Safety helmet mold quality prediction control method and system based on big data

Abstract

The invention relates to the technical field of safety helmet processing quality programmed control, and discloses a safety helmet mold quality prediction control method and system based on big data, and the system comprises a safety helmet injection molding information acquisition module, a safety helmet injection molding quality analysis module, and a safety helmet injection molding quality adjustment module. On the basis of numerical analysis science, movable mold and fixed mold injection fit clearance adjustment parameters of the safety helmet injection mold are constructed, intelligent generation of injection machining error adjustment parameters of the safety helmet injection mold is achieved, and meanwhile movable mold and fixed mold injection adjustment operation of the safety helmet injection mold is accurately and reliably executed according to an injection control system. The self-adaptive dynamic prediction and adjustment of the injection machining error of the safety helmet injection mold are realized, the applicability and the safety of the safety helmet injection mold are improved, and the quality and the qualified rate of safety helmet manufacturing are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of procedural control of helmet processing quality, and in particular to a helmet mold quality prediction control method and system based on big data. Background Art

[0002] The injection mold of the helmet is mainly composed of two parts: the movable mold and the fixed mold. After the movable mold and the fixed mold are combined, a cavity of the helmet is formed, and the plastic raw material is injection molded in this cavity. A cooling water channel is also designed inside the mold to quickly cool the helmet that has just been injection molded to improve production efficiency. The steps of helmet injection molding include the following steps: 1. Plastic melting: First, the plastic raw material is heated to a molten state to make it fluid and easy to inject into the mold. 2. Injection molding: Under high pressure, the molten plastic is quickly injected into the mold cavity. Since the shape of the mold cavity matches the shape of the helmet, the plastic will form the shape of the helmet after cooling and solidifying in the cavity. 3. Cooling and solidification: After the molten plastic is injected into the mold, it is quickly cooled through the cooling water channel inside the mold to solidify the plastic. 4. Demolding and removal: When the helmet is completely solidified, open the mold and remove the molded helmet from the mold. The precision and quality of the helmet injection mold are crucial to the performance of the helmet. High-precision molds can ensure that the produced helmets are accurate in size and regular in shape, thereby improving their protective performance. In addition, the material and manufacturing process of the mold will also affect the quality and durability of the helmet. High-quality molds can ensure that the produced helmets have a smooth and flawless surface and have a long service life. During the injection molding process of the helmet, due to the wear of the movable mold and the fixed mold, and the wear of the movable mold and the fixed mold positioning device, the manufacturing dimensions of the helmet cannot meet the design requirements. The existing injection molding control system of the injection molding process of the helmet cannot adaptively correct the injection fitting clearance between the movable mold and the fixed mold based on the manufacturing thickness dimensional deviation of the helmet injection molding product, which reduces the quality and pass rate of the helmet manufacturing.

[0003] The Chinese invention patent with announcement number CN104267683B discloses a product quality test start control system and a product quality test start control method. The product quality detector is set to include multiple groups of independent test units, and a single test unit includes a test container and a test terminal carrying a command translator; the command translator is used to establish a communication connection between the test terminal and the industrial computer, receive a first start test command sent by the industrial computer from the relevant port on the serial port expansion card, and interpret the first start test command into a second start test command that conforms to the programming language of the test terminal, so as to accurately control the test terminal to start the product quality test; however, the above product quality test control system cannot achieve accurate adjustment of the product processing quality, which reduces the product production and processing quality. Summary of the invention

[0004] 1. Technical issues to be solved

[0005] In order to solve the problem that the existing injection molding control system of the injection molding process of the safety helmet cannot adaptively correct the injection fitting clearance between the dynamic mold and the fixed mold based on the manufacturing thickness dimension deviation of the safety helmet injection molding product, which increases the quality and defective rate of the safety helmet manufacturing, the above purposes are achieved: accurately collect the characteristic information of the safety helmet injection molding product and the three-dimensional model of the safety helmet injection molding product manufacturing end, efficiently search the three-dimensional model of the safety helmet injection molding product design end, scientifically establish the measurement coordinates of the thickness dimension of the safety helmet injection molding product, accurately measure the manufacturing thickness dimension of the safety helmet injection molding product and the design thickness dimension of the safety helmet injection molding product, independently measure the manufacturing thickness dimension error of the safety helmet injection molding product and the mean value of the manufacturing thickness dimension error of the safety helmet injection molding product, intelligently analyze the manufacturing quality of the safety helmet injection molding product, accurately construct the injection fitting clearance adjustment data of the dynamic mold and the fixed mold of the safety helmet injection molding mold, and adaptively adjust the injection fitting clearance of the dynamic mold and the fixed mold of the safety helmet injection molding mold.

[0006] (II) Technical solution

[0007] The present invention is implemented by the following technical scheme: a method for predicting and controlling the quality of a helmet mold based on big data, the method comprising the following steps:

[0008] S1. Collecting the characteristic text data of the helmet injection molding product and the 3D model data of the helmet injection molding product manufacturing end;

[0009] S2, performing a 3D model parameter search process on the design end of the safety helmet injection molding product based on the safety helmet injection molding product feature text data and the 3D model data on the design end of the safety helmet injection molding product, and generating the 3D model data on the design end of the target safety helmet injection molding product;

[0010] S3, according to the manufacturing end 3D model parameters and the design end 3D model parameters of the target helmet injection molding product, the structural thickness dimension measurement position of the helmet injection molding manufacturing end and the design end product is established and processed, and the thickness dimension measurement position coordinate data of the helmet injection molding manufacturing product and the thickness dimension measurement position coordinate data of the helmet injection molding design product are generated;

[0011] S4, based on the thickness dimension measurement position coordinate data of the helmet injection molding product, the thickness dimension measurement position coordinate data of the helmet injection molding design product, the three-dimensional model data of the helmet injection molding product manufacturing end, and the three-dimensional model data of the target helmet injection molding product design end, perform the structural thickness dimension measurement operation of the helmet injection molding product and the design product, and generate the manufacturing thickness dimension data of the helmet injection molding product and the design thickness dimension data of the helmet injection molding product;

[0012] S5, performing manufacturing thickness dimension error measurement processing of the safety helmet injection molded product according to the manufacturing thickness dimension data of the safety helmet injection molded product and the design thickness dimension data of the safety helmet injection molded product, generating manufacturing thickness dimension error data of the safety helmet injection molded product and performing manufacturing thickness dimension error mean statistical processing of the safety helmet injection molded product, generating manufacturing thickness dimension error mean of the safety helmet injection molded product;

[0013] S6, performing manufacturing quality analysis processing of the safety helmet injection molding product according to the mean value of the manufacturing thickness dimension error of the safety helmet injection molding product, generating manufacturing quality analysis data of the safety helmet injection molding product, and ending the current safety helmet injection molding product manufacturing quality adjustment operation when the data is qualified;

[0014] S7. When the result is unqualified, the movable mold and fixed mold injection molding fit clearance adjustment data of the safety helmet injection mold is constructed and the movable mold and fixed mold injection molding adjustment operation of the safety helmet injection mold is performed.

[0015] Preferably, the operation steps of collecting the characteristic text data of the helmet injection molding product and the three-dimensional model data of the helmet injection molding product manufacturing end are as follows:

[0016] S11, obtaining product feature text data of the injection-molded safety helmet online through the material ERP, and generating safety helmet injection-molded product feature text data R, wherein the safety helmet injection-molded product feature text data includes model information, specification information, material information, and production date information of the safety helmet;

[0017] The three-dimensional solid model of the overall structure of the helmet product is completed by online scanning with a three-dimensional laser scanner, and the three-dimensional model data W of the helmet injection molding product manufacturing end is generated.

[0018] The present invention obtains the feature information of the injection molding product of the helmet and the 3D model information of the manufacturing end of the injection molding product of the helmet online through the material ERP and the 3D laser scanner, so as to provide the real data support for the scientific analysis of the manufacturing quality of the helmet mold.

[0019] Preferably, the steps of performing a 3D model parameter search process on the design end of the safety helmet injection molding product based on the safety helmet injection molding product feature text data and the 3D model data on the design end of the safety helmet injection molding product to generate the target 3D model data on the design end of the safety helmet injection molding product are as follows:

[0020] S21. Establish the three-dimensional model data set U of the helmet injection molding product design end = (u 1 ,…,u a ,…,u φ ), a=1,2,3,…,φ; where u arepresents the three-dimensional model data of the design end of the injection molding product of the helmet corresponding to the a-th type of helmet, and φ represents the maximum number of types of helmets; the three-dimensional model data of the design end of the injection molding product of the helmet represents the three-dimensional model data generated in the theoretical design stage of different types of injection molding products of helmets;

[0021] S22, the helmet injection molding product feature text data R and the helmet injection molding product design end three-dimensional model data set U of the helmet injection molding product design end three-dimensional model data set U. a Perform keyword matching of the safety helmet product feature to search for the safety helmet injection product design end three-dimensional model data u corresponding to the safety helmet injection product feature text data R a , and generate the target helmet injection product design end 3D model data through data identification Execute and generate the three-dimensional model data of the target helmet injection product design end The specific steps are as follows:

[0022] S221, initializing the model recognition cheetah initial position in the search space of the φ-dimensional three-dimensional model data set U of the helmet injection product design end is described as: H i,j =ψ+rand(ξ-ψ), where H i,j represents the position of the cheetah identified by the i-th head model in the search space with spatial dimension j, that is, the position of the cheetah identified by the i-th head model in the search space of the three-dimensional model data set U of the helmet injection molding product design end with spatial dimension φ, ξ and ψ are the upper limit and lower limit of the j-th dimension search space respectively, rand is a random number in the interval (0,1), and T represents the maximum number of iterations;

[0023] S222, execute the search strategy to search for prey, the model recognition cheetah performs a full range scan in the search space of the three-dimensional model data set U of the helmet injection molding product design end, or actively searches for the three-dimensional model data u of the helmet injection molding product design end corresponding to the characteristic text data R of the helmet injection molding product. a ; The search strategy is mathematically described as in It represents the position of the cheetah in the search space with the spatial dimension j after the i-th model recognizes the cheetah for the t+1th iteration in the stage of executing the search strategy to search for prey, that is, the position of the three-dimensional model data set U of the helmet injection molding product design end in the search space with the spatial dimension φ after the i-th model recognizes the cheetah for the t+1th iteration; It represents the position of the cheetah in the search space with the spatial dimension j after the i-th model recognizes the cheetah for the t-th iteration in the stage of executing the search strategy to search for prey, that is, the position of the three-dimensional model data set U of the helmet injection molding product design end in the search space with the spatial dimension φ after the i-th model recognizes the cheetah for the t-th iteration, The random number that is normally distributed in the search space with spatial dimension j for the i-th model to identify the cheetah, The search step length of the i-th model recognition cheetah in the search space with spatial dimension j after the t-th iteration;

[0024] S223, execute the search for prey using the sit-and-wait strategy, in the search mode, in the search space of the three-dimensional model data set U of the design end of the helmet injection molding product, the three-dimensional model data u corresponding to the feature text data R of the helmet injection molding product design end a The prey is exposed to the model recognition cheetah's field of vision, and the model recognition cheetah adopts a sit-and-wait ambush strategy to approach the three-dimensional model data u of the design end of the helmet injection molding product corresponding to the characteristic text data R of the helmet injection molding product a The sit-and-wait ambush strategy includes lying on the ground or hiding in the bushes. The sit-and-wait strategy is mathematically described as in It represents the position of the cheetah in the search space with a spatial dimension of j after the i-th model recognizes the cheetah for the t+1th iteration in the stage of executing the sit-and-wait strategy for searching prey, that is, the position of the cheetah in the search space with a spatial dimension of φ after the i-th model recognizes the cheetah for the t+1th iteration; It represents the position of the cheetah in the search space with a spatial dimension of j after the i-th head model recognizes the cheetah for the t-th iteration in the stage of executing the search for prey using the sit-and-wait strategy, that is, the position of the three-dimensional model data set U of the helmet injection molding product design end in the search space with a spatial dimension of φ after the i-th head model recognizes the cheetah for the t-th iteration;

[0025] S224, execute the sit-and-wait strategy to approach the prey and attack the prey according to the attack strategy. In the search space of the three-dimensional model data set U of the helmet injection molding product design end, each model recognition cheetah adjusts its position according to the position of the fleeing prey, the leading model recognition cheetah or the nearby model recognition cheetah to obtain the best attack. The three-dimensional model data set U of the helmet injection molding product design end that matches the feature text data R of the helmet injection molding product a Prey position, attack strategy mathematically described as in It represents the position of the prey in the search space with a spatial dimension of j after the i-th model recognizes the cheetah for the t+1th iteration in the attack phase according to the attack strategy, that is, the position of the three-dimensional model data set U of the helmet injection molding product design end with a spatial dimension of φ after the i-th model recognizes the cheetah for the t+1th iteration; represents the position of the prey in the search space with a spatial dimension of j after the t-th iteration of the cheetah identified by the i-th model in the attack phase according to the attack strategy, that is, the position of the three-dimensional model data set U of the helmet injection molding product design end in the search space with a spatial dimension of φ after the t-th iteration of the cheetah identified by the i-th model; Π i,j represents the turning factor of the cheetah identified by the i-th model in the search space with spatial dimension j; Identify the interaction factors of the cheetah at the tth iteration in the search space with spatial dimension j for the i-th model;

[0026] S225, repeating steps S222, S223, and S224 until the maximum number of iterations is met, and outputting the three-dimensional model data u of the design end of the helmet injection molding product that matches the characteristic text data R of the helmet injection molding product a ;

[0027] S226, the output in step S225 matches the three-dimensional model data u of the design end of the helmet injection molding product with the characteristic text data R of the helmet injection molding product a , and generate the target helmet injection product design end 3D model data through data identification

[0028] The present invention achieves the effect of efficient retrieval of different types of three-dimensional models of helmet injection molding product designs by intelligently searching the theoretical design three-dimensional model information of helmet injection molding products based on the three-dimensional model data of the helmet injection molding product design end stored in big data combined with the artificial intelligence cheetah optimization algorithm and the characteristic text data of the helmet injection molding product.

[0029] Preferably, the steps of establishing the structural thickness dimension measurement position of the helmet injection molding manufacturing end and design end products based on the manufacturing end 3D model parameters and the design end 3D model parameters of the target helmet injection molding product, and generating the thickness dimension measurement position coordinate data of the helmet injection molding manufacturing product and the thickness dimension measurement position coordinate data of the helmet injection molding design product are as follows:

[0030] S31, respectively, the three-dimensional model data W of the manufacturing end of the helmet injection molding product and the three-dimensional model data W of the design end of the target helmet injection molding product Import the product 3D model design software and run it. At the same time, according to the uniform specifications, the 3D model data W of the helmet injection molding product manufacturing end and the 3D model data of the target helmet injection molding product design end are respectively The corresponding outer contour surface of the helmet is meshed, and the mesh intersection is used as the measurement position of the thickness dimension of the helmet structure; the product three-dimensional model design software includes any one of Rhino, Cinema4D and Autodesk Inventor;

[0031] S32, collect the spatial coordinates of the thickness dimension measurement position of the helmet structure in step S31 online through the coordinate measurement module in the product three-dimensional model design software, and generate the thickness dimension measurement position coordinate data set of the helmet injection molding product respectively Thickness dimension measurement position coordinate data set of helmet injection design products where y b represents the safety helmet injection molding product thickness dimension measurement position coordinate data corresponding to the b-th thickness dimension measurement position in the safety helmet corresponding to the three-dimensional model data W of the safety helmet injection molding product manufacturing end,

[0032] Indicates the maximum number of thickness measurement positions; y' b Represents the three-dimensional model data of the target helmet injection product design end The coordinate data of the thickness dimension measurement position of the safety helmet injection molding design product corresponding to the bth thickness dimension measurement position in the corresponding safety helmet, the coordinate data of the thickness dimension measurement position of the safety helmet injection molding manufacturing product includes the horizontal coordinate, vertical coordinate and vertical coordinate of the thickness dimension measurement position of the manufacturing product; the coordinate data of the thickness dimension measurement position of the safety helmet injection molding design product includes the horizontal coordinate, vertical coordinate and vertical coordinate of the thickness dimension measurement position of the design product.

[0033] The present invention independently and accurately establishes the thickness dimension measurement position coordinate parameters of the helmet injection molding manufacturing and design products based on the three-dimensional models of the helmet injection molding product manufacturing and design ends in combination with the product three-dimensional model design software, thereby achieving the effect of intelligently and reliably establishing the thickness dimension measurement position of the helmet injection molding manufacturing and design products.

[0034] Preferably, based on the thickness dimension measurement position coordinate data of the helmet injection molding product, the thickness dimension measurement position coordinate data of the helmet injection molding design product, the three-dimensional model data of the helmet injection molding product manufacturing end, and the three-dimensional model data of the target helmet injection molding product design end, the structural thickness dimension measurement operation of the helmet injection molding product and the design product is performed, and the operation steps of generating the thickness dimension data of the helmet injection molding product manufacturing and the thickness dimension data of the helmet injection molding product design are as follows:

[0035] S41, through the thickness measurement module in the product three-dimensional model design software combined with the thickness measurement position coordinate data set Y of the safety helmet injection molding product thickness measurement position coordinate data y b , perform a thickness dimension measurement operation on the safety helmet corresponding to the three-dimensional model data W of the safety helmet injection molding product manufacturing end; and generate a safety helmet injection molding product manufacturing thickness dimension data set Among themb Indicates the thickness dimension measurement position coordinate data y of the helmet injection molding product b Corresponding thickness dimension data of helmet injection molding products, o b The unit is millimeter;

[0036] The thickness measurement module in the product three-dimensional model design software is combined with the thickness measurement position coordinate data set Y' of the safety helmet injection molding design product thickness size measurement position coordinate data y' b , the three-dimensional model data of the target helmet injection product design end The corresponding helmet is used to measure the thickness of the helmet injection molding product structure; and a data set of the thickness of the helmet injection molding product design is generated. where o′ b Represents the thickness dimension measurement position coordinate data y′ of the helmet injection molding product b The corresponding thickness dimension measurement position coordinate data of the helmet injection molding design product, o′ b The unit is millimeters.

[0037] The present invention achieves the effect of scientifically and accurately measuring the thickness dimensions of the helmet body structure of the helmet injection molding manufacturing end and the design end products by measuring the position coordinate parameters of the thickness dimensions of the helmet injection molding manufacturing and design products and combining the product three-dimensional model design software.

[0038] Preferably, the manufacturing thickness dimension error measurement processing of the safety helmet injection molded product is performed according to the manufacturing thickness dimension data of the safety helmet injection molded product and the design thickness dimension data of the safety helmet injection molded product, the manufacturing thickness dimension error data of the safety helmet injection molded product is generated, and the manufacturing thickness dimension error mean value of the safety helmet injection molded product is statistically processed, and the operating steps for generating the manufacturing thickness dimension error mean value of the safety helmet injection molded product are as follows:

[0039] S51, the thickness dimension data set O of the manufacturing thickness dimension data of the injection molded product of the helmet is b The thickness dimension measurement position numbers are ordered according to the thickness dimension measurement position coordinate data o′ of the safety helmet injection molding product design thickness dimension measurement position in the safety helmet injection molding product design thickness dimension data set O′. b Perform difference processing between the manufacturing and design thickness dimension values ​​of the helmet injection molding products, and generate a data set of thickness dimension error of the helmet injection molding products where o″ b Indicates the manufacturing thickness dimension error data of the helmet injection molding product corresponding to the bth thickness dimension measurement position of the helmet injection molding product, o″ b =o b-o′ b ,o″ b The unit is millimeter, o″ b The values ​​include positive, negative and zero, where o″ b When it is a positive number, it means that the manufacturing thickness of the injection molded helmet product is greater than the design thickness; o″ b When the number is negative, it means that the manufacturing thickness of the injection molded helmet product is smaller than the designed thickness; o″ b When it is a positive number, it means that the manufacturing thickness dimension of the injection molded product of the safety helmet is equal to the design thickness dimension; the manufacturing thickness dimension error data of the injection molded product of the safety helmet represents the error parameter of the thickness dimension of the cap body structure from the outer contour surface to the inner contour surface of the injection molded product of the safety helmet and the thickness dimension of the cap body structure from the outer contour surface to the inner contour surface of the injection molded design product of the safety helmet; because during the injection molding process of the safety helmet, the outer contour surface of the injection molded product of the safety helmet fits with the inner wall of the fixed mold cavity of the injection mold, and the inner contour surface fits with the outer surface of the movable mold of the injection mold, and the injection fitting clearance error between the movable mold and the fixed mold of the injection mold causes the injection structure thickness dimension from the inner contour surface to the outer contour surface of the injection molded product of the safety helmet to fail to meet the standard;

[0040] S52, the thickness dimension error data o″ of the safety helmet injection molding product manufacturing thickness dimension error data set O″ is counted. b Perform numerical measurement of the mean value of the manufacturing thickness dimension error of the helmet injection molding product, and generate the mean value of the manufacturing thickness dimension error of the helmet injection molding product in The unit is millimeter; The values ​​include positive, negative and zero. When it is a positive number, it means that the manufacturing thickness of the injection molded helmet product is greater than the design thickness; When the number is negative, it means that the manufacturing thickness of the injection molded helmet product is smaller than the designed thickness. When it is a positive number, it means that the manufacturing thickness dimension of the injection molded helmet product is equal to the design thickness dimension.

[0041] The present invention efficiently calculates the thickness dimension error and error mean data of the safety helmet injection molding product manufacturing based on the manufacturing and design thickness dimension parameters of the safety helmet injection molding product in combination with numerical analysis, thereby achieving the effect of digitalizing the manufacturing error of the safety helmet injection molding product through intelligent analysis.

[0042] Preferably, the manufacturing quality analysis of the injection molded helmet product is performed according to the mean value of the manufacturing thickness dimension error of the injection molded helmet product to generate the manufacturing quality analysis data of the injection molded helmet product. When the manufacturing quality adjustment operation of the injection molded helmet product is qualified, the operation steps of ending the manufacturing quality adjustment operation of the injection molded helmet product are as follows:

[0043] S61, calling the mean value of thickness dimension error of the injection molded helmet product

[0044] S62, the mean value of the thickness dimension error of the injection molded helmet product Conduct thickness dimension numerical analysis, and generate manufacturing quality analysis data L of helmet injection molding products based on the thickness dimension numerical analysis results;

[0045] when The absolute value of is equal to zero, indicating that the manufacturing quality of the helmet injection molding product is qualified, and the output of the helmet injection molding product manufacturing quality analysis data L is qualified, and the current helmet injection molding product manufacturing quality adjustment operation is directly terminated;

[0046] when The absolute value of is not equal to zero, indicating that the manufacturing quality of the injection molding product of the safety helmet is unqualified, and the output manufacturing quality analysis data L of the injection molding product of the safety helmet is unqualified.

[0047] The present invention accurately analyzes the manufacturing quality information of the injection molded helmet products according to the mean value of the thickness dimension error of the injection molded helmet products in combination with numerical analysis, thereby achieving the effect of intelligently managing the manufacturing quality of the injection molded helmet products.

[0048] Preferably, when the result is unqualified, the operation steps of constructing the adjustment data of the movable mold and fixed mold injection molding fit clearance of the helmet injection mold and performing the injection molding adjustment operation of the movable mold and fixed mold of the helmet injection mold are as follows:

[0049] S71, when the manufacturing quality analysis data L of the helmet injection molding product is unqualified, according to the mean value of the manufacturing thickness dimension error of the helmet injection molding product Perform data identification processing to construct the adjustment data V of the injection fit clearance of the dynamic mold and fixed mold of the helmet injection mold, where

[0050] When V is greater than zero, it means that the manufacturing thickness of the helmet injection molded product is smaller than the designed thickness. At this time, the injection fitting clearance between the movable mold and the fixed mold of the helmet injection mold needs to be increased according to |V|;

[0051] When V is less than zero, it means that the manufacturing thickness of the helmet injection molded product is greater than the designed thickness. At this time, it is necessary to reduce the injection fit clearance between the movable mold and the fixed mold of the helmet injection mold according to |V|;

[0052] S72, the injection molding control system performs the injection molding adjustment operation of the movable mold and fixed mold of the safety helmet injection mold according to the injection molding matching clearance adjustment data V of the movable mold and fixed mold of the safety helmet injection mold.

[0053] The present invention scientifically constructs the injection molding fit clearance adjustment parameters of the dynamic mold and the fixed mold of the safety helmet injection mold based on numerical analysis, and accurately and reliably performs the injection molding adjustment operation of the dynamic mold and the fixed mold of the safety helmet injection mold according to the injection molding control system, so as to achieve the effect of intelligently generating the injection molding processing error adjustment parameters of the safety helmet injection mold and adaptively dynamically predicting and adjusting the injection molding processing error of the safety helmet injection mold.

[0054] A safety helmet mold quality prediction and control system based on big data, used to implement the safety helmet mold quality prediction and control method based on big data, the system includes a safety helmet injection molding information acquisition module, a safety helmet injection molding quality analysis module, and a safety helmet injection molding quality adjustment module;

[0055] The helmet injection molding information acquisition module includes a helmet injection molding product feature information acquisition unit, a helmet injection molding product manufacturing end three-dimensional model acquisition unit, a helmet injection molding product design end three-dimensional model storage unit, and a helmet injection molding product design end three-dimensional model recognition unit;

[0056] The safety helmet injection molding product feature information collection unit collects safety helmet injection molding product feature text data through material ERP; the safety helmet injection molding product manufacturing end three-dimensional model collection unit collects safety helmet injection molding product manufacturing end three-dimensional model data through a three-dimensional laser scanner; the safety helmet injection molding product design end three-dimensional model storage unit is used to store the safety helmet injection molding product design end three-dimensional model data; the safety helmet injection molding product design end three-dimensional model recognition unit performs a safety helmet injection molding product design end three-dimensional model parameter search process based on the safety helmet injection molding product feature text data and the safety helmet injection molding product design end three-dimensional model data to generate target safety helmet injection molding product design end three-dimensional model data;

[0057] The helmet injection molding quality analysis module includes a helmet injection molding product thickness dimension measurement position establishment unit, a helmet injection molding product manufacturing thickness dimension measurement unit, a helmet injection molding product design thickness dimension measurement unit, a helmet injection molding product manufacturing thickness dimension error measurement unit, a helmet injection molding product manufacturing thickness dimension error mean measurement unit, and a helmet injection molding product manufacturing quality analysis unit;

[0058] The safety helmet injection molding product thickness dimension measurement position establishment unit performs structural thickness dimension measurement position establishment processing of the safety helmet injection molding manufacturing end and design end products according to the manufacturing end 3D model parameters and the design end 3D model parameters of the target safety helmet injection molding product in combination with the product 3D model design software, and generates the safety helmet injection molding manufacturing product thickness dimension measurement position coordinate data and the safety helmet injection molding design product thickness dimension measurement position coordinate data; the safety helmet injection molding product manufacturing thickness dimension measurement unit performs structural thickness dimension measurement operation of the safety helmet injection molding product based on the safety helmet injection molding product thickness dimension measurement position coordinate data, the safety helmet injection molding product manufacturing end 3D model data and the product 3D model design software, and generates the safety helmet injection molding product manufacturing thickness dimension data; the safety helmet injection molding product design thickness dimension measurement unit performs structural thickness dimension measurement operation of the safety helmet injection molding product based on the safety helmet injection molding design product thickness dimension measurement position coordinate data and the target safety helmet injection molding product design end 3D model data. The model data is combined with the product three-dimensional model design software to perform structural thickness dimension measurement of the helmet injection molding design product, and generate the design thickness dimension data of the helmet injection molding product; the manufacturing thickness dimension error measurement unit of the helmet injection molding product performs manufacturing thickness dimension error measurement processing of the helmet injection molding product according to the manufacturing thickness dimension data of the helmet injection molding product and the design thickness dimension data of the helmet injection molding product, and generates the manufacturing thickness dimension error data of the helmet injection molding product; the manufacturing thickness dimension error mean measurement unit of the helmet injection molding product performs statistical processing of the mean manufacturing thickness dimension error of the helmet injection molding product according to the manufacturing thickness dimension error data of the helmet injection molding product, and generates the manufacturing thickness dimension error mean of the helmet injection molding product; the manufacturing quality analysis unit of the helmet injection molding product performs manufacturing quality analysis processing of the helmet injection molding product according to the manufacturing thickness dimension error mean of the helmet injection molding product, and generates the manufacturing quality analysis data of the helmet injection molding product;

[0059] The helmet injection molding quality adjustment module comprises a helmet injection mold movable mold fixed mold injection molding fit clearance adjustment parameter construction unit and a helmet injection mold movable mold fixed mold injection molding adjustment unit;

[0060] The safety helmet injection mold movable mold and fixed mold injection fitting clearance adjustment parameter construction unit is used to construct the safety helmet injection mold movable mold and fixed mold injection fitting clearance adjustment data; the safety helmet injection mold movable mold and fixed mold injection adjustment unit performs the safety helmet injection mold movable mold and fixed mold injection adjustment operation according to the safety helmet injection mold movable mold and fixed mold injection fitting clearance adjustment data combined with the injection control system.

[0061] (III) Beneficial effects

[0062] The present invention provides a method and system for predicting and controlling the quality of helmet molds based on big data. It has the following beneficial effects:

[0063] 1. Obtain the feature information of the injection molding products and the 3D model information of the manufacturing end of the injection molding products online through material ERP and 3D laser scanners, so as to provide real data support for the scientific analysis of the manufacturing quality of the helmet molds; conduct intelligent search for the theoretical design 3D model information of the injection molding products based on the 3D model data of the injection molding products of the helmets, combined with the intelligent recognition algorithm and the feature text data of the injection molding products of the helmets, so as to realize the efficient retrieval of the 3D models of the injection molding products of different types, and improve the accuracy of the quality control of the manufacturing of the helmet molds.

[0064] 2. Based on the 3D models of the manufacturing and design ends of the helmet injection molding products and combined with the product 3D model design software, the thickness dimension measurement position coordinate parameters of the helmet injection molding manufacturing and design products are independently and accurately established, and the thickness dimension parameters of the helmet injection molding manufacturing and design products are accurately measured in combination with the product 3D model design software, so as to achieve scientific and accurate measurement of the thickness dimension of the hat body structure of the helmet injection molding manufacturing and design end products, and improve the accuracy of the quality control of the helmet mold manufacturing; based on the manufacturing and design thickness dimension parameters of the helmet injection molding products and combined with numerical analysis, the thickness dimension error and error mean data of the helmet injection molding product manufacturing are efficiently counted, and the digital intelligent analysis of the manufacturing error of the helmet injection molding product is realized, and the manufacturing quality of the helmet injection molding mold is improved; based on the mean value of the thickness dimension error of the helmet injection molding product manufacturing and combined with numerical analysis, the manufacturing quality information of the helmet injection molding product is accurately analyzed, and the intelligent management of the manufacturing quality of the helmet injection molding product is realized, and the intelligent production of the helmet mold is improved.

[0065] 3. By scientifically constructing the injection matching clearance adjustment parameters of the dynamic mold and fixed mold of the safety helmet injection mold based on numerical analysis, the intelligent generation of the injection molding error adjustment parameters of the safety helmet injection mold can be achieved. At the same time, the dynamic mold and fixed mold injection molding adjustment operation of the safety helmet injection mold can be accurately and reliably performed according to the injection molding control system, and the adaptive dynamic prediction and adjustment of the injection molding error of the safety helmet injection mold can be achieved, thereby improving the applicability and safety of the safety helmet injection mold, and effectively improving the quality and pass rate of safety helmet manufacturing. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 A schematic diagram of a module of a helmet mold quality prediction control system based on big data provided by the present invention;

[0067] Figure 2 A flowchart of a helmet mold quality prediction and control method based on big data provided by the present invention. DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] The embodiments of the method and system for predicting and controlling the quality of helmet molds based on big data are as follows:

[0070] Embodiment 1:

[0071] See also Figure 1 - Figure 2 , a helmet mold quality prediction and control method based on big data, the method comprises the following steps:

[0072] S1. Collecting the characteristic text data of the helmet injection molding product and the 3D model data of the helmet injection molding product manufacturing end;

[0073] S2, performing a 3D model parameter search process on the design end of the safety helmet injection molding product based on the feature text data of the safety helmet injection molding product and the 3D model data on the design end of the safety helmet injection molding product, and generating the 3D model data on the design end of the target safety helmet injection molding product;

[0074] S3, according to the manufacturing end 3D model parameters and the design end 3D model parameters of the target helmet injection molding product, the structural thickness dimension measurement position of the helmet injection molding manufacturing end and the design end product is established and processed, and the thickness dimension measurement position coordinate data of the helmet injection molding manufacturing product and the thickness dimension measurement position coordinate data of the helmet injection molding design product are generated;

[0075] S4, based on the thickness dimension measurement position coordinate data of the helmet injection molding manufacturing product, the thickness dimension measurement position coordinate data of the helmet injection molding design product, the three-dimensional model data of the helmet injection molding product manufacturing end, and the three-dimensional model data of the target helmet injection molding product design end, the structural thickness dimension measurement operation of the helmet injection molding manufacturing and design product is performed, and the manufacturing thickness dimension data of the helmet injection molding product and the design thickness dimension data of the helmet injection molding product are generated;

[0076] S5. Perform manufacturing thickness dimension error measurement processing of the safety helmet injection molding product according to the manufacturing thickness dimension data of the safety helmet injection molding product and the design thickness dimension data of the safety helmet injection molding product, generate the manufacturing thickness dimension error data of the safety helmet injection molding product and perform manufacturing thickness dimension error mean statistical processing of the safety helmet injection molding product, generate the manufacturing thickness dimension error mean of the safety helmet injection molding product;

[0077] S6. Perform manufacturing quality analysis of the injection molding products of the helmets according to the mean value of the manufacturing thickness dimension error of the injection molding products of the helmets, generate manufacturing quality analysis data of the injection molding products of the helmets, and when the data is qualified, end the manufacturing quality adjustment operation of the injection molding products of the helmets;

[0078] S7. When the result is unqualified, the movable mold and fixed mold injection molding fit clearance adjustment data of the safety helmet injection mold is constructed and the movable mold and fixed mold injection molding adjustment operation of the safety helmet injection mold is performed.

[0079] For further information, see Figure 1 - Figure 2 The steps for collecting the characteristic text data of the helmet injection molding product and the 3D model data of the helmet injection molding product manufacturing end are as follows:

[0080] S11. Obtain product feature text data of the injection-molded safety helmet online through the material ERP, and generate product feature text data R of the injection-molded safety helmet, where the product feature text data R of the injection-molded safety helmet includes model information, specification information, material information, and production date information of the safety helmet;

[0081] The three-dimensional solid model of the overall structure of the helmet product is completed by online scanning with a three-dimensional laser scanner, and the three-dimensional model data W of the helmet injection molding product manufacturing end is generated.

[0082] The steps for searching and processing the 3D model parameters of the helmet injection molding product design end are performed based on the characteristic text data of the helmet injection molding product and the 3D model data of the helmet injection molding product design end, and generating the target helmet injection molding product design end 3D model data are as follows:

[0083] S21. Establish the three-dimensional model data set U of the helmet injection molding product design end = (u 1 ,…,u a ,…,u φ ), a=1,2,3,…,φ; where u a represents the three-dimensional model data of the design end of the safety helmet injection molding product corresponding to the a-th safety helmet type, and φ represents the maximum number of safety helmet types; the three-dimensional model data of the design end of the safety helmet injection molding product represents the three-dimensional model data generated in the theoretical design stage of different types of safety helmet injection molding products;

[0084] S22, the helmet injection molding product feature text data R and the helmet injection molding product design end three-dimensional model data set U of the helmet injection molding product design end three-dimensional model data u a Perform keyword matching of helmet product features and search for the helmet injection product feature text data R corresponding to the helmet injection product design end 3D model data u a , and generate the target helmet injection product design end 3D model data through data identification Execute and generate the target helmet injection product design end 3D model data The specific steps are as follows:

[0085] S221. Initialize the model recognition cheetah's initial position in the search space of the φ-dimensional three-dimensional model data set U of the helmet injection product design end, and describe it as: H i,j =ψ+rand(ξ-ψ), where H i,j It represents the position of the cheetah identified by the i-th model in the search space with spatial dimension j, that is, the position of the cheetah identified by the i-th model in the search space of the three-dimensional model data set U of the helmet injection molding product design end with spatial dimension φ, ξ and ψ are the upper and lower limits of the j-th dimensional search space respectively, rand is a random number in the interval (0,1), and T represents the maximum number of iterations;

[0086] S222, execute the search strategy to search for prey, the model recognition cheetah performs a full range scan in the search space of the three-dimensional model data set U of the helmet injection molding product design end, or actively searches for the three-dimensional model data u of the helmet injection molding product design end corresponding to the feature text data R of the helmet injection molding product. a ; The search strategy is mathematically described as in It represents the position of the cheetah in the search space with the spatial dimension j after the i-th model recognizes the cheetah for the t+1th iteration in the prey search stage of executing the search strategy, that is, the position of the three-dimensional model data set U of the helmet injection product design end in the search space with the spatial dimension φ after the i-th model recognizes the cheetah for the t+1th iteration; It represents the position of the cheetah in the search space with spatial dimension j after the t-th iteration of the i-th model recognition of the cheetah in the stage of executing the search strategy to search for prey, that is, the position of the three-dimensional model data set U of the helmet injection product design end in the search space with spatial dimension φ after the t-th iteration of the i-th model recognition of the cheetah, The random number that is normally distributed in the search space with spatial dimension j for the i-th model to identify the cheetah, The search step length of the i-th model recognition cheetah in the search space with spatial dimension j after the t-th iteration;

[0087] S223, execute the search for prey using the sit-and-wait strategy, in the search mode, in the safety helmet injection molding product design end three-dimensional model data set U search space safety helmet injection molding product feature text data R corresponding to the safety helmet injection molding product design end three-dimensional model data u a The prey is exposed to the model recognition cheetah's field of vision. The model recognition cheetah adopts a sit-and-wait ambush strategy to approach the helmet injection molding product feature text data R corresponding to the helmet injection molding product design end 3D model data u a Prey, sit and wait ambush strategy includes lying on the ground or hiding in the bushes, sit and wait strategy mathematically described as in It represents the position of the cheetah in the search space with the spatial dimension j after the i-th model recognizes the cheetah for the t+1th iteration in the stage of searching for prey using the sit-and-wait strategy, that is, the position of the cheetah in the search space of the three-dimensional model data set U of the helmet injection molding product design end with the spatial dimension φ after the i-th model recognizes the cheetah for the t+1th iteration; It represents the position of the cheetah in the search space with the spatial dimension j after the i-th model recognizes the cheetah for the t-th iteration in the stage of searching for prey using the sit-and-wait strategy, that is, the position of the cheetah in the search space of the three-dimensional model data set U of the helmet injection molding product design end with the spatial dimension φ after the i-th model recognizes the cheetah for the t-th iteration;

[0088] S224, execute the sit-and-wait strategy to approach the prey and attack the prey according to the attack strategy. In the search space of the three-dimensional model data set U of the helmet injection molding product design end, each model recognition cheetah adjusts its position according to the position of the fleeing prey, the leading model recognition cheetah or the nearby model recognition cheetah to obtain the best attack and the helmet injection molding product design end three-dimensional model data u that matches the helmet injection molding product feature text data R. a Prey position, attack strategy mathematically described as in It represents the position of the prey in the search space with a spatial dimension of j after the i-th model recognizes the cheetah for the t+1th iteration in the attack phase according to the attack strategy, that is, the position of the cheetah in the search space of the three-dimensional model data set U of the helmet injection molding product design end with a spatial dimension of φ after the i-th model recognizes the cheetah for the t+1th iteration; Π represents the position of the prey in the search space with a spatial dimension of j after the t-th iteration of the cheetah identified by the i-th model in the attack phase according to the attack strategy, that is, the position of the cheetah in the search space of the three-dimensional model data set U of the helmet injection molding product design end with a spatial dimension of φ after the t-th iteration of the cheetah identified by the i-th model; i,j represents the turning factor of the cheetah identified by the i-th model in the search space with spatial dimension j; Identify the interaction factors of the cheetah at the tth iteration in the search space with spatial dimension j for the i-th model;

[0089] S225, repeating steps S222, S223, and S224 until the maximum number of iterations is met, and outputting the three-dimensional model data u of the helmet injection molding product design end that matches the characteristic text data R of the helmet injection molding product. a ;

[0090] S226, the output in step S225 matches the helmet injection molding product feature text data R with the helmet injection molding product design end three-dimensional model data u a, and generate the target helmet injection product design end 3D model data through data identification

[0091] Through the cooperation of the safety helmet injection molding product feature information collection unit and the safety helmet injection molding product manufacturing end 3D model collection unit, material ERP and 3D laser scanners are used to obtain safety helmet injection molding product feature information and safety helmet injection molding product manufacturing end 3D model information online, providing real data support for the scientific analysis of safety helmet mold manufacturing quality; the safety helmet injection molding product design end 3D model storage unit and the safety helmet injection molding product design end 3D model recognition unit cooperate with each other, and the safety helmet injection molding product design end 3D model data based on the big data storage combined with the intelligent recognition algorithm and the safety helmet injection molding product feature text data are used to perform intelligent search for the safety helmet injection molding product theoretical design 3D model information, so as to realize efficient retrieval of different types of safety helmet injection molding product design 3D models and improve the accuracy of safety helmet mold manufacturing quality control.

[0092] For further information, see Figure 1 - Figure 2 The steps for establishing and processing the structural thickness dimension measurement position of the helmet injection molding manufacturing end and design end products according to the manufacturing end 3D model parameters and design end 3D model parameters of the target helmet injection molding product, and generating the thickness dimension measurement position coordinate data of the helmet injection molding manufacturing product and the thickness dimension measurement position coordinate data of the helmet injection molding design product are as follows:

[0093] S31, respectively, the three-dimensional model data W of the helmet injection molding product manufacturing end and the three-dimensional model data W of the target helmet injection molding product design end Import the product 3D model design software and run it. At the same time, according to the uniform specifications, the 3D model data W of the helmet injection molding product manufacturing end and the 3D model data of the target helmet injection molding product design end are respectively The corresponding outer contour surface of the helmet is meshed, and the mesh intersection is used as the measurement position of the thickness dimension of the helmet structure; the product 3D model design software includes any one of Rhino, Cinema4D and Autodesk Inventor;

[0094] S32, collect the spatial coordinates of the thickness dimension measurement position of the helmet structure in step S31 online through the coordinate measurement module in the product three-dimensional model design software, and generate the thickness dimension measurement position coordinate data set of the helmet injection molding product respectively Thickness dimension measurement position coordinate data set of helmet injection design products where y b represents the thickness dimension measurement position coordinate data of the helmet injection molding product corresponding to the b-th thickness dimension measurement position in the helmet corresponding to the three-dimensional model data W of the helmet injection molding product manufacturing end, Indicates the maximum number of thickness measurement positions; y' b Indicates the 3D model data of the target helmet injection product design end The coordinate data of the thickness dimension measurement position of the safety helmet injection molding design product corresponding to the bth thickness dimension measurement position in the corresponding safety helmet, the coordinate data of the thickness dimension measurement position of the safety helmet injection molding manufacturing product includes the horizontal coordinate, vertical coordinate and vertical coordinate of the thickness dimension measurement position of the manufacturing product; the coordinate data of the thickness dimension measurement position of the safety helmet injection molding design product includes the horizontal coordinate, vertical coordinate and vertical coordinate of the thickness dimension measurement position of the design product.

[0095] Based on the thickness dimension measurement position coordinate data of the helmet injection molding manufacturing product, the thickness dimension measurement position coordinate data of the helmet injection molding design product, the 3D model data of the helmet injection molding product manufacturing end, and the 3D model data of the target helmet injection molding product design end, the structural thickness dimension measurement operation of the helmet injection molding manufacturing and design product is performed, and the operation steps of generating the thickness dimension data of the helmet injection molding product manufacturing and the thickness dimension data of the helmet injection molding product design are as follows:

[0096] S41, through the thickness measurement module in the product three-dimensional model design software combined with the safety helmet injection molding product thickness dimension measurement position coordinate data set Y, the safety helmet injection molding product thickness dimension measurement position coordinate data y b , perform the thickness dimension measurement operation on the safety helmet corresponding to the three-dimensional model data W of the safety helmet injection molding product manufacturing end; and generate the safety helmet injection molding product manufacturing thickness dimension data set Among them b Indicates the thickness dimension measurement position coordinate data y of the helmet injection molding product b Corresponding thickness dimension data of helmet injection molding products, o b The unit is millimeter;

[0097] Through the thickness measurement module in the product 3D model design software combined with the safety helmet injection design product thickness size measurement position coordinate data set Y', the safety helmet injection design product thickness size measurement position coordinate data y' b , the 3D model data of the target helmet injection product design end The corresponding helmet is used to measure the thickness of the helmet injection molding product structure; and a data set of the thickness of the helmet injection molding product design is generated. where o′ b Represents the thickness dimension measurement position coordinate data y′ of the injection molded helmet product b The corresponding thickness dimension measurement position coordinate data of the helmet injection molding design product, o′ b The unit is millimeters.

[0098] According to the manufacturing thickness dimension data of the helmet injection molding products and the design thickness dimension data of the helmet injection molding products, the manufacturing thickness dimension error of the helmet injection molding products is measured, the manufacturing thickness dimension error data of the helmet injection molding products is generated, and the mean value of the manufacturing thickness dimension error of the helmet injection molding products is statistically processed. The operation steps for generating the mean value of the manufacturing thickness dimension error of the helmet injection molding products are as follows:

[0099] S51, the thickness dimension data set O of the manufacturing thickness dimension data of the injection molding product of the helmet is b The thickness dimension measurement position numbers are ordered and the thickness dimension measurement position coordinate data o' of the safety helmet injection molding product design thickness dimension data set O' b Perform difference processing between the manufacturing and design thickness dimension values ​​of the helmet injection molding products, and generate a data set of thickness dimension error of the helmet injection molding products where o″ b Indicates the manufacturing thickness dimension error data of the helmet injection molding product corresponding to the bth thickness dimension measurement position of the helmet injection molding product, o″ b =o b -o′ b ,o″ b The unit is millimeter, o″ b The values ​​include positive, negative and zero, where o″ b When it is a positive number, it means that the manufacturing thickness of the injection molded helmet product is greater than the design thickness; o″ b When the number is negative, it means that the manufacturing thickness of the injection molded helmet product is smaller than the designed thickness; o″ b When it is a positive number, it means that the manufacturing thickness dimension of the injection molded helmet product is equal to the design thickness dimension; the manufacturing thickness dimension error data of the injection molded helmet product represents the error parameter between the thickness dimension of the helmet body structure from the outer contour surface to the inner contour surface of the injection molded helmet manufacturing product and the thickness dimension of the helmet body structure from the outer contour surface to the inner contour surface of the injection molded helmet design product; because during the injection molding process of the helmet, the outer contour surface of the injection molded helmet product fits with the inner wall of the fixed mold cavity of the injection mold, and the inner contour surface fits with the outer surface of the movable mold of the injection mold, and the injection fitting clearance error between the movable mold and the fixed mold of the injection mold causes the injection structure thickness dimension from the inner contour surface to the outer contour surface of the injection molded helmet product to fail to meet the standard;

[0100] S52, the thickness dimension error data o″ of the thickness dimension error data set O″ of the thickness dimension error data set O″ of the injection molded product of the helmet b Perform numerical measurement of the mean value of the manufacturing thickness dimension error of the helmet injection molding product, and generate the mean value of the manufacturing thickness dimension error of the helmet injection molding product in The unit is millimeter; The values ​​include positive, negative and zero. When it is a positive number, it means that the manufacturing thickness of the injection molded helmet product is greater than the design thickness; When the number is negative, it means that the manufacturing thickness of the injection molded helmet product is smaller than the designed thickness. When it is a positive number, it means that the manufacturing thickness dimension of the injection molded helmet product is equal to the design thickness dimension.

[0101] According to the mean value of the thickness dimension error of the helmet injection molding product manufacturing, the manufacturing quality analysis of the helmet injection molding product is carried out to generate the manufacturing quality analysis data of the helmet injection molding product. When it is qualified, the operation steps to end the manufacturing quality adjustment operation of the helmet injection molding product are as follows:

[0102] S61, call the average thickness error of the injection molding product manufacturing of the helmet

[0103] S62. Average thickness error of injection molded helmets Conduct thickness dimension numerical analysis, and generate manufacturing quality analysis data L of helmet injection molding products based on the thickness dimension numerical analysis results;

[0104] when The absolute value of is equal to zero, indicating that the manufacturing quality of the helmet injection molding product is qualified, and the output of the helmet injection molding product manufacturing quality analysis data L is qualified, and the current helmet injection molding product manufacturing quality adjustment operation is directly terminated;

[0105] when The absolute value of is not equal to zero, indicating that the manufacturing quality of the injection molding product of the safety helmet is unqualified, and the output manufacturing quality analysis data L of the injection molding product of the safety helmet is unqualified.

[0106] Through the mutual cooperation among the safety helmet injection molding product thickness dimension measurement position establishment unit, the safety helmet injection molding product manufacturing thickness dimension measurement unit, and the safety helmet injection molding product design thickness dimension measurement unit, based on the safety helmet injection molding product manufacturing and design end three-dimensional model combined with the product three-dimensional model design software, the safety helmet injection molding manufacturing and design product thickness dimension measurement position coordinate parameters are independently and accurately established, and the thickness dimension parameters of the safety helmet injection molding manufacturing and design products are accurately measured in combination with the product three-dimensional model design software, so as to realize the scientific and accurate measurement of the thickness dimension of the cap body structure of the safety helmet injection molding manufacturing and design end products, and improve the accuracy of the safety helmet mold manufacturing quality control; safety helmet injection molding The product manufacturing thickness dimension error measurement unit and the helmet injection molding product manufacturing thickness dimension error mean measurement unit cooperate with each other, based on the manufacturing and design thickness dimension parameters of the helmet injection molding products combined with numerical analysis to efficiently count the manufacturing thickness dimension error and error mean data of the helmet injection molding products, realize the digital intelligent analysis of the manufacturing error of the helmet injection molding products, and improve the manufacturing quality of the helmet injection molding mold; the helmet injection molding product manufacturing quality analysis unit, based on the mean value of the manufacturing thickness dimension error of the helmet injection molding products combined with numerical analysis, accurately analyzes the manufacturing quality information of the helmet injection molding products, realizes the intelligent management of the manufacturing quality of the helmet injection molding products, and improves the intelligence of the helmet mold production.

[0107] For further information, see Figure 1 - Figure 2 When it is unqualified, the operation steps of constructing the adjustment data of the movable mold and fixed mold injection matching clearance of the helmet injection mold and performing the injection adjustment operation of the movable mold and fixed mold of the helmet injection mold are as follows:

[0108] S71. When the manufacturing quality analysis data L of the helmet injection molding product is unqualified, the average thickness dimension error of the helmet injection molding product manufacturing Perform data identification processing to construct the adjustment data V of the injection fit clearance of the dynamic mold and fixed mold of the helmet injection mold, where

[0109] When V is greater than zero, it means that the manufacturing thickness of the helmet injection molded product is smaller than the designed thickness. At this time, the injection fitting clearance between the movable mold and the fixed mold of the helmet injection mold needs to be increased according to |V|;

[0110] When V is less than zero, it means that the manufacturing thickness of the helmet injection molded product is greater than the designed thickness. At this time, it is necessary to reduce the injection fit clearance between the movable mold and the fixed mold of the helmet injection mold according to |V|;

[0111] S72, the injection molding control system performs the injection molding adjustment operation of the movable mold and fixed mold of the safety helmet injection mold according to the injection molding matching clearance adjustment data V of the movable mold and fixed mold of the safety helmet injection mold.

[0112] Through the cooperation of the dynamic mold and fixed mold injection fitting clearance adjustment parameter construction unit of the safety helmet injection mold and the dynamic mold and fixed mold injection adjustment unit of the safety helmet injection mold, the dynamic mold and fixed mold injection fitting clearance adjustment parameters of the safety helmet injection mold are scientifically constructed based on numerical analysis, and the injection molding processing error adjustment parameters of the safety helmet injection mold are intelligently generated. At the same time, the dynamic mold and fixed mold injection molding adjustment operation of the safety helmet injection mold is accurately and reliably performed according to the injection molding control system, and the injection molding processing error of the safety helmet injection mold is adaptively predicted and adjusted, thereby improving the applicability and safety of the safety helmet injection mold, and effectively improving the quality and qualified rate of safety helmet manufacturing.

[0113] Embodiment 2:

[0114] See also Figure 1 - Figure 2 , a helmet mold quality prediction control system based on big data, used to implement a helmet mold quality prediction control method based on big data, the system includes a helmet injection molding information acquisition module, a helmet injection molding quality analysis module, and a helmet injection molding quality adjustment module;

[0115] The helmet injection molding information acquisition module includes a helmet injection molding product feature information acquisition unit, a helmet injection molding product manufacturing end 3D model acquisition unit, a helmet injection molding product design end 3D model storage unit, and a helmet injection molding product design end 3D model recognition unit;

[0116] The feature information collection unit of the injection molding product of the safety helmet collects the feature text data of the injection molding product of the safety helmet through the material ERP; the 3D model collection unit of the injection molding product manufacturing end of the safety helmet collects the 3D model data of the injection molding product manufacturing end of the safety helmet through the 3D laser scanner; the 3D model storage unit of the injection molding product design end of the safety helmet is used to store the 3D model data of the injection molding product design end of the safety helmet; the 3D model recognition unit of the injection molding product design end of the safety helmet performs the 3D model parameter search and processing of the injection molding product design end of the safety helmet based on the feature text data of the injection molding product of the safety helmet and the 3D model data of the injection molding product design end of the safety helmet, and generates the 3D model data of the injection molding product design end of the target safety helmet;

[0117] The helmet injection molding quality analysis module includes a helmet injection molding product thickness dimension measurement position establishment unit, a helmet injection molding product manufacturing thickness dimension measurement unit, a helmet injection molding product design thickness dimension measurement unit, a helmet injection molding product manufacturing thickness dimension error measurement unit, a helmet injection molding product manufacturing thickness dimension error mean measurement unit, and a helmet injection molding product manufacturing quality analysis unit;

[0118] The unit for establishing the thickness dimension measurement position of the injection molding product of the safety helmet performs the structural thickness dimension measurement position establishment processing of the products at the injection molding manufacturing end and the design end according to the manufacturing end 3D model parameters and the design end 3D model parameters of the target injection molding product of the safety helmet in combination with the product 3D model design software, and generates the thickness dimension measurement position coordinate data of the injection molding manufacturing product of the safety helmet and the thickness dimension measurement position coordinate data of the injection molding design product of the safety helmet; the unit for measuring the manufacturing thickness dimension of the injection molding product of the safety helmet performs the structural thickness dimension measurement operation of the injection molding manufacturing product of the safety helmet based on the thickness dimension measurement position coordinate data of the injection molding manufacturing product of the safety helmet and the 3D model data of the injection molding product of the safety helmet in combination with the 3D model design software of the product, and generates the manufacturing thickness dimension data of the injection molding product of the safety helmet; the unit for measuring the design thickness dimension of the injection molding product of the safety helmet performs the structural thickness dimension measurement operation of the injection molding product of the safety helmet based on the thickness dimension measurement position coordinate data of the injection molding design product of the safety helmet and the 3D model data of the injection molding product of the target The model data is combined with the product three-dimensional model design software to measure the structural thickness dimensions of the helmet injection molding design product, and generate the design thickness dimension data of the helmet injection molding product; the manufacturing thickness dimension error measurement unit of the helmet injection molding product performs the manufacturing thickness dimension error measurement processing of the helmet injection molding product according to the manufacturing thickness dimension data of the helmet injection molding product and the design thickness dimension data of the helmet injection molding product, and generates the manufacturing thickness dimension error data of the helmet injection molding product; the manufacturing thickness dimension error mean measurement unit of the helmet injection molding product performs the statistical processing of the mean manufacturing thickness dimension error of the helmet injection molding product according to the manufacturing thickness dimension error data of the helmet injection molding product, and generates the mean manufacturing thickness dimension error of the helmet injection molding product; the manufacturing quality analysis unit of the helmet injection molding product performs the manufacturing quality analysis processing of the helmet injection molding product according to the mean manufacturing thickness dimension error of the helmet injection molding product, and generates the manufacturing quality analysis data of the helmet injection molding product;

[0119] The helmet injection molding quality adjustment module includes a helmet injection mold movable mold fixed mold injection molding fit clearance adjustment parameter construction unit and a helmet injection mold movable mold fixed mold injection molding adjustment unit;

[0120] The safety helmet injection mold movable mold and fixed mold injection fitting clearance adjustment parameter construction unit is used to construct the safety helmet injection mold movable mold and fixed mold injection fitting clearance adjustment data; the safety helmet injection mold movable mold and fixed mold injection adjustment unit performs the safety helmet injection mold movable mold and fixed mold injection adjustment operation according to the safety helmet injection mold movable mold and fixed mold injection fitting clearance adjustment data combined with the injection control system.

[0121] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A helmet mold quality prediction and control method based on big data, characterized in that: The method comprises the following steps: S1. Collecting the characteristic text data of the helmet injection molding product and the 3D model data of the helmet injection molding product manufacturing end; S2, performing a 3D model parameter search process on the design end of the safety helmet injection molding product based on the safety helmet injection molding product feature text data and the 3D model data on the design end of the safety helmet injection molding product, and generating the 3D model data on the design end of the target safety helmet injection molding product; S3, according to the manufacturing end 3D model parameters and the design end 3D model parameters of the target helmet injection molding product, the structural thickness dimension measurement position of the helmet injection molding manufacturing end and the design end product is established and processed, and the thickness dimension measurement position coordinate data of the helmet injection molding manufacturing product and the thickness dimension measurement position coordinate data of the helmet injection molding design product are generated; S4, based on the thickness dimension measurement position coordinate data of the helmet injection molding product, the thickness dimension measurement position coordinate data of the helmet injection molding design product, the three-dimensional model data of the helmet injection molding product manufacturing end, and the three-dimensional model data of the target helmet injection molding product design end, perform the structural thickness dimension measurement operation of the helmet injection molding product and the design product, and generate the manufacturing thickness dimension data of the helmet injection molding product and the design thickness dimension data of the helmet injection molding product; S5, performing manufacturing thickness dimension error measurement processing of the safety helmet injection molded product according to the manufacturing thickness dimension data of the safety helmet injection molded product and the design thickness dimension data of the safety helmet injection molded product, generating manufacturing thickness dimension error data of the safety helmet injection molded product and performing manufacturing thickness dimension error mean statistical processing of the safety helmet injection molded product, generating manufacturing thickness dimension error mean of the safety helmet injection molded product; S6, performing manufacturing quality analysis processing of the safety helmet injection molding product according to the mean value of the manufacturing thickness dimension error of the safety helmet injection molding product, generating manufacturing quality analysis data of the safety helmet injection molding product, and ending the current safety helmet injection molding product manufacturing quality adjustment operation when the data is qualified; S7. When the result is unqualified, the movable mold and fixed mold injection molding fit clearance adjustment data of the safety helmet injection mold is constructed and the movable mold and fixed mold injection molding adjustment operation of the safety helmet injection mold is performed.

2. The method for predicting and controlling the quality of helmet molds based on big data according to claim 1 is characterized in that: The S1 comprises the following steps: S11, obtaining product feature text data of the injection-molded safety helmet online through the material ERP, and generating product feature text data R of the injection-molded safety helmet; The three-dimensional solid model of the overall structure of the helmet product is completed by online scanning with a three-dimensional laser scanner, and the three-dimensional model data W of the helmet injection molding product manufacturing end is generated.

3. The method for predicting and controlling the quality of helmet molds based on big data according to claim 2 is characterized in that: The S2 comprises the following steps: S21. Establish the three-dimensional model data set U = (u1,…,u a ,…,u φ ), a=1,2,3,…,φ; where u a represents the three-dimensional model data of the helmet injection product design end corresponding to the a-th helmet type, and φ represents the maximum number of helmet types; S22, compare the R with the u in the U a Perform keyword matching of helmet product features and search for the u corresponding to the R a , and generate the target helmet injection product design end 3D model data through data identification Execute the generated The specific steps are as follows: S221, initializing the model in the search space U of φ dimension to identify the initial position of the cheetah; S222, execute the search strategy to search for prey, the model recognizes that the cheetah performs a full range scan in the U search space or actively searches for the u corresponding to the R a ; S223, execute the search for prey using the sit-and-wait strategy, in the search mode, the u corresponding to the R in the U search space a The prey is exposed to the model recognition cheetah's field of vision, and the model recognition cheetah adopts a sit-and-wait ambush strategy to approach the u corresponding to R a prey; S224, execute the sit-and-wait strategy to approach the prey and attack the prey according to the attack strategy. In the U search space, each model-recognized cheetah adjusts its position according to the position of the escaping prey, the leading model-recognized cheetah or the nearby model-recognized cheetah to obtain the best attack and the u matching the R. a Prey location; S225, repeating steps S222, S223, and S224 until the maximum number of iterations is met, and outputting the u that matches the R a ; S226, matching the output in step S225 with the u a , and generate the target helmet injection product design end 3D model data through data identification 4. The method for predicting and controlling the quality of helmet molds based on big data according to claim 3 is characterized in that: The S3 comprises the following steps: S31, respectively, the W and the Import the product 3D model into the design software and run it. At the same time, squares of uniform specifications are respectively placed in the W and the The corresponding outer contour surface of the helmet is meshed, and the mesh intersection is used as the measurement position of the thickness dimension of the helmet structure; S32, collect the spatial coordinates of the thickness dimension measurement position of the helmet structure in step S31 online through the coordinate measurement module in the product three-dimensional model design software, and generate the thickness dimension measurement position coordinate data set of the helmet injection molding product respectively Thickness dimension measurement position coordinate data set of helmet injection design products where y b represents the thickness dimension measurement position coordinate data of the helmet injection molding product corresponding to the b-th thickness dimension measurement position in the helmet corresponding to W, Indicates the maximum number of thickness measurement positions; y' b Indicates the The corresponding b-th thickness dimension measurement position in the safety helmet corresponds to the thickness dimension measurement position coordinate data of the safety helmet injection molding design product.

5. The method for predicting and controlling the quality of helmet molds based on big data according to claim 4 is characterized in that: The S4 comprises the following steps: S41, through the thickness measurement module in the product three-dimensional model design software combined with the y in the Y b , perform a thickness dimension measurement operation on the safety helmet corresponding to W; and generate a safety helmet injection molding product manufacturing thickness dimension data set Among them b Indicates that y b Corresponding thickness dimension data of helmet injection molding products, o b The unit is millimeter; By combining the thickness measurement module in the product 3D model design software with the Y' b , for the The corresponding helmet is used to measure the thickness of the helmet injection molding product structure; and a data set of the thickness of the helmet injection molding product design is generated. Among them o' b Indicates that y' b Corresponding helmet injection design product thickness dimension measurement position coordinate data, o' b The unit is millimeters.

6. The method for predicting and controlling the quality of helmet molds based on big data according to claim 5 is characterized in that: The S5 comprises the following steps: S51, the o in the O b According to the thickness dimension measurement position numbering order with O' in O' b Perform difference processing between the manufacturing and design thickness dimension values ​​of the helmet injection molding products, and generate a data set of thickness dimension error of the helmet injection molding products where o” b Indicates the manufacturing thickness dimension error data of the helmet injection molding product corresponding to the bth thickness dimension measurement position of the helmet injection molding product, o” b The unit is millimeter; S52, the o" in the O" b Perform numerical measurement of the mean value of the manufacturing thickness dimension error of the helmet injection molding product, and generate the mean value of the manufacturing thickness dimension error of the helmet injection molding product in The unit is millimeters.

7. The method for predicting and controlling the quality of helmet molds based on big data according to claim 6 is characterized by: The S6 comprises the following steps: S61, calling the S62. Conduct thickness dimension numerical analysis, and generate manufacturing quality analysis data L of helmet injection molding products based on the thickness dimension numerical analysis results; when If the absolute value of is equal to zero, the output safety helmet injection molding product manufacturing quality analysis data L is qualified, and the safety helmet injection molding product manufacturing quality adjustment operation is directly terminated at this time; when If the absolute value of is not equal to zero, the output safety helmet injection molding product manufacturing quality analysis data L is unqualified.

8. The method for predicting and controlling the quality of helmet molds based on big data according to claim 7 is characterized in that: The S7 comprises the following steps: S71, when the L is unqualified, according to the average thickness error of the injection molded product of the helmet Perform data identification processing to construct the injection fit clearance adjustment data V of the dynamic mold and fixed mold of the helmet injection mold; When V is greater than zero, the clearance between the movable mold and the fixed mold of the helmet injection mold needs to be increased according to |V|; When V is less than zero, it is necessary to reduce the gap between the movable mold and the fixed mold of the helmet injection mold according to |V|; S72, the injection molding control system performs the injection molding adjustment operation of the movable mold and the fixed mold of the safety helmet injection mold according to the V.

9. A helmet mold quality prediction and control system based on big data, used to implement a helmet mold quality prediction and control method based on big data as claimed in any one of claims 1 to 8, characterized in that: The system comprises a safety helmet injection molding information acquisition module, a safety helmet injection molding quality analysis module, and a safety helmet injection molding quality adjustment module.

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