Multi-layer composite material intelligent mold visual platform system and manufacturing method thereof

Abstract

The invention belongs to the technical field of injection molding process control, and particularly relates to a multi-layer composite material intelligent mold visual platform system and a manufacturing method thereof.The system comprises a modular layered mold, and each layer is provided with an independent temperature control unit and an electromagnetic compensation ejector pin; the multi-physical-field sensor module integrates temperature, pressure and vibration sensors and monitors technological parameters of the mold closing stage, the injection molding stage and the cooling stage in real time; the visual quality inspection module comprises a linear array camera and a laser displacement sensor and is used for detecting interlayer defects and dimensional tolerance on line; the control platform dynamically optimizes process parameters based on the sensor and visual data and performs data interaction with an external management system; according to the invention, interlayer pressure dynamic compensation and temperature zoning control can be realized, and the problem of deformation caused by the fact that a traditional die cannot adapt to thermal expansion difference of multi-layer materials is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of injection molding process control, and particularly relates to an intelligent mold vision platform system for multi-layer composite materials and a manufacturing method thereof. Background Art

[0002] With the increasing demand for lightweight materials, the application of multi-layer composite structures (such as honeycomb materials with a foamed core layer + a dense surface layer) in fields such as automobiles and aerospace is becoming increasingly widespread. Traditional molds for manufacturing such products face the following technical bottlenecks: Unstable interlayer bonding quality: Due to the differences in the thermal expansion coefficients of each layer of materials and insufficient matching of process parameters, defects such as delamination and bubbles are likely to occur. Moreover, although most current injection molds can achieve alternating injection, they rely on fixed temperature gradient settings and cannot dynamically compensate for material shrinkage, resulting in large fluctuations in interlayer shear strength; Low quality inspection efficiency: Existing technologies mostly use manual sampling inspection or off-line detection, and cannot real-time associate defects with process parameters. The traditional method has a high missed detection rate for delamination defects, and the detection time accounts for a relatively large proportion in the production cycle; Therefore, there is an urgent need for a systematic solution that integrates intelligent perception, real-time quality inspection, and dynamic control to improve the manufacturing efficiency and quality consistency of laminated composite materials. Summary of the Invention

[0003] The purpose of the present invention is to provide an intelligent mold vision platform system for multi-layer composite materials and a manufacturing method thereof to solve the problems raised in the background art.

[0004] To achieve the above purpose, the present invention provides the following technical solution: An intelligent mold vision platform system for multi-layer composite materials, the system includes: A modular stratified mold, each layer is provided with an independent temperature control unit and an electromagnetic compensation ejector pin; A multi-physical field sensor module, integrating temperature, pressure, and vibration sensors to real-time monitor the process parameters in the mold closing, injection molding, and cooling stages; A vision quality inspection module, including a line array camera and a laser displacement sensor, for on-line detection of interlayer defects and dimensional tolerances; A control platform, dynamically optimizing process parameters based on sensor and vision data, and performing data interaction with an external management system.

[0005] For the intelligent mold vision platform system for multi-layer composite materials of the present invention, wherein, the stroke accuracy of the electromagnetic compensation ejector pin is ±5μm, the response time ≤ 10ms, and the interlayer contact pressure deviation is adjusted by a PID algorithm to be less than 3%.

[0006] For the intelligent mold vision platform system for multi-layer composite materials of the present invention, wherein, the multi-physical field sensor module includes: Distributed FBG fiber grating sensors are arranged in each layer of the mold, with a temperature monitoring accuracy of ±0.2°C; Piezoelectric thin film sensor array, with a range of 0 - 50 MPa, generating a pressure distribution thermal map; Piezoelectric ceramic actuator, outputting mechanical vibrations with a frequency of 5 - 50 Hz.

[0007] In the visual platform system of the multi-layer composite material intelligent mold described in the present invention, the visual quality inspection module adopts an improved YOLOX-s algorithm.

[0008] In addition, the present invention also provides a manufacturing method for multi-layer composite materials based on the visual platform system of the multi-layer composite material intelligent mold. This method includes the following steps: Step S1: Set the temperature gradient of the layered mold and the initial process parameters; Step S2: Dynamically adjust the interlayer gap through an electromagnetic compensation ejector pin during the mold closing stage; Step S3: Apply vibration-assisted pressure holding during the injection molding stage, with a frequency of 20 Hz and an amplitude of 100 μm; Step S4: After mold opening, conduct on-line inspection through the visual quality inspection module, and the defect data is fed back to the control platform in real time.

[0009] In the manufacturing method for multi-layer composite materials based on the visual platform system of the multi-layer composite material intelligent mold described in the present invention, in step S3, the duration of vibration-assisted pressure holding is dynamically adjusted according to the rheological characteristics of the material, and the adjustment range is 10 - 30 s.

[0010] In the manufacturing method for multi-layer composite materials based on the visual platform system of the multi-layer composite material intelligent mold described in the present invention, in step S4, if a delamination defect is detected, the control platform automatically increases the pressure holding time by 0.3 - 0.8 s and increases the mold temperature gradient by 5 - 10°C.

[0011] In the manufacturing method for multi-layer composite materials based on the visual platform system of the multi-layer composite material intelligent mold described in the present invention, the specific process of setting the temperature gradient of the layered mold in step S1 includes: Calculate the initial temperature gradient ΔT = α×E×h / (1 - V 2 ), where α is the material expansion coefficient, E is the elastic modulus, h is the layer thickness, and V is the Poisson's ratio; Set the outer layer mold temperature to 180 - 220°C, and reduce the core layer mold temperature by 20 - 50°C; Real-time calibrate the temperature deviation to ±0.5°C through the distributed FBG fiber grating sensors.

[0012] The manufacturing method of multi-layer composite materials based on the visual platform system of intelligent molds for multi-layer composite materials according to the present invention, wherein the control logic of vibration-assisted pressure holding in step S3 is as follows: When the detected interlayer pressure difference ≥ 5 MPa, the vibration frequency is triggered to increase from the reference value of 20 Hz to 25 - 30 Hz; The amplitude is dynamically adjusted according to the material viscosity, and the calculation formula is: A = μ × V / (σ × t), where μ is the melt viscosity, V is the injection volume, σ is the interlayer contact stress, and t is the pressure holding time. The amplitude range is 80 - 150 μm.

[0013] The manufacturing method of multi-layer composite materials based on the visual platform system of intelligent molds for multi-layer composite materials according to the present invention, wherein the feedback optimization in step S4 includes: For the honeycomb structure collapse defect, synchronously adjust the electromagnetic compensation ejector pin pressure of the mold to increase by 10% - 15%; Predict the process parameters of the next cycle based on the LSTM model, and the output variables include the pressure holding time ± 0.3 s, the vibration frequency ± 5 Hz, and the mold temperature gradient ± 5 °C.

[0014] Compared with the prior art, the beneficial effects of the present invention are: Dynamic compensation of interlayer pressure and temperature zone control are realized, solving the deformation problem caused by the inability of traditional molds to adapt to the thermal expansion differences of multi-layer materials; moreover, it can also avoid the disadvantages of the prior art that manual sampling inspection or off-line detection cannot real-time associate defects with process parameters, further improving the missed detection rate of delamination defects, and greatly reducing the proportion of detection time consumption in the production cycle. Description of the Drawings

[0015] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0016] Figure 1 It is a process method step flow chart of the present invention. Specific Embodiments

[0017] In the description, claims and drawings of the present invention, terms such as "first", "second", "third" and "fourth" are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally further include steps or units not listed, or may optionally further include other steps or units inherent to these processes, methods, products or devices.

[0018] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in connection with the embodiments can be included in at least one embodiment of the present invention. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0019] "Plurality" means two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after.

[0020] Moreover, terms indicating directions such as "upper", "lower", "left", "right", "upper end", "lower end", "longitudinal", etc. are all referenced based on the attitude position of the device or equipment described in this solution during normal use.

[0021] In order to make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are partial embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the protection scope of the present invention.

[0022] This embodiment discloses a vision platform system for a multi-layer composite material intelligent mold, and the system includes: A modular layered mold, each layer is provided with an independent temperature control unit and an electromagnetic compensation ejector pin; A multi-physical field sensor module, integrating temperature, pressure and vibration sensors, and real-time monitoring of process parameters in the mold closing, injection molding and cooling stages; A vision quality inspection module, including a line array camera and a laser displacement sensor, for online detection of interlayer defects and dimensional tolerances; The control platform dynamically optimizes process parameters based on sensor and vision data and interacts with the data of the external management system.

[0023] Attracted by the present invention, dynamic compensation of the interlayer pressure and temperature zone control can be achieved, solving the deformation problem caused by the inability of traditional molds to adapt to the thermal expansion differences of multi-layer materials. Moreover, it can also avoid the drawbacks of the existing technology that manual sampling inspection or off-line detection cannot real-time associate defects with process parameters, further improving the missed detection rate of delamination defects and greatly reducing the proportion of the detection time-consuming in the production cycle.

[0024] In this embodiment, the stroke accuracy of the electromagnetic compensation ejector pin is ±5μm, the response time ≤ 10ms, and the deviation of the interlayer contact pressure is adjusted by the PID algorithm to be less than 3%, so as to reduce the fluctuation of the interlayer contact pressure and avoid the problem of uneven pressure caused by the response delay of the traditional mechanical ejector pin.

[0025] In this embodiment, the multi-physical field sensor module includes: Distributed FBG fiber Bragg grating sensors are arranged on each layer of the mold, and the temperature monitoring accuracy is ±0.2°C; Piezoelectric film sensor array with a range of 0 - 50MPa to generate a pressure distribution thermal map; Piezoelectric ceramic actuator to output mechanical vibration with a frequency of 5 - 50Hz; By establishing a three-parameter coupling model of pressure - temperature - vibration, the process controllability is further improved, avoiding the problem that single-parameter control cannot cope with the non-linear characteristics of material rheology.

[0026] In this embodiment, the vision quality inspection module adopts the improved YOLOX-s algorithm to improve the accuracy of defect classification and eliminate the problem of high missed inspection rate in manual sampling inspection.

[0027] Embodiment 2

[0028] This embodiment is basically the same as Embodiment 1, and the same parts will not be described again. The differences are that a manufacturing method for multi-layer composite materials based on the system of Embodiment 1 is also provided, as Figure 1 shown, and the method includes the following steps: Step S1: Set the temperature gradient of the layered mold and the initial process parameters; Step S2: Dynamically adjust the interlayer gap through the electromagnetic compensation ejector pin during the mold closing stage; Step S3: Apply vibration pressure holding during the injection molding stage, with a frequency of 20Hz and an amplitude of 100μm. Vibration pressure holding can further increase the interlayer molecular diffusion coefficient and solve the problem of weak interface bonding caused by insufficient penetration depth in traditional static pressure holding; Step S4: After mold opening, conduct on-line inspection through the vision quality inspection module, and the defect data is fed back to the control platform in real time.

[0029] In this embodiment, in step S3, the duration of vibration-assisted pressure holding is dynamically adjusted according to the rheological properties of the material, and the adjustment range is 10 - 30 s, so as to automatically match the amplitude for materials with different viscosities (80 - 150 μm self-adaptive), and avoid the fiber orientation disorder caused by general parameters.

[0030] In this embodiment, in step S4, if a delamination defect is detected, the control platform automatically increases the pressure holding time by 0.3 - 0.8 s and increases the mold temperature gradient by 5 - 10 °C, so as to improve the system feedback response time and reduce the callback fluctuation of traditional PID control.

[0031] In this embodiment, the specific process of setting the delamination mold temperature gradient in step S1 includes: Calculating the initial temperature gradient ΔT = α×E×h / (1 - V 2 ), where α is the material expansion coefficient, E is the elastic modulus, h is the layer thickness, and V is the Poisson's ratio; The outer layer mold temperature is set to 180 - 220 °C, and the core layer mold temperature is reduced by 20 - 50 °C; Real-time calibrating the temperature deviation to ±0.5 °C through a distributed FBG fiber Bragg grating sensor; Through this step, the thermal stress deformation amount can be reduced, and the uniformity of the cooling shrinkage of anisotropic materials can be ensured.

[0032] In this embodiment, the control logic of vibration-assisted pressure holding in step S3 is as follows: When the detected interlayer pressure difference ≥ 5 MPa, trigger the vibration frequency to increase from the reference value of 20 Hz to 25 - 30 Hz; The amplitude is dynamically adjusted according to the material viscosity, and the calculation formula is: A = μ×V / (σ×t), where μ is the melt viscosity, V is the injection volume, σ is the interlayer contact stress, t is the pressure holding time, and the amplitude range is 80 - 150 μm; Through the above control logic, the stability of the melt front flow velocity can be improved, and the jet flow phenomenon during the injection molding process can be weakened.

[0033] In this embodiment, the feedback optimization in step S4 includes: For the honeycomb structure collapse defect, synchronously adjust the electromagnetic compensation ejector pin pressure of the mold to increase by 10% - 15%; Predict the process parameters of the next cycle based on the LSTM model, and the output variables include the pressure holding time ±0.3 s, the vibration frequency ±5 Hz, and the mold temperature gradient ±5 °C.

[0034] It should be understood that for those of ordinary skill in the art, modifications or variations can be made according to the above description, and all such modifications and variations should fall within the protection scope of the appended claims of the present invention.

Claims

1. An intelligent die vision platform system for multi-layer composite materials, characterized in that, The system includes: A modular layered mold, with each layer equipped with an independent temperature control unit and an electromagnetic compensation ejector pin; A multi-physical field sensor module, integrating temperature, pressure and vibration sensors to monitor the process parameters in the mold closing, injection molding and cooling stages in real time; A vision quality inspection module, including a line array camera and a laser displacement sensor, used for on-line detection of interlayer defects and dimensional tolerances; A control platform, which dynamically optimizes the process parameters based on sensor and vision data and interacts with the external management system data.

2. The intelligent die vision platform system of the multi-layer composite material according to claim 1, characterized in that, The stroke accuracy of the electromagnetic compensation ejector pin is ±5μm, the response time ≤10ms, and the interlayer contact pressure deviation is adjusted by the PID algorithm to be less than 3%.

3. The intelligent die vision platform system of the multi-layer composite material according to claim 2, characterized in that, The multi-physical field sensor module includes: Distributed FBG fiber Bragg grating sensors, arranged on each layer of the mold, with a temperature monitoring accuracy of ±0.2℃; A piezoelectric film sensor array, with a range of 0~50MPa, generating a pressure distribution thermal map; A piezoelectric ceramic actuator, outputting mechanical vibration with a frequency of 5~50Hz.

4. The intelligent die vision platform system of the multi-layer composite material according to claim 1, characterized in that The vision quality inspection module adopts an improved YOLOX-s algorithm.

5. A manufacturing method of multi-layer composite materials based on a visual platform system of an intelligent mold for multi-layer composite materials. According to the visual platform system of an intelligent mold for multi-layer composite materials described in any one of claims 1-4, it is characterized in that, It includes: Step S1: Set the temperature gradient of the layered mold and the initial process parameters; Step S2: Dynamically adjust the interlayer gap through the electromagnetic compensation ejector pin during the mold closing stage; Step S3: Apply vibration pressure holding during the injection molding stage, with a frequency of 20Hz and an amplitude of 100μm; Step S4: After mold opening, conduct on-line detection through the vision quality inspection module, and the defect data is fed back to the control platform in real time.

6. The manufacturing method of the multi-layer composite material based on the vision platform system of the intelligent mold for multi-layer composite materials according to claim 5, characterized in that, In step S3, the duration of vibration pressure holding is dynamically adjusted according to the rheological properties of the material, and the adjustment range is 10-30s.

7. The manufacturing method of the multi-layer composite material based on the vision platform system of the intelligent mold for multi-layer composite materials according to claim 6, characterized in that, In step S4, if delamination defects are detected, the control platform automatically increases the pressure holding time by 0.3-0.8s and raises the mold temperature gradient by 5-10℃.

8. The manufacturing method of the multi-layer composite material based on the vision platform system of the intelligent mold for multi-layer composite materials according to claim 5, characterized in that, The specific process of setting the temperature gradient of the layered mold in step S1 includes: Calculate the initial temperature gradient ΔT = α × E × h / (1 - V) based on the difference in the coefficient of thermal expansion of materials 2 , where α is the coefficient of thermal expansion of the material, E is the elastic modulus, h is the layer thickness, and V is the Poisson's ratio; The outer layer mold temperature is set to 180-220℃, and the core layer mold temperature is reduced by 20-50℃; The temperature deviation is calibrated in real time to ±0.5℃ through the distributed FBG fiber Bragg grating sensor.

9. The manufacturing method of the multi-layer composite material based on the vision platform system of the intelligent mold for multi-layer composite materials according to claim 8, characterized in that, The control logic of vibration pressure holding in step S3 is: When the detected interlayer pressure difference ≥5MPa, the vibration frequency is triggered to increase from the reference value of 20Hz to 25-30Hz; The amplitude is dynamically adjusted according to the material viscosity, and the calculation formula is: A = μ×V / (σ×t), where μ is the melt viscosity, V is the injection volume, σ is the interlayer contact stress, t is the pressure holding time, and the amplitude range is 80-150μm.

10. The manufacturing method of the multi-layer composite material based on the vision platform system of the intelligent mold for multi-layer composite materials according to claim 9, characterized in that, The feedback optimization in step S4 includes: For the honeycomb structure collapse defect, synchronously adjust the pressure of the mold electromagnetic compensation ejector pin to increase by 10%~15%; Predict the process parameters of the next cycle based on the LSTM model, and the output variables include the pressure holding time ±0.3s, the vibration frequency ±5Hz and the mold temperature gradient ±5℃.

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