Mold opening and closing stroke control method of electric injection molding machine

Abstract

The invention discloses a mold opening and closing stroke control method of an electric injection molding machine, which comprises the following steps: S1, acquiring key geometric parameters of a toggle rod system in a five-hinge-point inclined double-toggle mold closing mechanism, and adjusting main variable conditions of a mechanism kinematic chain based on theoretical formulas of a movable mold plate stroke (Sm) and a ball screw driving stroke (So), data association points between the driving stroke of the ball screw and the stroke of the movable template are obtained; s2, constructing a function mapping model between a moving template stroke (Sm) and a ball screw driving stroke (So) according to the data obtained in the step S1, and forming a fitting expression in a form of Sm = f (So); and S3, establishing a reverse fitting function, and forming function mapping of So = (Sm). Reverse control is realized by precisely fitting the relationship between the template and the stroke of the ball screw, so that errors caused by using an interpolation method are reduced, the system control precision in the mold opening and closing process is improved, the calculation efficiency is greatly improved, the storage space is saved, and the cost is further reduced.

Description

Technical Field

[0001] The present application relates to the technical field of electric injection molding machines, and in particular to a method for controlling the mold opening and closing strokes of an electric injection molding machine. Background Art

[0002] An electric injection molding machine is an injection molding machine that uses an AC servo motor, equipped with components such as ball screws, toothed belts, and gears to drive various mechanisms. It effectively ensures the stability of plasticization and metering, and is capable of high-quality injection molding of everything from general-purpose resins to engineering plastics.

[0003] At present, a five-hinge oblique double-toggle clamping mechanism is commonly used in electric injection molding machines to achieve the closing and opening of the mold. This type of mechanism usually uses a ball screw as a driving element, and uses linear reciprocating motion to drive the movable platen to complete the mold closing or opening operation. In the prior art, an open-loop control method is mostly used, and the driving stroke of the ball screw is used as the basis for estimating the displacement of the movable platen. However, due to the complex structure of the five-hinge toggle mechanism and the nonlinear coupling relationship between multiple moving pairs, there is no linear or analytical mapping relationship between the ball screw stroke and the movable platen displacement. For this reason, the prior art generally uses interpolation or table lookup to establish an approximate mapping between the two. However, when the degree of nonlinearity of the mechanism is high or the dynamic response changes significantly, the above method is difficult to maintain control accuracy and is prone to position deviation, thereby affecting the product dimensional accuracy and injection molding quality.

[0004] Therefore, there is an urgent need for a control method that can accurately describe the relationship between the ball screw drive stroke and the actual displacement of the movable platen, so as to improve the control accuracy of the injection molding process and the product quality. Summary of the Invention

[0005] The purpose of this application is to provide a method for controlling the mold opening and closing stroke of an electric injection molding machine, which realizes reverse control by accurately fitting the relationship between the template and the ball screw stroke, thereby reducing the error caused by the use of interpolation method to improve the system control accuracy, greatly improve the calculation efficiency, save storage space and further reduce costs.

[0006] To achieve the above-mentioned purpose, the technical solution adopted in this application is: a method for controlling the mold opening and closing stroke of an electric injection molding machine, comprising the following steps: S1: obtaining the key geometric parameters of the toggle system in the five-hinge oblique double toggle mold clamping mechanism, based on the movable plate stroke (S m ) and ball screw drive stroke (S o ) theoretical formula, adjust the main variable conditions of the mechanism motion chain to obtain the data correlation point between the movable plate stroke and the ball screw drive stroke; S2: Construct the movable plate stroke (S according to the data obtained in step S1 m ) and ball screw drive stroke (S o ) to form a function mapping model between Sm =f(S o ) form of fitting expression; S3: Establish the inverse fitting function to form S o = (S m ) function mapping.

[0007] Compared with the prior art, the beneficial effects of the present application are: in the traditional control method, the position of the moving template is calculated based on the ball screw drive stroke, and the present invention adopts a reverse design, that is, the target moving template stroke is used as input to reversely calculate the required ball screw drive stroke, thereby reversely driving the clamping mechanism to complete the expected motion trajectory, realize reverse drive control, and ensure the requirements of the mold opening process; through mathematical models and kinematic simulations, an accurate mapping relationship between the template stroke and the ball screw stroke is established, and the relationship between the template and the ball screw stroke is accurately fitted; thereby reducing the error caused by the use of the interpolation method to improve the system control accuracy, ensuring that the actual stroke of the ball screw can be accurately calculated under a given template stroke, and replacing the traditional interpolation method with a fitting function to simplify system storage and calculation complexity, save storage space, greatly improve calculation efficiency, save storage space and further reduce costs.

[0008] As an improvement, in step S1, the five-hinge oblique double-toggle clamping mechanism includes a first crank slider group and a second crank slider group, the first crank slider group includes a large crank rod L1, a large connecting rod L2 and a movable template, the second crank slider group includes a small crank rod L3, a small connecting rod L4 and a crosshead, the large crank rod is fixedly connected to the small crank rod, and rotates synchronously around the hinge D at the same angular velocity, and the crosshead drives the connection point B to make the movable template perform reciprocating linear motion along the horizontal guide rail; through the above improvements, by obtaining the key positions to obtain the main variable conditions of the mechanism motion chain, a kinematic relationship can be established and a fitting data set can be generated, which is convenient for data analysis and further reverse control.

[0009] As an improvement, step S1 includes step S11: the variable conditions of the mechanism motion chain include the mechanism position variables, and the movable plate stroke (S m ) and ball screw drive stroke (S o ) motion relationship model, the corresponding stroke conversion equation is derived through the position variables of the mechanism to form a calculation expression: S m =( + ) ; Where, is the elbow rotation angle, are the mechanism's oblique angle and elbow wedge angle, L1~L4 are the length parameters of each member of the mechanism, L5 is the distance parameter between the intersection of the small crankshaft rod L3 and the small connecting rod L4 and the hinge point D, is the oblique installation angle of the mechanism, is the angle between the small connecting rod and the horizontal line, is the angle between the large connecting rod and the horizontal line, is the angle between the large and small elbows, is the angle of rotation of the elbow from the initial position of mold closing; H is the vertical distance from the hinge point D to the center of the crosshead, H1 is the half length of the crosshead, S m and S o and represent the strokes of the movable platen and the crosshead respectively; through the above improvements, according to the transmission structure between the movable platen and the ball screw, and based on the geometric constraints and kinematic characteristics of the toggle mechanism, the precise fitting relationship between the platen and the ball screw stroke is achieved.

[0010] As an improvement, an equal probability sampling method is set to select the elbow rotation angle within the preset range. Randomly select N sample points and calculate the corresponding ball screw drive stroke S o With the movable template stroke S m , thereby generating a numerical data set containing N groups of paired samples Through the above improvements, under the premise of keeping all structural parameters unchanged, the data set is evenly distributed in the angular space, which can fully cover the entire motion range of the clamping mechanism from mold opening to mold closing, and serve as the basic input for subsequent forward and inverse function fitting modeling, providing data support for high-precision modeling of complex nonlinear relationships.

[0011] As an improvement, the step S2 further includes a step S22: obtaining the N groups of sample data Follow the template itinerary S m Arrange in ascending order, perform first-order difference processing on them, and calculate the slope between two adjacent points:

[0012] , i=1, 2, ..., N 1;

[0013] According to the slope sequence obtained Construct a slope change curve by identifying local slope mutation points Or the second-order difference significant transition The larger one, determine the corresponding interval division points x1, x2, ..., x k Through the above improvements, by introducing the piecewise polynomial function fitting strategy, and The mapping relationship between them is partitioned and modeled to improve the overall fitting accuracy and local response sensitivity.

[0014] As an improvement, the step S2 further comprises a step S23: fitting function construction, according to the divided stage interval, using n j order polynomial function fitting in each segment interval m , and using the least square method to solve the parameters of the selected polynomial coefficients, constructing the fitting residual sum of squares as the objective function, and by taking the partial derivative of each coefficient and setting it to zero to form a linear equation group; solving the linear equation group to obtain the optimal polynomial coefficients with the minimum fitting error sum of squares; the optimal polynomial coefficients with the minimum fitting error sum of squares can introduce derivative continuity constraints; through the above improvement, the fitting coefficient solving and model optimization, by taking the partial derivative of each coefficient and setting it to zero, the normal equation group that meets the minimum error condition can be obtained, by solving the above linear equation group, the optimal polynomial coefficients that minimize the fitting error sum of squares can be obtained, and the continuity of the overall fitting function at the segment junctions is ensured, ensuring that the fitting curve has smooth transition characteristics.

[0015] As an improvement, the step S3 comprises a step S31: based on the data-driven reverse fitting modeling method, a function mapping relationship between the template stroke and the screw stroke is established, and reverse solving and model optimization are performed; through the above improvement, based on the data-driven reverse fitting modeling method, the application requirement of inversely calculating the required screw displacement from the target template stroke in actual control is fully realized.

[0016] As an improvement, the step S3 further comprises a step S32: by reverse data fitting, S m is taken as the independent variable, and S o is taken as the dependent variable, based on the fitted data set , the reverse mapping model is reconstructed; through the above improvement, reverse solving and model optimization, through the strategy of reverse data fitting, the reverse mapping model is reconstructed, thereby establishing a stable and high-precision reverse mapping relationship in the entire working stroke range.

[0017] As an improvement, the step S3 further comprises a step S33: when the function value is in the low stroke segment, a composite logarithmic fitting function is used to express: ; when the function value is in the middle displacement segment or the high displacement segment, low-order polynomial modeling is used, the middle segment fitting function can be expressed as ; the high segment fitting function can be expressed as ; in the formula, a k , b k are to-be-fitted parameters, It represents a logarithmic function with base 2. Through the above improvements, the inverse model uses multiple logarithmic functions and polynomial functions for segmented modeling in each sub-interval. In the low-stroke segment, a composite logarithmic fitting function is used to improve the modeling accuracy. In the medium and high displacement segments, the data distribution tends to be smooth, and the change trend is more suitable for modeling using low-order polynomials.

[0018] As an improvement, step S3 also includes step S34: using the least squares method to optimize the fitting of the coefficients of each piecewise function, constructing the sum of squares of the fitting residuals as the objective function, and forming a system of linear equations by taking partial derivatives of each coefficient and setting the partial derivatives to zero; solving the system of linear equations to obtain the optimal polynomial coefficients with the smallest sum of squares of fitting errors; fitting the optimal polynomial system with the smallest sum of squares of fitting errors, and introducing derivative continuity constraints. Through the above improvements, the fitting curve is smoothly transitioned at the connection of each segment to ensure that the overall function has good continuity and smoothness at the dividing point. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic diagram of the kinematics of the five-hinge oblique double-toggle mold clamping mechanism of this application;

[0020] Figure 2 This is the data graph after sampling 500 samples;

[0021] Figure 3 This is a mapping diagram of the displacement of the moving plate based on the displacement input of the ball screw;

[0022] Figure 4 This is a mapping diagram of the displacement of the ball screw based on the displacement input of the moving plate;

[0023] Figure 5 This is the relationship diagram of the moving template displacement fitting mapping based on the interpolation method;

[0024] Figure 6 This is the ball screw displacement fitting mapping relationship diagram based on the interpolation method;

[0025] Figure 7 This is the unsmoothed moving template displacement fitting mapping relationship diagram;

[0026] Figure 8 This is the unsmoothed ball screw displacement fitting mapping diagram. DETAILED DESCRIPTION

[0027] Below, the present application is further described in conjunction with specific implementation methods. It should be noted that, in the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" and the like are intended to mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms should not be understood as necessarily referring to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification.

[0028] In the description of this application, it should be noted that for directional words, such as the terms "center", "horizontal", "longitudinal", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and so on, indicating the orientation and position relationship are based on the orientation or position relationship shown in the accompanying drawings, which is only for the convenience of describing this application and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and cannot be understood as limiting the specific scope of protection of this application.

[0029] It should be noted that the terms "first", "second", etc. in the description and claims of this application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0030] In this application, unless otherwise expressly specified or limited, terms such as "mounted," "connected," "connect," and "fixed" should be understood in a broad sense. For example, they may refer to connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; and internal communication between two components or interaction between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on specific circumstances.

[0031] In this application, unless otherwise expressly specified or limited, a first feature being "above" or "below" a second feature may include the first and second features being in direct contact, or may include the first and second features being in contact not directly but through another feature between them. Moreover, a first feature being "above," "above," and "above" a second feature may include the first feature being directly above or obliquely above the second feature, or may simply mean that the first feature is higher in level than the second feature. A first feature being "below," "below," and "below" a second feature may include the first feature being directly below or obliquely below the second feature, or may simply mean that the first feature is lower in level than the second feature.

[0032] The terms "comprises" and "having" and any variations thereof in the specification and claims of this application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus that includes a series of steps or units is not necessarily limited to those steps or units expressly listed, but may include other steps or units not expressly listed or inherent to such process, method, product or apparatus.

[0033] This application discloses a method for controlling the mold opening and closing stroke of an electric injection molding machine. Figure 1-8 As shown, the method includes the following steps: S1: kinematic modeling and data collection, obtaining the key geometric parameters of the toggle system in the five-hinge oblique double-toggle clamping mechanism, based on the dynamic plate stroke (S m ) and ball screw drive stroke (S o ) theoretical formula, adjust the main variable conditions of the mechanism motion chain to obtain the data correlation point between the movable plate stroke and the ball screw drive stroke; S2: Fitting function construction, according to the data obtained in step S1, select the appropriate function type to construct the movable plate stroke (S m ) and ball screw drive stroke (S o ) to form a function mapping model between S m =f(S o ) form of fitting expression; S3: Establish an inverse fitting function. For the application scenario in which the target stroke of the moving template is a known quantity in the actual control system, further establish an inverse fitting function. By inverting the original fitting function or constructing a new fitting model, S o = (S m ) is used to calculate the required driving stroke of the ball screw from the template stroke, thereby completing the precise control of the mold opening process.

[0034] It should be noted that this application collects actual data on the ball screw drive stroke and the corresponding movable template stroke, combines the kinematic characteristics and geometric constraints of the five-hinge double-bend clamping mechanism, and adopts a fitting modeling method to establish a functional mapping relationship between the movable template stroke and the ball screw stroke, thereby achieving high-precision control of the actual stroke of the movable template in the clamping mechanism. Different from the "forward design" approach of the traditional control method that calculates the position of the movable template based on the ball screw drive stroke, the present invention adopts the "reverse design" concept, that is, using the target movable template stroke as input, inverting the required ball screw drive stroke, and thus reversely driving the clamping mechanism to complete the expected motion trajectory and realize reverse drive control. By optimizing the design goals, it is possible to achieve accurate fitting of the relationship between the template and the ball screw stroke, improve the system control accuracy, and simplify the system storage and calculation complexity.

[0035] Specifically, such as Figure 1 As shown, in the five-hinge oblique double toggle clamping mechanism adopted by the present invention, the power transmission between the ball screw and the movable platen is completed by the first crank slider group and the second crank slider group. In step S1, the five-hinge oblique double toggle clamping mechanism includes the first crank slider group and the second crank slider group. The first crank slider group includes a large crankshaft rod L1, a large connecting rod L2 and a movable platen, and the second crank slider group includes a small crankshaft rod L3, a small connecting rod L4 and a crosshead. The large crankshaft rod and the small crankshaft rod are fixedly connected and rotate synchronously around the hinge point D at the same angular velocity. The crosshead drives the connection point B to make the movable platen perform reciprocating linear motion along the horizontal guide rail.

[0036] It should be noted that Figure 1 The solid line represents the mechanism's limit position in the mold closing condition, while the dashed line corresponds to the limit position in the mold opening condition. The movable platen acts as a slider in this mechanism, the crosshead serves as the slider element, and the large crankshaft rod L1 and the small toggle rod L3 are rigidly connected to form a single unit.

[0037] Specifically, step S1 includes step S11: the variable conditions of the mechanism motion chain include the mechanism position variables, and the movable plate stroke (S m ) and ball screw drive stroke (S o ) motion relationship model, the corresponding stroke conversion equation is derived through the mechanism position variables (including elbow angle, connecting rod length, arrangement angle, etc.) to form a calculation expression: S m =( + ) . Where, is the rotation angle of the elbow rod, respectively, the oblique angle of the mechanism and the wedge angle of the elbow, L1~L4 are the length parameters of each rod of the mechanism, L5 is the distance parameter of the intersection of the small crankshaft rod L3 and the small connecting rod L4 compared to the hinge point D, is the oblique installation angle of the mechanism, is the angle (wedge angle) between the small connecting rod and the horizontal line, is the angle between the large connecting rod and the horizontal line, is the angle between the large and small elbows, and is the angle of rotation of the elbow from the initial position of the mold clamping; H is the vertical distance from the hinge point D to the center of the crosshead, H1 is the half-length dimension of the crosshead, S m and S o and represent the travel distances of the movable platen and the crosshead respectively.

[0038] Specifically, step S2 includes step S21: setting an equal probability sampling method to sample the elbow rotation angle within a preset range. Randomly select N sample points and calculate the corresponding ball screw drive stroke S o With the movable template stroke S m , thereby generating a numerical data set containing N groups of paired samples .

[0039] Example 1: Figure 2 As shown in the figure, under the premise of keeping all structural parameters unchanged, in order to fully cover the motion characteristics of the entire workspace, an equal probability sampling method is used to calculate the elbow rotation angle within a reasonable design range. 500 sample points are randomly selected. Each set of rotation angle values ​​is substituted into the above kinematic model to calculate the corresponding ball screw drive stroke. With dynamic template stroke , thus generating a numerical data set containing 500 pairs of samples The dataset is evenly distributed in the angular space, fully covering the entire motion range of the mold clamping mechanism from mold opening to mold closing. It serves as the basic input for subsequent forward and inverse function fitting modeling, providing data support for high-precision modeling of complex nonlinear relationships.

[0040] It is understandable that since the mapping relationship between the movable plate stroke and the ball screw stroke is highly nonlinear and the segment variation law is inconsistent, it is difficult to take into account both accuracy and stability if a global single function is used for fitting. It is easy to have problems such as excessive fitting deviation or numerical oscillation in some areas. Therefore, a piecewise polynomial function fitting strategy is used to fit the and The mapping relationship between them is partitioned and modeled to improve the overall fitting accuracy and local response sensitivity.

[0041] Specifically, step S2 further includes step S22: obtaining N groups of sample data Follow the template itinerary S mArrange in ascending order, perform first-order difference processing on them, and calculate the slope between two adjacent points: , i=1, 2, ..., N 1; According to the slope sequence obtained Construct a slope change curve, and then analyze the change trend of the curve in the entire travel range, by identifying the local slope mutation point Or the second-order difference significant transition The larger one, determine the corresponding interval division points x1, x2, ..., x k .

[0042] Specifically, step S2 also includes step S23: constructing a fitting function, according to the divided stage intervals, in each segment interval Inside, use n j Fitting of polynomial functions. Right now: ,in, are the multinomial coefficients of the j-th segment.

[0043] Preferably, the fitting coefficients are solved and the model is optimized. In order to obtain the optimal polynomial fitting function in each segmented interval, the coefficients of the selected polynomial are parameterized using the least squares method, and the sum of squares of the fitting residuals is constructed as the objective function: .

[0044] in, Represents the true value of the i-th template stroke in the sample, Indicates the predicted value of the fitting function at this point. By finding the partial derivative and setting it to zero, we can obtain the normal equations that satisfy the minimum error condition: 0, m=0,1,……, , the above equations can be arranged into the following linear equations in matrix form: .

[0045] in, The coefficient matrix, whose elements are expressed as: , p,q=0,1,…… ; is the polynomial coefficient vector to be found; is the right-hand term vector, whose elements are expressed as: , p=0,1,……, .

[0046] Preferably, by solving the above linear equations, the optimal polynomial coefficients that minimize the sum of squares of the fitting errors can be obtained. Then, to ensure the continuity of the overall fitting function at each segment junction, the fitting function must satisfy the function value continuity at the dividing point: = , and introduce derivative continuity constraints when necessary: = , to ensure that the fitting curve has smooth transition characteristics.

[0047] Example 2: Figure 3 As shown, the fitting function experimental results are analyzed.

[0048] The first fitting section: The ball screw displacement is in the range of 0~85mm, and the following fifth-order polynomial is used for fitting: .

[0049] in, is the displacement of the moving plate; x is the displacement of the ball screw , = -1.4927e-08, =1.7395e-05, = -1.5153e-04, = 0.0233, = -0.0925. When the ball screw displacement = 65mm, the above formula can be substituted to calculate the movable plate displacement = 5.2924mm, which has an error of 0.0476mm compared with the original given data of 5.34mm.

[0050] Second fitting: The ball screw displacement is in the range of 85~445mm, and the following fifth-order polynomial is used for fitting: .

[0051] in, =3.914837e-08, = -5.85635e-05, =0.032683886, =-5.793008, =369.3053885. For example, when the ball screw displacement = 395 mm, the displacement of the movable platen can be calculated by substituting it into the above formula to be =524.3295, which has an error of 0.0795 mm compared to the original given data of 524.25 mm.

[0052] Specifically, step S3 includes step S31: after completing the construction of the forward mapping model of the ball screw stroke and the movable platen stroke, based on the data-driven inverse fitting modeling method, a function mapping relationship from the movable platen stroke to the screw stroke is established, and inverse solution and model optimization are performed.

[0053] Specifically, step S3 further includes step S32: by reverse data fitting, with S m As an independent variable, S o As the dependent variable, based on the fitted data set , reconstruct the reverse mapping model. Since the forward model is a highly nonlinear relationship, its inverse function is difficult to directly solve analytically, so the reverse solution and model optimization are performed in step S32.

[0054] Preferably, taking into account the functional characteristics of different sections, the inverse model selects multiple logarithmic functions and polynomial functions in each sub-interval for segmented modeling, thereby establishing a stable and high-precision inverse mapping relationship within the entire working range.

[0055] Specifically, step S3 further includes step S33: when the function value is in the low-travel segment, the data change rate is significantly nonlinear, conventional polynomial fitting is difficult to converge, and the fitting error is relatively large, a composite logarithmic fitting function is used, and the fitting function can be expressed as: When the function value is in the middle displacement segment or high displacement segment, the data distribution tends to be smooth, and the change trend is more suitable for modeling using a low-order polynomial. The middle segment fitting function can be expressed as: ; The high-segment fitting function can be expressed as: ; Where a k , b k are the parameters to be fitted, It represents the logarithmic function with base 2.

[0056] Specifically, step S3 also includes step S34: using the least squares method to optimize the fitting of the coefficients of each piecewise function, constructing the sum of squares of the fitting residuals as the objective function, and forming a system of linear equations by taking partial derivatives of each coefficient and setting the partial derivatives to zero; solving the system of linear equations to obtain the optimal polynomial coefficients with the smallest sum of squares of fitting errors; fitting the optimal polynomial system with the smallest sum of squares of fitting errors, and introducing derivative continuity constraints.

[0057] Example 3: Analysis of the experimental results of the fitting function in the low-stroke segment. When the movable platen displacement range is 0 to 35 mm, the original data distribution in this range has strong nonlinear characteristics, and the traditional linear or polynomial model fitting effect is poor. Therefore, a fourth-order logarithmic polynomial model is selected for modeling. The fitting function expression is as follows: ; Where x represents the displacement of the moving plate S m , =0.00748246, =0.06791582, =1.1941003, =10.458000, =31.492866. When the movable plate displacement x = 2.2mm, the screw displacement can be calculated by substituting it into the above formula to = 45.04643, which has an error of 0.04643mm compared with the corresponding original given data of 45.0mm.

[0058] Example 4: Analysis of experimental results of the fitting function for the mid-stroke segment. When the movable platen displacement range is within 35 to 155 mm, the data distribution in the mid-stroke segment is relatively smooth, making it suitable for fitting using a fourth-order polynomial function. The fitting function expression is as follows: + + + ; in, = -1.785110e-07, = 8.6516e-05, = -0.016958, = 2.2116234, = 70.82277. When the movable plate displacement x = 61.28 mm, the screw displacement can be calculated by substituting the above formula into =160.0615, which is 0.0615mm compared with the corresponding original given data 160mm.

[0059] Example 5: Analysis of the experimental results of the fitting function for the high stroke section. The displacement of the movable plate is in the range of 155-642 mm, and the following formula is used for fitting: + + + ; in, = 3.54869978e-07, = -0.000483, = 0.6508975, = 134.93843; when the displacement of the movable plate is x=3196.4mm, the screw displacement can be calculated by substituting it into the above formula: =305.2325, which has an error of 0.2325mm compared with the corresponding original given data 305mm.

[0060] It is understandable that if Figure 5 and Figure 6 As shown in , the uniform interpolation method is significantly inferior to the method proposed in this design in terms of fitting accuracy and transition smoothness. Figure 7 and Figure 8 As shown in Figure 3, the data results further demonstrate the performance of the proposed stage function before and after smoothing: before smoothing, the function has obvious fluctuations at the contact points, while after smoothing, it achieves better continuity and transition, verifying the advantages of this method in segmented connection processing.

[0061] The above describes the basic principles, main features, and advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above-described embodiments. The above-described embodiments and the specification merely illustrate the principles of the present application. Various changes and improvements may be made to the present application without departing from the spirit and scope of the present application. These changes and improvements fall within the scope of the present application for which protection is sought. The scope of protection claimed by the present application is defined by the appended claims and their equivalents.

Claims

1. A method for controlling the mold opening and closing stroke of an electric injection molding machine, characterized in that: The steps include: S1: Obtain the key geometric parameters of the toggle system in the five-hinge oblique double toggle clamping mechanism, based on the movable plate stroke (S m ) and ball screw drive stroke (S o ) theoretical formula, adjust the main variable conditions of the mechanism motion chain to obtain the data correlation point between the moving plate stroke and the ball screw drive stroke; S2: Construct the dynamic template stroke (S m ) and ball screw drive stroke (S o ) to form a function mapping model between S m =f(S o ) form of fitting expression; S3: Establish the inverse fitting function to form S o = (S m ) function mapping.

2. The method for controlling the mold opening and closing stroke of an electric injection molding machine according to claim 1, wherein: In step S1, the five-hinge oblique double-toggle clamping mechanism includes a first crank slider group and a second crank slider group, the first crank slider group includes a large crank rod L1, a large connecting rod L2 and a movable plate, the second crank slider group includes a small crank rod L3, a small connecting rod L4 and a crosshead, the large crank rod is fixedly connected to the small crank rod, and rotates synchronously around the hinge D at the same angular velocity, and the crosshead drives the connection point B to make the movable plate perform reciprocating linear motion along the horizontal guide rail.

3. The method for controlling the mold opening and closing stroke of an electric injection molding machine according to claim 2, wherein: Step S1 includes step S11: the variable conditions of the mechanism motion chain include the mechanism position variables, and the movable plate stroke (S m ) and ball screw drive stroke (S o ) motion relationship model, the corresponding stroke conversion equation is derived through the position variables of the mechanism to form a calculation expression: S m =（ + ） ； Where, is the elbow rotation angle, are the mechanism's oblique angle and elbow wedge angle, L1~L4 are the length parameters of each member of the mechanism, L5 is the distance parameter between the intersection of the small crankshaft rod L3 and the small connecting rod L4 and the hinge point D, is the oblique installation angle of the mechanism, is the angle between the small connecting rod and the horizontal line, is the angle between the large connecting rod and the horizontal line, is the angle between the large and small elbows, is the angle of rotation of the elbow from the initial position of mold closing; H is the vertical distance from the hinge point D to the center of the crosshead, H1 is the half length of the crosshead, S m S0 and S1 represent the travel distances of the movable platen and the crosshead respectively.

4. The method for controlling the mold opening and closing stroke of an electric injection molding machine according to claim 1, wherein: Step S2 includes step S21: setting an equal probability sampling method to sample the elbow rotation angle within a preset range. Randomly select N sample points and calculate the corresponding ball screw drive stroke S o With the movable template stroke S m , thereby generating a numerical data set containing N groups of paired samples .

5. The method for controlling the mold opening and closing stroke of an electric injection molding machine according to claim 4, wherein: The step S2 further includes step S22: obtaining the N groups of sample data Follow the template itinerary S m Arrange in ascending order, perform first-order difference processing on them, and calculate the slope between two adjacent points: ,i=1,2,……,N 1; According to the slope sequence obtained Construct a slope change curve by identifying local slope mutation points Or the second-order difference significant transition The larger one, determine the corresponding interval division points x1, x2, ..., x k .

6. The method for controlling the mold opening and closing stroke of an electric injection molding machine according to claim 5, wherein: The step S2 also includes step S23: constructing a fitting function, according to the divided stage intervals, in each segment interval Inside, use n j The method fits a polynomial function of order, and uses the least squares method to solve the parameters of the coefficients of the selected polynomial, constructs the sum of squares of the fitting residuals as the objective function, and forms a linear equation system by taking the partial derivative of each coefficient and setting it to zero; solves the linear equation system to obtain the optimal polynomial coefficients with the smallest sum of squares of fitting errors; and finds the optimal polynomial system with the smallest sum of squares of fitting errors, and can introduce derivative continuity constraints.

7. The method for controlling the mold opening and closing stroke of an electric injection molding machine according to claim 1, wherein: The step S3 includes step S31: establishing a moving plate stroke based on a data-driven inverse fitting modeling method. To screw stroke , and perform reverse solution and model optimization.

8. The mold opening stroke control method of an electric injection molding machine according to claim 7, wherein: The step S3 further includes a step S32: performing reverse data fitting with S m As an independent variable, S o As the dependent variable, based on the fitted data set , reconstruct the inverse mapping model.

9. The method for controlling the mold opening and closing stroke of an electric injection molding machine according to claim 8, wherein: The step S3 further includes step S33: When the function value is in the low stroke segment, a composite logarithmic fitting function is used, and the fitting function can be expressed as: When the function value is in the middle displacement segment or high displacement segment, a low-order polynomial model is used, and the middle segment fitting function can be expressed as: ； The high-segment fitting function can be expressed as ; Where a k , b k are the parameters to be fitted, It represents the logarithmic function with base 2.

10. The method for controlling mold opening and closing stroke of an electric injection molding machine according to claim 9, wherein: The step S3 also includes a step S34: using the least squares method to optimize the fitting of the coefficients of each piecewise function, constructing the sum of squares of the fitting residuals as the objective function, and forming a linear equation system by taking the partial derivative of each coefficient and setting the partial derivative to zero; solving the linear equation system to obtain the optimal polynomial coefficients with the minimum sum of squares of fitting errors; and fitting the optimal polynomial system with the minimum sum of squares of fitting errors, and introducing derivative continuity constraints.

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