Intelligent 3D model design method and system based on small-mouth pressure-blow molding equipment

Abstract

The present invention relates to the technical field of data processing and three-dimensional model construction, and in particular to an intelligent three-dimensional model design method and system based on small-mouth pressure-blow molding equipment. The method comprises the following steps: using an intelligent sensor to collect parameters of the small-mouth pressure-blow molding equipment and record initial equipment parameter information; using computer-aided design software to establish a three-dimensional initial model of the equipment according to the initial equipment parameter information; obtaining glass droplet characteristic data and inputting the glass droplet characteristic data into the three-dimensional initial model of the equipment, using simulation software to simulate the pressure-blow molding process, and performing an impact assessment on the simulated pressure-blow molding process to obtain a molding process assessment result. The present invention uses data processing technology, three-dimensional modeling technology and automated control technology to perform correlation assessment on various parameters of the small-mouth pressure-blow molding equipment and construct an intelligent three-dimensional model of the equipment; improves the accuracy of the control operation of the molding equipment so that the produced glass containers maintain specification consistency.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing and three-dimensional model construction, and in particular to an intelligent three-dimensional model design method and system based on small-mouth pressure-blow molding equipment. Background Art

[0002] Small-mouth pressure-blow molding technology is a common process for producing lightweight glass bottles or glass jars. During the small-mouth pressure-blow molding process, the molten glass liquid is cut by the shear blade at the material temperature to form cylindrical glass droplets, called "droplets". The droplets fall under the action of gravity and first enter the primary mold to form a prefabricated shape. Then the flip arm flips the preformed glass bottle (parison) into the mold. The mold is closed and clamped to wrap the parison. The blowing head descends to the top of the mold, and compressed air is poured into the parison from the middle, squeezing the glass to expand into the mold to form the final bottle shape. The traditional small-mouth pressure-blow molding device has a flipping structure, which leads to a larger overall structure of the device, occupies more production space, and flipping requires a certain amount of time; and the parison will further cool and harden during the transfer process, affecting the subsequent small-mouth molding. However, the existing small-mouth pressure-blow molding equipment has low control operation accuracy and fails to conduct a correlation evaluation of the various parameters of the small-mouth pressure-blow molding equipment, resulting in low efficiency of small-mouth pressure-blow molding and difficulty in achieving uniform specifications for the produced glass containers. Summary of the invention

[0003] Based on this, it is necessary to provide an intelligent three-dimensional model design method and system based on small-mouth pressure-blow molding equipment to solve at least one of the above technical problems.

[0004] To achieve the above object, an intelligent three-dimensional model design method based on a small-mouth pressure-blow molding device is provided, the method comprising the following steps:

[0005] Step S1: using intelligent sensors to collect parameters of small-mouth pressure-blow molding equipment and record initial equipment parameter information; using computer-aided design software to establish a three-dimensional initial model of the equipment based on the initial equipment parameter information;

[0006] Step S2: obtaining glass droplet characteristic data and inputting the glass droplet characteristic data into the three-dimensional initial model of the equipment, simulating the press-blow molding process by using simulation software, and performing an impact evaluation on the simulated press-blow molding process to obtain a molding process evaluation result;

[0007] Step S3: adjusting the initial equipment 3D geometric model according to the molding process evaluation result; if the molding process evaluation result does not meet the preset molding process result, optimizing the molding structure design parameters of the equipment 3D initial model to obtain the equipment 3D optimized model;

[0008] Step S4: Integrate the equipment three-dimensional optimization model into the intelligent pressure-blowing control device to monitor the actual pressure-blowing bottle weight and obtain actual pressure-blowing feedback data; perform feedback adjustment on the equipment three-dimensional optimization model according to the actual pressure-blowing feedback data to generate an intelligent pressure-blowing three-dimensional model.

[0009] The present invention uses intelligent sensors to collect small-mouth pressure-blow molding equipment parameters and records initial equipment parameter information, which can ensure that the collected data has high precision and high reliability, and provide a solid data foundation for subsequent equipment model establishment and optimization. The three-dimensional initial model of the equipment is established by computer-aided design software, and the structure and parameters of the equipment are intuitively displayed, which is convenient for designers to analyze and modify, thereby improving design efficiency and accuracy. The glass droplet characteristic data are obtained and input into the three-dimensional initial model of the equipment, so that the simulation process is closer to the actual production conditions and the accuracy of the simulation is improved. The pressure-blow molding process is simulated by simulation software, and the impact of the simulated pressure-blow molding process is evaluated to obtain the molding process evaluation result, which is helpful to predict the problems that occur in the molding process, such as uneven material flow, product defects, etc., so as to prevent and optimize before actual production. The initial equipment three-dimensional geometric model is adjusted according to the molding process evaluation result. If the molding process evaluation result does not meet the preset molding process result, the molding structure design parameter of the three-dimensional initial model of the equipment is optimized to obtain the three-dimensional optimization model of the equipment. It can ensure that the equipment design is more in line with production requirements and improve the molding quality and production efficiency of the product. By optimizing the equipment model, material waste is reduced, production costs are reduced, and the consistency and reliability of the product are improved. The three-dimensional optimization model of the equipment is integrated into the intelligent pressure-blowing control device to monitor the actual pressure-blowing bottle weight, and the actual pressure-blowing feedback data is obtained, which realizes the close integration of the equipment model and the actual production process, making the equipment control more accurate. The three-dimensional optimization model of the equipment is feedback-adjusted according to the actual pressure-blowing feedback data to generate an intelligent pressure-blowing three-dimensional model, further improving the intelligence level of the equipment, realizing precise control of the pressure-blowing molding process, and improving product consistency and production efficiency. Through real-time monitoring and adjustment, errors in production can be reduced, product quality can be improved, and energy consumption and costs can be reduced. Therefore, the present invention uses data processing technology, three-dimensional modeling technology and automated control technology to perform correlation evaluation on various parameters of small-mouth pressure-blowing molding equipment, and construct an intelligent three-dimensional model of small-mouth pressure-blowing molding equipment; improve the accuracy of the control operation of small-mouth pressure-blowing molding equipment, improve the efficiency of small-mouth pressure-blowing molding, and make the produced glass containers maintain specification consistency.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: using a laser scanner to scan the external contour of the small-mouth pressure-blow molding equipment to obtain the external contour data of the equipment;

[0012] Step S12: using an angle sensor to measure the movable angle of the connection joint of the small-mouth pressure-blow molding equipment, and using a displacement sensor to measure the telescopic length of the connection part of the small-mouth pressure-blow molding equipment to obtain the movable range of the connection part;

[0013] Step S13: using a contact measuring instrument to measure the diameter, length and width of the mold punch of the small-mouth pressure-blow molding equipment to obtain the mold punch size parameters;

[0014] Step S14: integrating the equipment external contour data, the movable range of the connection part and the die punch size parameters to obtain initial equipment parameter information;

[0015] Step S15: Using computer-aided design software to build a three-dimensional initial model of the device based on the initial device parameter information.

[0016] The present invention uses a laser scanner to scan the external contour of the small-mouth pressure-blow molding equipment, obtains the external contour data of the equipment, ensures the accurate capture of the appearance features of the equipment, and provides accurate external shape information for subsequent model establishment. An angle sensor is used to measure the movable angle of the connection joint of the small-mouth pressure-blow molding equipment, and a displacement sensor is used to measure the telescopic length of the connection part to obtain the movable range of the connection part, which provides key data support for the kinematic analysis and dynamic simulation of the equipment. A contact measuring instrument is used to measure the diameter, length and width of the mold punch of the small-mouth pressure-blow molding equipment to obtain the mold punch size parameters, which provides an accurate size basis for mold design and manufacturing, and ensures the accurate matching and molding quality of the mold. The equipment external contour data, the movable range of the connection part and the mold punch size parameters are integrated into the equipment parameters to achieve comprehensive integration of the equipment parameters, laying a solid data foundation for establishing an accurate three-dimensional model of the equipment. According to the initial equipment parameter information, a three-dimensional initial model of the equipment is established using computer-aided design software to achieve three-dimensional visualization of the equipment design, which provides an intuitive model basis for subsequent equipment optimization and simulation.

[0017] Preferably, step S15 comprises the following steps:

[0018] Step S151: Start computer-aided design software and create a three-dimensional model project file of the equipment;

[0019] Step S152: inputting the equipment external contour data into the equipment three-dimensional model project file, and drawing the equipment external contour diagram;

[0020] Step S153: parameterizing the device connection parts of the device external contour diagram according to the movable range of the connection parts to obtain a device parameterized contour diagram;

[0021] Step S154: perform two-dimensional dimensioning on the equipment parameterized outline drawing based on the die punch size parameters to obtain two-dimensional geometric dimensioning data; perform three-dimensional structural modeling on the two-dimensional geometric dimensioning data to generate a three-dimensional initial model of the equipment.

[0022] The present invention starts computer-aided design software and creates a three-dimensional model project file of an equipment, provides a systematic working environment for the construction of the equipment model, and ensures the orderly progress of subsequent design work; inputs the equipment external contour data into the equipment three-dimensional model project file, and draws the equipment external contour diagram, realizes the digital expression of the equipment appearance characteristics, and lays a foundation for accurately constructing the equipment model; parameterizes the equipment connection parts of the equipment external contour diagram according to the movable range of the connection parts, and obtains the equipment parameterized contour diagram, so that the equipment model can accurately reflect the actual dynamic characteristics; two-dimensional dimension annotation is performed on the equipment parameterized contour diagram based on the size parameters of the mold punch, and these data are used for three-dimensional structure modeling to generate a three-dimensional initial model of the equipment, which ensures the accurate coordination of the mold punch with other parts of the equipment and provides an accurate size reference for subsequent production and assembly.

[0023] Preferably, step S2 comprises the following steps:

[0024] Step S21: obtaining a glass drop sample; using an electronic scale to measure the mass of a single glass drop sample, repeating the measurement 10-20 times, and recording each measurement value; averaging each measurement value to obtain an average mass of the glass drop;

[0025] Step S22: using a measuring cylinder and water displacement method to measure the volume of the glass drop sample, the glass drop is completely immersed in a measuring cylinder filled with a known volume of water, and the volume of the water level rise is recorded to obtain the volume of the glass drop;

[0026] Step S23: Calculating the density of the glass drop according to the average mass of the glass drop and the volume of the glass drop to obtain a density value of the glass drop;

[0027] Step S24: placing the glass drop sample into a high temperature furnace, heating it at a rate of 1-5 degrees Celsius per minute, recording the temperature at which the glass drop changes from solid to liquid, and obtaining the melting point value of the glass drop;

[0028] Step S25: using a universal material testing machine to perform a tensile test on the glass drop sample, and recording the stress and strain data of the glass drop during the tensile process to obtain the elastic modulus of the glass drop;

[0029] Step S26: Integrate the glass drop density value, the glass drop melting point value and the glass drop elastic modulus to obtain glass drop characteristic data;

[0030] Step S27: inputting the glass drop characteristic data into the three-dimensional initial model of the equipment, simulating the press-blow molding process using simulation software, and performing an impact evaluation on the simulated press-blow molding process to obtain a molding process evaluation result.

[0031] The present invention obtains glass drop samples and uses an electronic scale to measure the mass of a single sample for multiple times, thereby ensuring the accuracy and reliability of drop mass data; uses a measuring cylinder and a water displacement method to measure the volume of the glass drop samples, thereby providing accurate measurement of the drop volume; calculates the density according to the average mass and volume of the glass drop, thereby obtaining the density value of the glass drop; places the glass drop samples in a high-temperature furnace to increase the temperature and records the melting point; uses a universal material testing machine to perform a tensile test, records stress and strain data, thereby providing data support for material mechanical property analysis; integrates density values, melting point values, and elastic modulus, thereby providing comprehensive data for comprehensive property analysis of the material; inputs the glass drop property data into a three-dimensional initial model of the equipment, thereby obtaining a molding process evaluation result, thereby providing a scientific basis for optimizing the molding process.

[0032] Preferably, step S27 includes the following steps:

[0033] Step S271: start the simulation software and create a small-mouth pressure-blow molding simulation project; import the 3D initial model of the equipment into the small-mouth pressure-blow molding simulation project, and input the glass drop characteristic data into the 3D initial model of the equipment;

[0034] Step S272: setting the compression blow molding parameters in the simulation software, wherein the molding temperature is set to 800-1000 degrees Celsius, the molding pressure is set to 50-150 MPa, and the molding time is set to 120-150 seconds;

[0035] Step S273: setting glass drop injection parameters in the simulation software, wherein the injection speed is 10-15 mm / s and the injection amount is 100-150 g;

[0036] Step S274: Start the simulation software to simulate the press-blow molding process, continuously monitor the changing characteristic values ​​of the glass drop during the molding process, and record the punch position information, glass drop temperature and glass drop pressure value at the corresponding time point every 10 seconds as a time unit;

[0037] Step S275: continuously monitoring the molding process parameter information; when the molding process parameter information is a press-blow molding parameter, determining that the small-mouth press-blow molding simulation is finished; outputting the punch position information, the glass drop temperature and the glass drop pressure value in the simulated press-blow molding process to obtain simulated press-blow molding process data;

[0038] Step S276: Perform an impact assessment on the simulated press-blow molding process data to obtain a molding process assessment result.

[0039] The present invention starts simulation software to create a small-mouth press-blow molding simulation project, and imports a three-dimensional initial model of the equipment and glass drop characteristic data, thereby realizing accurate construction of a simulation environment; sets press-blow molding parameters in the simulation software, including temperature, pressure and time, to ensure that parameter control of the simulation process matches actual production conditions; sets glass drop injection parameters, including injection speed and injection volume, to simulate the dynamic process of drop injection in actual production; starts simulation software to simulate the press-blow molding process, and records the punch position, glass drop temperature and pressure value in time units, thereby realizing continuous monitoring of key variables of the molding process; continuously monitors molding process parameter information, determines that the simulation ends when the set press-blow molding parameters are reached, and outputs relevant data; evaluates the simulated press-blow molding process data, and obtains a molding process evaluation result.

[0040] Preferably, step S276 includes the following steps:

[0041] Step S2761: determining the maximum stroke and the minimum stroke of the punch position information to obtain the maximum stroke and the minimum stroke of the punch; evaluating the impact of the forming speed based on the maximum stroke and the minimum stroke of the punch to obtain the forming speed impact data;

[0042] Step S2762: determining the maximum temperature and the minimum temperature of the glass drop to obtain the maximum temperature and the minimum temperature of the glass drop; evaluating the impact of molding fluidity based on the maximum temperature and the minimum temperature of the glass drop to obtain molding flow impact data;

[0043] Step S2763: determining the pressure peak value and the pressure valley value of the glass drop pressure value to obtain the glass drop pressure peak value and the glass drop pressure valley value; evaluating the molding pressure influence based on the glass drop pressure peak value and the glass drop pressure valley value to obtain molding pressure influence data;

[0044] Step S2764: The molding speed influence data, the molding flow influence data and the molding pressure influence data are combined for influence evaluation to obtain a molding process evaluation result.

[0045] The present invention determines the maximum stroke and minimum stroke of the punch position information to evaluate the influence of the molding speed; determines the maximum temperature and the minimum temperature of the glass drop to evaluate the influence of the molding fluidity; determines the pressure peak value and the pressure valley value of the glass drop to evaluate the influence of the molding pressure; combines the molding speed, molding flow and molding pressure influence data to obtain the molding process evaluation result, comprehensively evaluates the key influencing factors in the molding process, and provides a comprehensive analysis result for optimizing the molding process.

[0046] Preferably, step S3 comprises the following steps:

[0047] Step S31: presetting a standard molding process result for the molding process evaluation result to generate a preset molding process result;

[0048] Step S32: if the molding process evaluation result does not meet the preset molding process result, the computer-aided design software is started to adjust the three-dimensional initial model of the equipment;

[0049] Step S33: identifying the uneven wall thickness region of the equipment three-dimensional initial model according to the molding speed influence data to obtain the molding wall thickness uneven region; adjusting the molding wall thickness uneven region to 2-4 mm to obtain the wall thickness adjustment parameter;

[0050] Step S34: identifying the concave-convex area of ​​the surface of the equipment three-dimensional initial model according to the molding flow influence data to obtain the concave-convex area of ​​the molding surface; performing surface smoothing optimization on the concave-convex area of ​​the molding surface to obtain the surface optimization parameters;

[0051] Step S35: identifying the abnormal air pressure area of ​​the airway on the three-dimensional initial model of the equipment according to the molding pressure influence data to obtain the abnormal air pressure area of ​​the molding airway; adjusting the airway diameter of the abnormal air pressure area of ​​the molding airway to 3-6 mm to obtain the airway diameter adjustment parameter;

[0052] Step S36: Optimizing the molding structure design parameters of the three-dimensional initial model of the equipment based on the wall thickness adjustment parameters, the curved surface optimization parameters, and the airway diameter adjustment parameters to obtain a three-dimensional optimized model of the equipment.

[0053] The present invention sets a preset of standard molding process results to ensure the accuracy and consistency of molding process evaluation; when the molding process evaluation result is inconsistent with the preset result, the computer-aided design software is started to adjust the three-dimensional initial model of the equipment to ensure that the equipment model can be optimized according to the actual molding effect; the wall thickness uneven area is identified according to the molding speed influence data, and adjusted to the standard range; the surface concave and convex area is identified according to the molding flow influence data to improve the appearance quality of the product and the molding accuracy; the airway abnormal pressure area is identified according to the molding pressure influence data, and the airway diameter is adjusted to ensure the uniformity of the air pressure distribution and improve the molding efficiency of the product.

[0054] Preferably, step S34 further includes the following steps:

[0055] Step S341: enhancing the geometric features of the molding surface of the equipment three-dimensional initial model according to the molding flow influence data to obtain molding geometric enhancement features;

[0056] Step S342: scanning the molding geometric enhancement features point by point, and marking the position points of the concave and convex regions; connecting the position points of the concave and convex regions to obtain the concave and convex curved surface regions;

[0057] Step S343: performing surface parameter detection on the concave-convex surface area to obtain concave-convex surface parameters;

[0058] Step S344: dynamically adjusting the parameters of the concave-convex surface area based on the concave-convex surface parameters, performing local optimization on each concave-convex area, and obtaining surface feature adjustment data by adjusting the curvature and tension of the surface;

[0059] Step S345: Input the surface feature adjustment data into the three-dimensional initial model of the device for iterative geometric correction. Through multiple rounds of iterations, the surface parameters are continuously optimized to obtain the surface optimization parameters.

[0060] The present invention enhances the geometric features of the molding surface of the three-dimensional initial model of the equipment, thereby improving the model's responsiveness to flow characteristics during the molding process; scans the molding geometric enhancement features point by point and marks the position points of the concave and convex areas, accurately identifying geometrically irregular areas in the model; performs parameter detection on the concave and convex surface areas, providing detailed geometric data for subsequent surface optimization; performs dynamic parameter adjustment based on the concave and convex surface parameters, locally optimizes each concave and convex area, adjusts the surface curvature and tension, and achieves fine control of the model's geometric features; and optimizes the surface parameters through multiple rounds of iterations to ensure high precision of the model geometry and stability of the molding quality.

[0061] Preferably, step S4 comprises the following steps:

[0062] Step S41: exporting the equipment three-dimensional optimization model from the computer-aided design software and converting the model format to obtain the equipment three-dimensional optimization model file, and transmitting the equipment three-dimensional optimization model file to the intelligent pressure-blow control device;

[0063] Step S42: extracting model pressure-blowing parameters from the three-dimensional optimization model of the equipment to obtain the model pressure-blowing parameters, and setting the blowing parameters of the intelligent pressure-blowing control device according to the model pressure-blowing parameters;

[0064] Step S43: starting the intelligent blow molding control device to monitor the actual blow molding bottle weight;

[0065] Step S44: During the pressure-blowing monitoring process, the weight data of the small-mouth bottle is recorded every 10 seconds, and the bottle weight data is recorded and stored to obtain actual pressure-blowing feedback data;

[0066] Step S45: Feedback adjustment is performed on the equipment three-dimensional optimization model according to the actual press-blow feedback data to generate an intelligent press-blow three-dimensional model.

[0067] The present invention exports a three-dimensional optimization model of the equipment and converts the format to achieve seamless connection between the model and the control device; extracts the model pressure-blowing parameters and sets an intelligent pressure-blowing control device, so that the control device can perform precise control according to the model parameters; extracts the model pressure-blowing parameters and sets an intelligent pressure-blowing control device to improve the automation level and control accuracy of the pressure-blowing process; regularly records and stores bottle weight data during the pressure-blowing monitoring process, providing detailed weight change information for evaluating and optimizing the pressure-blowing process; adjusts the three-dimensional optimization model of the equipment according to actual pressure-blowing feedback data, generates an intelligent pressure-blowing three-dimensional model, and achieves continuous optimization of the model and improvement of the pressure-blowing quality.

[0068] Preferably, step S45 includes the following steps:

[0069] Step S451: extracting the small-mouth bottle weight and time data of the actual blow molding feedback data to obtain bottle weight-time data; performing bottle weight change analysis on the bottle weight-time data to generate bottle weight change data;

[0070] Step S452: performing weight distribution range identification on the bottle weight change data to obtain the weight distribution range of the small-mouth bottle; performing average weight calculation based on the weight distribution range to generate an average weight value of the small-mouth bottle;

[0071] Step S453: determining the adjustment area of ​​the equipment three-dimensional optimization model based on the weight distribution range of the small-mouth bottles and the average weight value of the small-mouth bottles to obtain the model feedback adjustment area;

[0072] Step S454: determining the model parameters to be adjusted in the model feedback adjustment area to obtain the model parameters to be adjusted; and performing intelligent parameter adjustment on the model parameters to be adjusted to generate an intelligent pressure-blowing three-dimensional model.

[0073] The present invention extracts bottle weight-time data and performs bottle weight change analysis, accurately captures the weight change trend of small-mouth bottles over time; identifies the weight distribution range in the bottle weight change data, and determines the weight distribution characteristics of the small-mouth bottles; determines the model feedback adjustment area based on the weight distribution and the average weight value of the small-mouth bottles, and accurately locates the part that needs to be adjusted in the three-dimensional optimization model of the equipment; determines the model parameters to be adjusted and performs intelligent parameter adjustment, realizes the accurate adjustment of the model parameters, optimizes the compression and blowing process, and improves the consistency of product quality.

[0074] In this specification, an intelligent three-dimensional model design system based on a small-mouth pressure-blow molding device is provided, which is used to execute the above-mentioned intelligent three-dimensional model design method based on a small-mouth pressure-blow molding device. The intelligent three-dimensional model design system based on a small-mouth pressure-blow molding device includes:

[0075] The equipment parameter acquisition module uses intelligent sensors to collect the parameters of the small-mouth pressure-blow molding equipment and records the initial equipment parameter information; based on the initial equipment parameter information, a three-dimensional initial model of the equipment is established using computer-aided design software;

[0076] The press-blow molding process simulation module is used to obtain the glass drop characteristic data and input the glass drop characteristic data into the 3D initial model of the equipment, simulate the press-blow molding process by using simulation software, and conduct an impact assessment on the simulated press-blow molding process to obtain the molding process assessment results;

[0077] The equipment 3D model parameter optimization module is used to adjust the initial equipment 3D geometric model according to the molding process evaluation result. If the molding process evaluation result does not meet the preset molding process result, the molding structure design parameter optimization of the equipment 3D initial model is performed to obtain the equipment 3D optimized model.

[0078] The intelligent press-blow three-dimensional model integration test module is used to integrate the equipment's three-dimensional optimization model into the intelligent press-blow control device to monitor the actual press-blow bottle weight and obtain actual press-blow feedback data; the equipment's three-dimensional optimization model is feedback-adjusted according to the actual press-blow feedback data to generate an intelligent press-blow three-dimensional model.

[0079] The present invention uses an equipment parameter acquisition module and an intelligent sensor to collect parameters and record initial equipment parameter information of a small-mouth pressure-blow molding equipment, thereby ensuring accurate data capture and providing an accurate reference basis for subsequent model construction and optimization. The three-dimensional initial model created using computer-aided design software improves the intuitiveness and interactivity of design work, enhances the flexibility and accuracy in the design process, and thus optimizes the design process. Through the pressure-blow molding process simulation module, the characteristic data of the glass drop is integrated into the three-dimensional initial model of the equipment, so that the simulation process can more realistically reflect the actual production environment and enhance the credibility of the simulation results. Through the simulation software, the pressure-blow molding process is simulated and the impact evaluation is performed, and potential molding problems such as uneven material distribution or product defects can be identified and solved in advance, providing scientific guidance for the optimization of the production process. Through the equipment three-dimensional model parameter optimization module, the three-dimensional geometric model of the equipment is adjusted according to the molding process evaluation result. If the evaluation result does not meet expectations, the structural design parameters of the equipment model are optimized to form a three-dimensional optimization model of the equipment. The equipment design is optimized to make it more adaptable to production needs, improve molding efficiency and product quality, while reducing material loss, reducing manufacturing costs, and enhancing the overall consistency of the product. Through the intelligent pressure-blowing three-dimensional model integrated test module, the three-dimensional optimization model of the equipment is combined with the intelligent pressure-blowing control device to monitor the actual pressure-blowing bottle weight and obtain actual pressure-blowing feedback data, so as to achieve seamless connection between the model and production practice and improve the accuracy of equipment control. The equipment model is adjusted according to the actual feedback data to generate an intelligent pressure-blowing three-dimensional model, further improving the intelligence level of the equipment, ensuring the stability and controllability of the pressure-blowing molding process, improving product consistency, reducing production costs, and optimizing energy consumption. Therefore, the present invention uses data processing technology, three-dimensional modeling technology and automatic control technology to perform correlation evaluation on various parameters of small-mouth pressure-blowing molding equipment, and construct an intelligent three-dimensional model of small-mouth pressure-blowing molding equipment; improve the accuracy of the control operation of small-mouth pressure-blowing molding equipment, improve the efficiency of small-mouth pressure-blowing molding, and make the produced glass containers maintain specification consistency. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 A schematic diagram of the steps of an intelligent three-dimensional model design method based on a small-mouth pressure-blow molding device;

[0081] Figure 2 for Figure 1 Detailed implementation steps of step S1 in FIG.

[0082] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0083] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0084] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0085] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0086] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0087] To achieve this, please refer to Figures 1 to 3 , an intelligent three-dimensional model design method based on a small-mouth pressure-blow molding device, the method comprising the following steps:

[0088] Step S1: using intelligent sensors to collect parameters of small-mouth pressure-blow molding equipment and record initial equipment parameter information; using computer-aided design software to establish a three-dimensional initial model of the equipment based on the initial equipment parameter information;

[0089] Step S2: obtaining glass droplet characteristic data and inputting the glass droplet characteristic data into the three-dimensional initial model of the equipment, simulating the press-blow molding process by using simulation software, and performing an impact evaluation on the simulated press-blow molding process to obtain a molding process evaluation result;

[0090] Step S3: adjusting the initial equipment 3D geometric model according to the molding process evaluation result; if the molding process evaluation result does not meet the preset molding process result, optimizing the molding structure design parameters of the equipment 3D initial model to obtain the equipment 3D optimized model;

[0091] Step S4: Integrate the equipment three-dimensional optimization model into the intelligent pressure-blowing control device to monitor the actual pressure-blowing bottle weight and obtain actual pressure-blowing feedback data; perform feedback adjustment on the equipment three-dimensional optimization model according to the actual pressure-blowing feedback data to generate an intelligent pressure-blowing three-dimensional model.

[0092] The present invention uses intelligent sensors to collect small-mouth pressure-blow molding equipment parameters and records initial equipment parameter information, which can ensure that the collected data has high precision and high reliability, and provide a solid data foundation for subsequent equipment model establishment and optimization. The three-dimensional initial model of the equipment is established by computer-aided design software, and the structure and parameters of the equipment are intuitively displayed, which is convenient for designers to analyze and modify, thereby improving design efficiency and accuracy. The glass droplet characteristic data are obtained and input into the three-dimensional initial model of the equipment, so that the simulation process is closer to the actual production conditions and the accuracy of the simulation is improved. The pressure-blow molding process is simulated by simulation software, and the impact of the simulated pressure-blow molding process is evaluated to obtain the molding process evaluation result, which is helpful to predict the problems that occur in the molding process, such as uneven material flow, product defects, etc., so as to prevent and optimize before actual production. The initial equipment three-dimensional geometric model is adjusted according to the molding process evaluation result. If the molding process evaluation result does not meet the preset molding process result, the molding structure design parameter of the three-dimensional initial model of the equipment is optimized to obtain the three-dimensional optimization model of the equipment. It can ensure that the equipment design is more in line with production requirements and improve the molding quality and production efficiency of the product. By optimizing the equipment model, material waste is reduced, production costs are reduced, and the consistency and reliability of the product are improved. The three-dimensional optimization model of the equipment is integrated into the intelligent pressure-blowing control device to monitor the actual pressure-blowing bottle weight, and the actual pressure-blowing feedback data is obtained, which realizes the close integration of the equipment model and the actual production process, making the equipment control more accurate. The three-dimensional optimization model of the equipment is feedback-adjusted according to the actual pressure-blowing feedback data to generate an intelligent pressure-blowing three-dimensional model, further improving the intelligence level of the equipment, realizing precise control of the pressure-blowing molding process, and improving product consistency and production efficiency. Through real-time monitoring and adjustment, errors in production can be reduced, product quality can be improved, and energy consumption and costs can be reduced. Therefore, the present invention uses data processing technology, three-dimensional modeling technology and automated control technology to perform correlation evaluation on various parameters of small-mouth pressure-blowing molding equipment, and construct an intelligent three-dimensional model of small-mouth pressure-blowing molding equipment; improve the accuracy of the control operation of small-mouth pressure-blowing molding equipment, improve the efficiency of small-mouth pressure-blowing molding, and make the produced glass containers maintain specification consistency.

[0093] In the embodiment of the present invention, reference Figure 1 As shown, in this example, the intelligent three-dimensional model design method based on the small-mouth pressure-blow molding equipment includes the following steps:

[0094] Step S1: using intelligent sensors to collect parameters of small-mouth pressure-blow molding equipment and record initial equipment parameter information; using computer-aided design software to establish a three-dimensional initial model of the equipment based on the initial equipment parameter information;

[0095] In the embodiment of the present invention, a strain gauge pressure sensor is used, which can convert the pressure change of the equipment into an electrical signal and transmit it to the data acquisition system through a digital interface. The temperature measurement adopts a platinum resistance temperature sensor, which uses the resistance change caused by the temperature change to measure the temperature and transmits the signal to the data acquisition system. All sensors have a self-calibration function to ensure the accuracy and reliability of the measured data. The data acquisition system has an analog-to-digital conversion function, which converts the analog signal of the sensor into a digital signal and transmits it to the database through a digital interface; the data acquisition system stores the collected parameter information in a relational database, and each data record contains a sensor ID, a timestamp, a pressure value, and a temperature value. The database design adopts a standardized data table structure to ensure the consistency and queryability of the data, and supports SQL query language for data retrieval and analysis. Computer-aided design software is used for parametric modeling, and the collected equipment parameter information is input as the constraint condition of the model. According to the pressure and temperature parameters, a three-dimensional model of the equipment is constructed, including the detailed geometry of the appearance, internal structure and key components. The model is edited, modified and optimized through the user interface of the computer-aided design software to ensure that the model meets the design requirements and engineering standards. During the model building process, parametric design methods were used to allow the model size and shape to be quickly adjusted to accommodate different design changes.

[0096] Step S2: obtaining glass droplet characteristic data and inputting the glass droplet characteristic data into the three-dimensional initial model of the equipment, simulating the press-blow molding process by using simulation software, and performing an impact evaluation on the simulated press-blow molding process to obtain a molding process evaluation result;

[0097] In an embodiment of the present invention, a binocular vision system is used for non-contact measurement. The system filters, removes noise and extracts feature contours of the collected images by building a binocular vision model. A disparity map with complete edge information is obtained based on a Census transform stereo matching algorithm that integrates the gradient information of the droplet image, thereby obtaining three-dimensional information of the glass droplet. The influence of the droplets deflected in the camera plane direction and the camera depth of field direction on the accuracy of the horizontal slicing method is analyzed, and the droplets deflected in the camera plane direction are corrected using the minimum circumscribed rectangle algorithm. The droplets deflected in the camera depth of field direction are corrected using the disparity information, and finally the volume and mass of the droplets are obtained by accumulating the horizontal slices based on the horizontal slicing method. The measured volume and mass data of the glass droplet are used as input parameters and imported into the three-dimensional model of the equipment. These data will be used as the physical properties of the glass droplets in the simulation of the blow molding process. In the three-dimensional model, the volume and mass data of the glass droplet are used to define the initial state of the droplet, providing the necessary physical parameters for the simulation software. Glass molding simulation software is used, which can simulate the flow of viscoelastic materials and is suitable for extrusion molding, blow molding and other processing processes of polymer materials such as plastics and resins. Finite element algorithm and robust solver are used to adjust the deformation boundary through Euler-Lagrangian deformation grid technology to simulate the flow and heat transfer of glass droplets in the press-blow molding process. The simulation software provides viscoelasticity and complex rheology, contact detection, grid overlap technology, nonlinear thermal effects including radiation, sliding grid technology and other functions to evaluate various influencing factors in the molding process. Through the simulation results, the feasibility of the molding process is evaluated, and the molding defects such as cracks, twists, waves and bends are predicted. Specifically, the pressure distribution, velocity distribution, viscosity distribution and shear rate distribution can be intuitively obtained, providing data support for the impact evaluation. According to the evaluation results of the simulation software, a detailed evaluation report of the molding process is obtained, including key parameters and potential problems in the molding process. The evaluation results will guide the actual press-blow molding process, optimize mold design and molding conditions, and improve product quality and production efficiency.

[0098] Step S3: adjusting the initial equipment 3D geometric model according to the molding process evaluation result; if the molding process evaluation result does not meet the preset molding process result, optimizing the molding structure design parameters of the equipment 3D initial model to obtain the equipment 3D optimized model;

[0099] In the embodiment of the present invention, numerical simulation and defect prediction technology are used to preset the molding process results of the molding process to obtain the preset molding process results. Specifically, injection pressure is one of the key parameters in injection molding, and is usually set to a pressure that can ensure that the plastic melt completely fills the mold cavity. The preset value is generally 70% to 90% of the maximum injection pressure, and the specific value depends on the fluidity of the plastic and the complexity of the mold; the holding time refers to the time after the injection is completed to maintain the pressure in the cavity to compensate for the shrinkage of the material to ensure the stability of the product size. The preset value is generally set according to the thickness of the product and the characteristics of the material. For example, for a plastic part with a wall thickness of 2-3mm, the holding time is set between 5 and 15 seconds; the cooling time refers to the time required for the plastic to cool in the mold to a temperature that can be safely demolded. The preset value depends on the thermal properties of the plastic and the geometric shape of the product. For common thermoplastics, the cooling time ranges from tens of seconds to several minutes. According to the preset molding process results, the parametric geometric model adjustment technology is used to adjust the three-dimensional geometric model of the equipment. By changing the boundary shape of the mold cavity, the profile curve of the mold cavity is optimized to reduce the concentration of compressive stress during the molding process. The dynamic mesh model and master-slave element method are used to perform a full three-dimensional simulation of the RTM process with preform deformation, realize the calculation of preform deformation based on the viscoelastic model, and improve the simulation accuracy. The sensitivity analysis technology is used to calculate the sensitivity of the velocity component at a specific point at the mold cavity outlet to the design variables, and analyze the influence of the design variables on the molding process. The optimization algorithm is used to optimize the process parameters of the molding process to improve the comprehensive dimensional accuracy of the solid model. The weight of the influence of each variable on the molding effect is determined, and then the optimal parameter solution is calculated. The surface reconstruction technology is used to reconstruct a complete three-dimensional surface model from the point cloud data, reduce the influence of noise, and improve the surface accuracy of the model. The model is refined by using geometric operation techniques such as curve drawing and surface subdivision, creating shapes and paths, and improving the geometric details of the model. The model is post-processed through filter application and model refinement processing technology to optimize the geometric features of the model and improve the physical properties and molding quality of the model.

[0100] Step S4: Integrate the equipment three-dimensional optimization model into the intelligent pressure-blowing control device to monitor the actual pressure-blowing bottle weight and obtain actual pressure-blowing feedback data; perform feedback adjustment on the equipment three-dimensional optimization model according to the actual pressure-blowing feedback data to generate an intelligent pressure-blowing three-dimensional model.

[0101] In an embodiment of the present invention, a three-dimensional model integration technology is used to import the optimized three-dimensional geometric model into an intelligent pressure-blowing control device. The technology involves converting model data into a format recognizable by the control device and ensuring that the geometric features of the model match the control logic. The actual pressure-blowing bottle weight monitoring is achieved through integrated sensor technology. The sensor collects bottle weight data in real time and transmits the data to the control device for processing. Sensor technology includes but is not limited to pressure sensors, weighing sensors, etc., which can accurately measure and record the weight changes of the bottle. During the actual pressure-blowing process, the intelligent pressure-blowing control device collects feedback data through a data acquisition system, including the weight, molding time and quality control parameters of the bottle. Using data processing technology, the collected actual pressure-blowing feedback data is analyzed to identify deviations from the preset molding process results. Data processing technology includes data filtering, outlier detection and statistical analysis. Parameter optimization technology is used to adjust the design parameters of the three-dimensional model according to the feedback data. It involves modifying the geometric dimensions, material properties or molding process parameters of the model to optimize the molding process. Applying the three-dimensional model update technology, the adjusted parameters are applied to the three-dimensional model to generate a new intelligent pressure-blowing three-dimensional model. This includes updating the model's geometry, material properties, and molding process parameters to ensure that the model reflects actual production conditions. Model validation is performed using computer-aided engineering software to simulate and analyze the performance of the new model to ensure that it meets design requirements and production standards. Simulation analysis includes finite element analysis, fluid dynamics simulation, etc. to predict the performance of the model in actual production.

[0102] As an example of the present invention, refer to Figure 2 As shown, in this example, step S1 includes:

[0103] Step S11: using a laser scanner to scan the external contour of the small-mouth pressure-blow molding equipment to obtain the external contour data of the equipment;

[0104] Step S12: using an angle sensor to measure the movable angle of the connection joint of the small-mouth pressure-blow molding equipment, and using a displacement sensor to measure the telescopic length of the connection part of the small-mouth pressure-blow molding equipment to obtain the movable range of the connection part;

[0105] Step S13: using a contact measuring instrument to measure the diameter, length and width of the mold punch of the small-mouth pressure-blow molding equipment to obtain the mold punch size parameters;

[0106] Step S14: integrating the equipment external contour data, the movable range of the connection part and the die punch size parameters to obtain initial equipment parameter information;

[0107] Step S15: Using computer-aided design software to build a three-dimensional initial model of the device based on the initial device parameter information.

[0108] In the embodiment of the present invention, the laser scanner first sets parameters according to the operation process, including scanning resolution (commonly used range is 0.5mm to 50mm), scanning range (360° panoramic scanning or local area scanning can be selected), and color scanning or intensity scanning mode. When performing the scanning operation, ensure that the device remains absolutely stable during the scanning process to avoid any vibration, monitor the scanning progress, and check whether there are any missed or abnormal areas, and perform additional scanning if necessary. When scanning at multiple sites, ensure that there is a 30%-40% overlap area between adjacent scanning sites to facilitate later splicing, and use targets or feature points to assist in registration to improve splicing accuracy. Real-time data inspection, using the preview function of the device, quickly check the quality of the point cloud data. If a problem area is found, immediately rescan or supplement the scan. The angle sensor is based on the working principle of the magnetostrictive displacement sensor, and uses Faraday's law of electromagnetic induction, the magnetostrictive effect of ferromagnets, the principle of ultrasonic transmission, etc., in conjunction with modern electronic technology and semiconductor technology, to perform high-precision displacement measurement. The sensor structure design includes sealing plug, retaining ring, position magnet, waveguide wire protection tube, etc., and is equipped with waveguide wire, waveguide wire shielding layer, echo elimination device, etc. inside, which is used to sense the change of waveguide wire magnetic field strength caused by torsion wave, so as to obtain the displacement of the object to be measured. The displacement sensor is also based on the working principle of magnetostrictive displacement sensor. The interrogation pulse is loaded onto the waveguide wire through the pulse generator, and the position of the movable magnet is measured to obtain the displacement of the object to be measured. The contact measuring instrument measures the diameter, length and width of the die punch by directly contacting the surface of the object to be measured, and provides accurate dimensional parameters. The measuring instrument has a variety of extraction and analysis tools and special measurement tools, which can extract edge points, circular extraction edge points, ellipse extraction, etc., as well as geometric quantities such as measurement points, lines, and circles. Through data integration technology, the external contour data obtained by the laser scanner, the movable range data of the connection part obtained by the angle sensor and displacement sensor, and the die punch size parameters obtained by the contact measuring instrument are integrated to form the initial equipment parameter information. Using computer-aided design software, the integrated equipment parameter information is input to establish a three-dimensional initial model of the equipment. The software can process point cloud data, perform surface reconstruction, and optimize the model to improve the geometric structure accuracy and topological structure regularity of the model. The 3D reconstruction and model optimization methods in the software can improve the model quality and meet the requirements of the application scenario for model accuracy.

[0109] Preferably, step S15 comprises the following steps:

[0110] Step S151: Start computer-aided design software and create a three-dimensional model project file of the equipment;

[0111] Step S152: inputting the equipment external contour data into the equipment three-dimensional model project file, and drawing the equipment external contour diagram;

[0112] Step S153: parameterizing the device connection parts of the device external contour diagram according to the movable range of the connection parts to obtain a device parameterized contour diagram;

[0113] Step S154: perform two-dimensional dimensioning on the equipment parameterized outline drawing based on the die punch size parameters to obtain two-dimensional geometric dimensioning data; perform three-dimensional structural modeling on the two-dimensional geometric dimensioning data to generate a three-dimensional initial model of the equipment.

[0114] In an embodiment of the present invention, a computer-aided design software is started, and the software has a three-dimensional model editor function. A new project file is created in the software, named "equipment three-dimensional model project", and project parameters are set. The external contour data of the equipment obtained by the laser scanner is imported into the project file using the import function of the software, and the data format is usually a point cloud or STEP file. According to the imported data, the external contour diagram of the equipment is drawn in the software, and the external shape of the equipment is constructed using modeling tools such as primitive operations such as stretching and rotation. According to the movable range data of the connection part obtained by the angle sensor and the displacement sensor, the connection part in the external contour diagram of the equipment is parameterized. In the software, the movable parameters of the connection part, such as the angle range and the telescopic length, are defined by parametric modeling technology, and applied to the corresponding parts of the equipment model. The die punch size parameters obtained by the contact measuring instrument, including diameter, length and width, are marked in two dimensions on the parametric contour diagram of the equipment. The dimensioning tool in the software is used to add accurate dimension lines and texts on the contour diagram to ensure that all dimension parameters match the actual measured values. The three-dimensional structure modeling function in the software is used to construct the three-dimensional structure of the equipment according to the two-dimensional geometric dimensioning data. Through parametric modeling technology, the two-dimensional dimensional data is converted into three-dimensional geometric shapes, the three-dimensional initial model of the equipment is generated, and necessary model optimization is performed, such as smoothing and structural strengthening.

[0115] Preferably, step S2 comprises the following steps:

[0116] Step S21: obtaining a glass drop sample; using an electronic scale to measure the mass of a single glass drop sample, repeating the measurement 10-20 times, and recording each measurement value; averaging each measurement value to obtain an average mass of the glass drop;

[0117] Step S22: using a measuring cylinder and water displacement method to measure the volume of the glass drop sample, the glass drop is completely immersed in a measuring cylinder filled with a known volume of water, and the volume of the water level rise is recorded to obtain the volume of the glass drop;

[0118] Step S23: Calculating the density of the glass drop according to the average mass of the glass drop and the volume of the glass drop to obtain a density value of the glass drop;

[0119] Step S24: placing the glass drop sample into a high temperature furnace, heating it at a rate of 1-5 degrees Celsius per minute, recording the temperature at which the glass drop changes from solid to liquid, and obtaining the melting point value of the glass drop;

[0120] Step S25: using a universal material testing machine to perform a tensile test on the glass drop sample, and recording the stress and strain data of the glass drop during the tensile process to obtain the elastic modulus of the glass drop;

[0121] Step S26: Integrate the glass drop density value, the glass drop melting point value and the glass drop elastic modulus to obtain glass drop characteristic data;

[0122] Step S27: inputting the glass drop characteristic data into the three-dimensional initial model of the equipment, simulating the press-blow molding process using simulation software, and performing an impact evaluation on the simulated press-blow molding process to obtain a molding process evaluation result.

[0123] In the embodiment of the present invention, an electronic scale is used for measurement. The device converts weight into an electrical signal through a pressure sensor and has a measurement capability with an accuracy of 0.01 grams. A single glass droplet sample is measured 10 to 20 times, and the mass reading is recorded each time. For example, the recorded value is , , , ; The average mass (M) of the glass gob is determined by averaging these measurements, using the formula:

[0124]

[0125] Using the measuring cylinder and water displacement method, according to Archimedes' principle, when an object is completely immersed, its volume is equal to the volume of the displaced liquid. Completely immerse the glass goblet in a measuring cylinder filled with a known volume of water and record the volume of the water level rise, for example, the recorded value is V_displaced. The volume of the glass goblet is the volume of the water level rise, that is, V_goblet=V_displaced;

[0126] Based on the average mass of the glass gob and the volume of the glass gob, the density is calculated using the formula:

[0127]

[0128] Place a glass gob sample in a high temperature furnace, heat it up at a rate of 1-5 degrees Celsius / minute, and record the temperature at which the glass gob changes from solid to liquid, for example, T_melt. Use techniques such as the electrical resistance method or differential scanning calorimetry (DSC) to record this transition temperature.

[0129] Use a universal material testing machine to perform tensile tests on glass drop samples and record stress and strain data. Calculate the elastic modulus ( ), using the formula:

[0130]

[0131] The density value, melting point value and elastic modulus are integrated to form a complete set of glass gob characteristic data, for example, recorded as Data_goblet={density, T_melt, E}; Data_goblet is input into the 3D initial model of the equipment, and the press-blow molding process is simulated using simulation software. The simulation software simulates the behavior of the glass gob in the press-blow molding process based on the input physical property data, and evaluates the impact of the simulation process to obtain the molding process evaluation results.

[0132] Preferably, step S27 includes the following steps:

[0133] Step S271: start the simulation software and create a small-mouth pressure-blow molding simulation project; import the 3D initial model of the equipment into the small-mouth pressure-blow molding simulation project, and input the glass drop characteristic data into the 3D initial model of the equipment;

[0134] Step S272: setting the compression blow molding parameters in the simulation software, wherein the molding temperature is set to 800-1000 degrees Celsius, the molding pressure is set to 50-150 MPa, and the molding time is set to 120-150 seconds;

[0135] Step S273: setting glass drop injection parameters in the simulation software, wherein the injection speed is 10-15 mm / s and the injection amount is 100-150 g;

[0136] Step S274: Start the simulation software to simulate the press-blow molding process, continuously monitor the changing characteristic values ​​of the glass drop during the molding process, and record the punch position information, glass drop temperature and glass drop pressure value at the corresponding time point every 10 seconds as a time unit;

[0137] Step S275: continuously monitoring the molding process parameter information; when the molding process parameter information is a press-blow molding parameter, determining that the small-mouth press-blow molding simulation is finished; outputting the punch position information, the glass drop temperature and the glass drop pressure value in the simulated press-blow molding process to obtain simulated press-blow molding process data;

[0138] Step S276: Perform an impact assessment on the simulated press-blow molding process data to obtain a molding process assessment result.

[0139] In an embodiment of the present invention, the simulation software is started, and the software has a project management function. Create a new simulation project, named "Small Mouth Press-Blow Molding Simulation Project", and set the project parameters, including units, material properties, and initial conditions. Import the three-dimensional initial model of the equipment into the simulation project, and use the import function of the software to support multiple three-dimensional model formats, such as STL and IGS; input the glass droplet characteristic data into the three-dimensional initial model of the equipment, including parameters such as density, melting point, and elastic modulus, and input them through the parameter setting interface of the software; set the molding temperature to 800-1000 degrees Celsius, the molding pressure to 50-150 MPa, and the molding time to 120-150 seconds in the software, and configure them through the process parameter setting interface of the software. Set the injection speed to 10-15 mm / s and the injection volume to 100-150 grams; input the above parameters through the injection molding module of the software to simulate the injection process of the glass droplet; start the simulated press-blow molding process, and the software will perform simulation calculations based on the input parameters and models. Continuously monitor the changing characteristic values ​​of the glass drop during the molding process, including the punch position, glass drop temperature and pressure, and record data every 10 seconds. The software has data recording and processing functions, and can automatically record and store key data in the simulation process; monitor the molding process parameter information, and when the parameters meet the press-blow molding parameter settings, determine that the simulation is over. Output the punch position information, glass drop temperature and pressure values ​​in the simulated press-blow molding process to obtain the simulated press-blow molding process data; use the software's post-processing module to perform an impact assessment on the simulated press-blow molding process data. The evaluation results include molding quality, defect prediction and process optimization suggestions. The software can provide detailed evaluation reports and visualization results, such as pressure distribution, velocity distribution, etc.

[0140] Preferably, step S276 includes the following steps:

[0141] Step S2761: determining the maximum stroke and the minimum stroke of the punch position information to obtain the maximum stroke and the minimum stroke of the punch; evaluating the impact of the forming speed based on the maximum stroke and the minimum stroke of the punch to obtain the forming speed impact data;

[0142] Step S2762: determining the maximum temperature and the minimum temperature of the glass drop to obtain the maximum temperature and the minimum temperature of the glass drop; evaluating the impact of molding fluidity based on the maximum temperature and the minimum temperature of the glass drop to obtain molding flow impact data;

[0143] Step S2763: determining the pressure peak value and the pressure valley value of the glass drop pressure value to obtain the glass drop pressure peak value and the glass drop pressure valley value; evaluating the molding pressure influence based on the glass drop pressure peak value and the glass drop pressure valley value to obtain molding pressure influence data;

[0144] Step S2764: The molding speed influence data, the molding flow influence data and the molding pressure influence data are combined for influence evaluation to obtain a molding process evaluation result.

[0145] In an embodiment of the present invention, the punch position time series data is formed by recording the position change of the punch during the molding process. Through data analysis technology, the maximum and minimum values ​​of the punch position are determined from these data, representing the maximum stroke (Max_Stroke) and the minimum stroke (Min_Stroke) of the punch, respectively. In this process, the maximum stroke and the minimum stroke of the punch reflect the range of activity of the punch during the molding process, which has a direct impact on the evaluation of the molding speed. The extreme values ​​of the punch stroke determine the maximum and minimum speed requirements during the molding process, thereby evaluating the impact of the molding speed on product quality and production efficiency. The temperature time series data is formed by recording the temperature change of the glass drop during the molding process. Through data analysis technology, the highest value and the lowest value in the temperature time series data are determined, representing the highest temperature (Max_Temp) and the lowest temperature (Min_Temp) of the glass drop, respectively. The highest temperature and the lowest temperature of the glass drop are important parameters for molding fluidity. The highest temperature affects the fluidity of the glass, while the lowest temperature is related to the hardening speed of the glass. Temperature control is crucial to avoiding product defects such as microcracks and residual stress. The pressure time series data is formed by real-time recording of the pressure change of the glass drop during the molding process. Through data analysis technology, the peak and valley values ​​in the pressure time series data are determined, which represent the pressure peak (Peak_Pressure) and pressure valley (Valley_Pressure) of the glass droplet respectively. The pressure peak and pressure valley are crucial for the impact assessment of the molding pressure. The pressure peak will lead to excessive compression of the glass, while the pressure valley will lead to insufficient molding, affecting the strength and integrity of the product. Through data fusion technology, the molding speed influence data, molding flow influence data and molding pressure influence data are combined for impact assessment to obtain the molding process evaluation results.

[0146] Preferably, step S2762 further includes the following steps:

[0147] Step S27621: Use a K-type thermocouple as a temperature measurement device to measure the temperature of the glass gob with a measurement accuracy of ±0.5 degrees Celsius;

[0148] Step S27622: placing a thermocouple probe at a predetermined temperature measurement point 1 cm from the surface of the glass gob, and bringing the device into full contact with the glass gob to obtain an accurate temperature reading;

[0149] Step S27623: Record the temperature change of the glass drop during the heating process, and use a data recorder to record the temperature value of each temperature measuring point at a frequency of once per second;

[0150] Step S27624: determining the maximum temperature of the glass drop from the recorded temperature data to obtain the maximum temperature of the glass drop; determining the minimum temperature of the glass drop from the recorded temperature data to obtain the minimum temperature of the glass drop;

[0151] Step S27625: inputting the maximum temperature of the glass drop and the minimum temperature of the glass drop into the molding fluidity evaluation software to simulate the fluidity of the glass drop at different temperatures to obtain molding fluidity data;

[0152] Step S27626: continuously monitor the molding fluidity data, and record the flow curves and flow characteristic parameters at different temperatures; perform a blow molding flow impact assessment based on the flow curves and flow characteristic parameters to obtain molding flow impact data.

[0153] In the embodiment of the present invention, a K-type thermocouple is used as a temperature measurement device. The thermocouple is composed of a nickel-chromium alloy (positive electrode) and a nickel-silicon alloy (negative electrode), and has the characteristics of good linearity and high sensitivity. The measurement accuracy is set to ±0.5 degrees Celsius, which meets most industrial detection requirements. The K-type thermocouple probe is placed at a predetermined temperature measurement point 1 cm away from the surface of the glass drop, ensuring that the probe is in full contact with the surface of the glass drop to obtain an accurate temperature reading; a data recorder is used to record the temperature value of each temperature measurement point at a frequency of once per second to capture the temperature change of the glass drop during the heating process; the highest and lowest temperatures of the glass drop are determined from the recorded temperature data to obtain the highest temperature of the glass drop respectively; the highest and lowest temperatures of the glass drop are input into the molding fluidity evaluation software to simulate the fluidity of the glass drop at different temperatures to obtain molding fluidity data; the molding fluidity data is continuously monitored, and the flow curves and flow characteristic parameters at different temperatures are recorded. The flow impact evaluation of the press-blow molding is performed based on the flow curve and flow characteristic parameters to obtain the molding flow impact data.

[0154] Preferably, step S2763 further includes the following steps:

[0155] Step S27631: installing a pressure sensor in a molding mold of a glass drop, and bringing the measuring end of the sensor into direct contact with the glass drop, starting the molding process, and simultaneously turning on a data recorder to record the pressure value measured by the pressure sensor at a frequency of once per second, thereby obtaining a measured pressure value of the glass drop;

[0156] Step S27632: continuously monitoring the measured pressure value of the glass drop, and when the measured pressure value is at the highest pressure value in the molding process, recording the peak value of the glass drop pressure; when the measured pressure value is at the lowest pressure value in the molding process, recording the valley value of the glass drop pressure;

[0157] Step S27633: inputting the peak value of the glass drop pressure and the valley value of the glass drop pressure into the molding pressure impact evaluation software to perform molding pressure simulation, and recording the pressure distribution and drop flow conditions of the simulation process;

[0158] Step S27634: After the software simulation is completed, the molding pressure distribution and droplet flow conditions are output, including the molding time and droplet filling efficiency under different pressure values;

[0159] Step S27635: Perform molding pressure influence analysis based on molding time and droplet filling efficiency to determine the optimal pressure range in the molding process to obtain molding pressure influence data.

[0160] In an embodiment of the present invention, a pressure sensor is installed in a molding mold of a glass droplet to ensure that the measuring end of the sensor is in direct contact with the glass droplet. The molding process is started, and the data recorder is turned on at the same time to record the pressure value measured by the pressure sensor at a frequency of once per second to obtain the measured pressure value of the glass droplet. In the embodiment, the pressure sensor is composed of a pressure transducer, a signal amplifier and a data acquisition system, and can monitor the pressure-time curve, the pressure holding stage and the cooling stage. The measured pressure value of the glass droplet is continuously monitored. When the measured pressure value is at the highest pressure value of the molding process, the peak value (Peak_Pressure) of the glass droplet pressure is recorded; when the measured pressure value is at the lowest pressure value of the molding process, the valley value (Valley_Pressure) of the glass droplet pressure is recorded. In this process, by analyzing the cavity pressure data, any process deviation or defect is identified, such as low cavity pressure indicating insufficient filling, and high cavity pressure indicating excessive pressure holding or overflow. The peak value and valley value of the glass droplet pressure are input into the molding pressure impact evaluation software for molding pressure simulation. The software can simulate the fluidity of the glass droplet under different pressures and record the pressure distribution and droplet flow of the simulation process. The application of MPI / Flow software is involved, which helps process personnel understand the causes of defects in essence by simulating the filling and holding processes. After the software simulation is completed, the molding pressure distribution and droplet flow conditions are output, including the molding time and droplet filling efficiency under different pressure values. These data provide key information for the subsequent molding pressure impact analysis. In Moldex3D software, chip packaging modules can be analyzed, which will help improve product quality. Molding pressure impact analysis is performed based on molding time and droplet filling efficiency to determine the optimal pressure range during the molding process. It involves a comprehensive evaluation of pressure distribution and flow efficiency data to obtain molding pressure impact data (Pressure_Impact_Data). By analyzing cavity pressure data and correlating it with other process data, the process can be optimized and part quality can be improved.

[0161] As an example of the present invention, refer to Figure 3As shown, in this example, step S3 includes:

[0162] Step S31: presetting a standard molding process result for the molding process evaluation result to generate a preset molding process result;

[0163] Step S32: if the molding process evaluation result does not meet the preset molding process result, the computer-aided design software is started to adjust the three-dimensional initial model of the equipment;

[0164] Step S33: identifying the uneven wall thickness region of the equipment three-dimensional initial model according to the molding speed influence data to obtain the molding wall thickness uneven region; adjusting the molding wall thickness uneven region to 2-4 mm to obtain the wall thickness adjustment parameter;

[0165] Step S34: identifying the concave-convex area of ​​the surface of the equipment three-dimensional initial model according to the molding flow influence data to obtain the concave-convex area of ​​the molding surface; performing surface smoothing optimization on the concave-convex area of ​​the molding surface to obtain the surface optimization parameters;

[0166] Step S35: identifying the abnormal air pressure area of ​​the airway on the three-dimensional initial model of the equipment according to the molding pressure influence data to obtain the abnormal air pressure area of ​​the molding airway; adjusting the airway diameter of the abnormal air pressure area of ​​the molding airway to 3-6 mm to obtain the airway diameter adjustment parameter;

[0167] Step S36: Optimizing the molding structure design parameters of the three-dimensional initial model of the equipment based on the wall thickness adjustment parameters, the curved surface optimization parameters, and the airway diameter adjustment parameters to obtain a three-dimensional optimized model of the equipment.

[0168] In the embodiment of the present invention, the standard results of the molding process are set according to historical data and industry standards, including the standard range of punch stroke, glass drop temperature and pressure. These preset values ​​are used as the basis for evaluation and are used for subsequent model adjustment and optimization. Specifically, injection pressure is one of the key parameters in injection molding, and is usually set to a pressure that can ensure that the plastic melt completely fills the mold cavity. The preset value is generally 70% to 90% of the maximum injection pressure, and the specific value depends on the fluidity of the plastic and the complexity of the mold; the holding time refers to the time to maintain the pressure in the cavity to ensure the stability of the product size after the injection is completed in order to compensate for the shrinkage of the material. The preset value is generally set according to the thickness of the product and the characteristics of the material. For example, for a plastic part with a wall thickness of 2-3mm, the holding time is set between 5 and 15 seconds; the cooling time refers to the time required for the plastic to cool in the mold to a temperature that can be safely demolded. The preset value depends on the thermal properties of the plastic and the geometric shape of the product. For common thermoplastics, the cooling time ranges from tens of seconds to several minutes. When the evaluation result shows that the molding process does not meet the preset standard, the computer-aided design software is started to adjust the three-dimensional initial model of the equipment. For example, if the punch stroke exceeds the preset range, the punch design parameters are adjusted to meet the standard stroke. Using the molding speed influence data, the areas with uneven wall thickness in the equipment 3D model are identified. By analyzing the speed changes during the molding process, the areas with uneven wall thickness are determined, and the wall thickness of these areas is adjusted to the standard range of 2-4 mm to generate wall thickness adjustment parameters. Using the molding flow influence data, the areas with concave and convex surfaces in the equipment 3D model are identified. These areas are surface smoothed and optimized to reduce the flow resistance during the molding process, and the surface optimization parameters are generated. Using the molding pressure influence data, the areas with abnormal airway pressure in the equipment 3D model are identified. The airway diameters in these areas are adjusted to the standard range of 3-6 mm to generate airway diameter adjustment parameters. The three-dimensional initial model of the equipment is comprehensively optimized by combining the wall thickness adjustment parameters, surface optimization parameters, and airway diameter adjustment parameters. Involves fine-tuning the geometric structure of the model to ensure that the speed, fluidity, and pressure during the molding process are all in the optimal state, thereby obtaining a three-dimensional optimized model of the equipment.

[0169] Preferably, step S34 further includes the following steps:

[0170] Step S341: enhancing the geometric features of the molding surface of the equipment three-dimensional initial model according to the molding flow influence data to obtain molding geometric enhancement features;

[0171] Step S342: scanning the molding geometric enhancement features point by point, and marking the position points of the concave and convex regions; connecting the position points of the concave and convex regions to obtain the concave and convex curved surface regions;

[0172] Step S343: performing surface parameter detection on the concave-convex surface area to obtain concave-convex surface parameters;

[0173] Step S344: dynamically adjusting the parameters of the concave-convex surface area based on the concave-convex surface parameters, performing local optimization on each concave-convex area, and obtaining surface feature adjustment data by adjusting the curvature and tension of the surface;

[0174] Step S345: Input the surface feature adjustment data into the three-dimensional initial model of the device for iterative geometric correction. Through multiple rounds of iterations, the surface parameters are continuously optimized to obtain the surface optimization parameters.

[0175] In an embodiment of the present invention, a three-dimensional mesh model repair algorithm is used to enhance the geometric features of the molding surface of the three-dimensional initial model of the device. The algorithm identifies and enhances the geometric features in the model through geometric-topological iterative optimization to obtain the molding geometric enhancement features. The molding geometric enhancement features are scanned point by point, and the point cloud data is edited and processed using point cloud data processing software such as GeomagicWrap to mark the position points of the concave and convex areas. Subsequently, these concave and convex area position points are connected to obtain the concave and convex surface area. The concave and convex surface area is tested for surface parameters, including the calculation of curvature and tension. The surface parameters are tested by the geometric iteration method to obtain the concave and convex surface parameters. Based on the concave and convex surface parameters, the concave and convex surface area is dynamically adjusted. By adjusting the curvature and tension of the surface, local optimization is performed to obtain the surface feature adjustment data. This process involves intelligent parameter adjustment, such as a hyperparameter adjustment algorithm, to achieve intelligent optimization of parameters. The surface feature adjustment data is input into the three-dimensional initial model of the device for iterative geometric correction. Through multiple rounds of iteration, the surface parameters are continuously optimized to finally obtain the surface optimization parameters. The geometric iteration method and its application are used to achieve precise control and optimization of the three-dimensional model through the progressive iterative approximation (PIA) property.

[0176] Preferably, step S4 comprises the following steps:

[0177] Step S41: exporting the equipment three-dimensional optimization model from the computer-aided design software and converting the model format to obtain the equipment three-dimensional optimization model file, and transmitting the equipment three-dimensional optimization model file to the intelligent pressure-blow control device;

[0178] Step S42: extracting model pressure-blowing parameters from the three-dimensional optimization model of the equipment to obtain the model pressure-blowing parameters, and setting the blowing parameters of the intelligent pressure-blowing control device according to the model pressure-blowing parameters;

[0179] Step S43: starting the intelligent blow molding control device to monitor the actual blow molding bottle weight;

[0180] Step S44: During the pressure-blowing monitoring process, the weight data of the small-mouth bottle is recorded every 10 seconds, and the bottle weight data is recorded and stored to obtain actual pressure-blowing feedback data;

[0181] Step S45: Feedback adjustment is performed on the equipment three-dimensional optimization model according to the actual press-blow feedback data to generate an intelligent press-blow three-dimensional model.

[0182] In an embodiment of the present invention, the computer-aided design software is started to open the three-dimensional optimization model file of the device. Select the "Export" option in the "File" menu and select the target format, such as STL or OBJ, which are widely used in the fields of 3D printing and CNC machining. Confirm the export settings, including unit conversion (such as from millimeters to inches) and precision settings (such as the density of the surface grid). Click "OK" to complete the export and save the three-dimensional optimization model of the device as a file in the specified format. In the computer-aided design software, the wall thickness of the three-dimensional optimization model of the device is measured using a measuring tool, and the wall thickness values ​​of each area are recorded. Using the analysis tools of the software, the surface shape of the model is analyzed to extract the geometric parameters of the surface, such as curvature and inclination angle. The airway in the model is dimensional analyzed to record the diameter, length and distribution of the airway. The extracted pressure-blowing parameters are input into the control software of the intelligent pressure-blowing control device. In the control software, the wall thickness control parameters are set to ensure the consistency of the wall thickness during the blowing process. Set the surface shape control parameters to adjust the air pressure and speed during the blowing process to match the geometric characteristics of the model. Set the airway control parameters, including the opening time and pressure of the airway, to control the gas flow during the blowing process. Start the intelligent pressure-blowing control device and start the pressure-blowing process. The built-in weighing sensor of the control device starts working to monitor the weight changes of the small-mouth bottle in real time. The control software is set to automatically record the weight data every 10 seconds and save the data in the built-in memory. After the pressure-blowing process is completed, export the bottle weight data from the memory of the control device. Analyze the bottle weight data and compare it with the preset weight range to determine whether there is a deviation. If the bottle weight data exceeds the preset range, adjust the parameters of the equipment's three-dimensional optimization model, such as wall thickness, surface shape, and airway size, according to the deviation. Re-export the adjusted three-dimensional optimization model of the equipment, and repeat steps S42 to S45 until the bottle weight data meets the preset range.

[0183] Preferably, step S45 includes the following steps:

[0184] Step S451: extracting the small-mouth bottle weight and time data of the actual blow molding feedback data to obtain bottle weight-time data; performing bottle weight change analysis on the bottle weight-time data to generate bottle weight change data;

[0185] Step S452: performing weight distribution range identification on the bottle weight change data to obtain the weight distribution range of the small-mouth bottle; performing average weight calculation based on the weight distribution range to generate an average weight value of the small-mouth bottle;

[0186] Step S453: determining the adjustment area of ​​the equipment three-dimensional optimization model based on the weight distribution range of the small-mouth bottles and the average weight value of the small-mouth bottles to obtain the model feedback adjustment area;

[0187] Step S454: determining the model parameters to be adjusted in the model feedback adjustment area to obtain the model parameters to be adjusted; and performing intelligent parameter adjustment on the model parameters to be adjusted to generate an intelligent pressure-blowing three-dimensional model.

[0188] In an embodiment of the present invention, the weight data of the small-mouth bottle during the pressure-blowing process is exported from the data storage system of the intelligent pressure-blowing control device, and the data is stored in the form of a timestamp and a corresponding weight value. The exported data is parsed using data processing software, such as the automation control software Beckhoff TwinCAT, to extract the bottle weight data corresponding to each time point to form a bottle weight-time data set; statistical analysis methods are applied, such as calculating the standard deviation and mean of the data, to determine the distribution range of the weight data. By identifying the maximum and minimum values ​​of the bottle weight data, the weight distribution range of the small-mouth bottle, that is, the upper and lower limits of the weight, is determined; the average weight value of the small-mouth bottle is calculated using the average mass calculation formula, that is, the sum of all values ​​divided by the number of values. The calculated average weight value is compared with the weight distribution range to evaluate the consistency and uniformity of the weight; based on the weight distribution range and the average weight value of the small-mouth bottle, the area that needs to be adjusted in the three-dimensional optimization model of the equipment, that is, the model feedback adjustment area, is determined. Using the 3D mesh model repair algorithm, the uneven wall thickness areas, concave and convex areas of the curved surface and the abnormal airway pressure areas in the model are identified and marked as parameters to be adjusted; hyperparameter adjustment algorithms such as grid search, random search and Bayesian optimization are applied to intelligently adjust the parameters to be adjusted. The adjusted parameters are input into the computer-aided design software to modify the 3D optimization model of the equipment and generate a new intelligent 3D press-blow model. Through iterative optimization, the press-blow parameters of the new model are ensured to be more accurate to improve the production quality and consistency of small-mouth bottles.

[0189] Preferably, step S453 includes the following steps:

[0190] Step S4531: performing data normalization processing on the weight distribution range of the small-mouth bottle to obtain weight range normalized data; sorting the data on the weight distribution range, and extracting the upper and lower limits of the weight distribution to obtain the upper and lower limits of the weight distribution;

[0191] Step S4532: verify the average weight value of the small-mouth bottle to obtain average weight value verification data; identify the weight characteristics of the small-mouth bottle based on the upper and lower limits of the weight distribution and obtain the weight distribution characteristics of the small-mouth bottle;

[0192] Step S4533: comparing the weight distribution characteristics of the small-mouth bottle with the three-dimensional optimization model of the equipment, identifying the area in the model related to the weight distribution, so as to obtain the model weight distribution related area;

[0193] Step S4534: marking the model area related to the model weight distribution to obtain the model feedback adjustment area.

[0194] In the embodiment of the present invention, the data of the weight distribution range of the small-mouth bottle is normalized, and the original weight data is converted into a data set in the interval [0,1]. The specific operation is to first determine the maximum and minimum values ​​of the weight data, and then apply the linear normalization formula:

[0195]

[0196] After obtaining the normalized data of the weight range, the normalized data is sorted and sorted by the quick sort algorithm, and the first and last values ​​after sorting are extracted as the upper and lower limits of the weight distribution. The average weight value of the small-mouth bottle is verified, and the accuracy of the average weight value is verified by repeated measurements and statistical methods. The specific operation is to collect a certain number of small-mouth bottle weight data, calculate the sample mean, and compare it with the overall mean. The t-test is used to determine whether there is a significant difference between the sample mean and the overall mean, and the average weight value verification data is obtained. Based on the average weight value verification data, the upper and lower limits of the weight distribution are feature identified, and the weight data is divided into several weight intervals by the cluster analysis method to obtain the weight distribution characteristics of the small-mouth bottle. The weight distribution characteristics of the small-mouth bottle are compared with the three-dimensional optimization model of the equipment, and the areas related to the weight distribution in the model are identified by the geometric analysis method. The specific operation is to match the weight distribution characteristics of the small-mouth bottle with the geometric parameters of the model, obtain the precise geometric data of the model by three-dimensional scanning technology, and then apply the finite element analysis method to simulate the small-mouth bottle molding process, identify the areas in the model where the weight distribution does not match the actual weight characteristics, and obtain the model weight distribution related areas. Mark the areas related to the weight distribution of the model, and use graphic recognition technology to mark the areas that need to be adjusted on the 3D optimization model of the equipment. The specific operation is to use image processing software to analyze the 3D image of the model, and mark the areas related to the weight distribution on the model by color coding or labeling. Through these marks, the parts that need to be adjusted in the model can be intuitively identified, and the model feedback adjustment area can be obtained.

[0197] In this specification, an intelligent three-dimensional model design system based on a small-mouth pressure-blow molding device is provided, which is used to execute the above-mentioned intelligent three-dimensional model design method based on a small-mouth pressure-blow molding device. The intelligent three-dimensional model design system based on a small-mouth pressure-blow molding device includes:

[0198] The equipment parameter acquisition module uses intelligent sensors to collect the parameters of the small-mouth pressure-blow molding equipment and records the initial equipment parameter information; based on the initial equipment parameter information, a three-dimensional initial model of the equipment is established using computer-aided design software;

[0199] The press-blow molding process simulation module is used to obtain the glass drop characteristic data and input the glass drop characteristic data into the 3D initial model of the equipment, simulate the press-blow molding process by using simulation software, and conduct an impact assessment on the simulated press-blow molding process to obtain the molding process assessment results;

[0200] The equipment 3D model parameter optimization module is used to adjust the initial equipment 3D geometric model according to the molding process evaluation result. If the molding process evaluation result does not meet the preset molding process result, the molding structure design parameter optimization of the equipment 3D initial model is performed to obtain the equipment 3D optimized model.

[0201] The intelligent press-blow three-dimensional model integration test module is used to integrate the equipment's three-dimensional optimization model into the intelligent press-blow control device to monitor the actual press-blow bottle weight and obtain actual press-blow feedback data; the equipment's three-dimensional optimization model is feedback-adjusted according to the actual press-blow feedback data to generate an intelligent press-blow three-dimensional model.

[0202] The present invention uses an equipment parameter acquisition module and an intelligent sensor to collect parameters and record initial equipment parameter information of a small-mouth pressure-blow molding equipment, thereby ensuring accurate data capture and providing an accurate reference basis for subsequent model construction and optimization. The three-dimensional initial model created using computer-aided design software not only improves the intuitiveness and interactivity of the design work, but also enhances the flexibility and accuracy in the design process, thereby optimizing the design process. Through the pressure-blow molding process simulation module, the characteristic data of the glass droplet is integrated into the three-dimensional initial model of the equipment, so that the simulation process can more realistically reflect the actual production environment and enhance the credibility of the simulation results. Through the simulation software, the pressure-blow molding process is simulated and the impact is evaluated, which can identify and solve potential molding problems in advance, such as uneven material distribution or product defects, and provide scientific guidance for the optimization of the production process. Through the equipment three-dimensional model parameter optimization module, the three-dimensional geometric model of the equipment is adjusted according to the molding process evaluation results. If the evaluation results do not meet expectations, the structural design parameters of the equipment model are optimized to form a three-dimensional optimization model of the equipment. The equipment design is optimized to make it more adaptable to production needs, improve molding efficiency and product quality, reduce material loss, reduce manufacturing costs, and enhance the overall consistency of the product. Through the intelligent pressure-blowing three-dimensional model integrated test module, the equipment three-dimensional optimization model is combined with the intelligent pressure-blowing control device to monitor the actual pressure-blowing bottle weight, obtain actual pressure-blowing feedback data, achieve seamless connection between the model and production practice, and improve the accuracy of equipment control. The equipment model is adjusted according to the actual feedback data to generate an intelligent pressure-blowing three-dimensional model, which further improves the intelligence level of the equipment, ensures the stability and controllability of the pressure-blowing molding process, improves product consistency, reduces production costs, and optimizes energy consumption. Therefore, the present invention uses data processing technology, three-dimensional modeling technology and automatic control technology to perform correlation evaluation on various parameters of small-mouth pressure-blowing molding equipment, and constructs an intelligent three-dimensional model of small-mouth pressure-blowing molding equipment; improves the accuracy of the control operation of small-mouth pressure-blowing molding equipment, improves the efficiency of small-mouth pressure-blowing molding, and makes the produced glass containers maintain specification consistency.

[0203] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0204] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. An intelligent three-dimensional model design method based on small-mouth pressure-blow molding equipment, characterized in that: Applied to small-mouth pressure-blow molding equipment, the small-mouth pressure-blow molding equipment is equipped with an intelligent sensor measuring device and an intelligent pressure-blow control device, and the intelligent three-dimensional model design method based on the small-mouth pressure-blow molding equipment includes the following steps: Step S1: using intelligent sensors to collect parameters of small-mouth pressure-blow molding equipment and record initial equipment parameter information; using computer-aided design software to establish a three-dimensional initial model of the equipment based on the initial equipment parameter information; Step S2: Obtain glass droplet characteristic data and input the glass droplet characteristic data into the three-dimensional initial model of the equipment, simulate the press-blow molding process using simulation software, and perform an impact assessment on the simulated press-blow molding process to obtain a molding process assessment result; wherein the simulated press-blow molding process includes punch position information, glass droplet temperature, and glass droplet pressure value, and the impact assessment is specifically as follows: The maximum stroke and the minimum stroke of the punch position information are determined to obtain the maximum stroke and the minimum stroke of the punch; the forming speed influence is evaluated based on the maximum stroke and the minimum stroke of the punch to obtain the forming speed influence data; Determine the maximum temperature and the minimum temperature of the glass drop to obtain the maximum temperature and the minimum temperature of the glass drop; evaluate the molding fluidity influence based on the maximum temperature and the minimum temperature of the glass drop to obtain molding flow influence data; Determine the pressure peak and the pressure valley of the glass drop pressure value to obtain the glass drop pressure peak and the glass drop pressure valley; evaluate the molding pressure influence based on the glass drop pressure peak and the glass drop pressure valley to obtain molding pressure influence data; The molding speed influence data, molding flow influence data and molding pressure influence data are combined for influence evaluation to obtain the molding process evaluation result; Step S3: adjusting the initial equipment 3D geometric model according to the molding process evaluation result; if the molding process evaluation result does not meet the preset molding process result, optimizing the molding structure design parameters of the equipment 3D initial model to obtain the equipment 3D optimized model; Step S4: Integrate the three-dimensional optimization model of the equipment into the intelligent pressure-blowing control device to monitor the actual pressure-blowing bottle weight and obtain actual pressure-blowing feedback data; perform feedback adjustment on the three-dimensional optimization model of the equipment according to the actual pressure-blowing feedback data to generate an intelligent pressure-blowing three-dimensional model; Step S4 includes the following steps: Step S41: exporting the equipment three-dimensional optimization model from the computer-aided design software and converting the model format to obtain the equipment three-dimensional optimization model file, and transmitting the equipment three-dimensional optimization model file to the intelligent pressure-blow control device; Step S42: extracting model pressure-blowing parameters from the three-dimensional optimization model of the equipment to obtain the model pressure-blowing parameters, and setting the blowing parameters of the intelligent pressure-blowing control device according to the model pressure-blowing parameters; Step S43: starting the intelligent blow molding control device to monitor the actual blow molding bottle weight; Step S44: During the pressure-blowing monitoring process, the weight data of the small-mouth bottle is recorded every 10 seconds, and the bottle weight data is recorded and stored to obtain actual pressure-blowing feedback data; Step S45: Feedback adjustment is performed on the equipment three-dimensional optimization model according to the actual press-blow feedback data to generate an intelligent press-blow three-dimensional model.

2. The intelligent three-dimensional model design method based on small-mouth pressure-blow molding equipment according to claim 1 is characterized in that: The intelligent sensor measuring device includes an angle sensor, a displacement sensor, a laser scanner and a contact measuring instrument. Step S1 includes the following steps: Step S11: using a laser scanner to scan the external contour of the small-mouth pressure-blow molding equipment to obtain the external contour data of the equipment; Step S12: using an angle sensor to measure the movable angle of the connection joint of the small-mouth pressure-blow molding equipment, and using a displacement sensor to measure the telescopic length of the connection part of the small-mouth pressure-blow molding equipment to obtain the movable range of the connection part; Step S13: using a contact measuring instrument to measure the diameter, length and width of the mold punch of the small-mouth pressure-blow molding equipment to obtain the mold punch size parameters; Step S14: integrating the equipment external contour data, the movable range of the connection part and the die punch size parameters to obtain initial equipment parameter information; Step S15: Using computer-aided design software to build a three-dimensional initial model of the device based on the initial device parameter information.

3. The intelligent three-dimensional model design method based on small-mouth pressure-blow molding equipment according to claim 2 is characterized in that: Step S15 includes the following steps: Step S151: Start computer-aided design software and create a three-dimensional model project file of the equipment; Step S152: inputting the equipment external contour data into the equipment three-dimensional model project file, and drawing the equipment external contour diagram; Step S153: parameterizing the device connection parts of the device external contour diagram according to the movable range of the connection parts to obtain a device parameterized contour diagram; Step S154: perform two-dimensional dimensioning on the equipment parameterized outline drawing based on the die punch size parameters to obtain two-dimensional geometric dimensioning data; perform three-dimensional structural modeling on the two-dimensional geometric dimensioning data to generate a three-dimensional initial model of the equipment.

4. The intelligent three-dimensional model design method based on small-mouth pressure-blow molding equipment according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: obtaining a glass drop sample; using an electronic scale to measure the mass of a single glass drop sample, repeating the measurement 10-20 times, and recording each measurement value; averaging each measurement value to obtain an average mass of the glass drop; Step S22: using a measuring cylinder and water displacement method to measure the volume of the glass drop sample, the glass drop is completely immersed in a measuring cylinder filled with a known volume of water, and the volume of the water level rise is recorded to obtain the volume of the glass drop; Step S23: Calculating the density of the glass drop according to the average mass of the glass drop and the volume of the glass drop to obtain a density value of the glass drop; Step S24: placing the glass drop sample into a high temperature furnace, heating it at a rate of 1-5 degrees Celsius per minute, recording the temperature at which the glass drop changes from solid to liquid, and obtaining the melting point value of the glass drop; Step S25: using a universal material testing machine to perform a tensile test on the glass drop sample, and recording the stress and strain data of the glass drop during the tensile process to obtain the elastic modulus of the glass drop; Step S26: Integrate the glass drop density value, the glass drop melting point value and the glass drop elastic modulus to obtain glass drop characteristic data; Step S27: inputting the glass drop characteristic data into the three-dimensional initial model of the equipment, simulating the press-blow molding process using simulation software, and performing an impact evaluation on the simulated press-blow molding process to obtain a molding process evaluation result.

5. The intelligent three-dimensional model design method based on small-mouth pressure-blow molding equipment according to claim 4 is characterized in that: Step S27 includes the following steps: Step S271: start the simulation software and create a small-mouth pressure-blow molding simulation project; import the 3D initial model of the equipment into the small-mouth pressure-blow molding simulation project, and input the glass drop characteristic data into the 3D initial model of the equipment; Step S272: setting the compression blow molding parameters in the simulation software, wherein the molding temperature is set to 800-1000 degrees Celsius, the molding pressure is set to 50-150 MPa, and the molding time is set to 120-150 seconds; Step S273: setting glass drop injection parameters in the simulation software, wherein the injection speed is 10-15 mm / s and the injection amount is 100-150 g; Step S274: Start the simulation software to simulate the press-blow molding process, continuously monitor the changing characteristic values ​​of the glass drop during the molding process, and record the punch position information, glass drop temperature and glass drop pressure value at the corresponding time point every 10 seconds as a time unit; Step S275: continuously monitoring the molding process parameter information; when the molding process parameter information is a press-blow molding parameter, determining that the small-mouth press-blow molding simulation is finished; outputting the punch position information, the glass drop temperature and the glass drop pressure value in the simulated press-blow molding process to obtain simulated press-blow molding process data; Step S276: Perform an impact assessment on the simulated press-blow molding process data to obtain a molding process assessment result.

6. The intelligent three-dimensional model design method based on small-mouth pressure-blow molding equipment according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: presetting a standard molding process result for the molding process evaluation result to generate a preset molding process result; Step S32: if the molding process evaluation result does not meet the preset molding process result, the computer-aided design software is started to adjust the three-dimensional initial model of the equipment; Step S33: identifying the uneven wall thickness region of the equipment three-dimensional initial model according to the molding speed influence data to obtain the molding wall thickness uneven region; adjusting the molding wall thickness uneven region to 2-4 mm to obtain the wall thickness adjustment parameter; Step S34: identifying the concave-convex area of ​​the surface of the equipment three-dimensional initial model according to the molding flow influence data to obtain the concave-convex area of ​​the molding surface; performing surface smoothing optimization on the concave-convex area of ​​the molding surface to obtain the surface optimization parameters; Step S35: identifying the abnormal air pressure area of ​​the airway on the three-dimensional initial model of the equipment according to the molding pressure influence data to obtain the abnormal air pressure area of ​​the molding airway; adjusting the airway diameter of the abnormal air pressure area of ​​the molding airway to 3-6 mm to obtain the airway diameter adjustment parameter; Step S36: Optimize the molding structure design parameters of the three-dimensional initial model of the equipment based on the wall thickness adjustment parameters, the surface optimization parameters and the airway diameter adjustment parameters to obtain a three-dimensional optimized model of the equipment.

7. The intelligent three-dimensional model design method based on small-mouth pressure-blow molding equipment according to claim 1 is characterized in that: Step S45 includes the following steps: Step S451: extracting the small-mouth bottle weight and time data of the actual blow molding feedback data to obtain bottle weight-time data; performing bottle weight change analysis on the bottle weight-time data to generate bottle weight change data; Step S452: performing weight distribution range identification on the bottle weight change data to obtain the weight distribution range of the small-mouth bottle; performing average weight calculation based on the weight distribution range to generate an average weight value of the small-mouth bottle; Step S453: determining the adjustment area of ​​the equipment three-dimensional optimization model based on the weight distribution range of the small-mouth bottles and the average weight value of the small-mouth bottles to obtain the model feedback adjustment area; Step S454: determining the model parameters to be adjusted in the model feedback adjustment area to obtain the model parameters to be adjusted; and performing intelligent parameter adjustment on the model parameters to be adjusted to generate an intelligent pressure-blowing three-dimensional model.

8. An intelligent three-dimensional model design system based on small-mouth pressure-blow molding equipment, characterized in that: Used to execute the intelligent three-dimensional model design method based on small-mouth pressure-blow molding equipment according to claim 1, the intelligent three-dimensional model design system based on small-mouth pressure-blow molding equipment comprises: The equipment parameter acquisition module uses intelligent sensors to collect the parameters of the small-mouth pressure-blow molding equipment and records the initial equipment parameter information; based on the initial equipment parameter information, a three-dimensional initial model of the equipment is established using computer-aided design software; The press-blow molding process simulation module is used to obtain the glass drop characteristic data and input the glass drop characteristic data into the 3D initial model of the equipment, simulate the press-blow molding process by using simulation software, and conduct an impact assessment on the simulated press-blow molding process to obtain the molding process assessment results; The equipment 3D model parameter optimization module is used to adjust the initial equipment 3D geometric model according to the molding process evaluation result. If the molding process evaluation result does not meet the preset molding process result, the molding structure design parameter optimization of the equipment 3D initial model is performed to obtain the equipment 3D optimized model. The intelligent press-blow three-dimensional model integration test module is used to integrate the equipment's three-dimensional optimization model into the intelligent press-blow control device to monitor the actual press-blow bottle weight and obtain actual press-blow feedback data; the equipment's three-dimensional optimization model is feedback-adjusted according to the actual press-blow feedback data to generate an intelligent press-blow three-dimensional model.

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