Machining method for injection mold of automobile lampshade

Abstract

The invention relates to the technical field of injection molds, in particular to an automobile lampshade injection mold processing method, which has the technical scheme that a conformal cooling system is designed based on topological optimization, so that the cooling efficiency is improved, and the injection molding period is shortened; a composite exhaust system comprising a main exhaust groove, a micro exhaust structure and ejector pin exhaust is arranged on a parting surface, so that the production efficiency is improved, the service life of the mold is prolonged, and the surface smoothness of the mold and the light transmission and appearance quality of the lampshade are improved by implementing a five-axis precision polishing and gradient coating composite surface treatment process; a modular mold core structure is configured to reduce the machining difficulty and cost and the maintenance cost, a hydraulic-mechanical composite demolding mechanism is configured to improve the production efficiency and the product quality, and an online detection and technological parameter self-adaptive regulation and control system is integrated, so that the complex defect recognition accuracy and the abnormal working condition disposal efficiency can be improved; and the process parameter optimization response time can be shortened, so that the system control adaptivity is improved, and the product surface defect rate is reduced.

Description

Technical Field

[0001] The invention relates to the technical field of injection molds, and in particular to a method for processing an automobile lampshade injection mold. Background Art

[0002] In the production of automotive lampshades, flip-chip injection molds are required. These molds offer high precision, greatly facilitating production. However, conventional methods for manufacturing automotive lampshade injection molds have the following challenges: insufficient mold surface finish affects the light transmittance and appearance of the lampshades; low mold cooling efficiency results in long injection cycles and low production efficiency; complex mold structures make processing difficult and costly; and short mold life, resulting in high wear and tear and high maintenance costs.

[0003] In view of this, we propose a car lampshade injection mold processing method to solve the existing problems. Summary of the Invention

[0004] The object of the present invention is to provide a method for processing an automobile lampshade injection mold to solve the problems raised in the above background technology.

[0005] To achieve the above-mentioned object, the present invention provides the following technical solution: a method for processing an automobile lampshade injection mold, the working steps comprising:

[0006] S1: Designing a conformal cooling system based on topology optimization and using metal 3D printing to manufacture mold inserts with double-helix circulating water channels;

[0007] S2: A composite exhaust system including main exhaust grooves, micro exhaust structures and ejector exhaust is set on the parting surface;

[0008] S3: Implement five-axis precision polishing and gradient coating composite surface treatment process;

[0009] S4: Equipped with modular core structure and hydraulic-mechanical composite demoulding mechanism;

[0010] S5: Integrated online detection and process parameter adaptive control system.

[0011] Furthermore, in S1, the water channel network of the conformal cooling system maintains an equidistant distribution of 4.8-5.2 mm from the cavity surface, including spiral channels with diameters of 3 mm and 5 mm alternatingly arranged, with 8 independent temperature control zones and integrated fiber Bragg grating temperature sensors.

[0012] Furthermore, in S2, the main exhaust groove of the composite exhaust system adopts a trapezoidal cross-section with a depth of 0.04mm and a width of 10mm, the micro-exhaust structure is a hexagonal honeycomb array with a unit side length of 2mm, a 0.025mm annular exhaust gap is provided at the ejector pin, and the exhaust channel includes a three-level cross-section contraction structure.

[0013] Furthermore, in S3, the mirror polishing of the surface treatment process uses a diamond grinding paste with a particle size of 0.1 μm, the DLC coating includes a Cr transition layer and a gradient carbon layer, the surface hardness reaches HV2500±100, and a hydrophobic treatment with a contact angle greater than 110° is used.

[0014] Furthermore, in S4, the modular core structure adopts a combination of H13 steel matrix and tungsten-copper alloy inserts, has a 0.02mm thermal expansion compensation gap, is equipped with a pneumatic quick locking device, and includes 12 sets of standard interchangeable units.

[0015] Furthermore, the specific working steps of S5 include:

[0016] A1: Build a multimodal sensor network;

[0017] A2: Deploy intelligent control algorithm system;

[0018] A3: Establish a dynamic adjustment mechanism;

[0019] A4: Configure an intelligent decision-making terminal that supports AR interaction and remote access.

[0020] Furthermore, in A1, the multimodal sensing network includes a laser scanning unit, infrared thermal imaging, a MEMS pressure array, an integrated melt viscosity online detection unit, a melt crystallinity online analysis unit, a mold surface stress distribution monitoring system, an ambient temperature and humidity compensation module, and a data security encryption transmission protocol; the laser scanning unit uses a 1550nm wavelength laser with an accuracy of ±0.002mm; the resolution of the infrared thermal imaging reaches 640×512 pixels, and the thermal sensitivity is 0.03°C; the MEMS pressure array contains 16 orthogonally distributed measuring points; the measurement frequency of the integrated melt viscosity online detection unit is 10Hz; the melt crystallinity online analysis unit uses Raman spectroscopy detection; the mold surface stress distribution monitoring system uses a fiber grating array; the control accuracy of the ambient temperature and humidity compensation module is ±0.5°C / ±3%RH.

[0021] Furthermore, in A2, the intelligent control algorithm system includes a time series prediction model constructed by an LSTM neural network, a fuzzy PID controller, a digital twin model, and a process knowledge graph; the number of hidden layer nodes of the time series prediction model constructed by the LSTM neural network is 128, the rule base of the fuzzy PID controller contains 50 expert experiences, the refresh frequency of the digital twin model is greater than or equal to 100Hz, and the process knowledge graph contains the mapping relationship between materials, defects, and parameters.

[0022] Furthermore, in A3, the dynamic adjustment mechanism includes mold cavity deformation compensation, melt viscosity-pressure collaborative compensation, and a wear feature recognition model based on a convolutional neural network. The parameter adjustment execution cycle is less than or equal to 0.1s.

[0023] Furthermore, the specific working steps of A4 include: AR interface displays three-dimensional temperature / pressure / flow cloud maps; abnormal diagnosis supports sound and light alarms and autonomous shutdown; 5G communication module supports multi-terminal data synchronization.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] The present invention improves the cooling efficiency and shortens the injection molding cycle by designing a conformal cooling system based on topological optimization. A composite exhaust system including a main exhaust groove, a micro exhaust structure and an ejector pin exhaust is arranged on the parting surface to improve production efficiency and extend the service life of the mold. The implementation of five-axis precision polishing and gradient coating composite surface treatment process improves the surface finish of the mold and the light transmittance and appearance quality of the lampshade. The configuration of a modular core structure reduces the processing difficulty, cost and maintenance cost. The configuration of a hydraulic-mechanical composite demoulding mechanism improves production efficiency and product quality. The integrated online detection and process parameter adaptive control system can not only improve the accuracy of complex defect recognition and the efficiency of handling abnormal working conditions, but also shorten the process parameter optimization response time, thereby improving the adaptability of system control, thereby reducing the surface defect rate of the product. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a schematic flow chart of a method for manufacturing an automobile lampshade injection mold according to the present invention;

[0027] Figure 2 It is a flow chart of the integrated online detection and process parameter adaptive control system of the present invention. DETAILED DESCRIPTION

[0028] The technical solution of the present invention is further described below with reference to the accompanying drawings and specific embodiments.

[0029] Example 1

[0030] like Figure 1 As shown, a method for processing an automobile lampshade injection mold includes the following steps:

[0031] S1: A conformal cooling system was designed based on topology optimization, and a mold insert with a double-helix circulating water channel was manufactured using metal 3D printing. The conformal cooling system's water channel network is evenly spaced 4.8-5.2 mm from the cavity surface and includes alternating spiral channels with diameters of 3 mm and 5 mm. It features eight independent temperature control zones and an integrated fiber Bragg grating temperature sensor.

[0032] S2: A composite exhaust system including a main exhaust groove, a micro exhaust structure, and ejector pin exhaust is set on the parting surface; the main exhaust groove of the composite exhaust system adopts a trapezoidal cross-section with a depth of 0.04mm and a width of 10mm, the micro exhaust structure is a hexagonal honeycomb array with a unit side length of 2mm, and a 0.025mm annular exhaust gap is provided at the ejector position. The exhaust channel includes a three-level cross-sectional contraction structure;

[0033] S3: Implement a composite surface treatment process of five-axis precision polishing and gradient coating. The mirror polishing of the surface treatment process uses a diamond grinding paste with a particle size of 0.1μm. The DLC coating includes a Cr transition layer and a gradient carbon layer. The surface hardness reaches HV2500±100, and a hydrophobic treatment with a contact angle greater than 110° is used.

[0034] S4: Equipped with a modular core structure and a hydraulic-mechanical composite demolding mechanism; the modular core structure uses an H13 steel base and a tungsten-copper alloy insert, has a 0.02mm thermal expansion compensation gap, is equipped with a pneumatic quick-locking device, and includes 12 sets of standard interchangeable units;

[0035] S5: Integrated online detection and process parameter adaptive control system.

[0036] like Figure 2 As shown, the specific working steps of S5 include:

[0037] A1: Construct a multimodal sensing network; the multimodal sensing network includes a laser scanning unit, infrared thermal imaging, a MEMS pressure array, an integrated melt viscosity online detection unit, a melt crystallinity online analysis unit, a mold surface stress distribution monitoring system, an ambient temperature and humidity compensation module, and a data security encryption transmission protocol. The laser scanning unit uses a 1550nm wavelength laser with an accuracy of ±0.002mm. The infrared thermal imaging resolution reaches 640×512 pixels and a thermal sensitivity of 0.03°C. The MEMS pressure array contains 16 orthogonally distributed measurement points. The measurement frequency of the integrated melt viscosity online detection unit is 10Hz. The melt crystallinity online analysis unit uses Raman spectroscopy detection. The mold surface stress distribution monitoring system uses a fiber Bragg grating array. The control accuracy of the ambient temperature and humidity compensation module is ±0.5°C / ±3%RH.

[0038] A2: Deploy an intelligent control algorithm system. This system includes a time series prediction model built using an LSTM neural network, a fuzzy PID controller, a digital twin model, and a process knowledge graph. The time series prediction model built using the LSTM neural network has 128 hidden layer nodes. The fuzzy PID controller's rule base contains 50 expert experiences. The digital twin model has a refresh rate greater than or equal to 100 Hz, and the process knowledge graph contains mappings between materials, defects, and parameters.

[0039] A3: Establish a dynamic adjustment mechanism; the dynamic adjustment mechanism includes mold cavity deformation compensation, melt viscosity-pressure coordinated compensation, and a wear feature recognition model based on a convolutional neural network. The parameter adjustment execution cycle is less than or equal to 0.1s.

[0040] A4: Configure an intelligent decision-making terminal that supports AR interaction and remote access. Specific steps include: displaying three-dimensional temperature / pressure / flow cloud maps on the AR interface; abnormal diagnosis supports audio-visual alarms and autonomous shutdown; and the 5G communication module supports multi-terminal data synchronization.

[0041] The working principle of the automobile lampshade injection mold processing method based on the first embodiment is:

[0042] In S1, the specific steps for designing a conformal cooling system based on topology optimization include:

[0043] B1. Determine the cooling target area: Classify the molded object into a flat structure or a three-dimensional structure, and determine the area that needs cooling;

[0044] B2. Decompose the cooling target surface: Decompose the cooling target area into two-dimensional cooling target surfaces. These two-dimensional shapes will reflect the three-dimensional shape information, especially the thickness information, because the thickness affects the required cooling amount;

[0045] B3. Preprocessing images: Preprocessing the images of the cooling object surface reflecting the heat load, including converting them into grayscale images, performing histogram equalization to enhance image contrast, and performing binarization steps to facilitate subsequent neural network processing;

[0046] B4. Using neural network learning: The pre-processed image is input into a pre-trained artificial intelligence neural network. Through learning, the neural network can predict the appropriate topology optimization design from the image reflecting the cooling object surface with heat load;

[0047] B5. Topology Optimization Design: Determine the design area for topology optimization, i.e., the surface to be cooled; define the boundary conditions of the design area, including the diameter, location, and flow rate of the cooling fluid inlet and outlet; define the objective function to minimize the pressure drop of the cooling fluid and the time required to cool to the extraction temperature; use sensitivity analysis to evaluate the impact of changes in design variables on the objective function, and repeatedly adjust the design variables to derive a topology optimization design that meets the convergence criteria;

[0048] B6. Forming conformal cooling channels: Based on the topology optimization design results, independent cooling channels are formed on each cooling object surface. These cooling channels are combined to form conformal cooling channels that adapt to the shape of the molded object.

[0049] B7. Verify the performance of conformal cooling channels through simulation or experiments, and perform further optimization as needed.

[0050] The method of designing conformal cooling channels using topology optimization can greatly improve cooling efficiency, reduce cooling time, and optimize pressure distribution and cooling uniformity.

[0051] The time series prediction model constructed using the LSTM neural network is a time series prediction method based on the long short-term memory (LSTM) neural network model. LSTM is a special type of recurrent neural network that effectively processes time series data, possessing memory capabilities and the ability to model long-term dependencies. This model's advantage lies in its ability to capture long-term dependencies in time series data, making it suitable for prediction tasks with complex temporal dependencies. In S2, LSTM automatically learns features and patterns in time series data, eliminating the need for manual feature extraction and thus improving prediction accuracy. In time series prediction, the LSTM model uses its unique gating mechanism (including a forget gate, input gate, and output gate) to control the flow and forgetting of information, enabling it to learn long-term dependencies in the data.

[0052] A fuzzy PID controller combines fuzzy logic control with traditional PID control. In S2, fuzzy logic algorithms are used to optimize the proportional (P), integral (I), and differential (D) coefficients of PID control in real time according to specific fuzzy rules to improve control system performance. Fuzzy PID control includes several key components, including fuzzification, fuzzy rule reasoning, and defuzzification. Fuzzy PID controllers offer advantages such as strong adaptability, robustness, and the ability to effectively handle system nonlinearities.

[0053] A digital twin is a virtual model of a physical entity or system created digitally. By integrating physical models, sensor data, operational history, and other information, a highly similar mirror image of the real entity is constructed in a virtual space. In S2, this model reflects the entity's state, behavior, and performance in real time, and can self-update and adjust based on real-time data, enabling comprehensive, dynamic tracking and simulation prediction of the entity.

[0054] A process knowledge graph is a structured network composed of nodes and edges, used to represent and organize knowledge elements in the process domain and their relationships. These knowledge elements can be entities, concepts, attributes, etc., while relationships describe the connections and interactions between these elements. The purpose of a process knowledge graph is to better understand and manage the complex connections within the process system. In S2, relevant knowledge from the process design process is integrated to assist in the selection and supplementation of parameters when establishing mechanism models. It can also extract data in a targeted manner for optimizing mechanism model parameters and model the relationships between different manufacturing links, helping companies to better collaborate, effectively improving work efficiency, and promoting the advancement of industrial intelligence. In equipment fault diagnosis, knowledge graphs, as a data-driven knowledge representation and reasoning method, effectively address problems such as multi-source heterogeneity and incomplete data in knowledge representation and reasoning. This not only promotes industrial intelligence, but also optimizes manufacturing processes and improves product quality.

[0055] Mold cavity deformation compensation refers to an adjustment or correction method for the deformation that may occur in the mold cavity during use during the mold design and manufacturing process. The mold will be affected by various factors during use, causing the mold cavity to deform, thereby affecting the dimensional accuracy and appearance quality of the product. To solve this problem, the deformation factor can be taken into account during the mold design stage, and the mold cavity can be designed with corresponding compensation. In S3, the mold cavity deformation compensation amount is calculated as follows: Δ = α·T 2 +β·P; where Δ is the mold cavity deformation compensation, in mm; T is the temperature, in °C; P is the pressure, in MPa; α = 0.0035 mm / °C 2 ;β=0.015mm / MPa.

[0056] Melt viscosity-pressure co-compensation is a comprehensive compensation method used during plastics processing processes such as injection molding and extrusion to address the impact of melt viscosity and pressure fluctuations on product quality. Melt viscosity and pressure are two critical process parameters in plastics processing, and their variations directly impact the dimensional accuracy, surface quality, and internal properties of the product. Because melt viscosity is affected by multiple factors such as temperature, shear rate, and material properties, while pressure is dependent on process conditions such as injection speed and dwell time, melt viscosity and pressure often fluctuate during actual production. In S3, changes in melt viscosity and pressure are monitored in real time and adjusted accordingly based on a pre-set compensation algorithm to maintain melt flow stability and consistent product quality. Melt viscosity-pressure co-compensation involves adjusting process parameters such as injection speed, dwell pressure, and mold temperature to compensate for the effects of melt viscosity and pressure fluctuations on product size, weight, and surface finish, thereby improving product quality stability and production efficiency while reducing scrap rates.

[0057] The wear feature recognition model based on convolutional neural networks (CNNs) automatically extracts and identifies wear features. Convolutional neural networks are a deep learning method that can adaptively extract effective features from raw data and are particularly well-suited for image data processing. In the wear feature recognition task, CNNs, through their convolutional and pooling layers, can automatically learn and extract features of worn areas, thereby enabling classification and identification of wear levels. In S3, accurate identification of wear levels is achieved through training and optimization, providing strong support for wear monitoring and prediction, and adapting to different types of wear conditions.

[0058] The above specific embodiments are only several preferred embodiments of the present invention. Based on the technical solutions of the present invention and the relevant inspirations of the above embodiments, those skilled in the art can make various alternative improvements and combinations to the above specific embodiments.

Claims

1. A method for processing an automobile lampshade injection mold, characterized in that: The working steps include: S1: Designing a conformal cooling system based on topology optimization and using metal 3D printing to manufacture mold inserts with double-helix circulating water channels; S2: A composite exhaust system including main exhaust grooves, micro exhaust structures and ejector exhaust is set on the parting surface; S3: Implement five-axis precision polishing and gradient coating composite surface treatment process; S4: Equipped with modular core structure and hydraulic-mechanical composite demoulding mechanism; S5: Integrated online detection and process parameter adaptive control system.

2. The method for processing an automobile lampshade injection mold according to claim 1, characterized in that: In S1, the water channel network of the conformal cooling system is evenly distributed at 4.8-5.2 mm from the cavity surface, including spiral channels with diameters of 3 mm and 5 mm alternatingly arranged, with 8 independent temperature control zones and integrated fiber Bragg grating temperature sensors.

3. The method for processing an automobile lampshade injection mold according to claim 1, characterized in that: In S2, the main exhaust groove of the composite exhaust system adopts a trapezoidal cross-section with a depth of 0.04mm and a width of 10mm. The micro-exhaust structure is a hexagonal honeycomb array with a unit side length of 2mm. The ejector pin is provided with a 0.025mm annular exhaust gap. The exhaust channel includes a three-level cross-section contraction structure.

4. The method for processing an automobile lampshade injection mold according to claim 1, characterized in that: In S3, the mirror polishing of the surface treatment process uses diamond grinding paste with a particle size of 0.1μm. The DLC coating contains a Cr transition layer and a gradient carbon layer. The surface hardness reaches HV2500±100, and a hydrophobic treatment with a contact angle greater than 110° is used.

5. The method for processing an automobile lampshade injection mold according to claim 1, characterized in that: In the S4, the modular core structure uses a combination of H13 steel matrix and tungsten-copper alloy inserts, has a 0.02mm thermal expansion compensation gap, is equipped with a pneumatic quick locking device, and includes 12 sets of standard interchangeable units.

6. The method for processing an automobile lampshade injection mold according to claim 1, characterized in that: The specific working steps of S5 include: A1: Build a multimodal sensor network; A2: Deploy intelligent control algorithm system; A3: Establish a dynamic adjustment mechanism; A4: Configure an intelligent decision-making terminal that supports AR interaction and remote access.

7. The method for manufacturing an automobile lampshade injection mold according to claim 6, characterized in that: In A1, the multimodal sensing network includes a laser scanning unit, infrared thermal imaging, a MEMS pressure array, an integrated melt viscosity online detection unit, a melt crystallinity online analysis unit, a mold surface stress distribution monitoring system, an ambient temperature and humidity compensation module, and a data security encryption transmission protocol; the laser scanning unit uses a 1550nm wavelength laser with an accuracy of ±0.002mm; the resolution of infrared thermal imaging reaches 640×512 pixels, and the thermal sensitivity is 0.03℃; the MEMS pressure array contains 16 orthogonally distributed measuring points; the measurement frequency of the integrated melt viscosity online detection unit is 10Hz; the melt crystallinity online analysis unit uses Raman spectroscopy detection; the mold surface stress distribution monitoring system uses a fiber Bragg grating array; the control accuracy of the ambient temperature and humidity compensation module is ±0.5℃ / ±3%RH.

8. The method for manufacturing an automobile lampshade injection mold according to claim 6, wherein: In A2, the intelligent control algorithm system includes a time series prediction model constructed by an LSTM neural network, a fuzzy PID controller, a digital twin model, and a process knowledge graph; the time series prediction model constructed by the LSTM neural network has 128 hidden layer nodes, the rule base of the fuzzy PID controller contains 50 expert experiences, the refresh frequency of the digital twin model is greater than or equal to 100Hz, and the process knowledge graph contains the mapping relationship between materials, defects, and parameters.

9. The method for manufacturing an automobile lampshade injection mold according to claim 6, wherein: In A3, the dynamic adjustment mechanism includes mold cavity deformation compensation, melt viscosity-pressure collaborative compensation, and a wear feature recognition model based on a convolutional neural network. The parameter adjustment execution cycle is less than or equal to 0.1s.

10. The method for processing an automobile lampshade injection mold according to claim 6, characterized in that: The specific working steps of A4 include: the AR interface displays three-dimensional temperature / pressure / flow cloud maps; abnormal diagnosis supports sound and light alarms and autonomous shutdown; and the 5G communication module supports multi-terminal data synchronization.

Images