Ultra-thin narrow frame type glue-iron integrated structure module and forming method thereof

Abstract

This invention proposes an ultra-thin, narrow-bezel integrated metal-plastic structure module and its molding method. It belongs to the field of precision electronic structural component manufacturing and advanced connection technology. The method includes: designing a gradient thickness for the metal skeleton to generate a three-dimensional model of the metal skeleton; performing nano-injection mold cavity adaptation processing based on the three-dimensional model data of the metal skeleton to construct a gradient interface injection molding system; dynamically controlling the holding pressure parameters based on the gradient interface injection molding system and collecting data on the coupled pressure and temperature fields within the mold; predicting residual stress based on the coupled pressure and temperature fields within the mold using a multiphysics simulation model, and generating dynamic holding pressure compensation commands. This method ensures the strength of the ultra-thin, narrow-bezel structure while reducing the narrowest part of the bezel to within 1.8mm and precisely controlling the overall thickness to 2.35mm, significantly improving space utilization and product thinness.

Description

Technical Field

[0001] This invention proposes an ultra-thin, narrow-bezel integrated glue-iron structure module and its molding method, belonging to the field of precision electronic structural component manufacturing and advanced connection technology. Background Technology

[0002] In an era where mobile display devices continue to evolve towards larger screens and thinner designs, traditional bezel structures face severe challenges. Early devices generally adopted a combination of a metal mid-frame and a separate plastic bracket, assembled using screws or clips.

[0003] This split design not only suffers from the drawbacks of numerous parts and complex assembly processes, but also faces bottlenecks in structural strength and space utilization. Especially when the screen size increases to the 6.95-inch level, the narrow bezels and thin and light body require the frame to have higher overall rigidity and dimensional accuracy to resist bending and impact.

[0004] Meanwhile, to accommodate full-screen displays and front-facing sensor layouts, the bezel needs to integrate precise positioning and support structures in certain areas. Traditional split-type solutions are no longer sufficient to balance strength, weight, and signal performance in such delicate and thin designs, necessitating a highly integrated and reliable new solution. Summary of the Invention

[0005] This invention provides an ultra-thin, narrow-bezel integrated glue-iron structure module and its molding method, to solve the problems mentioned in the background art above: The present invention proposes a molding method for an ultra-thin, narrow-bezel integrated glue-iron structure module, the method comprising: S1. Perform gradient thickness design on the metal skeleton to generate three-dimensional model data of the metal skeleton; perform nano-injection mold cavity adaptation processing based on the three-dimensional model data of the metal skeleton to construct a gradient interface injection molding system. S2. Dynamic pressure holding parameter control is performed based on the gradient interface injection molding system, and data of in-mold pressure and temperature coupling field are collected; residual stress is predicted based on the in-mold pressure and temperature coupling field data through a multi-physics simulation model, and dynamic pressure holding compensation command is generated; the injection pressure holding pressure is adjusted in real time according to the dynamic pressure holding compensation command to form a stress collaborative control closed loop. S3. Chemical bonding enhancement treatment is performed on the nano-injection molded iron-plastic composite. A plasma activation and silane coupling agent composite process is used to generate gradient chemical bonding interface layer data. The evolution of bonding strength of the gradient chemical bonding interface layer data is monitored in real time by Raman spectroscopy to complete the online feedback of interface bonding quality. S4. Obtain the actual molding contour data of the plastic-iron composite through three-dimensional laser scanning; compare the flatness deviation between the actual molding contour data and the three-dimensional model data of the metal skeleton to generate molding accuracy correction parameters; iteratively optimize the injection molding process parameters according to the molding accuracy correction parameters to complete the closed-loop control of the in-mold state and the final rigidity / flatness. S5. Based on the closed-loop control, the rigidity test of the integrated plastic-iron structure module is carried out, and three-point bending strength data and high and low temperature cycle failure data are collected. The data are processed by the weighted stability index algorithm to generate a structural reliability assessment report. Based on the structural reliability assessment report, an intelligent molding process optimization scheme is output to form a precision molding operating system.

[0006] This invention proposes an ultra-thin, narrow-bezel integrated glue-iron structure module, the module comprising: One or more processors; Memory, used to store one or more programs; Wherein, when the one or more programs are executed by the one or more processors, the one or more processors are made to implement the method described in any one of the above.

[0007] The beneficial effects of this invention are as follows: By integrating a unique metal skeleton gradient thickness design with nano-injection molding technology, this method ensures the strength of the ultra-thin narrow bezel structure while reducing the width of the narrowest part of the bezel to within 1.8mm, and precisely controlling the overall thickness to 2.35mm, significantly improving space utilization and product thinness. Dynamic pressure compensation and stress synergistic control technology reduce residual stress by more than 40%, effectively avoiding the problem of glue-iron interface debonding under high temperature and high humidity conditions, and enhancing structural reliability. Chemical bonding reinforcement treatment forms a gradient interface layer, increasing the interface bonding strength by 3 times and completely avoiding the failure risk of traditional physical anchoring methods. This process integrates the original 7-step assembly process into 1 pre-processing step, reducing the number of parts and CNC machining time by 60%, reducing material costs by 30%, and controlling the flatness tolerance within 0.1mm through an online feedback system, achieving a balance between high precision and low cost. Attached Figure Description

[0008] Figure 1 This is a diagram illustrating the steps of the method described in this invention. Detailed Implementation

[0009] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0010] One embodiment of the present invention, such as Figure 1 As shown, a molding method for an ultra-thin, narrow-bezel integrated glue-iron structure module is described, the method comprising: S1. Perform gradient thickness design on the metal skeleton to generate three-dimensional model data of the metal skeleton, wherein the three-dimensional model data of the metal skeleton has local thickening in the key stress area; perform nano-injection mold cavity adaptation processing based on the three-dimensional model data of the metal skeleton to construct a gradient interface injection molding system. S2. Dynamic pressure holding parameter control is performed based on the gradient interface injection molding system, and data of in-mold pressure and temperature coupling field are collected; residual stress is predicted based on the in-mold pressure and temperature coupling field data through a multi-physics simulation model, and dynamic pressure holding compensation command is generated; the injection pressure holding pressure is adjusted in real time according to the dynamic pressure holding compensation command to form a stress collaborative control closed loop. S3. Chemical bonding enhancement treatment is performed on the nano-injection molded iron-plastic composite. A plasma activation and silane coupling agent composite process is used to generate gradient chemical bonding interface layer data. The evolution of bonding strength of the gradient chemical bonding interface layer data is monitored in real time by Raman spectroscopy to complete the online feedback of interface bonding quality. S4. Obtain the actual molding contour data of the plastic-iron composite through three-dimensional laser scanning; compare the flatness deviation between the actual molding contour data and the three-dimensional model data of the metal skeleton to generate molding accuracy correction parameters; iteratively optimize the injection molding process parameters according to the molding accuracy correction parameters to complete the closed-loop control of the in-mold state and the final rigidity / flatness. S5. Based on the closed-loop control, the rigidity test of the integrated plastic-iron structure module is carried out, and three-point bending strength data and high and low temperature cycle failure data are collected. The three-point bending strength data and high and low temperature cycle failure data are processed by the weighted stability index algorithm to generate a structural reliability assessment report. Based on the structural reliability assessment report, an intelligent molding process optimization scheme is output to form a precision molding operating system with gradient interface, dynamic pressure holding, stress coordination and online feedback.

[0011] The working principle and effects of the above technical solution are as follows: The integrated glue-iron design reduces the number of parts and assembly steps, lowering material and processing costs. The gradient thickness design combined with nano-injection molding improves the overall structural rigidity and flatness accuracy, enhancing the module's stability in high and low temperature environments and preventing bending or cracking during use. Chemical bonding enhancement treatment improves interface adhesion, and with process iteration optimization, reduces molding deviations. This solution not only meets the design requirements of large-screen ultra-thin narrow bezels but also ensures compatibility with existing overall device structures without additional adaptation, while simultaneously improving product reliability and lifespan, and reducing potential maintenance risks.

[0012] In one embodiment of the present invention, S1 includes: S11. Select 6-series stainless steel as the base material for the metal frame. Combine the requirements of large screen and thinness of 6.95-inch devices and the stress characteristics, carry out the overall planning of the gradient thickness of the metal frame, highlight the reinforcement design of key stress areas, and produce the initial three-dimensional model data of the metal frame. S12. Based on the initial 3D model data, refine the local thickening dimensions and transition curvature of the key stress areas, optimize the thinning scheme of the non-stress areas, and generate 3D model data of the metal skeleton with the required accuracy. S13. Focusing on the injection flow characteristics of PBT+30% glass fiber reinforced engineering plastic, design the mold cavity gradient interface structure to match the fitting requirements of the three-dimensional model data of the metal skeleton. S14. Based on the cavity gradient interface design scheme, perform nano-injection mold cavity processing, and simultaneously carry out processing accuracy testing to ensure that the cavity size error meets the assembly standard. S15. Integrate the optimized 3D model data of the metal skeleton with the qualified mold cavity, build a gradient interface injection molding system, and complete the initial calibration of system parameters.

[0013] The working principle and effects of the above technical solution are as follows: By selecting high-strength materials and combining them with gradient thickness planning, the core support of the metal frame is enhanced. Simultaneously, the thinning design of non-stress areas is optimized, meeting the core requirements of a large, thin screen while reducing overall weight. Refining the dimensions and transition curvature of key areas improves the accuracy of the 3D model, preventing issues with loose fit during subsequent injection molding. Focusing on the material's injection molding characteristics, the mold cavity is designed, and combined with processing accuracy testing, cavity dimensional errors are reduced, resulting in a higher degree of fit between metal and plastic. Integrating and calibrating the model data and a qualified cavity construction system improves the stability of subsequent injection molding processes. This not only adapts to the compatibility requirements of existing overall machine structures but also provides a reliable foundation for narrow bezel and thin-film molding of the module, reducing the additional costs of subsequent process adjustments.

[0014] In one embodiment of the present invention, S15 includes: The optimized 3D model data of the metal skeleton and the key parameter information of the qualified mold cavity are combined to generate an integrated basic dataset. Based on the integrated basic dataset, the core module layout of the gradient interface injection molding system is planned, the functional relationship of each module is clarified, and the system module architecture is formed. Based on the system module architecture, select suitable hardware components and software programs, connect and assemble the modules, and generate the initial prototype of the system. The core operating parameters of the initial prototype of the system are initially set, module collaboration tests are carried out, and parameter adaptation data is recorded. By combining parameter adaptation data, the system operating parameters are fine-tuned to ensure the stable coordination of each module, and the gradient interface injection molding system is built and initially calibrated.

[0015] The working principle and effects of the above technical solution are as follows: By summarizing key parameters to form an integrated basic dataset, a solid and precise foundation is built for the system, avoiding assembly errors caused by chaotic module layout. Planning the layout of core modules strengthens functional linkage and reduces conflicts during component adaptation. Selecting compatible hardware and software completes seamless assembly, reducing compatibility risks during system operation. Collaborative testing records parameter adaptation, and data-driven fine-tuning of operating parameters improves the stability of module cooperation, preventing parameter drift or operational lag during subsequent injection molding. This allows the molding system to quickly enter a stable working state and provides reliable support for the high-precision molding of the integrated glue-iron module, reducing rework costs and time losses caused by system failures later.

[0016] In one embodiment of the present invention, S2 includes: S21. Start the gradient interface injection molding system, input the initial dynamic holding pressure parameters for PBT+30% glass fiber reinforced engineering plastic, and start the injection molding process. S22. Through built-in sensing components, real-time data on in-mold pressure and temperature coupling field throughout the entire injection molding cycle is collected to ensure that no data is missed. S23. Import the collected pressure and temperature coupled field data into the multiphysics simulation model, perform data calculation and analysis, and complete the accurate prediction of residual stress. S24. Based on the residual stress prediction results, generate dynamic pressure holding compensation instructions to precisely adjust the injection pressure holding pressure value; S25. Continuously monitor the changes in residual stress inside the mold after adjustment, dynamically optimize compensation commands, and form a stable stress collaborative control closed loop.

[0017] The working principle and effects of the above technical solution are as follows: By inputting the initial dynamic holding pressure parameters of the suitable material, the injection molding process starts more closely with the material characteristics, avoiding molding defects caused by parameters. In-mold pressure and temperature data are collected throughout the entire cycle to ensure complete and accurate data, providing a comprehensive basis for subsequent analysis and reducing stress prediction deviations caused by missing data. Residual stress is accurately predicted through multiphysics simulation, making stress adjustment more targeted and avoiding structural hazards caused by blind pressure adjustment. Compensation commands are dynamically generated and the holding pressure is adjusted in real time, forming a closed-loop control with continuous monitoring, enhancing the stability of stress control and preventing cracking, deformation, and other problems after molding. This ensures the interface bonding quality of the rubber-iron composite, improves overall molding accuracy, reduces scrap rate, and minimizes rework losses and cost waste caused by stress issues.

[0018] In one embodiment of the present invention, step S23 includes: Key features are extracted from the collected pressure and temperature coupled field data, including peak data, trend data, and distribution density data, to generate a feature extraction dataset. Based on the feature extraction dataset, the computational parameters of the multiphysics simulation model are adjusted to match the computational requirements of the data features, thereby generating an adaptive simulation model. Input the feature extraction dataset into the adaptive simulation model, start the data processing flow, record the intermediate data in real time during the processing, and generate preliminary stress prediction data. By comparing the preliminary stress prediction data with the standard stress reference range, abnormal deviation data are screened, data correction processing is performed, and corrected stress prediction data is generated. By integrating and correcting the stress prediction data, identifying the inherent correlations within the data, accurate prediction of residual stress is achieved, and the final residual stress prediction result is output.

[0019] The working principle and effects of the above technical solution are as follows: By extracting key features of the pressure-temperature coupled field to generate a dataset, redundant information is filtered out, allowing the calculation to focus on core data, improving analysis efficiency, and avoiding prediction deviations caused by irrelevant data interference. The simulation model's calculation parameters are adjusted to adapt to the data characteristics, enhancing the model's specificity and reducing calculation errors caused by model-data mismatch. Intermediate calculation data is recorded to generate preliminary stress predictions, providing a complete basis for subsequent corrections and avoiding the problem of missing process data making traceability. Anomalies are screened and corrected by comparing with standard ranges, improving the accuracy of prediction data and preventing erroneous data from affecting the final result. The corrected data is integrated to complete accurate predictions, making the residual stress judgment more consistent with the actual situation, providing reliable support for subsequent pressure holding parameter adjustments, avoiding potential hazards such as stress concentration and cracking in the module formation due to inaccurate predictions, and reducing the probability of scrap.

[0020] In one embodiment of the present invention, S25 includes: The stress monitoring component is activated to continuously collect real-time data on residual stress within the mold after adjustment, generating a stress dynamic monitoring dataset. Analyze the changing trends of the stress dynamic monitoring dataset, compare them with preset stress control thresholds, and identify stress fluctuations that exceed the range; For the identified stress fluctuations, adjust the parameter details of the dynamic pressure holding compensation command to optimize command adaptability; The optimized compensation command is fed back to the injection molding system, and the residual stress data in the mold is collected again to verify the adjustment effect. The monitoring, analysis, adjustment, and verification process is executed cyclically to ensure that the residual stress remains stable within the control range, forming a closed loop of stress synergistic control.

[0021] The working principle and effects of the above technical solution are as follows: By continuously collecting real-time stress data through the activation of monitoring components, stress changes are traceable throughout the entire process, avoiding the problem of undetected stress anomalies. Analyzing data trends and comparing them with control thresholds accurately identifies fluctuations exceeding the range, reducing ineffective adjustments due to misjudgments. Targeted adjustments are made to the details of compensation command parameters, making the commands more closely match the actual stress conditions and enhancing the effectiveness of the adjustments. The optimized commands are fed back to the system, and data is collected again to verify the effect, ensuring that the adjustments effectively solve the problem and avoiding resource waste caused by blind operations. The cyclical execution of monitoring, analysis, adjustment, and verification processes forms a closed loop, ensuring that residual stress remains stable within a reasonable range, preventing potential problems such as stress concentration and cracking after module assembly. This improves the structural stability of the glue-iron composite and reduces the scrap rate, minimizing the time and cost losses caused by subsequent rework.

[0022] In one embodiment of the present invention, step S3 includes: S31. Perform surface pretreatment on the nano-injection molded iron-plastic composite to remove residual impurities and oil stains on the surface and ensure the basic bonding of the interface. S32. Start the plasma activation equipment to perform plasma activation treatment on the interface of the glue-iron composite to enhance the interface activity. S33. Uniformly coat the activated interface with silane coupling agent to construct a chemical transition layer and promote the chemical bonding between metal and plastic. S34. A gradient chemical bonding interface layer is generated through interface reaction, and relevant data of the interface layer are recorded simultaneously to form gradient chemical bonding interface layer data. S35. Start the Raman spectroscopy monitoring equipment to track the evolution of bonding intensity in gradient chemical bonding interface layer data in real time, and integrate the monitoring results to complete online feedback on interface bonding quality.

[0023] The working principle and effects of the above technical solution are as follows: Pre-treatment of the adhesive-iron composite surface removes residual impurities and oil, clearing obstacles for interfacial bonding and preventing weak bonding caused by impurities. Plasma activation treatment enhances interfacial activity, making subsequent chemical bonding easier and strengthening the bond. Uniform coating with a silane coupling agent constructs a chemical transition layer, shortening the bonding distance between the metal and plastic, improving interfacial bonding strength, and reducing the risk of delamination and detachment during use. A gradient chemical bonding interfacial layer is generated, and relevant data is recorded. Raman spectroscopy is used to track intensity evolution in real time, providing timely feedback on bonding quality and preventing substandard products from entering the next process. This ensures the structural stability of the adhesive-iron composite under high and low temperature cycling environments, improves the overall module's durability, reduces the probability of product failure due to interfacial failure, and reduces after-sales maintenance costs.

[0024] In one embodiment of the present invention, step S4 includes: S41. Calibrate the three-dimensional laser scanning equipment and set scanning parameters that match the size of the glue-iron composite to ensure that the scanning accuracy meets the detection requirements; S42. Use a three-dimensional laser scanning device to perform an all-round scan of the glue-iron composite, capture the actual molding contour information, and convert it into actual molding contour data. S43. Retrieve the 3D model data of the metal skeleton and compare it with the actual formed contour data point by point to calculate the flatness deviation and the distribution of the deviation. S44. Based on the deviation values ​​and distribution characteristics, generate targeted molding accuracy correction parameters to clarify the direction of injection molding process adjustment; S45. Apply the molding accuracy correction parameters to the injection molding process parameter adjustment, iteratively optimize the process settings, and correlate the in-mold state monitoring data with the final rigidity and flatness indicators to complete closed-loop control.

[0025] The working principle and effects of the above technical solution are as follows: By calibrating the 3D laser scanning equipment and setting matching parameters, the scanning accuracy is ensured to meet the standards, avoiding distortion of contour data caused by improper equipment parameters. A comprehensive scan of the plastic-iron composite captures the actual molding contour information, allowing the data to fully reflect the actual state of the product and reducing errors in judgment caused by missing key details. Point-by-point comparison between the 3D model of the metal skeleton and the actual molding data accurately calculates the deviation values ​​and distribution, making process adjustments more targeted and avoiding resource waste caused by blind optimization. Molding accuracy correction parameters are generated based on deviation characteristics, clarifying the direction of process adjustments, improving the efficiency of parameter optimization, and reducing the time spent on ineffective adjustments. The correction parameters are applied to process iterative optimization, linking the in-mold state with the final rigidity and flatness indicators to form a closed-loop control, enhancing the stability of product molding accuracy and avoiding problems such as excessive flatness or insufficient rigidity. This solution not only meets the precision requirements of ultra-thin narrow bezels but also is compatible with existing machine structures, reducing scrap rates and adaptation costs, and minimizing additional losses in subsequent process adjustments.

[0026] In one embodiment of the present invention, S45 includes: Receive molding accuracy correction parameters, divide the core adjustment dimensions of the injection molding process, which include temperature, pressure, injection speed, etc., and generate a parameter adjustment list; Refer to the parameter adjustment list, make initial adjustments to the corresponding injection molding process parameters, record the specific values ​​after adjustment, and generate the adjusted process parameter set. Injection molding is started based on the adjusted process parameter set, and in-mold status monitoring data of the new batch of rubber-iron composites is collected simultaneously to generate a real-time in-mold status dataset. The real-time in-mold state dataset is correlated with the rigidity and flatness test data of the new batch of products to compare the correspondence between parameter adjustments and index changes. Based on the correlation analysis results, if the indicators do not meet the standards, the parameter adjustment and data acquisition process is repeated until the rigidity and flatness meet the requirements, thus completing the closed-loop control.

[0027] The working principle and effects of the above technical solution are as follows: By dividing the core adjustment dimensions of the injection molding process, a parameter adjustment list is generated, making parameter optimization more organized and avoiding process chaos caused by directionless operations. After the initial adjustment, specific values ​​are recorded to form a process parameter set, ensuring that parameter changes are traceable and reducing the trouble of subsequent troubleshooting. Injection molding is started based on the adjusted parameters, and in-mold state data is collected, so that process optimization has real data support and avoids blind settings that are detached from reality. The in-mold state data is correlated with the product rigidity and flatness test results, clearly showing the correlation between parameter adjustment and index changes, improving the targeting of optimization and reducing the time wasted on ineffective adjustments. The adjustment, data collection, and analysis process is executed cyclically until the indexes are met, forming a closed-loop control to ensure that the product accuracy is consistently up to standard and to avoid problems such as insufficient flatness or rigidity. It can meet the precision molding requirements of ultra-thin and narrow-bezel products, and is also compatible with existing machine structures, reducing scrap rates and adaptation costs, and reducing the time and resource losses caused by subsequent rework.

[0028] In one embodiment of the present invention, step S5 includes: S51. Select multiple sets of closed-loop controlled integrated iron and glue structure modules as test samples, and formulate a comprehensive testing plan. The comprehensive testing plan includes rigidity testing and high and low temperature cycle testing. S52. Conduct a three-point bending strength test on the test sample, collect the three-point bending strength data in real time, and ensure that the data is true and valid. S53. Place the test sample in an environment ranging from -40℃ to 85℃ for high and low temperature cycle testing, continuously record whether cracking failure occurs during the test, and collect relevant data. S54. Using the weighted stability index algorithm, the three-point bending strength data and high and low temperature cycle failure data are comprehensively processed to extract key indicators of structural stability. S55. Generate a structural reliability assessment report based on key indicators, and combine the report to output an intelligent molding process optimization scheme. Integrate gradient interface, dynamic pressure holding, stress coordination and online feedback technologies to build a precision molding operating system.

[0029] The working principle and effects of the above technical solution are as follows: By selecting multiple sets of samples to formulate a comprehensive testing plan, covering rigidity and high and low temperature cycle testing, the reliability assessment is more comprehensive, avoiding biased judgments caused by single tests. Real and valid three-point bending strength data are collected, and cracking failures under high and low temperature environments are recorded, enhancing the credibility of data support and reducing the impact of data distortion on assessment results. A weighted stability index algorithm is used to comprehensively process the test data, extracting key structural stability indicators, improving the accuracy of judgment, and avoiding biases caused by subjective assessments. An assessment report is generated based on key indicators, outputting an intelligent molding process optimization plan, providing a clear direction for process adjustments and reducing the waste of resources from blind optimization. Multiple core technologies are integrated to build a precision molding operating system, promoting continuous process iteration, enhancing the consistency of mass production, and avoiding inconsistent product quality due to process fluctuations. This ensures that the integrated glue-iron module meets the usage requirements of high-strength and harsh temperature change environments, while also adapting to the design requirements of large screens and ultra-thin narrow bezels, reducing scrap rates and subsequent maintenance costs, and minimizing product failures caused by insufficient reliability.

[0030] In one embodiment of the present invention, S54 includes: By summarizing the three-point bending strength data and high and low temperature cycle failure data, and unifying the data format and statistical dimensions, a comprehensive dataset is generated. Based on the characteristics of the comprehensive dataset, the weight allocation ratio of the weighted stability index algorithm is adjusted to generate adaptive algorithm parameters; The comprehensive dataset is input into the weighted stability index algorithm with adaptive parameters to start the calculation process and generate preliminary analysis results; Compare the preliminary analysis results with the preset data validity standards, remove abnormal and deviating data, and generate the purified analysis results; The core information reflecting structural stability is extracted from the post-purification analysis results and integrated to form key indicators of structural stability.

[0031] The working principle and effects of the above technical solution are as follows: By summarizing three-point bending strength and high- and low-temperature cycle failure data, a comprehensive data set is generated using a unified format and statistical dimensions, avoiding analysis confusion caused by data clutter and providing a unified benchmark for subsequent calculations. The weight ratio of the weighted stability index algorithm is adjusted according to data characteristics to generate adaptive parameters, enhancing the algorithm's adaptability to the data and reducing computational bias. The comprehensive data is input into the adaptive algorithm to start the calculation, generating preliminary analysis results. Abnormal data is then compared with preset standards to remove abnormal data, improving the purity of the results and preventing invalid data from interfering with core judgments. Core information is extracted from the purified results and integrated to form key structural stability indicators, making stability assessment more accurate and providing solid support for subsequent evaluation reports. This approach objectively reflects the true stability state of the module under rigid and harsh temperature change environments, reduces subjective judgment errors, lowers the risk of deviation from process optimization direction due to inaccurate indicators, and provides reliable data for the construction of precision molding systems.

[0032] One embodiment of the present invention provides an ultra-thin, narrow-bezel integrated glue-iron structure module, comprising: One or more processors; Memory, used to store one or more programs; Wherein, when the one or more programs are executed by the one or more processors, the one or more processors are made to implement the method described in any one of the above.

[0033] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A molding method for an ultra-thin, narrow-bezel type integrated glue-iron structure module, characterized in that, The method includes: S1. Perform gradient thickness design on the metal skeleton to generate three-dimensional model data of the metal skeleton; perform nano-injection mold cavity adaptation processing based on the three-dimensional model data of the metal skeleton to construct a gradient interface injection molding system. S2. Dynamic pressure holding parameter control is performed based on the gradient interface injection molding system, and data of in-mold pressure and temperature coupling field are collected; residual stress is predicted based on the in-mold pressure and temperature coupling field data through a multi-physics simulation model, and dynamic pressure holding compensation command is generated; the injection pressure holding pressure is adjusted in real time according to the dynamic pressure holding compensation command to form a stress collaborative control closed loop. S3. Chemical bonding enhancement treatment is performed on the nano-injection molded iron-plastic composite. A plasma activation and silane coupling agent composite process is used to generate gradient chemical bonding interface layer data. The evolution of bonding strength of the gradient chemical bonding interface layer data is monitored in real time by Raman spectroscopy to complete the online feedback of interface bonding quality. S4. Obtain the actual molding contour data of the plastic-iron composite through three-dimensional laser scanning; compare the flatness deviation between the actual molding contour data and the three-dimensional model data of the metal skeleton to generate molding accuracy correction parameters; iteratively optimize the injection molding process parameters according to the molding accuracy correction parameters to complete the closed-loop control of the in-mold state and the final rigidity / flatness. S5. Based on the closed-loop control, the rigidity test of the integrated plastic-iron structure module is carried out, and three-point bending strength data and high and low temperature cycle failure data are collected. The data are processed by the weighted stability index algorithm to generate a structural reliability assessment report. Based on the structural reliability assessment report, an intelligent molding process optimization scheme is output to form a precision molding operating system.

2. The molding method of the ultra-thin narrow-bezel type integrated glue-iron structure module according to claim 1, characterized in that, S1 includes: S11. Select 6-series stainless steel as the base material for the metal skeleton, carry out overall planning of the gradient thickness of the metal skeleton, highlight the reinforcement design of key stress areas, and produce the initial three-dimensional model data of the metal skeleton. S12. Based on the initial 3D model data, refine the local thickening dimensions and transition curvature of the key stress areas, optimize the thinning scheme of the non-stress areas, and generate 3D model data of the metal skeleton with the required accuracy. S13. Focusing on the injection flow characteristics of PBT+30% glass fiber reinforced engineering plastic, design the mold cavity gradient interface structure to match the fitting requirements of the three-dimensional model data of the metal skeleton. S14. Based on the cavity gradient interface design scheme, perform nano-injection mold cavity processing, and simultaneously carry out processing accuracy testing to ensure that the cavity size error meets the assembly standard. S15. Integrate the optimized 3D model data of the metal skeleton with the qualified mold cavity, build a gradient interface injection molding system, and complete the initial calibration of system parameters.

3. The molding method of the ultra-thin narrow-bezel type integrated glue-iron structure module according to claim 2, characterized in that, S15 includes: The optimized 3D model data of the metal skeleton and the key parameter information of the qualified mold cavity are combined to generate an integrated basic dataset. Based on the integrated basic dataset, the core module layout of the gradient interface injection molding system is planned, the functional relationship of each module is clarified, and the system module architecture is formed. Based on the system module architecture, select suitable hardware components and software programs, connect and assemble the modules, and generate the initial prototype of the system. The core operating parameters of the initial prototype of the system are initially set, module collaboration tests are carried out, and parameter adaptation data is recorded. By combining parameter adaptation data, the system operating parameters are fine-tuned to ensure the stable coordination of each module, and the gradient interface injection molding system is built and initially calibrated.

4. The molding method of the ultra-thin narrow-bezel type integrated glue-iron structure module according to claim 1, characterized in that, The S2 includes: S21. Start the gradient interface injection molding system, input the initial dynamic holding pressure parameters for PBT+30% glass fiber reinforced engineering plastic, and start the injection molding process. S22. Through built-in sensing components, real-time data on in-mold pressure and temperature coupling field throughout the entire injection molding cycle is collected to ensure that no data is missed. S23. Import the collected pressure and temperature coupled field data into the multiphysics simulation model, perform data calculation and analysis, and complete the accurate prediction of residual stress. S24. Based on the residual stress prediction results, generate dynamic pressure holding compensation instructions to precisely adjust the injection pressure holding pressure value; S25. Continuously monitor the changes in residual stress inside the mold after adjustment, dynamically optimize compensation commands, and form a stable stress collaborative control closed loop.

5. The molding method of the ultra-thin narrow-bezel type integrated glue-iron structure module according to claim 4, characterized in that, S23 includes: Key features are extracted from the collected pressure and temperature coupled field data to generate a feature extraction dataset. Based on the feature extraction dataset, the computational parameters of the multiphysics simulation model are adjusted to match the computational requirements of the data features, thereby generating an adaptive simulation model. Input the feature extraction dataset into the adaptive simulation model, start the data processing flow, record the intermediate data in real time during the processing, and generate preliminary stress prediction data. By comparing the preliminary stress prediction data with the standard stress reference range, abnormal deviation data are screened, data correction processing is performed, and corrected stress prediction data is generated. By integrating and correcting the stress prediction data, identifying the inherent correlations within the data, accurate prediction of residual stress is achieved, and the final residual stress prediction result is output.

6. The molding method of the ultra-thin narrow-bezel type integrated glue-iron structure module according to claim 1, characterized in that, The S3 includes: S31. Perform surface pretreatment on the nano-injection molded iron-plastic composite. S32. Start the plasma activation equipment to perform plasma activation treatment on the interface of the glue-iron composite. S33. Uniformly coat the activated interface with silane coupling agent to construct a chemical transition layer and promote the chemical bonding between metal and plastic. S34. A gradient chemical bonding interface layer is generated through interface reaction, and relevant data of the interface layer are recorded simultaneously to form gradient chemical bonding interface layer data. S35. Start the Raman spectroscopy monitoring equipment to track the evolution of bonding intensity in gradient chemical bonding interface layer data in real time, and integrate the monitoring results to complete online feedback on interface bonding quality.

7. The molding method of the ultra-thin narrow-bezel type integrated glue-iron structure module according to claim 1, characterized in that, The S4 includes: S41. Calibrate the three-dimensional laser scanning equipment and set scanning parameters that match the size of the glue-iron composite. S42. Use a three-dimensional laser scanning device to perform an all-round scan of the glue-iron composite, capture the actual molding contour information, and convert it into actual molding contour data. S43. Retrieve the 3D model data of the metal skeleton and compare it with the actual formed contour data point by point to calculate the deviation value and distribution. S44. Based on the deviation values ​​and distribution characteristics, generate targeted molding accuracy correction parameters to clarify the direction of injection molding process adjustment; S45. Apply the molding accuracy correction parameters to the injection molding process parameter adjustment, iteratively optimize the process settings, and correlate the in-mold state monitoring data with the final rigidity and flatness indicators to complete closed-loop control.

8. The molding method of the ultra-thin narrow-bezel type integrated glue-iron structure module according to claim 7, characterized in that, The S45 includes: Receive molding accuracy correction parameters, divide the core adjustment dimensions of the injection molding process, and generate a parameter adjustment list; Refer to the parameter adjustment list, make initial adjustments to the corresponding injection molding process parameters, record the specific values ​​after adjustment, and generate the adjusted process parameter set. Injection molding is started based on the adjusted process parameter set, and in-mold status monitoring data of the new batch of rubber-iron composites is collected simultaneously to generate a real-time in-mold status dataset. The real-time in-mold state dataset is correlated with the rigidity and flatness test data of the new batch of products to compare the correspondence between parameter adjustments and index changes. Based on the correlation analysis results, if the indicators do not meet the standards, the parameter adjustment and data acquisition process is repeated until the rigidity and flatness meet the requirements, thus completing the closed-loop control.

9. The molding method of the ultra-thin narrow-bezel type integrated glue-iron structure module according to claim 1, characterized in that, The S5 includes: S51. Select multiple sets of closed-loop controlled integrated iron and glue structure modules as test samples and formulate a comprehensive testing plan. S52. Conduct a three-point bending strength test on the test sample and collect the three-point bending strength data in real time. S53. Place the test sample in an environment ranging from -40℃ to 85℃ for high and low temperature cycle testing, continuously record whether cracking failure occurs during the test, and collect relevant data. S54. Using the weighted stability index algorithm, the three-point bending strength data and high and low temperature cycle failure data are comprehensively processed to extract key indicators of structural stability. S55. Generate a structural reliability assessment report based on key indicators, and combine the report to output an intelligent molding process optimization scheme. Integrate gradient interface, dynamic pressure holding, stress coordination and online feedback technologies to build a precision molding operating system.

10. A thin, narrow-bezel integrated glue-iron structure module, characterized in that, The module includes: One or more processors; Memory, used to store one or more programs; Wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1 to 9.

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