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CN-122021303-A - Injection molding processing technology for high-performance plastic products

CN122021303ACN 122021303 ACN122021303 ACN 122021303ACN-122021303-A

Abstract

The invention relates to an injection molding processing technology for high-performance plastic products, which belongs to the technical field of information and comprises the steps of obtaining initial viscosity data and foaming parameters of a high-fluidity material, constructing a melt flow balance model according to the initial viscosity data and the foaming parameters, obtaining a parameter change trend from a bubble control lifting scheme, processing related variables with unstable internal structures by using a neural network to obtain a product surface flaw prediction result, obtaining real-time monitoring data of uneven bubble distribution from a foaming lightweight configuration, fine-adjusting the parameters according to the real-time monitoring data to obtain an overall performance optimization path, updating the viscosity data of the high-fluidity material according to the overall performance optimization path, and adopting a finite element simulation to process a feedback loop of bubble control to obtain a melt flow balance solution.

Inventors

  • LI SHISHENG

Assignees

  • 惠州市聚鑫达实业有限公司

Dates

Publication Date
20260512
Application Date
20260128

Claims (10)

  1. 1. An injection molding processing technology for high-performance plastic products is characterized by comprising the steps of obtaining initial viscosity data and foaming parameters of high-fluidity materials, constructing a melt flow balance model according to the initial viscosity data and the foaming parameters, extracting geometric characteristics of an ultrathin electronic shell from the melt flow balance model, conducting bubble distribution simulation on the geometric characteristics through finite element simulation, adjusting the foaming parameters according to simulation results to obtain a bubble control lifting scheme, obtaining parameter variation trend from the bubble control lifting scheme, processing related variables with unstable internal structures through a neural network to obtain product surface flaw prediction results, extracting potential defect position data from the product surface flaw prediction results, conducting iterative optimization through a genetic algorithm and combining with the geometric characteristics, correcting the melt flow balance model according to optimization results to obtain foaming lightweight configuration, obtaining real-time monitoring data of bubble distribution imbalance from the foaming lightweight configuration, conducting fine adjustment on the parameters according to the real-time monitoring data to obtain an overall performance optimization path, updating the viscosity data of the high-fluidity materials according to the overall performance optimization path, and conducting feedback circulation scheme controlled by finite element simulation to obtain the bubble balance.
  2. 2. The injection molding process for high-performance plastic products according to claim 1, wherein the construction of the melt flow balance model according to the initial viscosity data and the foaming parameters comprises the steps of introducing a viscosity adjustment mechanism to the initial viscosity data, determining the contradiction of strength loss in the foaming and light weight process, simulating the bubble distribution condition through the foaming parameters, judging that if the bubble distribution is uneven and exceeds a preset uneven distribution threshold value, acquiring a distribution optimization simulation result, balancing the melt flow by adopting the distribution optimization simulation result to obtain a preliminary melt flow balance model, and extracting a parameter acquisition path of a high-fluidity material from the preliminary melt flow balance model.
  3. 3. The injection molding process for high-performance plastic products, which is characterized by comprising the steps of extracting geometric features of an ultrathin electronic shell from a melt flow balance model, conducting bubble distribution simulation on the geometric features by adopting finite element simulation, acquiring ultrathin electronic shell data with outstanding production requirements from the melt flow balance model, extracting geometric features suitable for complex structures, adopting a finite element simulation algorithm to simulate bubble distribution conditions according to the geometric features, judging whether the distribution non-uniformity exceeds a threshold value, acquiring a distribution optimization simulation result according to the simulated bubble distribution conditions if the distribution non-uniformity exceeds the threshold value, adopting the distribution optimization simulation result to adjust foaming parameters to obtain optimized foaming parameters, balancing melt flow according to the optimized foaming parameters, determining a shell thickness uniform control path, and fusing the distribution non-uniformity threshold value through the shell thickness uniform control path to obtain a bubble control lifting scheme.
  4. 4. The injection molding process for high-performance plastic products according to claim 1, wherein the obtaining of the parameter variation trend from the bubble control lifting scheme and the processing of the related variable with unstable internal structure by using the neural network comprise obtaining the parameter variation trend from the bubble control lifting scheme, extracting the related variable with unstable internal structure from the parameter variation trend, wherein the related variable comprises a stress fluctuation value and a density deviation value, obtaining a variable set, inputting the variable set into the neural network algorithm, processing the related variable with unstable internal structure by using the neural network algorithm as an input, calculating hidden layer node output by using a multi-layer perceptron, obtaining a stable processing result, fusing shell thickness control according to the stable processing result, fusing the stable processing result by using the shell thickness control based on a preset uniform threshold value, simulating internal stress distribution condition, determining a stress balance path, adjusting the uneven distribution threshold value by using the stress balance path, generating a surface defect prediction model, calculating a defect probability by using the stress balance path as an input defect probability, and obtaining a stable product surface defect prediction result by using the surface defect prediction model.
  5. 5. The injection molding process for high-performance plastic products according to claim 1, wherein the step of extracting potential defect position data from the product surface flaw prediction result and performing iterative optimization by adopting a genetic algorithm in combination with the geometric features comprises the steps of extracting potential defect position data from the stable product surface flaw prediction result, performing iterative optimization by adopting a genetic algorithm in combination with geometric features suitable for complex structures aiming at the potential defect position data to obtain an optimized defect position set, activating a correction flow if the defect position set is inconsistent with the strength loss, acquiring the current parameters of a melt flow balance model, determining a correction direction, adjusting the parameters of the melt flow balance model according to the correction direction, fusing internal stress distribution with external load simulation, generating a density uniform adjustment scheme, obtaining an adjusted melt flow model, adopting the adjusted melt flow model simulation foaming process, extracting configuration variables aiming at the density uniform adjustment scheme, judging that the configuration variables exceed a preset threshold value, performing iterative correction to obtain a stable configuration set, and integrating a final lightweight foaming index through the stable configuration set.
  6. 6. The injection molding process for high-performance plastic products according to claim 1, wherein the acquiring of the real-time monitoring data of the bubble maldistribution from the foaming lightweight configuration and the fine adjustment of parameters according to the real-time monitoring data comprise extracting the real-time monitoring data of the bubble maldistribution from the final foaming lightweight configuration, calculating a distribution deviation value according to the real-time monitoring data aiming at the production demand salient feature of an ultrathin electronic shell, judging that if the distribution deviation value deviates from a preset threshold value, activating a fine adjustment mechanism to obtain an initial optimization path, fusing an internal stress balance attribute according to the initial optimization path, acquiring a density adjustment parameter in material flow simulation, integrating the maldistribution correction according to the density adjustment parameter, determining an enhanced structural strength evaluation scheme, simulating shell thickness uniform control according to the enhanced structural strength evaluation scheme, extracting configuration variables according to the shell thickness uniform control, and if the configuration variables exceed the threshold value, iterating parameters, integrating lightweight demand attribute according to the adjusted performance optimization path, acquiring final real-time feedback data in density adjustment, and determining an overall performance optimization path.
  7. 7. The injection molding process for high-performance plastic products according to claim 1, wherein the updating of the viscosity data of the high-fluidity material according to the overall performance optimization path and the adoption of a feedback loop of finite element simulation processing bubble control comprise the steps of extracting initial viscosity data of the high-fluidity material from a material database according to the enhanced overall performance optimization path, adjusting and fusing the initial viscosity data for a heat conduction coefficient, obtaining an updated viscosity data set, processing bubble control promotion in the updated viscosity data set by adopting a finite element simulation algorithm, generating a mold temperature balance attribute for the feedback loop, determining an internal structure simulation result, calculating an instability index value by the internal structure simulation result, judging that balance adjustment is activated if the instability index value is reduced for the comparison of the instability index value with a preset threshold value, obtaining a structural stability evaluation index, obtaining flow deviation parameters in the structural stability evaluation index, correcting the flow deviation parameters for melt distribution unevenness, determining verification data, integrating high-fluidity selection attribute by the scheme verification data, fusing the scheme balance data for complete melt flow simulation, and obtaining a complete flow solution balance.
  8. 8. The injection molding process for high-performance plastic products according to claim 2, wherein the step of introducing a viscosity adjustment mechanism to the initial viscosity data to determine the contradiction of strength loss in the foaming and light-weight process comprises the steps of acquiring initial viscosity data and foaming parameters of a high-fluidity material from a preset material database, performing viscosity adjustment mechanism processing on the initial viscosity data to obtain an adjusted viscosity value, determining a bubble distribution uneven threshold corresponding to the contradiction of strength loss according to the matching relation between the adjusted viscosity value and the foaming parameters, and restricting a subsequent bubble distribution simulation process through the bubble distribution uneven threshold to ensure that the initial construction of a melt flow balance model has a coordination basis of strength and light weight.
  9. 9. An injection molding process for high performance plastic products according to claim 3, wherein the step of adopting a finite element simulation algorithm to simulate bubble distribution conditions for the geometric features and judging whether the distribution non-uniformity exceeds a threshold value comprises dividing the geometric features adapted to the complex structure into a plurality of local areas, respectively executing finite element grid division and melt flow-bubble growth coupling calculation for each local area, counting deviation of bubble size distribution and volume fraction of each local area according to calculation results, and triggering a distribution optimization simulation flow to obtain targeted foam parameter adjustment suggestion for determining a subsequent uniform shell thickness control path when the deviation value of any local area exceeds a preset distribution non-uniformity threshold value.
  10. 10. The injection molding process for high-performance plastic products according to claim 4, wherein the neural network algorithm is inputted through the variable set, and the neural network algorithm uses the variable set as an input to process related variables with unstable internal structures, and comprises the steps of normalizing the variable set formed by the stress fluctuation value and the density deviation value, inputting the normalized variable set into a multi-layer perceptron, calculating nodes of each hidden layer through forward propagation of the multi-layer perceptron, outputting the nodes, and obtaining an intermediate representation through an activation function, mapping the intermediate representation through an output layer to obtain an integrated quantization index for representing the stability degree of the internal structures, and determining whether further adjustment of the uneven distribution threshold is needed to generate a surface flaw prediction model according to a comparison result of the integrated quantization index and a preset stability threshold.

Description

Injection molding processing technology for high-performance plastic products Technical Field The invention relates to the technical field of information, in particular to an injection molding processing technology for high-performance plastic products. Background In the modern manufacturing industry, injection molding technology of plastic products takes a vital role, especially in the fields of high requirements such as electronic product shells, automobile parts and the like, and the process directly influences the performance and production efficiency of the products. Injection molding is required to meet production requirements of complex structures and ultrathin designs, and is balanced in terms of light weight, strength and appearance quality. The importance of the technology is that the technology can promote product innovation, and simultaneously reduce production cost and resource consumption, and becomes a key link for transformation and upgrading of the manufacturing industry. However, existing injection molding processes tend to be two-way in the face of the need for high flow materials. In order to make it easier to fill complex moulds with plastic melts, the viscosity is usually reduced by adding lubricants or increasing the processing temperature, but this tends to result in a loss of strength and toughness of the material and even in heat resistance of the product. More seriously, when the light weight is attempted to be realized by a foaming technology, the formation and distribution of bubbles are often difficult to control, so that flaws appear on the surface of a product, the internal structure is also not uniform enough, and the application scene with strict requirements on the appearance and the mechanical properties is difficult to meet. Focusing on the technical difficulty, the core problem is that the balance of melt fluidity and bubble structure cannot be considered. On the one hand, the melt has higher viscosity when flowing at a high speed, and is difficult to rapidly fill complex dies, and particularly in an ultrathin wall structure, the problems of insufficient filling or excessive internal stress easily occur. On the other hand, bubbles are easy to overgrow or merge in the foaming process, so that the internal holes are different in size, even defects are formed on the surface, and the attractive appearance and the service life of the product are influenced. These two problems are related to each other, and insufficient flowability can increase the non-uniformity of bubble distribution, and failure of bubble control can further impair material properties and appearance quality. Therefore, how to ensure the high fluidity of the melt to adapt to complex structures in the injection molding process and accurately control the size and distribution of bubbles to improve the strength and the surface quality of the product becomes a key problem of breakthrough of the prior art. This problem is particularly pronounced in practical applications, such as in the production of adapter housings, where ultra-thin designs require the material to quickly fill every minute corner of the mold, while also requiring smooth surfaces, no marks, and stable internal structures to withstand the shock of everyday use, which presents a significant challenge to the prior art. From the above analysis, the progressive relationship from industry demand to specific technical bottlenecks can be clearly seen, and an innovative method is needed to solve the core contradiction, and provide new possibilities for producing high-performance plastic products. Disclosure of Invention The invention provides an injection molding processing technology for high-performance plastic products, which mainly comprises the following steps: The method comprises the steps of obtaining initial viscosity data and foaming parameters of a high-fluidity material, constructing a melt flow balance model according to the initial viscosity data and the foaming parameters, extracting geometric characteristics of an ultrathin electronic shell from the melt flow balance model, conducting bubble distribution simulation on the geometric characteristics by adopting finite element simulation, adjusting the foaming parameters according to simulation results to obtain a bubble control lifting scheme, obtaining parameter variation trend from the bubble control lifting scheme, processing related variables with unstable internal structures by adopting a neural network to obtain a product surface flaw prediction result, extracting potential defect position data from the product surface flaw prediction result, conducting iterative optimization by adopting a genetic algorithm and combining the geometric characteristics, correcting the melt flow balance model according to the optimization result to obtain foaming lightweight configuration, obtaining real-time monitoring data of bubble distribution unevenness from the foaming lightweight configuration, c