Automobile part plastic shell green forming method based on intelligent control

Abstract

The invention discloses an automobile part plastic shell green forming method based on intelligent control, and belongs to the field of high polymer material forming. The method comprises the following steps: mixing polylactic acid and regenerated polyethylene glycol terephthalate according to a mass ratio of 7: 3, adding a nanocellulose reinforcing agent and a 3% glycerol triglycidyl ether compatibilizer, blending by a twin-screw extruder, injecting supercritical carbon dioxide for foaming, and preheating the raw materials to 60-80 DEG C by utilizing waste heat of a mold; through an intelligent mold with a built-in sensor and a graphene coating, data is transmitted by means of IoT, and injection molding parameters are dynamically adjusted in combination with a digital twin model. 20-40 kHz ultrasonic resonance and an LSTM algorithm are adopted in the pressure maintaining stage to optimize the process, and defects are reduced. After a main body is subjected to injection molding, complex features are complemented through FDM 3D printing, seamless switching of a visual system is achieved, and AI visual detection and waste heat power generation are matched. According to the method, whole-process environmental protection is achieved, product performance and production efficiency are improved, cost is reduced, and a solution is provided for green manufacturing of automobiles.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of material forming, and particularly relates to a green forming method for a plastic shell of an automobile part based on intelligent control. BACKGROUND

[0002] The patent method breaks through in green environmental protection and material performance optimization. By mixing biodegradable polylactic acid and regenerated polyethylene terephthalate at a mass ratio of 7:3, and adding nano-cellulose reinforcing agent and glycerol triglycidyl ether compatibilizer, the mechanical properties of the shell are improved, and the material compatibility is improved, and interface delamination is avoided. Using supercritical carbon dioxide as a physical foaming agent, the traditional chemical foaming agent is replaced, and the environmental pollution risk is eliminated. At the same time, the raw materials are preheated to 60-80 DEG C by using a plate-type exchanger to cool the waste heat of the mold, and the remaining waste heat is converted into electric energy by an organic Rankine cycle, realizing efficient recovery of energy, solving the problems of energy waste and insufficient environmental protection in traditional processes.

[0003] In the process cooperation and complex structure forming, the method realizes the balance between batch production and high precision. Through the combined process of injection molding main body+FDM 3D printing to complete the local complex features, relying on the visual system to realize seamless connection, the advantages of batch production of injection molding are played, and the complex details that are difficult to realize by traditional injection molding are precisely formed by FDM. The mold cavity adopts bionic micro-texture, which directly gives the shell surface functional characteristics, reduces subsequent processing procedures, and solves the contradiction between complex structure forming and large-scale production.

[0004] The intelligent control system greatly improves the process stability and cost control ability. The built-in sensor and graphene coating mold transmit data in real time through the Internet of Things IoT, dynamically adjust the scCO2 injection parameters and injection molding parameters combined with the digital twin model, and reduce stress concentration. In the pressure maintaining stage, 20-40 kHz ultrasonic resonance is used to cooperate with LSTM algorithm to process time series data, capture the nonlinear change of melt and reduce internal defects. The AI visual detection system forms a detection-feedback-optimization closed loop to reduce the waste rate. The use of recycled materials, intelligent regulation and integrated process comprehensively reduce the cost of raw materials, equipment and waste products, and provide an efficient solution for automobile green manufacturing. SUMMARY

[0005] To solve the problems raised in the background art, the application provides the following technical scheme: A green forming method for a plastic shell of an automobile part based on intelligent control, characterized by comprising the following steps: S1: Mix polylactic acid (PLA) with recycled polyethylene terephthalate (rPET), add nano-cellulose reinforcing agent and 3% glycerol triglycidyl ether compatibilizer in sequence, then blend through a twin-screw extruder, simultaneously inject supercritical carbon dioxide as a physical foaming agent to form a homogeneous mixture, preheat the raw material particles through a plate heat exchanger using waste heat generated by mold cooling during the forming process; S2: Use a self-adaptive intelligent mold with built-in sensors and a graphene coating, dynamically adjust the injection parameters of scCO2 during the injection molding stage by real-time transmission of temperature and pressure data through IoT, optimize melt filling by combining a digital twin model, and reduce stress concentration; S3: Use ultrasonic resonance during the holding pressure stage, dynamically adjust parameters based on melt characteristics through AI algorithms, and rely on a digital twin model to simulate temperature and pressure fields in real time, optimize injection speed and holding time parameters through a closed loop, and reduce internal defects; S4: The main structure is injection molded, and the local complex features are completed by integrated FDM nozzle 3D printing. The visual system triggers seamless switching of the process, and the mold cavity uses biomimetic micro-texture. S5: The AI vision detection system identifies surface defects in real time, automatically traces back and optimizes the process when the yield rate is abnormal, and waste heat is converted into electrical energy through an organic Rankine cycle.

[0006] Preferably, the polylactic acid and recycled polyethylene terephthalate in S1 are mixed in a mass ratio of 7:3.

[0007] Preferably, the melt blending temperature controlled by the twin-screw extruder in S1 is between 180-220°C.

[0008] Preferably, the preheating of the heat exchanger and the raw material particles in S1 is to 60-80°C.

[0009] Preferably, the injection of scCO2 in S2 controls its pressure range to be between 25-30 MPa, and dynamically adjusts the injection amount in combination with a digital twin model.

[0010] Preferably, the frequency of the ultrasonic resonance in S3 is set to 20-40 kHz.

[0011] Preferably, the AI algorithm in S3 collects temperature field distribution, pressure field data, melt viscosity, and current frequency of ultrasonic resonance through real-time data collection by sensors, then denoises and normalizes the original data to form a time series feature vector.

[0012] Preferably, the AI in S3 uses the advantage of LSTM (Long Short-Term Memory Network) to process time series data, capturing the nonlinear dynamic change law of the melt during the holding pressure stage.

[0013] Preferably, the nozzle diameter of the FDM in S4 is 0.2-0.4mm, the nozzle heating temperature is 190-230℃, the hot bed temperature is 60-80℃, the layer height is set to 0.05-0.15mm, and the printing speed is 10-30mm / s.

[0014] Preferably, the FDM in S4 is set to a temperature feedback frequency of 100Hz, a switching response time ≤0.5s, and an extrusion rate control of 5-15mm 3 / s and a wire diameter control of 1.75mm.

[0015] The effect and advantages of the green forming method of the automobile part plastic shell based on intelligent control in the application are: 1. The seamless switching of injection molding and 3D printing in the patent avoids the need for multiple process turnover or multiple sets of molds, shortens the production cycle, and reduces energy consumption in the melting stage through waste heat preheating of raw materials, indirectly improving production efficiency.

[0016] 2. The use of recycled materials (rPET) in the patent reduces raw material costs, intelligent control reduces waste rates, integrated molds and processes reduce equipment investment, and comprehensively reduces production costs.

[0017] 3. Through the innovative integration of green materials, intelligent control, and process synergy, the patent realizes the full-process environmental protection of automobile plastic shells from production to recycling, while considering product performance, process precision, and production efficiency, providing a solution for automobile lightweighting and green manufacturing.

[0018] 4. The nanocellulose reinforcing agent in the system improves the mechanical properties of the shell, enhances its strength and hardness, the glycerol triglycidyl ether compatibilizer improves the compatibility of PLA and rPET to avoid interface delamination, and supercritical carbon dioxide reduces the density of the system while maintaining structural integrity.

[0019] 5. The patent combines the synergistic injection molding system and 3D printing to complete local complex features, and combines a vision system to realize seamless connection, ensuring batch production while preserving complex details. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 is a flow chart of the green forming method of the automobile part plastic shell based on intelligent control in the application. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0022] It should be noted that, in this document, relational terms such as first and second and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. The terms "includes", "including", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0023] Embodiment 1 This embodiment provides a green forming method for a plastic shell of an automobile part based on intelligent control, which is suitable for a plastic protective shell of an automobile part, and the following implementation content is provided. Experimental purpose: To prepare a green and environmentally friendly plastic shell of an automobile part Experimental raw materials: Polylactic acid (PLA), recycled polyethylene terephthalate (rPET), nanocellulose, glycerol triglycidyl ether compatibilizer, supercritical carbon dioxide, graphene.

[0024] Experimental steps: S1: Mix polylactic acid (PLA) and recycled polyethylene terephthalate (rPET) at a mass ratio of 7:3, and finally add nanocellulose reinforcing agent and 3% glycerol triglycidyl ether compatibilizer. Then melt blend through a double-screw extruder at 180-220°C, and simultaneously inject supercritical carbon dioxide as a physical foaming agent to form a homogeneous mixture. Use the waste heat generated by mold cooling during the forming process to preheat the raw material particles to 60-80°C through a plate-type exchanger; S2: Use a self-adaptive intelligent mold with built-in sensors and a graphene coating. Through IoT, real-time transmission of temperature and pressure data is realized. During the injection molding stage, the injection parameters of scCO2 are dynamically adjusted to control the pressure range at 25-30 MPa. Combined with a digital twin model, the melt filling is optimized to reduce stress concentration. S3: 20-40 kHz ultrasonic resonance is applied in the holding pressure stage, and the parameters are dynamically adjusted by AI algorithm according to the characteristics of the melt, relying on the digital twin model to simulate the temperature field, pressure field, and closed-loop optimize the injection speed and holding time, etc., to reduce internal defects; S4: The main structure is injection molded, and the local complex features are completed by integrated FDM nozzle 3D printing. The visual system triggers seamless switching of the process, and the mold cavity adopts biomimetic micro-texture; S5: The AI visual inspection system identifies surface defects in real time, and automatically traces back to optimize the process when the yield rate is abnormal. Waste heat is converted into electrical energy through organic Rankine cycle.

[0025] Experimental results: see Table 1 for details.

[0026] Table 1: Test results of Example 1

[0027] Example 1 prepares a plastic protective shell for automobile parts by green materials + intelligent control + process synergy, verifying the effectiveness of the molding method. The experiment mixes PLA and rPET at a mass ratio of 7:3, adds nano-cellulose reinforcing agent and 3% glycerol triglycidyl ether compatibilizer, and then extrudes and blends at 180-220°C, and injects 25-30 MPa supercritical CO2 foaming. The mold waste heat is used to preheat the raw materials to 60-80°C. Through the IoT data transmission of the built-in sensor and graphene coating mold, combined with digital twin dynamic optimization of injection molding parameters. In the holding pressure stage, 20-40 kHz ultrasonic resonance and AI algorithm are used to reduce defects, and FDM 3D printing is used to complete complex features, realizing seamless switching of the process. The test results show that the product tensile strength reaches 60-70 MPa, impact strength 12-15 kJ / m 2 , yield rate 96%, raw material utilization rate ≥98%, which not only reflects the improvement of material performance, but also realizes green environmental protection through intelligent control and waste heat recovery, verifying the advantages of the method in considering performance, efficiency and environmental protection.

[0028] Comparative Example 1 This example provides a preparation of a traditional plastic shell for automobile parts, and the following implementation content: Experimental purpose: Prepare a plastic shell for automobile parts by injection molding.

[0029] Experimental raw materials: Acrylonitrile-butadiene-styrene copolymer (ABS), polypropylene (PP).

[0030] Experimental steps: S1: Dry ABS, PP or PC / ABS alloy, finally remove moisture to avoid bubbles after molding; S2: The pretreated plastic particles are added to the injection molding machine barrel, and the plastic is gradually melted into a homogeneous mixture by the barrel heating and screw rotation shearing action; S3: The injection molding machine screw is injected into the closed mold cavity at a high speed of 50-150 MPa and 50-150 mm / s to ensure that the melt fills the entire cavity; S4: After the melt fills the cavity, 50-80% of the injection pressure is maintained, and the missing part of the melt is injected to supplement the cooling shrinkage. Finally, the mold is cooled by cooling water to cool the plastic part in the cavity; S5: After cooling, the plastic part is removed from the gate, flash and other excess parts, and then the surface of the plastic part is painted and electroplated.

[0031] Experimental results: see Table 2 for details.

[0032] Table 2: Test results of Comparative Example 1

[0033] Comparative Example 1 uses a traditional injection molding process to prepare a plastic shell of an automobile part, which can meet the basic molding requirements, but has significant defects: in terms of performance, the impact strength is only 9-11 kJ / m 2 , the toughness is insufficient, and it is difficult to cope with the high-frequency impact environment during use of the automobile. The tensile strength of 30-35 MPa meets the basic requirements, but there is still room for improvement compared to the new composite process. In terms of process efficiency, due to the reliance on fixed parameter injection molding, there is a lack of dynamic adjustment mechanism, which is prone to produce bubbles, flash and other defects due to uneven melt filling, cooling stress concentration, etc., resulting in a good yield of only 85%. The raw material utilization rate is ≥83%, but the waste material recycling rate of the gate, flash and other materials is low, and the material waste is obvious. In terms of environmental protection, the ABS and PP used are non-degradable plastics, and the paint spraying and electroplating processes in the post-processing will also produce volatile pollutants, which do not meet the green manufacturing trend; and the process does not involve energy-saving design such as waste heat recovery, and the energy consumption is high. Overall, this traditional method has shortcomings in performance toughness, resource utilization efficiency and environmental protection, and is difficult to adapt to the demand for high performance, low loss and sustainability of modern automobile parts Comparative Example 2 A method for preparing a plastic shell of an automobile part by blow molding is provided, and the following implementation content is provided: Experimental raw materials: High-density polyethylene (HDPE).

[0034] Experimental purpose: Preparation of a plastic shell of an automobile part by blow molding Experimental steps: S1: The HDPE material is sent into the extruder after drying the raw materials, and then the plastic particles are melted in the extruder and extruded into a tubular blank through an annular die; S2: The tubular blank is clamped and sent into a split mold. After the mold is closed, compressed air is introduced from one end of the tubular blank, causing the tubular blank to blow up and tightly fit the inner wall of the mold, while the mold is cooled by cooling water. S3: After the plastic is cooled and solidified, the compressed air is released, the mold is opened, and the plastic part is taken out. The port is trimmed, and finally the plastic part is leak tested.

[0035] Experimental results: Details are shown in Table 3.

[0036] Table 3: Test results of Comparative Example 2

[0037] Comparative Example 2 uses a traditional blow molding process to prepare a plastic shell of an automobile part, which has many obvious defects. In terms of performance, the impact strength is only 5-9 kJ / m 2 , the toughness is very poor, and it is difficult to withstand the vibration and impact load during automobile operation. The tensile strength is 20-30 MPa, which is at a low level and cannot meet the use requirements of high-strength parts. The process stability is insufficient, as the blow-up relies on fixed parameters, the tubular blank wall thickness is prone to unevenness, and the mold closing and inflation timing control is rough, resulting in a good product rate of only 80%. The products are often accompanied by defects such as wrinkles and insufficient blow-up. Although the raw material utilization rate is ≥82%, the port waste generated by trimming has low recycling rate, and the material waste is significant. The environmental friendliness is lacking, as the HDPE used is non-degradable plastic, and the process has no waste recycling and energy recycling design, which does not meet the green manufacturing trend. In addition, blow molding has poor adaptability to complex structures, and can only produce simple hollow shells, limiting its application in various types of automobile parts. Overall, this method has obvious shortcomings in performance toughness, process stability, material utilization rate, and application range, and is difficult to adapt to the high performance and diversification needs of modern automobile parts.

[0038] Example 1 uses a green molding method based on intelligent control to prepare a plastic shell of an automobile part. Polylactic acid and recycled polyethylene terephthalate are mixed in a mass ratio of 7:3, nano-cellulose reinforcing agent and 3% glycerol triglycidyl ether compatibilizer are added, and then the mixture is extruded by a twin-screw extruder at 180-220°C and injected into supercritical carbon dioxide at 25-30 MPa for foaming. The raw materials are preheated to 60-80°C using mold waste heat. Through IoT data transmission of built-in sensors and graphene-coated molds, dynamic optimization of digital twin models, reduction of defects during the holding stage combined with 20-40 kHz ultrasonic resonance and LSTM algorithm, and completion of complex features with FDM 3D printing, the final product has a tensile strength of 60-70 MPa and an impact strength of 12-15 kJ / m 2, the yield rate is 96%, the raw material utilization rate is greater than or equal to 98%, and the unification of green environmental protection, high performance and high efficiency is realized.

[0039] The comparative example 1 uses a traditional injection molding process, uses non-degradable plastics such as ABS and PP as raw materials, and after drying, melting injection, cooling, the sprue and flash need to be removed and post-processing such as paint spraying and electroplating is carried out. The product tensile strength is 30-35 MPa, impact strength is 9-11 kJ / m 2 , the yield rate is only 85%, and the raw material utilization rate is greater than or equal to 83%. This process is prone to defects such as bubbles and flash due to the lack of dynamic parameter adjustment mechanism, and the non-degradable materials and post-processing process have environmental problems, high energy consumption and low resource utilization efficiency.

[0040] The comparative example 2 uses a blow molding process, uses high-density polyethylene as a raw material, extrudes a tube blank, blows and fits a mold, and trims the port after cooling and solidification. The product tensile strength is 20-30 MPa, impact strength is 5-9 kJ / m 2 , the yield rate is 80%, and the raw material utilization rate is greater than or equal to 82%. This process has problems such as poor performance, uneven tube blank wall thickness, and insufficient process stability, and is only suitable for simple hollow structures, which is obviously wasteful of materials and does not meet the green manufacturing trend.

[0041] Compared with the comparative examples 1 and 2, the embodiment 1 is significantly superior in many aspects. In terms of materials, biodegradable PLA and renewable rPET are used, and green additives are used to replace traditional non-degradable plastics, which is more environmentally friendly. In terms of performance, the tensile strength and impact strength are much higher, meeting the high requirements of automobile parts on mechanical properties; in terms of process, through intelligent control, waste heat recovery and FDM3D printing to complete complex features, a high yield rate of 96% and a raw material utilization rate of greater than or equal to 98% are realized, which is much higher than the yield rate of 80%-85% and the raw material utilization rate of 82%-83% of the comparative examples, and truly realizes the synergy of environmental protection, high performance and high efficiency in the whole process.

[0042] Those skilled in the art can realize that the modules and algorithm steps of the examples described in combination with the embodiments disclosed in the present application can be realized in electronic hardware or a combination of computer software and electronic hardware, and whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0043] In addition, the functional modules in each embodiment of the present application can be integrated in one processing module, or each module can exist physically, or two or more modules can be integrated in one module.

[0044] The above merely provides the specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, and all of them should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0045] Finally, the above merely provides the preferred embodiments of the present application, and is not used to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A green molding method for plastic housings of automotive parts based on intelligent control, characterized in that, Includes the following steps: S1: Polylactic acid and recycled polyethylene terephthalate are mixed, and nanocellulose reinforcing agent and 3% glycerol triglycidyl ether compatibilizer are added in sequence. Then, the mixture is blended through a twin-screw extruder, and supercritical carbon dioxide is injected simultaneously as a physical foaming agent to form a homogeneous mixture. The waste heat generated by the mold cooling during the molding process is used to preheat the raw material particles through a plate heat exchanger. S2: Adaptive smart mold with built-in sensors and graphene coating, which transmits temperature and pressure data in real time through IoT, dynamically adjusts supercritical carbon dioxide injection parameters during the injection molding stage, and optimizes melt filling by combining digital twin model to reduce stress concentration; S3: During the pressure holding stage, ultrasonic resonance is used, and parameters are dynamically adjusted according to the melt characteristics through AI algorithms. Relying on the digital twin model, the temperature field, pressure field, and closed-loop optimization of parameters such as injection speed and pressure holding time are simulated in real time to reduce internal defects. S4: The main structure is injection molded, and local complex features are completed by 3D printing with an integrated FDM nozzle. The vision system triggers seamless process switching, and its mold cavity adopts a biomimetic micro-texture. S5: The AI ​​vision inspection system identifies surface defects in real time, automatically backtracks and optimizes the process when the yield rate is abnormal, and waste heat is converted into electrical energy through organic Rankine cycle.

2. The green molding method for automotive component plastic housings based on intelligent control as described in claim 1, characterized in that, In S1, polylactic acid and recycled polyethylene terephthalate are mixed at a mass ratio of 7:

3.

3. The green molding method for automotive component plastic housings based on intelligent control as described in claim 1, characterized in that, The melt blending temperature controlled by the twin-screw extrusion in S1 is 180-220℃.

4. The green molding method for automotive component plastic housings based on intelligent control as described in claim 1, characterized in that, In step S1, the heat exchanger and raw material particles are preheated to 60-80°C.

5. The green molding method for automotive component plastic housings based on intelligent control as described in claim 1, characterized in that, The injection of scCO2 in S2 is controlled within a pressure range of 25-30 MPa, and the injection volume is dynamically adjusted in conjunction with a digital twin model.

6. The green molding method for automotive component plastic housings based on intelligent control as described in claim 1, characterized in that, The frequency of ultrasonic resonance in S3 is set to a range of 20-40kHz.

7. The green molding method for automotive component plastic housings based on intelligent control as described in claim 1, characterized in that, The AI ​​algorithm in S3 uses real-time temperature field distribution, pressure field data, melt viscosity, and current ultrasonic resonance frequency parameters collected by sensors to denoise and normalize the original data, forming a time-series feature vector.

8. The green molding method for plastic housings of automotive parts based on intelligent control as described in claim 1, characterized in that, In S3, AI utilizes the advantages of LSTM in processing time-series data to capture the nonlinear dynamic changes of the melt during the holding pressure stage.

9. The green molding method for automotive component plastic housings based on intelligent control as described in claim 1, characterized in that, In S4, the nozzle diameter of the FDM is between 0.2-0.4mm, the nozzle heating temperature is 190-230℃, the heated bed temperature is 60-80℃, the layer height is set to 0.05-0.15mm, and the printing speed is 10-30mm / s.

10. The green molding method for plastic housings of automotive parts based on intelligent control as described in claim 1, characterized in that, In S4, the fused deposition modeling setting has a temperature feedback frequency of 100Hz and a switching response time of ≤0.5s, and the extrusion rate is controlled at 5-15mm. 3 / s and wire diameter are controlled at 1.75mm.

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