A method for injection molding of an automobile lamp cover

Abstract

This application provides a vacuum-assisted injection molding method for automotive lamp covers, relating to the field of injection molding technology. The method includes the following steps: identifying specifications by reading an integrated label and initializing vacuum parameters, including vacuum level, evacuation timing, and exhaust valve opening reference; collecting the melt front position, velocity, and pressure using sensors, and predicting the front arrival time using a linear regression model; activating the vacuum pump to evacuate when the front approaches the end of the cavity, and coordinating with the exhaust valve and zoned cooling; quantifying the gas mark area and weld line length after demolding, and iterating the vacuum parameters using a support vector machine model; verifying the filling uniformity, and fine-tuning the vacuum delay or auxiliary mechanism if abnormal; repeating the above steps until the defect rate is <3%. This application has the effect of improving the first article yield and reducing consumption.

Description

Technical Field

[0001] This invention relates to the field of injection molding technology, and in particular to a vacuum-assisted injection molding method for automotive lamp covers. Background Technology

[0002] Existing technologies for automotive lamp cover injection molds include dedicated cooling devices for automotive lamp covers, efficient venting structures, easy ejection and mold opening mechanisms, precision mold closing mechanisms, stable guiding mechanisms, auxiliary mold core changing structures, multi-specification adaptability structures, and automatic gate removal devices. These patents emphasize cooling uniformity, demolding protection, venting stability, and rapid mold core switching. However, for large-size or thick-walled lamp covers, air is easily left behind during melt filling, leading to air marks, weld lines, or optical stress problems. Existing venting is mostly a passive structure, which cannot dynamically assist vacuum based on the real-time filling status. After switching between multiple specifications, vacuum parameters need to be manually adjusted, resulting in a high first-piece defect rate.

[0003] To address the aforementioned issues, automotive lamp cover injection molds urgently need to achieve dynamic air removal and defect minimization during the filling process. Summary of the Invention

[0004] To address the aforementioned issues, this application provides a vacuum-assisted injection molding method for automotive lamp covers.

[0005] The vacuum-assisted injection molding method for automotive lamp covers provided in this application adopts the following technical solution: A vacuum-assisted injection molding method for automotive lamp covers includes the following steps: S1. Mold core specification identification and parameter initialization: The specification is identified by reading the integrated tag, and the vacuum parameters are initialized, including the vacuum degree, pumping sequence and exhaust valve opening reference. S2. Melt front prediction and data acquisition: The position, velocity and pressure of the melt front are collected by sensors, and the arrival time of the front is predicted by a linear regression model. S3, Dynamic Vacuum Pumping Linkage: Based on prediction, the vacuum pump is activated to pump air when the leading edge approaches the end of the cavity, and the exhaust valve and zoned cooling are linked. S4. Defect Feedback and Machine Learning Iteration: After demolding, the area of ​​air marks and the length of the weld wire are quantified, and the vacuum parameters are iterated using a support vector machine model. S5. Anomaly Verification and Fine-tuning: Verify the uniformity of filling; if an anomaly is found, fine-tune the vacuum delay or auxiliary mechanism. S6. Repeat S2-S5 until the defect rate is <3%.

[0006] Optionally, the vacuum pump's pumping intensity gradually increases to -0.08 MPa, and the exhaust valve opening is increased to 80%-100%.

[0007] Optionally, the support vector machine model uses a radial basis function kernel with C parameters 1-10; the input includes sensor data and defect values, and the output is a vacuum degree offset of ±10%-30%; the upper limit of iteration is 4 cycles, and the convergence condition is a defect decrease of <5%.

[0008] Optionally, the defect quantification includes air mark area statistically determined by image binarization to be <0.5cm², and weld line length calculated by edge detection to be <1mm.

[0009] Optionally, the method is compatible with rapid thermal cycling, with pre-injection heating at 120-150°C and post-pressure cooling at 20-40°C; abnormal fine-tuning includes a delay of 0.5-1s when ejection resistance is >50N, or pre-tightening of +5%-10% when mold closing offset is >0.1mm.

[0010] In summary, this application includes at least one of the following beneficial technical effects: It reduces the air mark defect rate, improves the first-piece yield rate, and reduces vacuum energy consumption. By integrating the predictive model with learning iteration, a highly creative closed loop is formed, which can be extended to the field of precision optical component injection molding to improve versatility and intelligent manufacturing level. Attached Figure Description

[0011] Figure 1 This is the overall flowchart of this method. Detailed Implementation

[0012] The terminology used in the following embodiments is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to also include expressions such as “one or more,” unless the context clearly indicates otherwise. It should also be understood that in the following embodiments of this application, “at least one” and “one or more” refer to one, two, or more than two. The term “and / or” is used to describe the relationship between related objects, indicating that three relationships may exist; for example, A and / or B can indicate: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character “ / ” generally indicates that the preceding and following related objects are in an “or” relationship.

[0013] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0014] The following is in conjunction with the appendix Figure 1 The present invention will be described in further detail below.

[0015] This application discloses a vacuum-assisted injection molding method for automotive lamp covers, referring to... Figure 1 This includes the following steps: S1. Mold core specification identification and parameter initialization: When switching mold cores, the specification is identified by reading the integrated RFID / NFC tag, and the vacuum parameters are initialized from the preset database, including the initial vacuum value, pumping sequence, exhaust valve opening reference and zoned cooling flow rate. S2. Melt Front Prediction and Data Acquisition: Before injection filling, the position, flow velocity, and pressure distribution of the melt front within the mold cavity are acquired in real time using fiber optic sensors and cavity pressure sensors. A linear regression model is used to predict the arrival time of the melt front. The formula for the linear regression model is as follows: ,in For distance, For speed, The error correction term is used in the linear regression model, which is trained using the least squares method. Based on the mold temperature difference, when the mold temperature difference is >5℃, the time is increased by 0.5s; S3. Dynamic vacuum pumping linkage: Based on the predicted time, when the melt front is close to the end of the cavity, i.e., the remaining distance is <10%-20%, the vacuum pump is activated to pump air. The vacuum pumping intensity gradually increases to -0.08MPa, the linkage exhaust valve opening increases to 80%-100%, and the zoned cooling is started at the same time to remove residual air. S4. Defect Feedback and Machine Learning Iteration: After demolding, an industrial camera is used to quantify the air mark area and weld line length. The air mark area is statistically determined by image binarization and is <0.5cm², while the weld line length is calculated by edge detection and is <1mm. Using defects as negative feedback, a Support Vector Machine (SVM) model is used to iterate the vacuum parameters. The input is sensor data and defect values, and the output is a vacuum degree offset of ±10%-30% and an evacuation timing adjustment of ±1-2s. The kernel function is the Radial Basis Function (RBF), and the C parameter is set to 1-10. The initial training set consists of 10-20 sets of historical periodic data. The upper limit of the iteration is 4 periods, and the convergence condition is a defect decrease of <5%. If the defect exceeds the standard, optimization is applied in the next period. S5. Anomaly Verification and Fine-tuning: Verify filling uniformity. If ejection resistance > 50N or mold closing offset > 0.1mm, fine-tune vacuum delay by 0.5-1s or guide preload by +5%-10%. S6. Cyclic execution: Repeat S2-S5 until the defect rate is <3%, supporting continuous production of multiple specifications.

[0016] This method is compatible with rapid thermal cycling (RHCM), requiring heating to 120-150℃ before injection and cooling to 20-40℃ after pressure holding; it is suitable for related fields such as injection molding of medical optical lenses, with the vacuum threshold adjusted to -0.06MPa.

[0017] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. Any equivalent modifications or variations made by those skilled in the art based on the content disclosed in the present invention should be included within the scope of protection set forth in the claims.

Claims

1. A vacuum-assisted injection molding method for automotive lamp covers, characterized in that, Includes the following steps: S1. Mold core specification identification and parameter initialization: The specification is identified by reading the integrated tag, and the vacuum parameters are initialized, including the vacuum degree, pumping sequence and exhaust valve opening reference. S2. Melt front prediction and data acquisition: The position, velocity and pressure of the melt front are collected by sensors, and the arrival time of the front is predicted by a linear regression model. S3, Dynamic Vacuum Pumping Linkage: Based on prediction, the vacuum pump is activated to pump air when the leading edge approaches the end of the cavity, and the exhaust valve and zoned cooling are linked. S4. Defect Feedback and Machine Learning Iteration: After demolding, the area of ​​air marks and the length of the weld wire are quantified, and the vacuum parameters are iterated using a support vector machine model. S5. Anomaly Verification and Fine-tuning: Verify the uniformity of filling; if an anomaly is found, fine-tune the vacuum delay or auxiliary mechanism. S6. Repeat S2-S5 until the defect rate is <3%.

2. The vacuum-assisted injection molding method for automotive lamp covers according to claim 1, characterized in that: In step S3, the vacuum pump's pumping intensity gradually increases to -0.08 MPa, and the exhaust valve opening increases to 80%-100%.

3. The vacuum-assisted injection molding method for automotive lamp covers according to claim 1, characterized in that: In step S4, the support vector machine model uses a radial basis function kernel with C parameters 1-10; the input includes sensor data and defect values, and the output is a vacuum degree offset of ±10%-30%; the upper limit of iteration is 4 cycles, and the convergence condition is a defect decrease of <5%.

4. The vacuum-assisted injection molding method for automotive lamp covers according to claim 3, characterized in that: In step S4, the defect quantification includes the air mark area being statistically determined to be <0.5cm² through image binarization, and the weld line length being calculated to be <1mm through edge detection.

5. The vacuum-assisted injection molding method for automotive lamp covers according to claim 1, characterized in that: The method is compatible with rapid thermal cycling, with pre-injection heating at 120-150℃ and post-pressure cooling at 20-40℃; abnormal fine-tuning includes a delay of 0.5-1s when ejection resistance is >50N, or a pre-tightening of +5%-10% when mold closing offset is >0.1mm.

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