Forming process and system of high-precision ejection hook

Abstract

The invention relates to the technical field of information, in particular to a forming process and system of a high-precision injection hook in the technical field of injection hook forming. The forming process includes the steps that computer aided design and finite element analysis are applied, the forming process of the injection hook in a mold is simulated, the mold structure is optimized, and the mold testing frequency is reduced; a high-precision mold is integrated with a cooling channel of a preset ejection hook arrow-shaped structure, a temperature sensor is arranged in the cooling channel, the cooling state is monitored in real time, cooling parameters are adjusted based on monitoring data, and precise structure forming is achieved. And the machine vision system detects the integrity and direction consistency of the arrow-shaped structure of the molded ejection hook on line, identifies unqualified products, automatically removes the unqualified products, records defect types, and sends defect type data and corresponding product batch information to the production data acquisition module.

Description

Technical Field

[0001] The invention relates to the field of information technology, specifically to the field of injection hook molding technology, and in particular to a molding process and system for a high-precision injection hook. Background Art

[0002] In the mass production and manufacturing scenario of injection hooks, the core goal is to achieve efficient and rapid snap-fitting of the injection hooks and the grid. To achieve this goal, the surface of the injection hook is designed to be arrow-shaped to facilitate quick access to the grid. However, in the actual production process, there is a key technical contradiction: how to ensure the precise molding of the arrow-shaped structure of the injection hook under the requirement of high production efficiency, as well as the directional consistency and uniform distribution of the snap-fitting force of multiple hooks when they are staggered. Specifically, the improvement of production efficiency usually depends on the rapid opening and closing of the mold and the rapid demolding of the product in the injection molding process, which conflicts with the fine molding of the arrow-shaped structure. The fine arrow-shaped structure requires longer cooling time and more precise mold design to avoid deformation or damage during rapid demolding.

[0003] In addition, when multiple arrow-shaped injection hooks need to be arranged in a specific staggered pattern, how to ensure that each hook maintains a precise direction and position during the injection molding process to ensure that the snap-fitting force with the grid in different directions is evenly distributed is another technical problem that needs to be solved urgently. Although this staggered arrangement design can theoretically improve the firmness and snap-fitting efficiency of the overall structure, in actual production, it places extremely high demands on the accuracy of the mold and the control of the injection molding process. How to achieve the precision molding of the arrow-shaped structure of the injection hook and the consistency of the staggered arrangement while ensuring production efficiency has become a key technical issue. Summary of the invention

[0004] The present invention provides a molding process for a high-precision injection hook, the process comprising the following steps:

[0005] Step S101, applying computer-aided design and finite element analysis to simulate the molding process of the injection hook in the mold, optimize the mold structure, and reduce the number of mold trials;

[0006] Step S102, the high-precision mold is integrated with a preset cooling channel for injecting a hook-arrow-shaped structure, a temperature sensor is set in the cooling channel, the cooling state is monitored in real time, and the cooling parameters are adjusted based on the monitoring data to achieve precise structural molding;

[0007] Step S103, each cavity of the multi-cavity mold is configured with independent injection parameter control, the central control system adjusts the injection pressure and temperature of each cavity in real time, and the multi-stage injection speed control module intervenes. In the initial stage, low-speed injection is adopted, and the injection speed is gradually increased in the subsequent stages to balance production efficiency and structural accuracy;

[0008] Step S104, the mold integrates multiple positioning pins and guide mechanisms to ensure the position and direction of the injection hook in the mold, ensure the consistency of the staggered arrangement, design a special demoulding mechanism, adopt oblique demoulding, configure a demoulding force sensor, monitor the demoulding force in real time, and ensure a smooth demoulding process;

[0009] Step S105, the machine vision system detects the integrity and direction consistency of the arrow-shaped structure of the injection hook after molding online, identifies unqualified products and automatically removes them and records the defect type, and sends the defect type data and the corresponding product batch information to the production data acquisition module;

[0010] Step S106, the production data acquisition module records the injection molding parameters, cooling time, demoulding force data and defect data uploaded by the machine vision system in real time, and the data mining algorithm analyzes the influence of various parameters and defect data on the molding quality of the injection hook. The preset quality standards include the integrity, directional consistency and dimensional accuracy of the injection hook. The adaptive control algorithm dynamically adjusts the injection molding process parameters according to the real-time production data, defect data and preset quality standards to maintain the production efficiency at the optimal state.

[0011] The present invention provides a high-precision injection hook molding system, which is used to implement a high-precision injection hook molding process, and the system includes:

[0012] Molding simulation and optimization module, which is used to apply computer-aided design and finite element analysis to simulate the molding process of the injection hook in the mold, optimize the mold structure, and reduce the number of mold trials;

[0013] The cooling monitoring and control module is used for the cooling channel of the preset injection hook-arrow-shaped structure integrated with the high-precision mold. The temperature sensor is set in the cooling channel to monitor the cooling status in real time. Based on the monitoring data, the cooling parameters are adjusted to achieve precise structural molding;

[0014] The injection molding parameter control module is used to configure independent injection molding parameter control for each cavity of a multi-cavity mold. The central control system adjusts the injection molding pressure and temperature of each cavity in real time. The multi-stage injection molding speed control module intervenes. The initial stage uses low-speed injection molding, and the injection molding speed is gradually increased in the subsequent stages to balance production efficiency and structural accuracy.

[0015] Positioning and demoulding module, which is used to integrate multiple positioning pins and guide mechanisms in the mold to ensure the position and direction of the injection hook in the mold, ensure the consistency of the staggered arrangement, design a special demoulding mechanism, adopt oblique demoulding, configure a demoulding force sensor, monitor the demoulding force in real time, and ensure a smooth demoulding process;

[0016] Visual inspection module, used for online inspection of the integrity and directional consistency of the arrow-shaped structure of the injection hook after molding by the machine vision system, automatically rejecting unqualified products and recording the defect type, and sending the defect type data and the corresponding product batch information to the production data acquisition module;

[0017] The data acquisition and analysis module is used for the production data acquisition module to record the injection molding parameters, cooling time, demoulding force data and defect data uploaded by the machine vision system in real time. The data mining algorithm analyzes the influence of various parameters and defect data on the molding quality of the injection hook. The preset quality standards include the integrity, directional consistency and dimensional accuracy of the injection hook. The adaptive control algorithm dynamically adjusts the injection molding process parameters according to the real-time production data, defect data and preset quality standards to maintain the production efficiency at the optimal state.

[0018] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0019] The present invention discloses a molding process and system for high-precision injection hooks. The system optimizes the mold structure through computer-aided design and finite element analysis, integrates preset cooling channels and temperature sensors, and realizes real-time monitoring and adjustment. The multi-cavity mold adopts independent parameter control and multi-stage injection speed control to balance production efficiency and precision. The mold design includes positioning pins, guide mechanisms and special demoulding mechanisms to ensure the consistency of the position and direction of the injection hook. The machine vision system detects the quality of the finished product online and automatically removes unqualified products. The production data acquisition module records various parameters and defect data, analyzes the influencing factors through data mining algorithms, and dynamically adjusts the process parameters through adaptive control algorithms. The present invention realizes high-precision and high-efficiency production of injection hooks by integrating advanced design, monitoring and control technologies, while ensuring the consistency and traceability of product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 The present invention is a flow chart of a molding process of a high-precision injection hook.

[0021] Figure 2 The figure is a schematic diagram of the framework of a molding system for a high-precision injection hook according to the present invention. DETAILED DESCRIPTION

[0022] The following will describe the technical solutions in the embodiments of the present invention in detail in conjunction with the accompanying drawings in the embodiments of the present invention. The described embodiments are only part of the embodiments of the present invention.

[0023] like Figure 1-Figure 2 The present embodiment provides a high-precision injection hook molding process, the process comprising the following steps:

[0024] Step S101, applying computer-aided design and finite element analysis to simulate the molding process of the injection hook in the mold, optimize the mold structure, and reduce the number of mold trials.

[0025] According to the three-dimensional model of the injection hook, a digital model of the injection hook is established by computer-aided design software, and the digital model is meshed to obtain a finite element analysis model; according to the material properties and process parameters of the injection hook, the boundary conditions and load conditions of the finite element analysis model are set, and the material constitutive model and rheological model are selected; the forming process of the injection hook is numerically simulated by finite element analysis software to obtain the field distribution information of stress, strain, temperature and pressure in the flow, filling, cooling and solidification processes of the injection hook in the mold; according to the numerical simulation results, the injection hook in the forming process is analyzed The defect generation mechanism is determined to determine the type and severity of the defect; the mold structure is optimized by using an optimization algorithm, the layout and size parameters of the pouring system, cooling system and exhaust system of the mold structure are adjusted, and an optimized mold design scheme is obtained; according to the optimized mold design scheme, the mold is processed and manufactured, and the mold structure is fine-tuned during the mold trial until the molding quality of the injection hook meets the requirements; the optimized mold design scheme is applied to mass production, and the quality data in the production process is collected and analyzed to realize online monitoring and quality control of the injection hook molding process.

[0026] For example, the establishment of a digital model of the injection hook is a key step in product design. Taking the injection hook of a certain automotive interior as an example, the three-dimensional model can be constructed using SolidWorks software and imported into ANSYS Workbench for meshing. The quality of the mesh directly affects the analysis accuracy. Tetrahedral units are usually used, with a unit size of about 0.5 mm and a total number of meshes of about 500,000. Material properties and process parameter settings are crucial to the simulation results. Assume that the injection hook material is PA66, with a melting point of 220°C, a mold temperature of 80°C, and an injection pressure of 60MPa. The Cross-WLF viscosity model is selected to describe the rheological properties of the material, and the PVT state equation characterizes the compressibility of the material. Boundary conditions include no slip on the mold wall and set flow rate at the injection port. Numerical simulation can reveal the distribution of various physical fields in the molding process. For example, in the filling stage, it can be observed that the melt flows from the injection port to the surroundings, the temperature gradually drops from 240°C to 200°C, and the pressure drops from 60MPa to atmospheric pressure. In the cooling stage, the temperature at the center of the product drops slowly, which is prone to residual stress and leads to warping. Defect analysis is the key to improving product quality. Insufficient filling usually occurs in the thin-walled area far away from the injection port, which can be improved by increasing the holding time or adjusting the gate position. Weld marks often appear at the intersection of multiple melts, which can be reduced by optimizing the gate layout or increasing the mold temperature. Warpage is mainly caused by uneven cooling, which can be improved by adjusting the layout of the cooling water channel. Mold optimization design aims to solve the above problems. For example, changing the original single-point gate to a multi-point gate can improve filling uniformity. Adding a cavity venting groove helps to remove gas and reduce burning. Optimizing the layout of the cooling water channel so that the cooling rate of each part of the product tends to be consistent can effectively reduce warpage. Mold manufacturing and debugging are the key links to put the design plan into practice. The use of precision processing equipment such as five-axis CNC machine tools can ensure the accuracy of the mold cavity. During the mold trial, the product quality can be gradually improved by adjusting the injection molding parameters such as holding time and cooling time. If necessary, make local modifications to the mold, such as adjusting the gate size, adding a cooling circuit, etc. In the batch production stage, an online monitoring system can be used to collect data such as injection pressure and temperature in real time. These data can be analyzed through statistical process control methods to detect abnormalities in a timely manner and make adjustments to ensure stable product quality. This closed-loop control method not only improves production efficiency, but also provides data support for continuous improvement.

[0027] Step S102, the high-precision mold is integrated with a preset cooling channel for ejecting a hook-arrow-shaped structure, a temperature sensor is set in the cooling channel, the cooling status is monitored in real time, and the cooling parameters are adjusted based on the monitoring data to achieve precise structural molding.

[0028] A three-dimensional design model of a high-precision mold is obtained, wherein the three-dimensional design model includes a hook-arrow-shaped structure; a cooling channel layout is preset in the three-dimensional design model for the hook-arrow-shaped structure; at least one temperature sensor is arranged in the cooling channel, and temperature data in the cooling channel is collected in real time by the temperature sensor; the collected temperature data is transmitted to a central control system, and a preset temperature threshold is used to determine whether the cooling state meets the standard; if the temperature data exceeds the temperature threshold range, an alarm signal is triggered, and the alarm signal is used to prompt the need to adjust the cooling parameters; according to the alarm signal, the central control system automatically adjusts the flow and temperature of the cooling medium to restore the cooling state to normal; the temperature data collection, the cooling state judgment and the cooling parameter adjustment are repeated until the injection molding process is completed; by dynamically monitoring and regulating the cooling state, the dimensional accuracy and surface quality of the hook-arrow-shaped structure during the molding process are ensured.

[0029] Exemplarily, the three-dimensional design model of high-precision molds is the basis of precision molding. For hook-shaped structures, it is crucial to preset the layout of cooling channels. For example, dense cooling channels can be set at the tip of the arrow and the bend to ensure that these key areas are cooled quickly and evenly. In the straight section, spiral cooling channels can be used to improve cooling efficiency. The layout of temperature sensors needs to take into account hot spots and temperature gradients. Thermocouples or infrared sensors can be arranged at the tip of the arrow, the bend and the straight section to monitor temperature changes in real time. For example, 3 sensors are arranged at the tip of the arrow, 2 at the bend, and 1 every 50 mm in the straight section to form a comprehensive temperature monitoring network. The central control system receives temperature data and analyzes it. Multi-level temperature thresholds can be set, such as the temperature at the tip of the arrow does not exceed 150°C, the temperature at the bend does not exceed 140°C, and the temperature at the straight section does not exceed 130°C. If the temperature in a certain area exceeds the threshold, the system immediately issues an alarm signal. Cooling parameter adjustment is a dynamic process. For example, when the temperature at the tip of the arrow reaches 155°C, the system automatically increases the flow rate of the cooling medium in the area by 20% and reduces the medium temperature by 2°C. If the temperature does not drop within 5 seconds, the flow rate is increased by 10% again. This fine adjustment ensures fast and effective temperature control. Dynamic monitoring and control runs through the entire injection molding process. In the early stage of injection molding, it may be necessary to adjust the cooling parameters frequently, while in the later stage of molding, they tend to be stable. For example, in the first 30 seconds after the start of injection molding, the system performs temperature detection and parameter adjustment every 2 seconds; after 30 seconds, the detection interval can be extended to 5 seconds to balance the control accuracy and system load. The key to precision molding is to maintain a stable cooling state. Through dynamic control, the dimensional error of the hook arrow-shaped structure can be controlled within ±0.05 mm, and the surface roughness reaches Ra0.4 microns. This accuracy is essential for the function of the hook, ensuring that it can be accurately engaged and released when used. The implementation of this monitoring and control system not only improves product quality, but also optimizes production efficiency. For example, by accurately controlling the cooling process, the cooling time of each cycle can be shortened from the original 45 seconds to 35 seconds, increasing production efficiency by about 22%. At the same time, due to more precise temperature control, the first-time qualified rate of the product has increased from 95% to 98.5%, greatly reducing the scrap rate and rework costs. This intelligent cooling control system reflects the trend of modern manufacturing industry towards intelligence and refinement. By combining traditional mold design with advanced sensing technology, data analysis and automatic control, it achieves a dual improvement in product quality and production efficiency, providing a new technical path for precision parts manufacturing.

[0030] Step S103, each cavity of the multi-cavity mold is configured with independent injection parameter control, the central control system adjusts the injection pressure and temperature of each cavity in real time, and the multi-stage injection speed control module intervenes. Low-speed injection is used in the initial stage, and the injection speed is gradually increased in subsequent stages to balance production efficiency and structural accuracy.

[0031] The structural parameters and process requirements of the multi-cavity mold are obtained, and the initial values ​​and variation ranges of the injection pressure, injection temperature and injection speed of each cavity are determined according to the shape and size of each cavity; the communication connection between the central control system and the multi-cavity mold is established to collect the pressure and temperature sensor data of each cavity in real time; the injection pressure and temperature of each cavity are dynamically adjusted according to the preset process curve and feedback data, and the corresponding speed control curve is set for different stages; through the multi-stage injection speed control module, the screw speed and back pressure of the injection molding machine are automatically adjusted according to the speed curve of each stage to achieve a smooth transition of the injection speed; during the injection molding process, the pressure changes of each cavity are monitored in real time, and by comparing the pressure curve with the preset threshold, it is determined whether the molten plastic filling is completed; when the pressure of all cavities reaches the set value, it switches to the holding stage, and optimizes the holding time and cooling time according to the structural characteristics and precision requirements of the plastic parts; through the central control system, the injection parameters and quality data of each production cycle are counted, and a production report is generated; machine learning algorithms are used to analyze and model historical data, optimize injection molding process parameters, shorten the production cycle and improve production efficiency while meeting the quality requirements of plastic parts.

[0032] Exemplarily, multi-cavity mold injection molding is an efficient method for producing plastic products. The consistency of molding quality of each cavity can be ensured by reasonably setting the injection molding parameters. First, independent injection molding parameters are set for each cavity according to the mold structure and process requirements. For example, for a four-cavity mold, different injection molding pressure ranges may be set according to the shape and size differences of each cavity: cavity one is 80-100MPa, cavity two is 85-105MPa, cavity three is 90-110MPa, and cavity four is 95-115MPa. This differentiated setting can compensate for the uneven filling problem caused by the mold structure. The central control system dynamically adjusts the injection molding parameters by collecting the pressure and temperature data of each cavity in real time. For example, when it is detected that the pressure of a cavity is lower than the preset value, the system will automatically increase the injection molding pressure of the cavity to ensure complete filling. This closed-loop control method can effectively cope with the impact of factors such as changes in raw material batches and ambient temperature fluctuations, and ensure the stability of product quality. Multi-stage control of injection speed is a key technology to improve product quality. In the initial stage, a lower injection speed, such as 10-20 mm / s, can be used to reduce the shear heating of the molten plastic and the generation of bubbles. As the injection molding process progresses, the speed is gradually increased to 50-80 mm / s to speed up the filling speed. This speed control strategy can effectively balance the surface quality of the product and production efficiency. Pressure monitoring is an important basis for judging the completion of filling. By setting a pressure threshold, such as when the pressure in the cavity reaches 95% of the maximum set pressure, the system automatically switches to the pressure holding stage. The optimization of the pressure holding time is crucial for products with different structural characteristics. For example, for parts with thicker walls, the pressure holding time may need to be extended to 15-20 seconds, while for thin-walled parts, 8-12 seconds of pressure holding time may be sufficient. The control of cooling time directly affects the dimensional accuracy and production efficiency of the product. For products with high precision requirements, the cooling time may need to be extended to 30-40 seconds to ensure that the product is fully formed. For general products, a cooling time of 20-25 seconds may be sufficient. By optimizing the cooling time, production efficiency can be improved while ensuring product quality. The application of machine learning algorithms provides new possibilities for injection molding process optimization. By analyzing historical production data, the system can establish a correlation model between injection molding parameters and product quality. For example, by analyzing the nonlinear relationship between injection molding temperature and product warpage, the system can automatically adjust the temperature parameters to shorten the production cycle by 5-10% while ensuring product quality. This data-driven optimization method not only improves production efficiency, but also reduces the workload of manual debugging and realizes intelligent control of the injection molding process.

[0033] Step S104, the mold integrates multiple positioning pins and guide mechanisms to ensure the position and direction of the injection hook in the mold, ensure the consistency of the staggered arrangement, design a special demoulding mechanism, adopt oblique demoulding, configure a demoulding force sensor, monitor the demoulding force in real time, and ensure a smooth demoulding process.

[0034] The preset position and direction information of the injection hook in the mold is obtained, and the layout scheme of the positioning pin and the guide mechanism is determined according to the preset position and direction information; the size and position of the positioning pin and the guide mechanism are optimized and designed by using computer-aided design software to obtain an optimized positioning pin and guide mechanism design scheme; according to the optimized positioning pin and guide mechanism design scheme, if the verification is passed, the optimized positioning pin and guide mechanism design scheme is determined as the final positioning pin and guide mechanism design scheme; according to the shape and arrangement of the injection hook, an oblique demoulding mechanism is designed, and the oblique demoulding mechanism can reduce the resistance in the demoulding process. force; a demoulding force sensor is installed on the oblique demoulding mechanism to collect force data in the demoulding process in real time, and transmit the force data to the control system; the control system analyzes and judges the force data collected by the demoulding force sensor in real time according to a preset demoulding force threshold, and immediately issues a warning signal if it is detected that the demoulding force exceeds the demoulding force threshold; according to the analysis result of the force data collected by the demoulding force sensor, the operating parameters of the oblique demoulding mechanism are dynamically adjusted, and the operating parameters include the demoulding speed and the oblique angle, to obtain an integrated mold; and the integrated mold is used for multiple mold trials and debugging.

[0035] For example, the layout of the locating pins and the guide mechanism is a key link in the mold design. Taking a mobile phone shell mold as an example, the ejection hook is usually set on the side or bottom of the product. According to these preset positions, the locating pins can be arranged on the fixed template of the mold to cooperate with the guide sleeve on the movable template to ensure accurate positioning when the mold is opened and closed. Using computer-aided design software such as SolidWorks or CATIA for three-dimensional modeling, the position and size of the locating pins can be adjusted intuitively. Through finite element analysis software such as ANSYS, the stress distribution during the mold opening and closing process can be simulated, and the diameter and length of the locating pins can be optimized to withstand the mold opening force while minimizing mold deformation. The design of the dedicated demolding mechanism needs to consider the shape characteristics of the ejection hook. Assuming that the mobile phone shell has multiple slender ejection hooks, the oblique demolding method can effectively reduce the ejection resistance. In specific implementation, a wedge mechanism can be installed on the movable template to make the ejection hook move at a 45-degree angle during demolding. This design not only reduces the demolding force, but also avoids scratches between the ejection hook and the mold. The installation position of the ejection force sensor is crucial. Piezoelectric sensors can be used and embedded in the push rod of the demoulding mechanism to collect force data in real time during the demoulding process. The data is converted into digital signals by an analog signal converter and then transmitted to the control system. The control system can set the demoulding force threshold. For example, for ABS plastic mobile phone shells, the threshold can be set to 2000N. When the demoulding force exceeds this value, the system immediately issues an alarm and automatically reduces the demoulding speed or adjusts the oblique angle. Dynamic adjustment of demoulding parameters is an effective means to improve production efficiency and product quality. By analyzing the demoulding force data of all times, a relationship model between demoulding force and demoulding speed and angle can be established. For example, if it is found that the demoulding force increases significantly when the demoulding speed exceeds 50mm / s, the speed can be limited to 40-50mm / s. At the same time, if it is found that the demoulding force at some ejection hook positions is too high, the oblique angle at that location can be appropriately increased from the original 45 degrees to 50 degrees to reduce local stress concentration. The integration and debugging stages of the mold require repeated verification. After the first mold trial, it may be found that the position deviation of some injection hooks exceeds the tolerance requirement of ±0.1mm. At this time, it is necessary to fine-tune the position of the positioning pin, and it may be necessary to move a positioning pin 0.05mm to the left. After multiple mold trials and adjustments, the position accuracy of all injection hooks is finally achieved within ±0.05mm, meeting the production requirements of high-precision mobile phone shells. This refined debugging process not only ensures product quality, but also accumulates valuable process parameter data for subsequent mass production.

[0036] Step S105, the machine vision system detects the integrity and directional consistency of the arrow-shaped structure of the injection hook after molding online, identifies unqualified products and automatically removes them and records the defect type, and sends the defect type data and the corresponding product batch information to the production data acquisition module.

[0037] The image of the hook-arrow structure after injection molding is obtained, and the image is preprocessed to obtain a preprocessed image; for the preprocessed image, an edge detection algorithm is used to extract the contour features of the hook-arrow structure to obtain a contour coordinate point set; the contour coordinate point set is matched with a preset standard template to obtain the contour similarity; if the similarity is lower than a preset threshold, the hook-arrow structure is determined to be incomplete and it is classified as a defective product; for products with complete hook-arrow structures, the Hough transform algorithm is used to extract the direction features of each hook arrow; the direction angle between the hook arrows is calculated; if the direction angle exceeds a preset range, the hook arrow directions are determined to be inconsistent and it is classified as a defective product; according to the detection results, a manipulator or a pneumatic device is controlled to automatically remove the defective products; the defect type information is associated with the corresponding product batch information to obtain defect data; the defect data is sent to the production data acquisition module through a network communication interface; for the accumulated defect data, a data mining algorithm is used to analyze the defect occurrence law and optimize the production process parameters.

[0038] Exemplarily, image preprocessing is a key step to improve detection accuracy. Through image enhancement technology, such as histogram equalization, the contrast of the image can be improved to make the hook arrow structure clearer. De-noising can use methods such as median filtering or Gaussian filtering to effectively remove random noise in the image, laying the foundation for subsequent edge detection. Edge detection is an important means to extract the outline of the hook arrow. Commonly used algorithms include Canny algorithm and Sobel algorithm. The Canny algorithm has strong noise resistance and edge positioning accuracy, and is suitable for hook arrow outline extraction under complex backgrounds. The extracted contour coordinate point set can be used for subsequent contour matching analysis. Contour matching is the core of judging the structural integrity of the hook arrow. Template matching or shape descriptor methods can be used for similarity calculation. For example, using Hu moment as a shape descriptor, the Hu moment distance between the contour to be detected and the standard template is calculated. If the distance exceeds a preset threshold (such as 0.1), it is determined that the structure is incomplete. Direction consistency detection is crucial to ensuring product quality. Hough transform can effectively extract straight line features to characterize the main direction of the hook arrow. By calculating the angle between the arrows of adjacent hooks, if the angle deviates from the expected value (such as 90 degrees) and exceeds the allowable range (such as ±5 degrees), it is determined that the direction is inconsistent. The automatic rejection system can be implemented by a manipulator or a pneumatic device. For example, a manipulator with a vacuum suction cup is used to accurately locate and grab defective products based on the visual inspection results and transfer them to a designated area. At the same time, information such as defect type (such as structural defects or directional defects) and product batches are recorded in the database. Data uploading and management are the basis for achieving quality traceability. Defect data can be transmitted to the central database in real time through industrial communication protocols such as OPC-UA. Database design should consider multi-dimensional information such as defect type, occurrence time, product batch, etc., to facilitate subsequent data analysis and mining. Data mining helps to discover potential factors affecting product quality. Algorithms such as decision trees or association rules can be used to analyze the laws of defect occurrence. For example, through decision tree analysis, it may be found that when the mold temperature exceeds a certain threshold, the probability of structural defects increases significantly. Based on these findings, process parameters can be adjusted accordingly, such as reducing the mold temperature or adjusting the cooling time, thereby improving the product yield. Through the comprehensive application of the above-mentioned series of technical means, efficient and accurate detection of the hook-arrow structure can be achieved, and the production process can be continuously optimized through a data-driven approach, ultimately achieving the goal of improving product quality and production efficiency.

[0039] Step S106, the production data acquisition module records the injection molding parameters, cooling time, demoulding force data and defect data uploaded by the machine vision system in real time, and the data mining algorithm analyzes the influence of various parameters and defect data on the molding quality of the injection hook. The preset quality standards include the integrity, directional consistency and dimensional accuracy of the injection hook. The adaptive control algorithm dynamically adjusts the injection molding process parameters according to the real-time production data, defect data and preset quality standards to maintain the production efficiency at the optimal state.

[0040] The step S106 comprises the following steps:

[0041] Step 1: Obtain the injection molding parameters, cooling time and demoulding force data recorded in real time by the production data acquisition module of the injection molding machine and the defect data uploaded by the machine vision system;

[0042] Step 2: using a data mining algorithm to analyze the injection molding parameters, cooling time and demoulding force data and defect data, and obtaining association rules between each parameter and defect data and a pre-established injection hook molding quality standard;

[0043] Step 3: Based on the association rules obtained through analysis, a decision tree algorithm is used to establish a quality prediction model for the ejected hook;

[0044] Step 4: input the real-time collected production data and defect data into the injection hook quality prediction model to predict whether the integrity, directional consistency and dimensional accuracy of the injection hook meet the preset quality standards;

[0045] Step 5: If the prediction result shows that the quality of the ejected hook does not meet the standard, the adaptive control algorithm is triggered to determine the injection molding process parameters that need to be adjusted according to the output result of the prediction model, and the parameter adjustment instruction is sent to the injection molding machine control system;

[0046] Step 6: After receiving the parameter adjustment instruction, the injection molding machine control system dynamically adjusts the injection pressure, injection speed, material temperature and mold temperature process parameters, and transmits the adjusted parameter values ​​back to the data acquisition module;

[0047] Step 7, inputting the adjusted process parameter values ​​together with the real-time collected production data and defect data into the injection hook quality prediction model, and predicting again whether the adjusted injection hook quality meets the standard;

[0048] Step 8: If the prediction result shows that the quality of the ejected hook meets the standard, the current process parameter settings are maintained and production continues;

[0049] Step 9: If the predicted result still does not meet the standard, repeat steps 5-7 until the quality of the ejected hook stabilizes at an acceptable level.

[0050] For example, the establishment of quality standards for injection hooks is the basis of intelligent injection molding production. These standards may include indicators such as dimensional accuracy, surface finish, and strength of the hook. For example, the length standard of a certain model of injection hook may be 50±0.1mm, the surface roughness does not exceed Ra1.6μm, and the tensile strength is not less than 20MPa. The data acquisition module records injection parameters such as injection pressure, speed, temperature, etc. in real time. Cooling time and demolding force are also key data. The defect data uploaded by the machine vision system may include information such as incomplete structure and inconsistent direction. The data mining algorithm analyzes these data to find out their association with the quality of the injection hook. For example, it may be found that the product integrity is best when the injection pressure is 80-85MPa and the mold temperature is 60-65℃. The quality prediction model established by the decision tree algorithm can quickly determine the quality of the injection hook under the current production conditions. Assuming the input injection pressure is 82MPa, the mold temperature is 62℃, and the cooling time is 15s, the prediction model outputs that the injection hook integrity is 98%, which meets the standard. If the injection pressure is 90MPa, the mold temperature is 70℃, and the cooling time is 10s, the model may predict that the integrity is only 85%, which does not meet the standard. The adaptive control algorithm adjusts the process parameters according to the prediction results. If the predicted quality does not meet the standard, the algorithm may recommend reducing the injection pressure to 85MPa, the mold temperature to 65℃, and the cooling time to 13s. These adjustment instructions are sent to the injection molding machine control system through the network interface. After receiving the instructions, the injection molding machine control system accurately adjusts the parameters. For example, the injection pressure is reduced from 90MPa to 85MPa by controlling the injection molding screw through the servo motor; the mold temperature is reduced from 70℃ to 65℃ by adjusting the mold cooling water valve; and the cooling time is extended to 13s. The adjusted parameters are fed back to the data acquisition module in real time through sensors. The data acquisition module inputs the new process parameters, production data, and defect data into the prediction model again. If the prediction results show that the integrity of the ejection hook is improved to 97%, which meets the standard, the current parameters are maintained and production continues. If it still does not meet the standard, the system will conduct a new round of parameter optimization until the quality requirements are met. This closed-loop control method can quickly respond to production fluctuations and continuously optimize process parameters. It not only ensures the quality stability of the ejected hook, but also improves production efficiency while ensuring quality. For example, by accurately controlling the cooling time, each production cycle can be shortened by 1-2 seconds while ensuring product quality, significantly increasing production capacity. At the same time, this intelligent control can also reduce human intervention, reduce operating errors, and improve production consistency and reliability.

[0051] The present invention provides a high-precision injection hook molding system, which mainly includes:

[0052] Molding simulation and optimization module, which is used to apply computer-aided design and finite element analysis to simulate the molding process of the injection hook in the mold, optimize the mold structure, and reduce the number of mold trials;

[0053] The cooling monitoring and control module is used for the cooling channel of the preset injection hook-arrow-shaped structure integrated with the high-precision mold. The temperature sensor is set in the cooling channel to monitor the cooling status in real time. Based on the monitoring data, the cooling parameters are adjusted to achieve precise structural molding;

[0054] The injection molding parameter control module is used to configure independent injection molding parameter control for each cavity of a multi-cavity mold. The central control system adjusts the injection molding pressure and temperature of each cavity in real time. The multi-stage injection molding speed control module intervenes. The initial stage uses low-speed injection molding, and the injection molding speed is gradually increased in the subsequent stages to balance production efficiency and structural accuracy.

[0055] Positioning and demoulding module, which is used to integrate multiple positioning pins and guide mechanisms in the mold to ensure the position and direction of the injection hook in the mold, ensure the consistency of the staggered arrangement, design a special demoulding mechanism, adopt oblique demoulding, configure a demoulding force sensor, monitor the demoulding force in real time, and ensure a smooth demoulding process;

[0056] Visual inspection module, used for online inspection of the integrity and directional consistency of the arrow-shaped structure of the injection hook after molding by the machine vision system, automatically rejecting unqualified products and recording the defect type, and sending the defect type data and the corresponding product batch information to the production data acquisition module;

[0057] The data acquisition and analysis module is used for the production data acquisition module to record the injection molding parameters, cooling time, demoulding force data and defect data uploaded by the machine vision system in real time. The data mining algorithm analyzes the influence of various parameters and defect data on the molding quality of the injection hook. The preset quality standards include the integrity, directional consistency and dimensional accuracy of the injection hook. The adaptive control algorithm dynamically adjusts the injection molding process parameters according to the real-time production data, defect data and preset quality standards to maintain the production efficiency at the optimal state.

[0058] The above embodiments are only used to illustrate the technical solution of the present invention but not to limit it. The present invention is described in detail with reference to the preferred embodiments. It should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, and should be included in the scope of the claims of the present invention.

Claims

1. A high-precision injection hook molding process, characterized in that: The process comprises the following steps: Step S101, applying computer-aided design and finite element analysis to simulate the molding process of the injection hook in the mold, optimize the mold structure, and reduce the number of mold trials; Step S102, the high-precision mold is integrated with a preset cooling channel for injecting a hook-arrow-shaped structure, a temperature sensor is set in the cooling channel, the cooling state is monitored in real time, and the cooling parameters are adjusted based on the monitoring data to achieve precise structural molding; Step S103, each cavity of the multi-cavity mold is configured with independent injection parameter control, the central control system adjusts the injection pressure and temperature of each cavity in real time, and the multi-stage injection speed control module intervenes. In the initial stage, low-speed injection is adopted, and the injection speed is gradually increased in the subsequent stages to balance production efficiency and structural accuracy; Step S104, the mold integrates multiple positioning pins and guide mechanisms to ensure the position and direction of the injection hook in the mold, ensure the consistency of the staggered arrangement, design a special demoulding mechanism, adopt oblique demoulding, configure a demoulding force sensor, monitor the demoulding force in real time, and ensure a smooth demoulding process; Step S105, the machine vision system detects the integrity and direction consistency of the arrow-shaped structure of the injection hook after molding online, identifies unqualified products and automatically removes them and records the defect type, and sends the defect type data and the corresponding product batch information to the production data acquisition module; Step S106, the production data acquisition module records the injection molding parameters, cooling time, demoulding force data and defect data uploaded by the machine vision system in real time, and the data mining algorithm analyzes the influence of various parameters and defect data on the molding quality of the injection hook. The preset quality standards include the integrity, directional consistency and dimensional accuracy of the injection hook. The adaptive control algorithm dynamically adjusts the injection molding process parameters according to the real-time production data, defect data and preset quality standards to maintain the production efficiency at the optimal state.

2. A high-precision injection hook molding process according to claim 1, characterized in that: The step S101 comprises: According to the three-dimensional model of the shooting hook, a digital model of the shooting hook is established by using computer-aided design software, and the digital model is meshed to obtain a finite element analysis model; According to the material properties and process parameters of the ejection hook, the boundary conditions and load conditions of the finite element analysis model are set, and the material constitutive model and rheological model are selected; The forming process of the injection hook is numerically simulated by finite element analysis software to obtain the field distribution information of stress, strain, temperature and pressure of the injection hook during the flow, filling, cooling and solidification process in the mold; According to the numerical simulation results, the defect generation mechanism of the injection hook during the molding process is analyzed to determine the type and severity of the defect; The mold structure is optimized by using an optimization algorithm, and the layout and size parameters of the pouring system, cooling system and exhaust system of the mold structure are adjusted to obtain an optimized mold design scheme; According to the optimized mold design scheme, the mold is processed and manufactured, and the mold structure is fine-tuned during the mold trial process until the molding quality of the injection hook meets the requirements; The optimized mold design scheme is applied to mass production, and the quality data in the production process is collected and analyzed to realize online monitoring and quality control of the injection hook molding process.

3. A high-precision injection hook molding process according to claim 1, characterized in that: The step S102 comprises: Acquire a three-dimensional design model of a high-precision mold, wherein the three-dimensional design model includes a hook-arrow-shaped structure; For the hook-arrow-shaped structure, presetting a cooling channel layout in the three-dimensional design model; Arrange at least one temperature sensor in the cooling channel, and collect temperature data in the cooling channel in real time through the temperature sensor; The collected temperature data is transmitted to a central control system, and a preset temperature threshold is used to determine whether the cooling state meets the standard; If the temperature data exceeds the temperature threshold range, an alarm signal is triggered, and the alarm signal is used to prompt that the cooling parameters need to be adjusted; According to the alarm signal, the central control system automatically adjusts the flow rate and temperature of the cooling medium to restore the cooling state to normal; Repeating the temperature data acquisition, the cooling state determination and the cooling parameter adjustment until the injection molding process is completed; By dynamically monitoring and regulating the cooling state, the dimensional accuracy and surface quality of the hook-arrow-shaped structure during the molding process are ensured.

4. A high-precision injection hook molding process according to claim 1, characterized in that: The step S103 comprises: Obtain the structural parameters and process requirements of the multi-cavity mold, and determine the initial values ​​and variation ranges of the injection pressure, injection temperature and injection speed independently controlled for each cavity according to the shape and size of each cavity; Establish communication connection between the central control system and multi-cavity molds to collect pressure and temperature sensor data of each cavity in real time; According to the preset process curve and feedback data, the injection pressure and temperature of each cavity are dynamically adjusted, and the corresponding speed control curve is set for different stages; Through the multi-stage injection speed control module, the screw speed and back pressure of the injection molding machine are automatically adjusted according to the speed curve of each stage to achieve a smooth transition of the injection speed; During the injection molding process, the pressure changes of each cavity are monitored in real time, and the filling of molten plastic is judged by comparing the pressure curve with the preset threshold value; When the pressure of all cavities reaches the set value, it switches to the pressure holding stage. According to the structural characteristics and precision requirements of the plastic parts, the pressure holding time and cooling time are optimized; Through the central control system, the injection molding parameters and quality data of each production cycle are counted and production reports are generated; Machine learning algorithms are used to analyze and model historical data, optimize injection molding process parameters, shorten production cycles, and improve production efficiency while meeting plastic part quality requirements.

5. A high-precision injection hook molding process according to any one of claims 1 to 4, characterized in that: The step S104 comprises: Acquire the preset position and direction information of the injection hook in the mold, and determine the layout plan of the positioning pin and the guide mechanism according to the preset position and direction information; Using computer-aided design software to optimize the size and position of the positioning pin and the guide mechanism to obtain an optimized positioning pin and guide mechanism design; According to the optimized positioning pin and guide mechanism design scheme, if the verification is passed, the optimized positioning pin and guide mechanism design scheme is determined as the final positioning pin and guide mechanism design scheme; According to the shape and arrangement of the ejection hooks, an oblique demoulding mechanism is designed, which can reduce the resistance during the demoulding process; A demoulding force sensor is installed on the oblique demoulding mechanism to collect force data in the demoulding process in real time and transmit the force data to the control system; The control system performs real-time analysis and judgment on the force data collected by the demoulding force sensor according to a preset demoulding force threshold, and immediately issues a warning signal if it is detected that the demoulding force exceeds the demoulding force threshold; According to the analysis result of the force data collected by the demoulding force sensor, the operating parameters of the oblique demoulding mechanism are dynamically adjusted, wherein the operating parameters include the demoulding speed and the oblique angle, to obtain an integrated mold; The integrated mold is used to perform multiple mold trials and debugging.

6. A high-precision injection hook molding process according to any one of claims 1 to 4, characterized in that: The step S105 comprises: Acquire an image of the hook-arrow structure after injection molding, and preprocess the image to obtain a preprocessed image; For the preprocessed image, an edge detection algorithm is used to extract the contour features of the hook-arrow structure to obtain a contour coordinate point set; Performing contour matching on the contour coordinate point set and a preset standard template to obtain contour similarity; If the similarity is lower than a preset threshold, the hook-arrow structure is determined to be incomplete and the product is classified as a defective product; For products with complete hook and arrow structures, the Hough transform algorithm is used to extract the directional features of each hook and arrow; Calculating the direction angles between the hook arrows; If the direction angle exceeds the preset range, it is determined that the hook arrow direction is inconsistent and it is classified as a defective product; According to the test results, the robot or pneumatic device is controlled to automatically remove defective products; Associating the defect type information with the corresponding product batch information to obtain defect data; Sending the defect data to the production data acquisition module through a network communication interface; Based on the accumulated defect data, data mining algorithms are used to analyze the defect occurrence patterns and optimize production process parameters.

7. A high-precision injection hook molding process according to any one of claims 1 to 4, characterized in that: The step S106 comprises the following steps: Step 1: Obtain the injection molding parameters, cooling time and demoulding force data recorded in real time by the production data acquisition module of the injection molding machine and the defect data uploaded by the machine vision system; Step 2: using a data mining algorithm to analyze the injection molding parameters, cooling time and demoulding force data and defect data, and obtaining association rules between each parameter and defect data and a pre-established injection hook molding quality standard; Step 3: Based on the association rules obtained through analysis, a decision tree algorithm is used to establish a quality prediction model for the ejected hook; Step 4: input the real-time collected production data and defect data into the injection hook quality prediction model to predict whether the integrity, directional consistency and dimensional accuracy of the injection hook meet the preset quality standards; Step 5: If the prediction result shows that the quality of the ejected hook does not meet the standard, the adaptive control algorithm is triggered to determine the injection molding process parameters that need to be adjusted according to the output result of the prediction model, and the parameter adjustment instruction is sent to the injection molding machine control system; Step 6: After receiving the parameter adjustment instruction, the injection molding machine control system dynamically adjusts the injection pressure, injection speed, material temperature and mold temperature process parameters, and transmits the adjusted parameter values ​​back to the data acquisition module; Step 7, inputting the adjusted process parameter values ​​together with the real-time collected production data and defect data into the injection hook quality prediction model, and predicting again whether the adjusted injection hook quality meets the standard; Step 8: If the prediction result shows that the quality of the ejected hook meets the standard, the current process parameter settings are maintained and production continues; Step 9: If the predicted result still does not meet the standard, repeat steps 5-7 until the quality of the ejected hook stabilizes at an acceptable level.

8. A high-precision injection hook molding system, characterized in that: The system is used to implement a molding process of a high-precision injection hook as described in any one of claims 1 to 7, and the system comprises: Molding simulation and optimization module, which is used to apply computer-aided design and finite element analysis to simulate the molding process of the injection hook in the mold, optimize the mold structure, and reduce the number of mold trials; The cooling monitoring and control module is used for the cooling channel of the preset injection hook-arrow-shaped structure integrated with the high-precision mold. The temperature sensor is set in the cooling channel to monitor the cooling status in real time. Based on the monitoring data, the cooling parameters are adjusted to achieve precise structural molding; The injection molding parameter control module is used to configure independent injection molding parameter control for each cavity of a multi-cavity mold. The central control system adjusts the injection molding pressure and temperature of each cavity in real time. The multi-stage injection molding speed control module intervenes. The initial stage uses low-speed injection molding, and the injection molding speed is gradually increased in the subsequent stages to balance production efficiency and structural accuracy. Positioning and demoulding module, which is used to integrate multiple positioning pins and guide mechanisms in the mold to ensure the position and direction of the injection hook in the mold, ensure the consistency of the staggered arrangement, design a special demoulding mechanism, adopt oblique demoulding, configure a demoulding force sensor, monitor the demoulding force in real time, and ensure a smooth demoulding process; Visual inspection module, used for online inspection of the integrity and directional consistency of the arrow-shaped structure of the injection hook after molding by the machine vision system, automatically rejecting unqualified products and recording the defect type, and sending the defect type data and the corresponding product batch information to the production data acquisition module; The data acquisition and analysis module is used for the production data acquisition module to record the injection molding parameters, cooling time, demoulding force data and defect data uploaded by the machine vision system in real time. The data mining algorithm analyzes the influence of various parameters and defect data on the molding quality of the injection hook. The preset quality standards include the integrity, directional consistency and dimensional accuracy of the injection hook. The adaptive control algorithm dynamically adjusts the injection molding process parameters according to the real-time production data, defect data and preset quality standards to maintain the production efficiency at the optimal state.

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