A mold article fitment detection system and method

Abstract

The application discloses a mold product matching degree detection system and method, and relates to the technical field of molds.The system comprises a measurement unit, a standard value acquisition unit, a comparison unit, a correction unit, a modeling unit, a judgment unit and a correction unit.The measurement unit is used for measuring the standard value of the mold specification.The comparison unit is used for comparing the measured value with the standard value, judging whether there is deviation, and correcting the measured value to obtain a corrected value.The modeling unit is used for establishing a three-dimensional model of the mold based on the standard value and the measured value, marking the actual value and the replacement value, imaging the movement process of the mold, constructing a movement model, simulating the movement state of the mold, and determining the matching degree after the simulation.The correction unit is used for determining a correction sequence and correcting the mold parameters to re-determine the matching degree of the mold.Based on the mold matching degree detection, the mold parameters and the corresponding mold matching degree are optimized, the best mold matching degree and the corresponding parameters are obtained, and the whole mold matching degree detection process is finally completed.

Description

Technical Field

[0001] This invention relates to the field of mold technology, specifically to a mold product fit testing system and method. Background Technology

[0002] A mold is a tool used to make shaped objects. It consists of various parts and mainly achieves the processing of the object's shape by changing the physical state of the material being molded. It is a variety of molds and tools used in industrial production to obtain the desired products through injection molding, blow molding, extrusion, die casting or forging, smelting, stamping and other methods.

[0003] Tolerance fit refers to the clearance value between the hole and the shaft. Due to issues such as equipment, labor costs, and technical requirements during the manufacturing process, there are errors in the manufacturing precision of the mold. A reasonable value is selected within this error range, and this value is the mold fit degree. Among them, the fit clearance is divided into transition fit, interference fit, and clearance fit.

[0004] After a period of use, the mold's fit needs to be tested to determine if its precision still meets requirements. Existing methods for testing mold fit typically involve measuring the mold's specifications, creating a model based on those parameters, and then calculating the fit based on the 3D model. However, due to inherent errors in the measurement data, the actual fit may differ from the calculated fit. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides a mold product fit testing system and method. The system involves: a measurement unit to acquire measured values ​​and standard values ​​of the mold specifications; comparing the measured values ​​with the standard values ​​to determine if deviations exist and making corrections; a modeling unit to generate correction values ​​based on the mold's standard and measured values, establishing a three-dimensional model of the mold, and marking the actual and replacement values; a judgment unit to image the mold's movement process, construct a motion model, simulate the mold's motion state, and determine the fit after the simulation test; and a correction unit to determine the correction sequence, correct the mold parameters, re-determine the mold fit, optimize the mold parameters and corresponding mold adaptability, and finally obtain the optimal mold adaptability and corresponding parameters, thus completing the entire mold fit testing process and solving the problems in the prior art.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention provides the following technical solution: a method for detecting the fit of molded products, comprising: Step 1, measuring the specifications of the mold and obtaining the measured values, and simultaneously obtaining the standard values ​​of the mold design; comparing the measured values ​​with the standard values ​​to determine whether there is a deviation; if there is a deviation, assessing whether the deviation exceeds expectations; if it does, making corrections and obtaining correction values.

[0009] Step 1 includes: Step 101: After determining the mold's usage time, clean the mold to keep its surface smooth and not affect the measurement; Step 102: Based on the mold design drawings, measure the mold's specifications. After several measurements, obtain the average value and record it as the measured value, along with the location where the data is generated; Step 103: Obtain the standard values ​​of the mold specifications from the mold design drawings, compare the measured values ​​with the average value, determine the error ratio, and output the error ratio; Step 104: Sum all error ratios to obtain the error value and compare it with the corresponding threshold to determine if the error value exceeds the threshold. If it does not exceed the threshold, output the measured value; if it exceeds the threshold, output both the measured value and the standard value.

[0010] Step 2: Based on the standard and measured values ​​of the mold, generate correction values, establish 3D models of the mold, and mark the actual and replacement values ​​respectively; Step 3: After obtaining the 3D model of the mold, image the movement process of the mold, construct a motion model based on the imaging information, simulate and test the motion state of the mold, determine the fit after the simulation test, and judge whether the fit meets the requirements; Step 4: Determine the correction sequence, correct the mold parameters, and build a model based on the corrected data. Based on the motion model of the mold, redetermine the mold fit.

[0011] Further, step two includes: Step 201: Sequentially obtain the standard value and measured value of the mold, and establish a standard value dataset and a measured value dataset; Step 202: Based on the design drawings of the mold, determine the error ratio of each measured value. When the error ratio exceeds the corresponding threshold, determine the measured value and the standard value at the corresponding position, obtain the average value of the two, and determine it as the correction value; replace the measured value with the correction value and mark the replacement position to form a correction value dataset; Step 203: Based on the standard value dataset, the measured value dataset, and the correction value dataset, establish a three-dimensional model of the mold respectively, and record it as a standard model, a measured model, and a correction model; in the correction model, mark the positions involving the correction value.

[0012] Furthermore, step three includes: step 301, setting an imaging device along the outer periphery of the mold to image the working state of the mold, acquiring imaging information, and uploading the acquired imaging information to the cloud; step 302, determining the normal working state of the mold based on the imaging information through image recognition, wherein the normal working state refers to the state of the mold when it is working normally, and extracting the working state data; step 303, visualizing the working process of the mold under normal conditions based on the imaging information and the working state data to form process information.

[0013] Furthermore, following step 303, the following steps are also included: Step 304: Combining process information and the 3D model of the mold, simulate the working state of the mold, establish a mold motion model, and upload the mold motion model to the cloud for backup; Step 305: Repeat the implementation of the mold motion model multiple times to achieve simulation testing. After the simulation test ends, obtain the mold fit degree and form the standard fit degree, measured fit degree, and corrected fit degree respectively; Step 306: Combine the standard fit degree, measured fit degree, and corrected fit degree, correlate them, and obtain the deviation value between the three. If the deviation value is unexpected, remeasure the mold data.

[0014] Furthermore, the method for calculating the deviation value is as follows: Obtain the standard compatibility degree Bp, the measured compatibility degree Cp, and the corrected compatibility degree Xp, perform normalization processing, obtain the average compatibility degree Pp, and correlate them to form the deviation value Pc. The correlation method conforms to the following formula:

[0015]

[0016] Where 0≤α≤1, 0≤β≤1, 0≤γ≤1, and α+β+γ=1, α, β, and γ are weights, the specific values ​​of which are adjusted and set by the user, and C is a correction coefficient, which is determined by the user setting or by function simulation.

[0017] Furthermore, after step 306, there are the following steps: Step 307: Compare the standard fit, measured fit, and corrected fit with the fit threshold to determine whether the mold fit is within the threshold; if at least one of the quasi-fit, measured fit, and corrected fit does not meet the fit threshold, then communicate externally; Step 308: Determine the quasi-fit, measured fit, and corrected fit that is furthest from the fit threshold, and determine it as the mold fit, and determine the data at each position of the model as the expected value of the mold parameters.

[0018] Furthermore, step four includes: step 401, based on the motion state of the mold, determining the degree of participation of each part of the mold in the forming of the component, assigning values ​​according to the degree of participation, thereby forming a participation value; step 402, obtaining the corresponding dimensional information and the deviation ratio value on each dimension, associating the two, and obtaining the correction priority.

[0019] Furthermore, the correlation method for the correction priority is as follows: obtain the participation value Cy and the deviation ratio Pl, perform normalization processing, and correlate them to form the correction priority Xz. The correlation method conforms to the following formula:

[0020]

[0021] Where 0≤δ≤1, 0≤θ≤1, and δ+θ=1, δ and θ are weights, the specific values ​​of which are adjusted and set by the user, and the correlation coefficient between the participating value Cy and the deviation ratio Pl is R.

[0022] Furthermore, after step 402, the process includes: Step 403: Sort the correction priorities, determine the correction order, and based on the expected values ​​of the mold parameters, correct each dimension position sequentially to determine the correction result; Step 404: Obtain the correction result and determine the degree of influence on the mold fit, determine whether the mold fit can change positively, and if it can change positively, that is, the mold fit can be optimized, then continue to perform multiple tests until the mold fit no longer changes positively; Step 405: Continue to correct other positions sequentially according to the correction order until all positions are corrected, and output the corrected data to determine the optimized value of the mold parameters; Step 406: Obtain the optimized value of the mold component dimension data after correction, and rebuild the mold three-dimensional model again, re-simulate the mold movement process, and after completing a preset number of movement simulation tests, obtain the mold fit again; determine whether the mold fit after the simulation test is within the preset range, if it is, output the corrected parameters, and determine the mold dimension parameters corresponding to the optimized value of the mold to achieve the fit standard; if not, issue an external alarm.

[0023] A mold product fit testing system includes: a measurement unit for acquiring measured values ​​and standard values ​​of mold specifications, comparing the measured values ​​with the standard values, determining whether there is a deviation and correcting it, and acquiring the correction value; a modeling unit for generating correction values ​​based on the standard values ​​and measured values ​​of the mold, establishing a three-dimensional model of the mold, and marking the actual values ​​and replacement values ​​respectively; a judgment unit for imaging the movement process of the mold, constructing a motion model, simulating the motion state of the mold, and determining the fit after the simulation test; and a correction unit for determining the correction sequence, correcting the mold parameters, modeling based on the corrected data, and redetermining the mold fit.

[0024] (III) Beneficial Effects

[0025] This invention provides a system and method for detecting the fit of molded products. It has the following beneficial effects:

[0026] By measuring the mold, standard values, measured values, and correction values ​​of the mold parameters are obtained. Based on modeling and simulation testing, different mold fit degrees are obtained. In different scenarios, users can select different mold fit degrees to evaluate the mold, making it suitable for more application scenarios.

[0027] By modeling and conducting simulation tests, we can obtain mold compatibility data that is more adaptable to actual usage scenarios based on actual measurements. At the same time, we can also establish expectations for changes in mold compatibility and indirectly predict the lifespan of the mold.

[0028] By establishing correction priorities and correcting the mold parameters sequentially, the mold parameters and corresponding mold compatibility can be optimized after completing the mold compatibility test, ultimately obtaining the best mold compatibility and corresponding parameters, and finally completing the entire mold compatibility test process. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the process for detecting the fit of molded products according to the present invention;

[0030] Figure 2 This is a schematic diagram of the product fit testing system of the present invention;

[0031] Figure 3 This is a schematic diagram illustrating the composition of the average compatibility and correction priority of the present invention.

[0032] In the diagram: 10, measurement unit; 20, modeling unit; 30, judgment unit; 40, correction unit. Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] Example 1

[0035] Please see Figure 1-3 This invention provides a method for detecting the fit of molded products, comprising the following steps:

[0036] Step 1: Measure the mold specifications and obtain the measured values. At the same time, obtain the standard values ​​of the mold design. Compare the measured values ​​with the standard values ​​to determine if there is a deviation. If there is, assess whether the deviation exceeds the expectation. If it does, make corrections and obtain the correction values.

[0037] Step one includes the following:

[0038] Step 101: After determining the usage time of the mold, clean the mold to keep its surface smooth and not affect the measurement of the mold; if there are a lot of contaminants on the mold surface, it will have a significant impact on the accuracy of the mold.

[0039] Step 102: Based on the mold design drawings, measure the mold specifications. After several measurements, obtain the average value and record it as the measured value, and record the location where the corresponding data is generated.

[0040] Step 103: Obtain the standard values ​​of the mold specifications from the mold design drawings, compare the measured values ​​with the average values, determine the error ratio, and output the error ratio; In reality, there are one or more places where the measured values ​​of the mold differ significantly from the standard values, so there are also multiple error ratios.

[0041] Step 104: Sum all error proportions to obtain the error value and compare it with the corresponding threshold. Determine whether the error value exceeds the threshold. If it does not exceed the threshold, output the measured value; if it exceeds the threshold, output the measured value and the standard value.

[0042] In use, combining the content of steps 101 to 104, the standard value and the measured value are obtained by measuring the size of the mold. Considering that the fit of the mold is generally satisfactory when it is first produced, but after a certain period of use, it will deform due to long-term exposure to a large amount of external force, the fit is calculated based on the standard value as the expected fit of the mold, and the fit is calculated based on the measured value as the actual fit.

[0043] Step 2: Based on the standard values ​​and measured values ​​of the mold, generate correction values, establish three-dimensional models of the mold, and mark the actual values ​​and replacement values ​​respectively; based on the standard values, measured values, and correction values, make a more comprehensive evaluation of the mold's fit.

[0044] Step two includes the following:

[0045] Step 201: Sequentially obtain the standard value and measured value of the mold, and establish a standard value dataset and a measured value dataset;

[0046] Step 202: Based on the design drawings of the mold, determine the error ratio of each measurement value. When the error ratio exceeds the corresponding threshold, determine the measurement value and the standard value at the corresponding position, obtain the average value of the two, and determine it as the correction value. Replace the measurement value with the correction value and mark the replacement position to form a correction value dataset.

[0047] Step 203: Based on the standard value dataset, the measured value dataset, and the correction value dataset, establish three-dimensional models of the mold respectively, and record them as the standard model, the measured model, and the correction model; in the correction model, mark the positions involving correction values;

[0048] This allows us to determine the location of the correction when we obtain the corrected mold, and to ascertain that the dimensional parameters at that location have been corrected.

[0049] When in use, correction values ​​are generated based on standard values ​​and measured values, and three-dimensional models of the mold are established respectively. This makes it convenient to determine the expected fit, actual fit, and corrected fit of the mold. The corrected fit can be used as a reference result for mold fit test.

[0050] Step 3: After obtaining the 3D model of the mold, image the movement process of the mold, construct a motion model based on the imaging information, simulate and test the motion state of the mold, determine the fit after the simulation test, and judge whether the fit meets the requirements.

[0051] By establishing a motion model of the mold and conducting simulation tests, it is possible to roughly detect the actual service life of the mold, determine its final fit, and facilitate mold maintenance.

[0052] Step three includes the following:

[0053] Step 301: Set up an imaging device along the outer periphery of the mold to image the working state of the mold, acquire imaging information, and upload the acquired imaging information to the cloud.

[0054] Step 302: Through image recognition, determine the normal working state of the mold based on the imaging information. The normal working state refers to the state of the mold when it is working normally. Extract working state data, such as working temperature, force and force application, material property data, movement speed, etc.

[0055] Step 303: Based on imaging information and working status data, visualize the working process of the mold under normal conditions to form process information; the visualization information can be realized by simulation software, and users can simulate the working status of the mold after inputting parameters, and judge the working performance of the mold based on the simulation data.

[0056] Step 304: Combining process information and the 3D model of the mold, simulate the working state of the mold and establish a mold motion model; In fact, considering that there are three 3D models of the mold, there are also three mold motion models; After determining the mold motion model, the mold motion model can be uploaded to the cloud for backup.

[0057] Step 305: Repeat the mold motion model multiple times to achieve simulation testing. After the simulation test ends, obtain the mold fit degree and form the standard fit degree, measured fit degree and corrected fit degree respectively.

[0058] Combining the content of steps 301 to 305, compared to using actual measurement values ​​to judge the mold fit, this step achieves this through modeling and simulation testing. Since the data of the basic test is obtained from actual measurement, compared to measuring the fit based on measurement data, it can reduce error accumulation, making the mold fit obtained by modeling and simulation testing closer to the real data. Moreover, compared to actual measurement, it has a higher degree of visualization and makes it easier to detect errors.

[0059] It should be noted that the measurement method for mold tolerance fit is a well-known method in the art and will not be disclosed in this solution. Considering that each type of mold has a corresponding tolerance fit standard table, the mold fit threshold can be determined according to the mold type. Similarly, the fit threshold is also common knowledge in the art and can be easily known by those skilled in the art.

[0060] Step 306: Combine the standard fit, measured fit, and corrected fit, correlate them, and obtain the deviation value between the three. If the deviation value is within the threshold, it means that the fit detection error is less than expected. If the deviation value is outside the expectation, the mold data is measured again.

[0061] The method for calculating the deviation value is as follows:

[0062] Obtain the standard compatibility Bp, measured compatibility Cp, and corrected compatibility Xp, perform normalization, obtain the average compatibility Pp, and correlate them to form the deviation value Pc. The correlation method conforms to the following formula:

[0063]

[0064] Where 0≤α≤1, 0≤β≤1, 0≤γ≤1, and α+β+γ=1, α and β are weights, whose specific values ​​are adjusted and set by the user, and C is a correction coefficient, which is determined by the user setting or by function simulation.

[0065] It should be noted that the above method is only one of several ways to calculate the deviation value. In fact, if other similar methods can be used, similar results can be obtained. That is to say, this method is only used as a way to evaluate the deviation between compatibility values ​​and does not limit the characteristic of compatibility deviation value.

[0066] Step 307: Compare the standard fit, measured fit, and corrected fit with the fit threshold to determine whether the mold fit is within the threshold.

[0067] If at least one of the quasi-fitness, measured fitness, and corrected fitness fails to meet the fitness threshold, then external communication is initiated.

[0068] In use, the fit is obtained through simulation testing, which is more realistic than obtaining the mold fit by directly measuring the value. During the use of the mold, due to the requirements of the working nature, the mold is subjected to a lot of pressure and force, and is prone to deformation. If the fit of the mold is judged solely by the actual measured value of the mold, there will be a large gap with the fit of the mold before use. It is generally not in line with the actual requirements. Moreover, since there will be a certain error in the actual measurement, the actual measured value may not be the same as the design value.

[0069] Step 308: Determine the quasi-fit degree, measure the fit degree, and correct the fit degree that is furthest from the fit degree threshold. This is determined as the mold fit degree. The data at each position of the model are then determined as the expected values ​​of the mold parameters.

[0070] This completes the detection of mold fit; by selecting the one furthest from the fit threshold as the mold fit, the measurement error is contained.

[0071] Step 4: Determine the correction sequence, correct the mold parameters, and build a model based on the corrected data. Based on the mold's motion model, redetermine the mold fit.

[0072] Step four includes the following:

[0073] Step 401: Based on the motion state of the mold, determine the degree of participation of each part of the mold in the forming of the component, and assign a value according to the degree of participation to form a participation value; the magnitude of the participation value is determined by the proportion of participation in the forming when the component participates in the component; the higher the degree of participation of the component in the forming, the more important the value of the corresponding dimensional data is, and if an error occurs, it will have a greater impact on the mold fit.

[0074] Step 402: After determining the participation value of each component, obtain the corresponding size information and the deviation ratio value of each size, correlate the two, and obtain the correction priority;

[0075] The correlation method for the correction priority is as follows: The participation value Cy and the deviation ratio Pl are obtained, normalized, and correlated to form the correction priority Xz. The correlation method conforms to the following formula:

[0076]

[0077] Where 0≤δ≤1, 0≤θ≤1, and δ+θ=1, δ and δθ are weights, the specific values ​​of which are adjusted and set by the user, and the correlation coefficient between the participating value Cy and the deviation ratio Pl is R.

[0078] It should be noted that the above association method is only one method for evaluating the correction priority. In fact, there are many other methods for obtaining the correction priority. The above method is only for public use and does not limit the feature of correction priority. In fact, choosing other similar methods to obtain the correction priority will not pose a substantial obstacle to the implementation of this solution.

[0079] Step 403: Sort the correction priorities and determine the correction order. Based on the expected values ​​of the mold parameters, correct each dimension position in turn and determine the correction results. Correcting the dimensions of several parts in turn according to the correction order can improve the efficiency of correction data and make the corrected mold fit degree meet the specified fit degree threshold as soon as possible.

[0080] Step 404: Obtain the correction results and determine the degree of impact on the mold fit. Determine whether the mold fit can change positively. If it can change positively, that is, the mold fit can be optimized, then continue to conduct multiple tests until the mold fit no longer changes positively. At this point, the correction of the mold fit is complete.

[0081] Step 405: Continue to correct the other positions in the corrected order until all positions have been corrected, and output the corrected data to determine the optimized values ​​of the mold parameters;

[0082] Step 406: Obtain the optimized values ​​of the mold component size data after correction, and rebuild the three-dimensional model of the mold again. Re-simulate the movement process of the mold. After completing the preset number of movement simulation tests, obtain the mold fit degree again.

[0083] Determine whether the mold fit after the simulation test is within the preset range. If it is, output the corrected parameters and determine the mold size parameters corresponding to the mold optimization value to achieve the fit.

[0084] If not present, an external alarm will be triggered.

[0085] When using it, in conjunction with steps 401 to 406, after the mold fit test has been completed, determine whether the mold fit meets the expected requirements. If it does not meet the fit threshold, the original mold parameters need to be optimized to optimize the mold fit until the mold fit reaches a better state, and then no further optimization is needed.

[0086] Combining the contents of steps 1 to 4, this application has at least the following effects:

[0087] By measuring the mold, standard values, measured values, and correction values ​​of the mold parameters are obtained. Based on modeling and simulation testing, different mold fit degrees are obtained. In different scenarios, users can select different mold fit degrees to evaluate the mold, making it suitable for more application scenarios.

[0088] By modeling and conducting simulation tests, we can obtain mold compatibility data that is more adaptable to actual usage scenarios based on actual measurements. At the same time, we can also establish expectations for changes in mold compatibility and indirectly predict the lifespan of the mold.

[0089] By establishing correction priorities and correcting the mold parameters sequentially, the mold parameters and corresponding mold compatibility can be optimized after completing the mold compatibility test, ultimately obtaining the best mold compatibility and corresponding parameters, and finally completing the entire mold compatibility test process.

[0090] Example 2

[0091] Please see Figure 1-3 This invention provides a mold product fit testing system, comprising:

[0092] Measurement unit 10: acquires the measured value and standard value of the mold specification, compares the measured value with the standard value, determines whether there is a deviation and corrects it, and acquires the correction value;

[0093] Modeling Unit 20: Based on the standard value and measured value of the mold, corrective values ​​are generated, and three-dimensional models of the mold are established respectively, with marks made at the actual value and the replacement value.

[0094] Judgment unit 30: Image the movement process of the mold, construct a motion model, simulate and test the motion state of the mold, and determine the fit after the simulation test;

[0095] Correction unit 40 determines the correction sequence and corrects the mold parameters, models the mold based on the corrected data, and redetermines the mold fit.

[0096] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0097] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0098] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0099] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division of a mold product fit detection system and method, and a waterway underwater topography change analysis system and method. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms.

[0100] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0101] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0102] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0103] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0104] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A mold article fitment detection method characterized by: include, Step 1: Measure the mold specifications and obtain the measured values. At the same time, obtain the standard values ​​of the mold design. Compare the measured values ​​with the standard values ​​to determine if there are any deviations. If the deviation exists, assess whether it exceeds expectations; if it does, make corrections and obtain the correction value. Step one includes: Step 101: After determining the usage time of the mold, clean the mold to keep the mold surface smooth and not affect the measurement of the mold. Step 102: Based on the mold design drawings, measure the mold specifications. After several measurements, obtain the average value and record it as the measured value, and record the location where the corresponding data is generated. Step 103: Obtain the standard values ​​of mold specifications from the mold design drawings, compare the measured values ​​with the average values, determine the error ratio, and output the error ratio. Step 104: Sum all error proportions to obtain the error value and compare it with the corresponding threshold. Determine whether the error value exceeds the threshold. If it does not exceed the threshold, output the measured value; if it exceeds the threshold, output the measured value and the standard value. Step 2: Based on the standard and measured values ​​of the mold, generate correction values, establish a three-dimensional model of the mold, and mark the actual and replacement values ​​respectively; Step 3: After obtaining the 3D model of the mold, image the movement process of the mold, construct a motion model based on the imaging information, simulate and test the motion state of the mold, determine the fit after the simulation test, and judge whether the fit meets the requirements. Step 4: Determine the correction sequence, correct the mold parameters, and build a model based on the corrected data. Based on the mold's motion model, redetermine the mold fit. Step two includes: Step 201: Sequentially obtain the standard value and measured value of the mold, and establish a standard value dataset and a measured value dataset; Step 202: Based on the design drawings of the mold, determine the error ratio of each measurement value. When the error ratio exceeds the corresponding threshold, determine the measurement value and the standard value at the corresponding position, obtain the average value of the two, and determine it as the correction value. Replace the measurement value with the correction value and mark the replacement position to form a correction value dataset. Step 203: Based on the standard value dataset, the measured value dataset, and the correction value dataset, establish three-dimensional models of the mold respectively, and record them as the standard model, the measured model, and the correction model; in the correction model, mark the positions involving correction values; Step three includes: Step 301: Set up an imaging device along the outer periphery of the mold to image the working state of the mold, acquire imaging information, and upload the acquired imaging information to the cloud. Step 302: Determine the normal working state of the mold based on the imaging information through image recognition, wherein the normal working state refers to the state of the mold when it is working normally, and extract the working state data; Step 303: Based on imaging information and working status data, visualize the working process of the mold under normal conditions to form process information.

2. The method of claim 1, wherein: After step 303, the following still exists: Step 304: Combining process information and the 3D model of the mold, simulate the working state of the mold, establish a mold motion model, and upload the mold motion model to the cloud for backup; Step 305: Repeat the mold motion model multiple times to achieve simulation testing. After the simulation test ends, obtain the mold fit degree and form the standard fit degree, measured fit degree and corrected fit degree respectively. Step 306: Combine the standard fit, measured fit, and corrected fit, correlate them, and obtain the deviation value among the three. If the deviation value is unexpected, remeasure the mold data.

3. The method of claim 2, wherein: The method for calculating the deviation value is as follows: Obtain the standard compatibility Bp, measured compatibility Cp, and corrected compatibility Xp, perform normalization, obtain the average compatibility Pp, and correlate them to form the deviation value Pc. The correlation method conforms to the following formula: ； in, , , ,and , is the weight, the specific value of which is adjusted and set by the user, and C is the correction coefficient, which is set by the user or obtained by function simulation.

4. The method of claim 2, wherein: After step 306, the following still exists: Step 307: Compare the standard fit, measured fit, and corrected fit with the fit threshold to determine whether the mold fit is within the threshold. If at least one of the quasi-fitness, measured fitness, and corrected fitness fails to meet the fitness threshold, then external communication is initiated. Step 308: Determine the quasi-fit degree, measure the fit degree, and correct the fit degree that is furthest from the fit degree threshold. This is determined as the mold fit degree. The data at each position of the model are then determined as the expected values ​​of the mold parameters.

5. The method of claim 1, wherein: Step four includes: Step 401: Based on the motion state of the mold, determine the degree of participation of each part of the mold in the forming of the component, and assign values ​​according to the degree of participation to form participation values; Step 402: Obtain the corresponding size information and the deviation ratio value of each size, correlate the two, and obtain the correction priority.

6. The method of claim 5, wherein: The correlation method for adjusting the priority is as follows: Obtain the participation value Cy and the deviation ratio Pl, perform normalization, and correlate them to form the adjustment priority Xz. The correlation method conforms to the following formula: ； wherein, , , and , is a weight, the specific value of which is adjusted by the user, the correlation coefficient between the participation value Cy and the deviation ratio Pl is .

7. The method of claim 5, wherein: Step 402 is followed by: Step 403: Sort the correction priorities, determine the correction order, and based on the expected values ​​of the mold parameters, correct each dimension position in sequence to determine the correction result. Step 404: Obtain the correction results and determine the degree of influence on the mold fit. Determine whether the mold fit can change positively. If it can change positively, that is, the mold fit can be optimized, then continue to conduct multiple tests until the mold fit no longer changes positively. Step 405: Continue to correct the other positions in the corrected order until all positions have been corrected, and output the corrected data to determine the optimized values ​​of the mold parameters; Step 406: Obtain the optimized values ​​of the mold component size data after correction, and rebuild the three-dimensional model of the mold again. Re-simulate the movement process of the mold. After completing the preset number of movement simulation tests, obtain the mold fit degree again. Determine whether the mold fit after the simulation test is within the preset range. If it is, output the corrected parameters and determine the mold size parameters corresponding to the mold optimization value to achieve the fit. If not, send an external alarm.

8. A mold article fitment detection system characterized by: include: The measuring unit (10) is used to obtain the measured value and standard value of the mold specification, compare the measured value with the standard value, determine whether there is a deviation and make corrections, and obtain the correction value. Modeling unit (20) is used to form correction values ​​based on the standard value and measured value of the mold, establish a three-dimensional model of the mold respectively, and mark the actual value and the replacement value respectively; The judgment unit (30) is used to image the motion process of the mold, construct a motion model, simulate and test the motion state of the mold, and determine the fit after the simulation test. The correction unit (40) is used to determine the correction sequence and correct the mold parameters, and to model the mold based on the corrected data and redetermine the mold fit. Among them, the modeling unit (20) is used to sequentially obtain the standard value and the measured value of the mold, and to establish the standard value dataset and the measured value dataset; It is also used to design drawings for corresponding molds, determine the error ratio of each measurement value, and when the error ratio exceeds the corresponding threshold, determine the measurement value and the standard value at the corresponding position, obtain the average value of the two, and determine it as the correction value; replace the measurement value with the correction value, mark the replacement position, and form a correction value dataset; It is also used to build three-dimensional models of molds based on standard value datasets, measured value datasets, and correction value datasets, and record them as standard models, measured models, and correction models; in the correction model, the positions involving correction values ​​are marked; The judgment unit (30) is used to set an imaging device along the outer periphery of the mold, to image the working state of the mold, to obtain imaging information, and to upload the obtained imaging information to the cloud. It is also used to determine the normal working state of the mold based on the imaging information through image recognition, wherein the normal working state refers to the state of the mold when it is working normally, and to extract the working state data; It is also used to visualize the working process of the mold under normal conditions based on imaging information and working status data, forming process information.

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