3D glass cover plate forming parameter optimization system and method based on dynamic temperature and pressure regulation and control

Abstract

The invention relates to the technical field of 3D glass, in particular to a 3D glass cover plate forming parameter optimization system and method based on dynamic temperature and pressure regulation, and the method comprises the steps: building a mathematical model which comprehensively considers the coupling effect of a temperature field, a stress field and a flow field, and processing a control equation set through a numerical discretization method, a discretization model of the glass forming process is obtained, and a discretization equation is solved; establishing a multi-target parameter optimization model by taking geometric accuracy and residual stress of the formed glass cover plate as optimization targets and taking mold temperature and pressure as optimization design variables; a dynamic temperature and pressure regulation and control strategy is designed, and an optimal mold temperature and pressure curve is obtained through searching by means of an intelligent optimization algorithm; the actual effect of the dynamic temperature and pressure regulation strategy is evaluated by measuring and statistically analyzing the geometric accuracy and residual stress of the formed glass cover plate. The method provides a new thought and method for high-quality and high-efficiency manufacturing of the 3D glass cover plate.

Description

Technical Field

[0001] This application relates to the technical field of 3D glass, specifically to an optimization system and method for the forming parameters of 3D glass cover plates based on dynamic temperature and pressure regulation and control. Background Art

[0002] At present, 3D glass cover plates are mainly manufactured by hot stamping forming process. During the hot stamping forming process, the glass is in a high-temperature softening state and deforms under the action of the mold to obtain the required three-dimensional shape. However, due to the complexity of the thermophysical and mechanical properties of the glass material, as well as the strong coupling effect of the temperature field, stress field, and flow field during the forming process, it is difficult to precisely control the forming process of the glass, resulting in quality problems such as low dimensional accuracy and large residual stress in the formed glass cover plate.

[0003] The existing hot stamping forming process for 3D glass cover plates generally has the following problems: on the one hand, the temperature and pressure control during the forming process are not precise enough, making it difficult to achieve uniform heating and deformation of the glass, resulting in low dimensional accuracy and uneven local thickness of the formed glass cover plate; on the other hand, it is difficult to effectively control the thermal stress and residual stress during the glass forming process, which easily leads to defects such as cracking and deformation of the glass cover plate, affecting the strength and reliability of the product.

[0004] In view of this, this application proposes an optimization system and method for the forming parameters of 3D glass cover plates based on dynamic temperature and pressure regulation and control. Summary of the Invention

[0005] To achieve the above object, this application provides an optimization method and system for the forming parameters of 3D glass cover plates based on dynamic temperature and pressure regulation and control. The specific technical solutions are as follows:

[0006] The optimization method for the forming parameters of 3D glass cover plates based on dynamic temperature and pressure regulation and control includes:

[0007] Establish a mathematical model for the 3D glass cover plate forming process, comprehensively consider the coupling effect of the temperature field, stress field, and flow field, and obtain a control equation set describing the glass forming process;

[0008] Use numerical methods to discretize the control equation set to obtain a discretized model of the glass forming process, and obtain the temperature, stress, and flow state of the glass forming process by solving the discretized equation;

[0009] Based on the numerical simulation of the glass forming process, taking the geometric accuracy and residual stress of the formed glass cover plate as the optimization objectives, and the mold temperature and pressure as the optimization design variables, establish a multi-objective parameter optimization model;

[0010] For the established multi-objective parameter optimization model, design a dynamic temperature and pressure regulation and control strategy, and use an intelligent optimization algorithm to search for the optimal mold temperature and pressure curves;

[0011] By measuring and statistically analyzing the quality indexes of the geometric accuracy and residual stress of the formed glass cover plate, the actual effect of the dynamic temperature and pressure control strategy is evaluated.

[0012] Preferably, a three-dimensional geometric model of the 3D glass cover plate is established, and the geometric dimensions, shape and relative position relationship of the 3D glass cover plate are defined;

[0013] Obtain the thermophysical properties of the glass, including density, specific heat capacity, thermal conductivity and viscosity; comprehensively consider the coupling effects of the temperature field, stress field and flow field, and construct a control equation set describing the glass forming process, including: considering the heat transfer mechanism in the glass forming process, establish a control equation describing the temperature field distribution; establish a stress field control equation, according to the mechanical behavior of the glass, establish a stress field control equation to describe the stress-strain relationship of the glass; considering the force balance condition in the glass forming process, establish a control equation describing the stress field distribution; according to the rheological properties of the glass, construct a flow field control equation to describe the relationship between the viscosity of the glass and temperature and shear rate; considering the mass conservation and momentum conservation in the glass forming process, establish a control equation describing the flow field distribution; considering the interaction and coupling effects between the temperature field, stress field and flow field, establish a coupled control equation set;

[0014] According to the characteristics of the glass forming process, define the initial conditions and boundary conditions of the temperature field, stress field and flow field.

[0015] Preferably, the continuous time domain is discretized into a finite number of time steps, and the finite difference method is used to discretize the time derivative term to obtain the time derivative; the continuous space domain is discretized, and the finite element method is used to discretize the space derivative term to obtain the space derivative; the discretized time derivative term and space derivative term are substituted into the control equation set to obtain the discretized equation;

[0016] The finite element method is used to discretize the stress field control equation; the finite volume method is used to discretize the flow field control equation;

[0017] According to the defined initial conditions and boundary conditions, corresponding constraints are imposed in the discretized equation; the discretized equations are combined to form a large algebraic equation set; the discretized equation set is solved to obtain the numerical solutions of the temperature, displacement and velocity field variables.

[0018] Preferably, taking the geometric accuracy and residual stress of the formed glass cover plate as the optimization objectives, quantifying the geometric accuracy and residual stress as the objective function, and taking the mold temperature and pressure as the optimization design variables, a multi-objective parameter optimization model is established.

[0019] Preferably, the dynamic temperature and pressure control curve is parameterized, and a piecewise linear function is used to fit the die temperature and pressure curves;

[0020] According to the parameterization of the dynamic temperature and pressure control curve, the starting temperature, starting pressure, duration, slope, etc. of each linear function of the die temperature and pressure curves are selected as the optimization design variables.

[0021] Preferably, for the multi-objective parameter optimization model, the non-dominated sorting genetic algorithm is used as the optimization algorithm; the value range of the optimization design variables of the optimization algorithm is constrained, and the penalty function method is used to handle the constraint conditions, converting the constraint conditions into penalty terms and adding them to the objective function, transforming the constrained optimization problem into an unconstrained optimization problem.

[0022] Preferably, the geometric dimensions of the formed 3D glass cover plate are measured to obtain the actual dimension data of the glass cover plate, and the actual dimension data obtained by measurement is compared with the designed dimensions of the 3D glass cover plate to calculate the dimensional deviation;

[0023] The residual stress distribution of the glass cover plate is measured. The glass cover plate is placed in a photoelastic stress instrument, polarized light is applied, the photoelastic fringe image is observed and recorded, the principal stress difference is calculated based on the photoelastic fringe image, and the maximum residual stress of the glass cover plate is calculated based on the principal stress difference and combined with the mechanical property parameters of the glass.

[0024] Preferably, the geometric accuracy measurement and residual stress test are carried out on multiple glass cover plate samples produced by the dynamic temperature and pressure control strategy to obtain a set of geometric accuracy indexes and the maximum residual stress;

[0025] Calculate the mean and standard deviation of the geometric accuracy indexes, as well as the mean and standard deviation of the maximum residual stress;

[0026] The statistical results of the geometric accuracy and residual stress under the dynamic temperature and pressure control strategy are compared with the results under the traditional process to evaluate the improvement effect of the dynamic temperature and pressure control strategy.

[0027] Preferably, according to the actual effect evaluation results of the dynamic temperature and pressure control strategy, the key factors affecting the quality of the glass cover plate are identified, including the temperature gradient and the pressure change rate;

[0028] Adjust the parameterization of the dynamic temperature and pressure curve, including: increasing the number of segments and adjusting the slope and intercept of the linear function to optimize the change law of temperature and pressure;

[0029] The optimized dynamic temperature and pressure curve is input into the multi-objective parameter optimization model, and the optimization solution is carried out again to obtain the improved dynamic temperature and pressure control strategy.

[0030] 3D Glass Cover Plate Molding Parameter Optimization System Based on Dynamic Temperature and Pressure Regulation, which is implemented according to the 3D Glass Cover Plate Molding Parameter Optimization Method Based on Dynamic Temperature and Pressure Regulation, includes: a mathematical fitting module, a discretization processing module, a multi-objective optimization module, a dynamic temperature and pressure regulation module, and an analysis and evaluation module;

[0031] The mathematical fitting module is used to establish a mathematical model for the 3D glass cover plate molding process, comprehensively considering the coupling effects of the temperature field, stress field, and flow field, and obtaining a control equation set that describes the glass molding process;

[0032] The discretization processing module uses numerical methods to discretize the control equation set, obtains a discretized model of the glass molding process, and obtains the temperature, stress, and flow states of the glass molding process by solving the discretized equation;

[0033] The multi-objective optimization module, based on the numerical simulation of the glass molding process, takes the geometric accuracy and residual stress of the molded glass cover plate as the optimization objectives, and the mold temperature and pressure as the optimization design variables, and establishes a multi-objective parameter optimization model;

[0034] The dynamic temperature and pressure regulation module designs a dynamic temperature and pressure regulation strategy for the established multi-objective parameter optimization model, and uses an intelligent optimization algorithm to search for the optimal mold temperature and pressure curves;

[0035] The analysis and evaluation module measures and statistically analyzes the quality indicators of the geometric accuracy and residual stress of the molded glass cover plate, and evaluates the actual effect of the dynamic temperature and pressure regulation strategy.

[0036] The beneficial effects of this application: By establishing a mathematical model that comprehensively considers the coupling effects of multiple fields, this application can accurately describe the complex physical mechanisms of the glass molding process, provide a theoretical basis for subsequent numerical simulation and parameter optimization, and improve the scientificity and reliability of the method.

[0037] This application uses a numerical discretization method to transform continuous control equations into discrete algebraic equation sets. By solving the discretized equations, it can efficiently and accurately obtain the temperature, stress, and flow states of the glass molding process, providing necessary data support for parameter optimization.

[0038] This application takes geometric accuracy and residual stress as the optimization objectives, and mold temperature and pressure as the optimization variables, and establishes a multi-objective parameter optimization model, which can comprehensively consider the key influencing factors of glass molding quality and lay a foundation for realizing the systematic optimization of molding process parameters.

[0039] This application designs a dynamic temperature and pressure control strategy, uses intelligent optimization algorithms to search for the optimal temperature and pressure curves, can give full play to the dynamic control effects of the mold temperature and pressure, achieve precise control of the glass forming process, and improve the forming quality and production efficiency of glass covers.

[0040] By measuring and statistically analyzing the geometric accuracy and residual stress of the formed glass cover, this application can quantitatively evaluate the actual effects of the dynamic temperature and pressure control strategy, verify the effectiveness of the method, and provide feedback and guidance for the further optimization of process parameters. Brief Description of the Drawings

[0041] Figure 1 It is a flow chart of the 3D glass cover forming parameter optimization method based on dynamic temperature and pressure control provided by this application;

[0042] Figure 2 It is a structure diagram of the 3D glass cover forming parameter optimization system based on dynamic temperature and pressure control provided by this application. Detailed Description of the Embodiments

[0043] To make the above objects, features, and advantages of this application more obvious and understandable, the following will provide a detailed description of the specific embodiments of this application in conjunction with the drawings in the specification.

[0044] Many specific details are set forth in the following description to facilitate a thorough understanding of this application, but this application can also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of this application, so this application is not limited by the specific embodiments disclosed below.

[0045] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation of this application. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or alternative embodiments that exclude each other from other embodiments.

[0046] Embodiment 1

[0047] Refer to Figure 1 , which is the first embodiment of this application, and provides a 3D glass cover forming parameter optimization method based on dynamic temperature and pressure control.

[0048] Step 1: Establish a mathematical model of the 3D glass cover forming process, comprehensively consider the coupling effects of the temperature field, stress field, and flow field, and obtain a control equation set describing the glass forming process.

[0049] Establish a 3D geometric model: According to the design requirements of the 3D glass cover plate, use CAD software, such as SolidWorks and CATIA, to construct 3D solid models of the glass cover plate, mold and other components, and define the geometric dimensions, shapes and relative positional relationships of each component.

[0050] Obtain the thermophysical properties of the glass, including density ρ, specific heat capacity c p , thermal conductivity k, viscosity μ, etc.; the glass properties can be obtained by referring to literature, manuals or experimental measurements.

[0051] Establish the temperature field control equation. Considering the heat transfer mechanism during the glass forming process, establish the control equation describing the temperature field distribution; for this non-crystalline material like glass, use the unsteady heat conduction equation: where T is the temperature, t is the time, and Q is the internal heat source.

[0052] According to the mechanical behavior of the glass, establish the stress field control equation to describe the stress-strain relationship of the glass; for linear elastic materials, use the generalized Hooke's law: σ ij = C ijkl ε kl ; where σ ij is the stress tensor, ε kl is the strain tensor, and C ijkl is the elastic constant tensor.

[0053] Considering the force balance condition during the glass forming process, establish the control equation describing the stress field distribution: where f is the body force.

[0054] According to the rheological properties of the glass, construct the flow field control equation to describe the relationship between the viscosity of the glass and temperature, shear rate; for non-Newtonian fluids, use the power-law model: where K is the viscosity coefficient, is the shear rate, and n is the power-law index.

[0055] Considering the mass conservation and momentum conservation during the glass forming process, establish the control equation describing the flow field distribution: where u is the velocity vector, p is the pressure, τ is the deviatoric stress tensor, and g is the gravitational acceleration vector.

[0056] Considering the interaction and coupling effects between the temperature field, stress field and flow field, establish the coupled control equation set; the coupling between the temperature field and stress field can be reflected by the strain caused by thermal expansion: where, represents the coupling of the temperature field and stress field, α is the linear thermal expansion coefficient, T ref is the reference temperature, and δ ijis the Kronecker symbol; the coupling of the temperature field and the flow field can be reflected by the influence of temperature on viscosity and the heat source term caused by viscous dissipation.

[0057] According to the characteristics of the glass forming process, the initial conditions and boundary conditions of the temperature field, stress field and flow field are defined; the initial conditions include the initial temperature distribution, initial stress distribution and initial velocity distribution; the boundary conditions include temperature boundary conditions (such as the temperature of the die wall), mechanical boundary conditions (such as the pressure of the die wall) and flow boundary conditions (such as the inlet and outlet velocities).

[0058] In this step, a mathematical model of the 3D glass cover plate forming process considering the coupling effects of the temperature field, stress field and flow field is established, and the control equation set describing the glass forming process is obtained; this lays a foundation for subsequent numerical simulation and parameter optimization.

[0059] Step 2: Discretize the control equation set by numerical methods to obtain a discretized model of the glass forming process, and obtain the temperature, stress and flow states of the glass forming process by solving the discretized equations.

[0060] The continuous time domain is discretized into a finite number of time steps, and the finite difference method is used to discretize the time derivative term; taking the implicit Euler format as the time discretization format, the time derivative term can be expressed as: where, T n and T n+1 represent the temperatures at the nth and (n + 1)th time steps respectively, and Δt is the time step.

[0061] The continuous space domain is discretized into a finite number of discrete nodes or elements, and the finite element method is used to discretize the space derivative term; including: first dividing the solution domain into a finite number of elements, introducing shape functions on each element, and approximating the continuous field variables (such as temperature, displacement, velocity, etc.) by the linear combination of node values and shape functions: where, N i (x) is the shape function, and T i is the node temperature value.

[0062] Substitute the discretized time derivative term and space derivative term into the control equation set to obtain the discretized equation; taking the heat conduction equation as the discretized equation, the discretized equation is: where, K is the heat conduction matrix, and Q n+1 is the heat flux load vector at the (n + 1)th time step.

[0063] Use the finite element method to discretize the stress field control equation, divide the solution domain into a finite number of elements, and then introduce the shape function of the displacement field on each element, and approximate the continuous displacement field u by the linear combination of node displacement values and shape functions: Among them, N i (x) is the shape function of the displacement field, and u i is the nodal displacement value.

[0064] According to the strain-displacement relationship, the strain tensor ε kl is expressed by the derivative of the displacement field:

[0065]

[0066] Substitute the discretized displacement field into the strain-displacement relationship to obtain the discretized strain tensor:

[0067]

[0068] Among them, B kli is the strain-displacement matrix, which contains the derivative information of the shape function;

[0069] Substitute the discretized strain tensor into the generalized Hooke's law to obtain the discretized stress tensor:

[0070]

[0071] Substitute the discretized stress tensor into the force equilibrium equation, and consider the body force term and boundary conditions to obtain the discretized stress field control equation:

[0072]

[0073] Among them, B is the strain-displacement matrix, C is the elastic constant matrix, N is the shape function matrix, f is the body force vector, t is the surface force vector, Ω is the solution domain, and Γ t is the boundary where the surface force is applied.

[0074] The discretized stress field control equation forms an algebraic equation system regarding the nodal displacement value u i . Solving this equation system can obtain the discretized displacement field and stress field.

[0075] Adopt the finite volume method to discretize the flow field control equation, divide the solution domain into a finite number of control volumes, and apply the conservation equation in integral form on each control volume; for the continuity equation, apply the Gauss divergence theorem on the control volume V to obtain the discretized equation: Among them, u f is the velocity vector on the control volume surface f, A f is the area vector of the control volume surface f, and N f is the number of control volume surfaces.

[0076] For the momentum conservation equation, applying the Gauss divergence theorem and the Euler backward time discretization scheme on the control volume V, the discretized equation is obtained:

[0077]

[0078] where, u n and u n+1 are the velocity vectors at the nth and (n + 1)th time steps respectively, is the mass flux on the control volume surface f, is the pressure value on the control volume surface f, is the viscous stress tensor on the control volume surface f.

[0079] The discretized continuity equation and momentum conservation equation form an algebraic equation system for the velocity u and pressure p; due to the coupling of velocity and pressure, special algorithms need to be used for solution, such as the SIMPLE algorithm, PISO algorithm, etc.

[0080] By the finite element method and the finite volume method, the stress field control equation and the flow field control equation are discretized into an algebraic equation system; solving these discretized equations can obtain the numerical solutions of the stress field and flow field in the glass forming process.

[0081] According to the defined initial conditions and boundary conditions, corresponding constraints are imposed in the discretized equations; the initial conditions can be directly assigned to the corresponding nodal variables, and the boundary conditions can be realized by modifying the corresponding terms in the discretized equations; for example, for the temperature boundary condition, the boundary nodal temperature can be directly set to a known value; for the mechanical boundary condition, corresponding force terms can be added to the discretized equations.

[0082] Combining the discretized equations to form a large algebraic equation system, which is solved by the iterative method or the direct method; common iterative methods include the Jacobi iterative method, Gauss - Seidel iterative method, conjugate gradient method, etc.; common direct methods include the Gaussian elimination method and LU decomposition method, etc.; during the solution process, it is necessary to simultaneously satisfy the discretized equations of the temperature field, stress field and flow field, and consider their coupling relationship.

[0083] Solving the discretized equations to obtain the numerical solutions of field variables such as temperature, displacement, velocity, etc. at the discrete nodes or elements; these discrete solutions characterize the temperature distribution, stress distribution and flow state in the glass forming process.

[0084] This step transforms the control equation system describing the glass forming process into a discretized model, and obtains the numerical solutions of the temperature, stress and flow state in the glass forming process by solving the discretized equations, providing the necessary numerical simulation results for subsequent parameter optimization.

[0085] Step 3: Based on the numerical simulation of the glass forming process, taking the geometric accuracy and residual stress of the formed glass cover plate as the optimization objectives, and the mold temperature and pressure as the optimization design variables, a multi-objective parameter optimization model is established.

[0086] Quantify the geometric accuracy as the objective function, and describe the relationship between the geometric accuracy and the optimization design variables with a mathematical expression. The geometric accuracy objective function is expressed as:

[0087]

[0088] where f1 is the geometric accuracy objective function, T and P are the design variables of the mold temperature and pressure respectively, N d is the number of measurement points of the geometric dimensions, w i is the weight factor of the i-th measurement point, d i is the design dimension of the i-th measurement point, is the actual dimension of the i-th measurement point (obtained from the numerical simulation results).

[0089] Quantify the residual stress as the objective function, and describe the relationship between the residual stress and the optimization design variables with a mathematical expression. The residual stress objective function is expressed as:

[0090] f2(T, P) = ∫ Ω σ v (T, P)dV

[0091] where f2 is the residual stress objective function, σ v (T, P) is the equivalent von Mises stress inside the glass cover plate (obtained from the numerical simulation results), and Ω is the solution domain of the glass cover plate;

[0092] Taking into account the geometric accuracy and residual stress comprehensively, a multi-objective optimization model is constructed; the multi-objective optimization model is expressed as:

[0093] min F(T, P) = [f1(T, P), f2(T, P)]

[0094] s.t. T L ≤ T ≤ T U

[0095] P L ≤ P ≤ P U

[0096] where F(T, P) is the multi-objective optimization function, T L and T U are the lower and upper limits of the mold temperature respectively, and P L and P U are the lower and upper limits of the mold pressure respectively.

[0097] Step 4: For the established multi-objective parameter optimization model, design a dynamic temperature and pressure control strategy, and use an intelligent optimization algorithm to search for the optimal die temperature and pressure curves.

[0098] Parametrically represent the dynamic temperature and pressure control curve, and use a piecewise linear function to fit the die temperature and pressure curves. Its mathematical expression is:

[0099]

[0100] where \(T(t)\) and \(P(t)\) are the time functions of the die temperature and pressure respectively, \(T\) i and \(P\) i are the starting temperature and pressure of the \(i\)-th linear function, \(k\) i and \(h\) i are the slopes of the \(i\)-th linear function, \(t\) i is the end time of the \(i\)-th linear function, and \(n\) is the number of segments of the linear function.

[0101] According to the parametric representation of the dynamic temperature and pressure control curve, select the starting temperature \(T\) i , starting pressure \(P\) i , duration \(\Delta t\) i , as well as slopes \(k\) i and \(h\) i as the optimization design variables, where \(i = 0, 1, \ldots, n - 1\). The vector representation of the optimization design variables is:

[0102] \(x = [T_0, P_0, \Delta t_0, k_0, h_0, T_1, P_1, \Delta t_1, k_1, h_1, \ldots, T\) n-1 , \(P\) n-1 , \(\Delta t\) n-1 , \(k\) n-1 , \(h\) n-1

[0103] For the multi-objective parameter optimization model, use the non-dominated sorting genetic algorithm (NSGA-II) as the optimization algorithm; the NSGA-II algorithm can effectively handle multi-objective optimization problems and obtain a uniformly distributed optimal solution set by introducing non-dominated sorting and crowding distance comparison mechanisms.

[0104] In the optimization solution process, it is necessary to constrain the value range of the optimization design variables to ensure the feasibility of the dynamic temperature and pressure control curve; use the penalty function method to handle the constraint conditions, transform the constraint conditions into penalty terms and add them to the objective function, so as to transform the constrained optimization problem into an unconstrained optimization problem; the constraint conditions include: value range constraints of temperature and pressure: \(T\) min \(\leq T\) i \(\leq T\) max , \(P\) min \(\leq P\) i \(\leq P\)​max ; Rate constraints of temperature and pressure: Total forming time constraint:

[0105] where, T min and T max are the lower and upper limits of temperature, P min and P max are the lower and upper limits of pressure, v T,max and v P,max are the upper limits of the change rates of temperature and pressure, and t max is the upper limit of the total forming time.

[0106] The NSGA-II algorithm is used to solve the multi-objective parameter optimization model to obtain the optimal solution set; the specific solution process is as follows:

[0107] Initialization: Randomly generate an initial population that satisfies the constraint conditions, and each individual corresponds to a parameterized representation of a dynamic temperature-pressure control curve;

[0108] Evaluation: Decode each individual to obtain the corresponding dynamic temperature-pressure control curve, and substitute it into the numerical simulation model to calculate the objective function value;

[0109] Non-dominated sorting: Perform non-dominated sorting on the population according to the objective function values to obtain non-dominated solution sets of different levels;

[0110] Crowding distance calculation: Calculate the crowding distance for each individual in the non-dominated solution set to maintain the diversity of the population;

[0111] Selection: Considering the non-dominated rank and crowding distance comprehensively, adopt the binary tournament selection strategy to select excellent individuals as the parents;

[0112] Crossover and mutation: Perform simulated binary crossover and polynomial mutation on the parent individuals to generate new offspring individuals;

[0113] Elite retention: Combine the parent and offspring individuals, and select elite individuals according to non-dominated sorting and crowding distance to form a new population;

[0114] Termination condition judgment: If the maximum number of iterations or other termination conditions are met, output the current optimal solution set; otherwise, return to the evaluation step to continue the iteration.

[0115] In this step, for the established multi-objective parameter optimization model, a dynamic temperature-pressure control strategy optimization method based on the NSGA-II algorithm is designed, and the optimal mold temperature and pressure curves are obtained through intelligent optimization algorithms; the optimization results provide theoretical guidance and process reference for the forming of high-quality 3D glass covers.

[0116] Step 5: Evaluate the actual effect of the dynamic temperature and pressure control strategy by measuring and statistically analyzing the quality indicators of the geometric accuracy and residual stress of the formed glass cover plate.

[0117] Use a coordinate measuring machine to measure the geometric dimensions of the formed 3D glass cover plate to obtain the actual dimension data of the glass cover plate; when measuring, select the key geometric features of the glass cover plate, such as length, width, height, inclination angle, etc., and measure each feature at least 3 times, and take the average value as the measurement result.

[0118] Compare the actual dimension data obtained by measurement with the designed dimensions of the 3D glass cover plate, and calculate the dimension deviation Δd i : Among them, is the measured value of the i-th geometric feature, is the designed value of the i-th geometric feature.

[0119] Calculate the geometric accuracy index G: Among them, n is the number of geometric features measured; the smaller the G value, the higher the geometric accuracy of the glass cover plate.

[0120] Use a photoelastic stress meter to measure the residual stress distribution of the glass cover plate; place the glass cover plate in the photoelastic stress meter, apply polarized light, observe and record the photoelastic fringe image; according to the photoelastic fringe image, calculate the principal stress difference Δσ: Among them, N is the number of photoelastic fringes, f σ is the photoelastic fringe value, which depends on the photoelastic constant of the glass material and the wavelength of the light source used, and h is the thickness of the glass cover plate.

[0121] According to the principal stress difference, combined with the mechanical property parameters of the glass, calculate the maximum residual stress σ of the glass cover plate r : Among them, σ x and σ y are the normal stress components of the glass cover plate in the x and y directions respectively, and can be estimated according to the mechanical property parameters of the glass material.

[0122] Conduct geometric accuracy measurement and residual stress test on multiple glass cover plate samples produced by the dynamic temperature and pressure control strategy to obtain a set of geometric accuracy indexes G i and the maximum residual stress σ ri ; calculate the mean value and standard deviation S G of the geometric accuracy index, as well as the mean value and standard deviation Among them, m is the number of glass cover plate samples.

[0123] Compare the statistical results of geometric accuracy and residual stress under the dynamic temperature and pressure control strategy with those under the traditional process to evaluate the improvement effect of the dynamic temperature and pressure control strategy; if and are significantly reduced, and S G and are small, it indicates that the dynamic temperature and pressure control strategy can effectively improve the forming quality and stability of the glass cover plate.

[0124] According to the evaluation results of the actual effect of the dynamic temperature and pressure control strategy, identify the key factors affecting the quality of the glass cover plate, including temperature gradient and pressure change rate, etc.; adjust the parametric representation of the dynamic temperature and pressure curve accordingly, such as increasing the number of segments and adjusting the slope and intercept of the linear function, etc., to optimize the change rules of temperature and pressure; input the optimized dynamic temperature and pressure curve into the multi-objective parameter optimization model, and re-optimize and solve to obtain the improved dynamic temperature and pressure control strategy.

[0125] In this step, through measuring and statistically analyzing the geometric accuracy and residual stress of the formed glass cover plate, quantitatively evaluate the actual effect of the dynamic temperature and pressure control strategy, and compare it with the traditional process to determine the advantages and improvement degree of the dynamic temperature and pressure control strategy; at the same time, according to the evaluation results, optimize and iterate the dynamic temperature and pressure curve specifically, continuously improve the effect and stability of the dynamic temperature and pressure control strategy, and ultimately achieve high-quality production of 3D glass cover plates.

[0126] Example 2

[0127] Referring to Figure 2 , which is the second embodiment of this application, provides a 3D glass cover plate forming parameter optimization system based on dynamic temperature and pressure control.

[0128] The system includes: a mathematical fitting module, a discretization processing module, a multi-objective optimization module, a dynamic temperature and pressure control module, and an analysis and evaluation module.

[0129] The mathematical fitting module is used to establish a mathematical model for the 3D glass cover plate forming process, comprehensively consider the coupling effects of the temperature field, stress field, and flow field, and obtain a control equation set describing the glass forming process.

[0130] The discretization processing module uses numerical methods to discretize the control equation set to obtain a discretized model of the glass forming process, and obtains the temperature, stress, and flow states of the glass forming process by solving the discretized equation.

[0131] The multi-objective optimization module, based on the numerical simulation of the glass forming process, takes the geometric accuracy and residual stress of the formed glass cover plate as optimization objectives, and takes the mold temperature and pressure as optimization design variables to establish a multi-objective parameter optimization model.

[0132] The dynamic temperature and pressure control module designs a dynamic temperature and pressure control strategy for the established multi-objective parameter optimization model, and uses an intelligent optimization algorithm to search for the optimal die temperature and pressure curves.

[0133] The analysis and evaluation module measures and statistically analyzes the quality indexes of the geometric accuracy and residual stress of the formed glass cover plate to evaluate the actual effect of the dynamic temperature and pressure control strategy.

[0134] Embodiment 3

[0135] This application provides a storage medium with a computer program stored thereon. When the computer program is executed by a processor, it runs the steps in the above method. Through the above technical solution, when the computer program is executed by the processor, it executes the method in any optional implementation manner of the above embodiment to achieve the following functions: obtaining multiple groups of historical electricity consumption data, dividing the historical electricity consumption data, and outputting specific electricity consumption groups; calculating the historical electricity consumption data and outputting a moving average value; analyzing the moving average value and outputting the equipment type and peak time; obtaining the current time and the real-time electricity consumption, analyzing and calculating the real-time electricity consumption based on the current time, peak time, and equipment type, and outputting abnormal electricity consumption information based on the calculation result.

[0136] In the above embodiments of this application, the descriptions of the respective embodiments have their own emphases. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0137] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. Among them, the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM for short), electrically erasable programmable read-only memory (EEPROM for short), erasable programmable read-only memory (EPROM for short), programmable read-only memory (PROM for short), read-only memory (ROM for short), magnetic memory, flash memory, magnetic disk or optical disc. These computer program instructions can also be stored in a computer-readable memory capable of guiding a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured article including an instruction device, and the instruction device implements the process Figure 1 in one process or multiple processes and / or Figure 1 in one block or multiple blocks as specified.

[0138] In the embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For another example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some communication interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0139] The embodiments of the present application have been described above in conjunction with the accompanying drawings, but the present application is not limited to the above specific implementation manners. The above specific implementation manners are only illustrative and not restrictive. Those of ordinary skill in the art, under the inspiration of the present application and without departing from the purpose of the present application and the scope protected by the claims, can also make changes, modifications, substitutions, and variations to the above embodiments, and these all fall within the protection scope of the present application.

Claims

1. A 3D glass cover molding parameter optimization method based on dynamic temperature and pressure control, characterized in that ,include: A mathematical model of the 3D glass cover forming process is established, and the coupling effects of temperature field, stress field and flow field are comprehensively considered to obtain the control equations describing the glass forming process. The control equations are discretized by numerical method to obtain the discretized model of glass forming process. The temperature, stress and flow state of glass forming process are obtained by solving the discretized equations. Based on the numerical simulation of glass forming process, a multi-objective parameter optimization model was established with the geometric accuracy and residual stress of the glass cover after forming as the optimization targets and the mold temperature and pressure as the optimization design variables. According to the established multi-objective parameter optimization model, a dynamic temperature and pressure control strategy is designed, and the optimal mold temperature and pressure curve is searched using an intelligent optimization algorithm; The actual effect of the dynamic temperature and pressure control strategy is evaluated by measuring and statistically analyzing the quality indicators of the geometric accuracy and residual stress of the glass cover after molding.

2. The 3D glass cover plate forming parameter optimization method based on dynamic temperature and pressure control according to claim 1, characterized in that ,Establish a three-dimensional geometric model of the 3D glass cover, and define the geometric size, shape and relative position relationship of the 3D glass cover; Obtain the thermophysical properties of glass, including density, specific heat capacity, thermal conductivity and viscosity; comprehensively consider the coupling effects of temperature field, stress field and flow field, and construct a group of control equations to describe the glass forming process, including: considering the heat transfer mechanism in the glass forming process, establishing a control equation to describe the temperature field distribution; establishing a stress field control equation, based on the mechanical behavior of the glass, establishing a stress field control equation to describe the stress-strain relationship of the glass; considering the force balance condition in the glass forming process, establishing a control equation to describe the stress field distribution; based on the rheological properties of the glass, construct a flow field control equation to describe the relationship between the viscosity of the glass and the temperature and shear rate; considering the conservation of mass and momentum in the glass forming process, establish a control equation to describe the flow field distribution; considering the interaction and coupling effects between the temperature field, stress field and flow field, establish a group of coupled control equations; According to the characteristics of glass forming process, the initial conditions and boundary conditions of temperature field, stress field and flow field are defined.

3. The method for optimizing the forming parameters of a 3D glass cover plate based on dynamic temperature and pressure control according to claim 2, characterized in that , discretize the continuous time domain into a finite number of time steps, use the finite difference method to discretize the time derivative term, and obtain the time derivative; discretize the continuous space domain, use the finite element method to discretize the space derivative term, and obtain the space derivative; Substitute the discretized time derivative and space derivative into the control equations to obtain the discretized equations; The finite element method is used to discretize the stress field control equations; the finite volume method is used to discretize the flow field control equations; According to the defined initial conditions and boundary conditions, corresponding constraints are imposed in the discretized equations; the discretized equations are combined to form a large algebraic equation system; the discretized equation system is solved to obtain numerical solutions of temperature, displacement and velocity field variables.

4. The method for optimizing forming parameters of a 3D glass cover plate based on dynamic temperature and pressure control according to claim 3 is characterized in that ,The geometric accuracy and residual stress of the glass cover after molding are taken as the optimization targets, the geometric accuracy and residual stress are quantified as the objective function, the mold temperature and pressure are taken as the optimization design variables, and a multi-objective parameter optimization model is established.

5. The method for optimizing forming parameters of a 3D glass cover plate based on dynamic temperature and pressure control according to claim 4, characterized in that , the dynamic temperature and pressure control curve is parametrically represented, and a piecewise linear function is used to fit the mold temperature and pressure curve; According to the parameterized representation of the dynamic temperature and pressure control curve, the starting temperature, starting pressure, duration and slope of each linear function of the mold temperature and pressure curve are selected as optimization design variables.

6. The method for optimizing forming parameters of a 3D glass cover plate based on dynamic temperature and pressure control according to claim 5, characterized in that ,For the multi-objective parameter optimization model, a non-dominated sorting genetic algorithm is used as the optimization algorithm; the value range of the optimization design variables of the optimization algorithm is constrained, and the penalty function method is used to deal with the constraints, the constraints are converted into penalty items and added to the objective function, and the constrained optimization problem is converted into an unconstrained optimization problem.

7. The method for optimizing forming parameters of a 3D glass cover plate based on dynamic temperature and pressure control according to claim 6, characterized in that , measure the geometric dimensions of the formed 3D glass cover plate, obtain the actual dimension data of the glass cover plate, compare the measured actual dimension data with the design dimension of the 3D glass cover plate, and calculate the dimension deviation; Measure the residual stress distribution of the glass cover plate. Place the glass cover plate in a photoelastic stress meter, apply polarized light, observe and record the photoelastic fringe image, calculate the principal stress difference based on the photoelastic fringe image, and calculate the maximum residual stress of the glass cover plate based on the principal stress difference and the mechanical properties parameters of the glass.

8. The method for optimizing forming parameters of a 3D glass cover plate based on dynamic temperature and pressure control according to claim 7, characterized in that ,The geometric accuracy measurement and residual stress test were carried out on multiple glass cover samples produced by the dynamic temperature and pressure control strategy to obtain a set of geometric accuracy indicators and maximum residual stress; Calculate the mean and standard deviation of geometric accuracy index, as well as the mean and standard deviation of maximum residual stress; The statistical results of geometric accuracy and residual stress under the dynamic temperature and pressure control strategy are compared with those under the traditional process to evaluate the improvement effect of the dynamic temperature and pressure control strategy.

9. The method for optimizing forming parameters of a 3D glass cover plate based on dynamic temperature and pressure control according to claim 8, characterized in that ,According to the actual effect evaluation results of the dynamic temperature and pressure control ,strategy, the key factors affecting the quality of the glass cover are identified, ,including temperature gradient and pressure change rate; Adjust the parameterized representation of the dynamic temperature and pressure curve, including: increasing the number of segments and adjusting the slope and intercept of the linear function to optimize the variation of temperature and pressure; The optimized dynamic temperature and pressure curve is input into the multi-objective parameter optimization model, and the optimization solution is re-performed to obtain the improved dynamic temperature and pressure control strategy.

10. A 3D glass cover molding parameter optimization system based on dynamic temperature and pressure regulation, which is implemented according to the 3D glass cover molding parameter optimization method based on dynamic temperature and pressure regulation according to any one of claims 1 to 9, characterized in that , including: mathematical fitting module, discrete processing module, multi-objective optimization module, dynamic temperature and pressure control module and analysis and evaluation module; The mathematical fitting module is used to establish a mathematical model of the 3D glass cover plate forming process, comprehensively consider the coupling effects of the temperature field, stress field and flow field, and obtain a control equation group describing the glass forming process; The discrete processing module uses a numerical method to discretize the control equation group to obtain a discretized model of the glass forming process, and obtains the temperature, stress and flow state of the glass forming process by solving the discretized equations; The multi-objective optimization module, based on the numerical simulation of the glass forming process, takes the geometric accuracy and residual stress of the glass cover after forming as the optimization targets, and takes the mold temperature and pressure as the optimization design variables, to establish a multi-objective parameter optimization model; The dynamic temperature and pressure control module designs a dynamic temperature and pressure control strategy based on the established multi-objective parameter optimization model, and uses an intelligent optimization algorithm to search for the optimal mold temperature and pressure curve; The analysis and evaluation module evaluates the actual effect of the dynamic temperature and pressure control strategy by measuring and statistically analyzing the quality indicators of the geometric accuracy and residual stress of the glass cover after molding.

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