sklearn library.

[0453] 3. Surface finish equation of rib winding:

[0454] The surface finish was found to be mainly affected by the winding speed, winding pressure, and the surface condition of the reinforcement. A generalized additive model (GAM) was used to capture the nonlinear relationship between these variables and surface finish. The Python pygam library was used to implement the GAM.

[0455] 4. Reinforcement tightness equation:

[0456] The tightness is mainly affected by the winding tension, winding pressure, steel bar diameter and steel bar elastic modulus. A nonlinear regression model is used to estimate the parameters using the curve_fit function in the scipy.optimize module.

[0457] When developing these equations, we took into account the issue of multicollinearity. We calculated the variance inflation factor (VIF), and addressed variables with a VIF greater than 10, such as merging correlated variables or using ridge regression.

[0458] To verify the appropriateness of the model, residual analysis was performed and QQ plots were drawn to test the normality of the residuals. A 5-fold cross validation was also used to evaluate the predictive performance of the model and the root mean square error (RMSE) and coefficient of determination R were calculated. 2 .

[0459] In step S30, the established set of equations is solved to obtain analytical solutions for the process parameters. Since the set of equations involves complex nonlinear relationships, numerical methods and approximation techniques are used.

[0460] First, the equations were simplified and reduced in order. Through sensitivity analysis, the parameters with the most significant impact on each quality indicator were determined, while the parameters with less significant impact were ignored.

[0461] Then, the simplified equation is linearized using the Taylor expansion method. The best parameter combination in the historical data is selected as the initial operating point, and a first-order Taylor expansion is performed around this point.

[0462] Symbolic computations were performed using the Python library SymPy to obtain partial derivatives of the parameters. For complex nonlinear functions where analytical partial derivatives are difficult to obtain directly, numerical differentiation methods, such as the central difference method, were employed.

[0463] Next, we applied the optimality criteria to find analytical solutions for each parameter. We used the Newton-Raphson method for iterative solution, with the termination condition being that the difference between two consecutive iterations was less than 10^-6.

[0464] After obtaining the analytical solution, we performed a stability analysis. We calculated the eigenvalues ​​of the Jacobian matrix and ensured that all eigenvalues ​​had negative real parts, guaranteeing the stability of the solution. We also checked the physical meaning of the solution to ensure that all parameters were within reasonable ranges.

[0465] In step S40, a multi-objective optimization model is established with the weighted sum of multiple analytical solutions as the objective function. Based on actual production, the following constraints are set:

[0466] 1. The winding speed shall not exceed the maximum speed of the equipment 35m / min

[0467] 2. The tension of the reinforcement should not exceed 80% of the yield strength of the steel bar

[0468] 3. The winding angle is between 30° and 90°

[0469] 4. The spacing between the reinforcement bars should not be less than 1.2 times the diameter of the reinforcement bars.

[0470] 5. The number of winding circles meets the design requirements

[0471] 6. The winding pressure does not exceed the maximum pressure of the equipment 0.8MPa

[0472] Use Python's pyomo library to construct and solve this multi-objective optimization problem.

[0473] In step S50, the multi-objective optimization model is solved. A weighting approach is employed to transform multiple objective functions into a single objective function. The weights are selected based on the importance of each quality indicator and determined through discussions with production management and quality control personnel. A genetic algorithm is used to solve this optimization problem because it is highly effective for handling nonlinear, multi-constrained optimization problems.

[0474] The solution obtained multiple sets of optimized process parameters, and five of them were selected for the next step of practical verification.

[0475] In step S60, actual rib winding tests were conducted using these five sets of optimized process parameters. Each set of parameters was repeated three times to reduce the impact of random errors. A comprehensive quality assessment of the rib winding results was conducted according to previously established methods, recording the winding uniformity, strength, surface finish, tightness, and position accuracy for each parameter set.

[0476] Finally, in step S70, based on the evaluation results of the winding quality indicators, the best-performing process parameters are selected as the optimal winding machine parameters. A comprehensive scoring method is used, assigning different weights to each quality indicator and calculating a comprehensive score for each parameter group. The weightings are determined based on product performance requirements and customer feedback, with winding strength and uniformity being given higher weights.

[0477] The parameter combination with the highest comprehensive score was determined as the optimal parameters of the rib winding machine, as follows:

[0478] 1. Reinforcement winding speed: 22m / min

[0479] 2. Reinforcement tension: 450N

[0480] 3. Reinforcement angle: 60°

[0481] 4. Reinforcement spacing: 15mm

[0482] 5. Number of winding circles: 40

[0483] 6. Reinforcement pressure: 0.4MPa

[0484] The above description is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for determining parameters of a rib winding machine, characterized in that: The following steps are involved: S10, conducting multiple sets of rebar winding tests, collecting rebar winding parameters and rebar winding quality under different process parameters and different rebar material parameters, wherein the process parameters include at least rebar winding speed, rebar winding tension, rebar winding angle, rebar winding spacing, rebar winding number, and rebar winding pressure; the rebar winding parameters include at least rebar winding speed, rebar winding tension, rebar winding angle, rebar winding spacing, rebar winding number, and rebar winding pressure; the rebar winding quality includes at least rebar winding uniformity, rebar winding strength, rebar winding surface finish, rebar winding tightness, and rebar winding position accuracy; the rebar material parameters include at least rebar diameter, rebar strength grade, rebar surface condition, rebar elastic modulus, rebar yield strength, and rebar elongation; the process parameters are used to set the rebar winding machine, and the rebar winding parameters are data collected during the rebar winding process; S20, establishing a set of equations for the quality of the winding process, including a winding uniformity equation, a winding strength equation, a winding surface finish equation, and a winding tightness equation; S30, solving the process quality equation group to obtain multiple analytical solutions, namely, an analytical solution for rebar winding speed, an analytical solution for rebar winding tension, an analytical solution for rebar winding angle, an analytical solution for rebar winding spacing, an analytical solution for rebar winding number, and an analytical solution for rebar winding pressure; S40, establishing a multi-objective optimization model using the weighted sum of multiple analytical solutions as the objective function and the boundary conditions of the winding machine process and the material performance limitations as constraints; S50, solving the multi-objective optimization model to obtain multiple sets of optimized process parameters; S60, performing an actual rib winding test using the optimized process parameters, and performing a quality assessment on the rib winding results, recording the assessment results of the rib winding quality indicators corresponding to each set of parameters; S70: According to the evaluation result of the rib winding quality index, select the best performing process parameters as the optimal rib winding machine parameters and output them.

2. A method for determining parameters of a rib winding machine according to claim 1, characterized in that: The uniformity equation of the reinforcement winding is specifically: ； The polynomial regression method is used, which is specifically expressed as: ； Where, is the undetermined coefficient, is the winding speed, is the tendon tension, is the winding angle, is the spacing of the reinforcement.

3. A method for determining parameters of a rib winding machine according to claim 2, characterized in that: The reinforcement strength equation is specifically: ； Where, is the reinforcement strength, is the winding speed, is the number of reinforcing turns, is the reinforcement pressure, is the diameter of the steel bar, is the steel strength grade, The surface state of the steel bar, is the elastic modulus of steel bars, is the yield strength of steel bars, is the elongation of the steel bar; the above formula adopts the polynomial regression method. Considering that there are many variables, the principal component analysis method is first used to reduce the dimension, and then the following regression equation is established: ； Where, As the main component, is the number of principal components selected, is the undetermined coefficient.

4. A method for determining parameters of a rib winding machine according to claim 3, characterized in that: The surface finish equation of the rib is specifically: ； Considering the surface finish and the winding speed , Reinforcement Tension , winding pressure , steel bar surface condition There is a nonlinear relationship, so a smooth function is used for fitting, as follows: ； Where, , , , is a smooth function, are constant terms, which respectively describe the nonlinear effects of winding speed, winding tension, winding pressure and steel bar surface condition on surface finish, and are fitted using spline function.

5. A method for determining parameters of a rib winding machine according to claim 4, characterized in that: The winding tightness equation is specifically: ； Considering the exponential relationship between tightness, tension and pressure, a nonlinear regression model is used, as follows: ； Where, is the undetermined coefficient.

6. A method for determining parameters of a rib winding machine according to claim 5, characterized in that: The objective function of the multi-objective optimization model is specifically: ； Where, is the weight coefficient, satisfying and ; The weight coefficient is determined by using the hierarchical analysis method or fuzzy comprehensive evaluation method.

7. A method for determining parameters of a rib winding machine according to claim 6, characterized in that: The method used to solve the multi-objective optimization model is a genetic algorithm.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores program instructions, and when the program instructions are executed, they are used to execute the method for determining parameters of a rib winding machine according to any one of claims 1 to 7.

9. A parameter determination system for a rib winding machine, characterized in that: Contains the computer-readable storage medium of claim 8.

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