Modular design and precision forming method of socket aluminum box

Abstract

The invention relates to a modular design and precision forming method of a socket aluminum box in the technical field of information, which comprises the following steps: carrying out modular design optimization on the internal structure of the socket aluminum box by adopting a topological optimization algorithm aiming at the functional independence and flexible configuration requirements of the socket aluminum box, and carrying out precise forming on the internal structure of the socket aluminum box on the premise of ensuring the strength and rigidity; quick replacement and cost reduction are realized to the greatest extent; on the basis of meeting the mechanical property requirement, the modular design of the internal structure is further refined, and a standardized interface is adopted, so that the functional module can be repeatedly used and is easy to assemble, and the functional module can be flexibly configured and is convenient to maintain; after an aluminum box forming part is demolded, the appearance quality of the aluminum box forming part is rapidly detected by adopting a machine vision technology, products with surface defects are removed, and qualified products are subjected to size precision detection by adopting a three-coordinate measuring machine, so that the key size is ensured to meet the matching requirement of a modular interface.

Description

Technical Field

[0001] The present invention relates to the field of information technology, specifically the field of production and manufacturing of socket aluminum boxes, and in particular to a modular design and precision molding method of a socket aluminum box. Background Art

[0002] In the production process of socket aluminum boxes, traditional processes usually require multiple parts to be processed separately and then assembled, which not only increases the production steps, but also may cause excessive matching clearances due to slight dimensional deviations between parts, affecting the assembly accuracy and stability of the socket aluminum box. In addition, the joints of multiple parts are also prone to become stress concentration points, which are prone to deformation or fracture failure during repeated plugging and unplugging of the socket. In order to overcome the above problems, a new production process is urgently needed to simplify the manufacturing process of the socket aluminum box, improve processing accuracy and consistency, and avoid structural weaknesses introduced by component connections. This process requires that a socket aluminum box with a complex internal structure, precise dimensions and strength that meets the requirements can be formed in one go without secondary processing or assembly. However, there are many technical challenges to achieving integrated molding, such as difficulty in mold design, defects such as shrinkage cavities and cracks that are prone to occur during the molding process, difficulty in demolding, and difficulty in ensuring the surface quality of the product. Therefore, how to overcome the process difficulties of integrated precision molding and achieve efficient production of socket aluminum boxes is a key technical problem that needs to be solved urgently. Summary of the invention

[0003] The present invention provides a modular design and precision molding method for a socket aluminum box, the method comprising the following steps:

[0004] S101. In view of the functional independence and flexible configuration requirements of the socket aluminum box, a topology optimization algorithm is used to optimize the modular design of its internal structure, so as to maximize rapid replacement and reduce costs while ensuring strength and rigidity;

[0005] S102. Based on the optimized three-dimensional model of the socket aluminum box, perform mechanical performance simulation through finite element analysis software, obtain stress-strain distribution cloud map, determine the stress concentration area, and if the stress exceeds the allowable stress of the material, increase the thickness or design reinforcement ribs for the weak parts of the structure, select high-strength materials, and use heat treatment and surface strengthening processes to improve rigidity;

[0006] S103. On the basis of meeting the requirements of mechanical properties, the modular design of the internal structure is further refined, and standardized interfaces are adopted to achieve the reusability and easy assembly of functional modules, which can be flexibly configured and easy to maintain;

[0007] S104. To ensure one-time precision molding and smooth demoulding of complex internal structures, a precision die-casting mold with multi-directional parting, inclined top and core pulling is designed. At the same time, the surface quality of the mold cavity is required to be high to ensure the surface finish of the aluminum box molded parts and improve the appearance quality;

[0008] S105. During the die-casting process, the aluminum liquid filling and solidification process is simulated by numerical simulation software, the gating system and cooling system design are optimized, and according to the simulation results, the process parameters of the aluminum liquid temperature, injection speed and pressure, and mold temperature are adjusted to obtain high-quality aluminum box molded parts without shrinkage cavities and cracks;

[0009] S106. Use online monitoring technology to monitor the die-casting process in real time, including key parameters such as aluminum liquid temperature, injection curve and mold temperature field. Once the monitoring data exceeds the preset threshold range, adjust the process parameters or shut down for maintenance in time to ensure the stability of mass production and the consistency of products.

[0010] S107. After the aluminum box molded parts are demoulded, the machine vision technology is used to quickly inspect their appearance quality, and surface defective products are eliminated. The qualified products are tested for dimensional accuracy using a three-coordinate measuring machine to ensure that the key dimensions meet the matching requirements of the modular interface;

[0011] S108. In order to verify the rigidity of the socket aluminum box molded parts, a vibration fatigue test is required. According to the test results, if necessary, the material selection, heat treatment process or structural design is further optimized to meet the requirements of long-term reliability. Through the integrated optimization of modular design and precision molding process, the high-strength, lightweight and configurable high-quality production of the socket aluminum box is finally achieved.

[0012] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0013] The present invention discloses a modular design and precision molding method for a socket aluminum box. The internal structure of the aluminum box is modularly designed through a topology optimization algorithm, so as to achieve rapid replacement and reduce costs while ensuring strength and rigidity. The stress concentration area is determined by finite element analysis, and the structure is optimized in a targeted manner. Standardized interfaces are designed to achieve flexible configuration and easy maintenance of functional modules. Precision die-casting mold technologies such as multi-directional parting and inclined top are used to ensure one-time precision molding of complex internal structures. Die-casting process parameters are optimized through numerical simulation, and the molding process is controlled in real time using online monitoring technology. Quality inspection is carried out in combination with machine vision and three-coordinate measurement technology, and reliability is verified through vibration fatigue testing. The present invention realizes high-strength, lightweight and configurable high-quality production of socket aluminum boxes, and improves product performance and production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1The present invention is a flowchart of a modular design and precision molding method of a socket aluminum box.

[0015] Figure 2 It is a schematic diagram of a modular design and precision molding method of a socket aluminum box of the present invention.

[0016] Figure 3 It is another schematic diagram of the modular design and precision molding method of a socket aluminum box of the present invention. DETAILED DESCRIPTION

[0017] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] like Figure 1-3 The modular design and precision molding method of a socket aluminum box in this embodiment may specifically include:

[0019] S101. In view of the functional independence and flexible configuration requirements of the socket aluminum box, a topology optimization algorithm is used to optimize the modular design of its internal structure, so as to maximize rapid replacement and reduce costs while ensuring strength and rigidity.

[0020] S101 includes: obtaining a three-dimensional model of a socket aluminum box, the three-dimensional model including the internal structure and the external structure of the socket aluminum box; using a topology optimization algorithm to analyze the internal structure in the three-dimensional model to obtain the position distribution and connection relationship of each functional module, the functional module including a power module, a switch module and a socket module; according to the position distribution and connection relationship, using a modular design method to divide the functional module into independent sub-modules, and establishing an interface relationship between the sub-modules; for each of the sub-modules, using a finite element analysis method to calculate the strength and stiffness of the sub-module, and determine whether the sub-module meets the preset strength threshold and stiffness threshold; if the If the submodules do not meet the preset strength threshold and stiffness threshold, return to the modular design step and re-divide the submodules; according to the position distribution and connection relationship of the submodules, use a genetic algorithm to optimize the layout of the submodules to obtain an optimal layout plan; according to the optimal layout plan, use parametric modeling technology to reconstruct the three-dimensional model of the socket aluminum box and generate an engineering drawing of the socket aluminum box; according to the three-dimensional model and engineering drawing, use rapid prototyping technology to make a sample of the socket aluminum box, and perform assembly and functional testing; if the sample passes the assembly and functional testing, formulate a production plan and quality control measures for the socket aluminum box according to the preset batch and production process.

[0021] Specifically, the topology optimization algorithm is first used to analyze the internal structure of the socket aluminum box. The design domain is discretized into a 100×100 grid by the SIMP method, and the volume fraction constraint is set to 50%. The OC method is used for iterative optimization. After 50 iterations, the optimal position distribution and connection relationship of each functional module are obtained. Then, the modular design method is used for each functional module. According to the similarity of functions and interfaces, it is divided into five independent sub-modules such as power supply module, protection module, and output module. The relationship between sub-modules is established by defining interface parameters (such as voltage, current, size, etc.). Then, the finite element analysis method is used, and the ANSYS software is used to mesh and apply loads to each sub-module. The maximum stress and deformation of the sub-module are calculated. The maximum stress of the power supply module is 15MPa, and the maximum deformation is 0.2mm, which meets the strength and stiffness requirements. After that, the genetic algorithm is used to optimize the sub-module layout. With assembly efficiency and heat dissipation performance as the objective function, the optimal layout scheme is obtained after 100 generations of evolution through selection, crossover, mutation and other operations. The assembly time between each sub-module is shortened by 30%. Finally, Creo software was used to perform 3D modeling and engineering drawing generation on the optimized layout plan, and a 3D printer was used to make samples for assembly and functional testing. The test results showed that the socket aluminum box can be quickly assembled and disassembled, and all performance indicators meet the design requirements. Through cost accounting and market research, it was determined that the die-casting process would be used for mass production, with a production scale of 1,000 pieces per month, and corresponding quality control measures were formulated, which reduced production costs by 20%.

[0022] S102. Based on the optimized 3D model of the socket aluminum box, the mechanical properties are simulated by finite element analysis software to obtain the stress-strain distribution cloud map and determine the stress concentration area. If the stress exceeds the allowable stress of the material, the thickness is increased or the reinforcement ribs are designed for the weak parts of the structure. At the same time, high-strength materials are selected, and heat treatment and surface strengthening processes are used to improve the rigidity.

[0023] S102 includes: establishing a finite element analysis model according to the three-dimensional model of the socket aluminum box, setting material properties, boundary conditions and load conditions; performing mechanical property simulation calculations through finite element analysis software to obtain a stress-strain distribution cloud map; determining the stress concentration area according to the stress-strain distribution cloud map, and judging whether the stress exceeds the allowable stress of the material; if the stress exceeds the allowable stress of the material, optimizing the weak parts of the structure by increasing the thickness or designing reinforcement ribs; while optimizing the structure, selecting high-strength materials to improve the overall strength and rigidity of the socket aluminum box; performing a heat treatment process on the optimized socket aluminum box to further improve the strength by adjusting the microstructure of the material; and treating the socket aluminum box with a surface strengthening process, wherein the surface strengthening process is shot peening or laser strengthening, and residual compressive stress is formed on the surface of the socket aluminum box to improve fatigue strength and wear resistance.

[0024] Specifically, according to the three-dimensional model of the socket aluminum box, a finite element analysis model was established in the ANSYS software, and the aluminum alloy material properties such as elastic modulus 70GPa and Poisson's ratio 33 were set. The bottom surface was constrained and a uniform load of 1000N was applied to the top surface. Through static analysis, the VonM i ses equivalent stress distribution cloud map was obtained, and the maximum stress was 120MPa, concentrated at the inner corner, exceeding the allowable stress of the aluminum alloy by 100MPa. The optimization was carried out by increasing the thickness locally to 4mm, and high-strength 7075 aluminum alloy was selected to improve the strength and stiffness. The optimized parts were subjected to T6 heat treatment, and fine precipitation phases were obtained through solid solution, quenching and aging processes to improve the strength. Finally, shot peening treatment was used to form a compressive stress of 200MPa on the surface, delaying the initiation and expansion of fatigue cracks, and improving fatigue strength and wear resistance. After a series of optimizations, the mechanical properties and reliability of the socket aluminum box were significantly improved.

[0025] S103. On the basis of meeting the requirements of mechanical properties, the modular design of the internal structure is further refined, and standardized interfaces are adopted to achieve reusable and easy assembly of functional modules, which can be flexibly configured and easy to maintain.

[0026] S103 includes: obtaining mechanical performance requirements, optimizing the design of the internal structure, using the finite element analysis method to simulate and analyze the structure, and determining the material selection and dimensional parameters of key parts. If the mechanical performance indicators are met, a modular design is performed for the optimized internal structure, and the system is divided into multiple functional modules. Each module implements a preset specific function, and the modules are connected and data is exchanged through standardized interfaces. For the modular design, a production and manufacturing management system is established to perform information management on the processing, assembly, and testing processes of the modules, and realize visual monitoring of the production process.

[0027] Specifically, according to the requirements of mechanical properties, the finite element analysis software ANSYS is used to simulate and analyze the internal structure. By establishing a geometric model, defining material properties (such as the elastic modulus of aluminum alloy is 70GPa and the Poisson's ratio is 33), applying loads and constraints, and discretizing the model using the tetrahedral meshing method, considering the calculation efficiency and accuracy, the mesh size is set to 2mm, and by solving the stress-strain distribution of the structure under load, the dimensional parameters of key parts are determined, such as the wall thickness is not less than 3mm, to ensure that the maximum stress is less than the allowable stress of the material. For the optimized internal structure, a modular design approach is adopted to divide the system into power module, transmission module, control module, etc. Each module is interconnected through standardized interfaces (such as bolt connection and snap connection). A parametric design library containing three-dimensional models of each functional module is established, and the rapid configuration and call of the module is realized through parametric modeling technology. Using the assembly design function of the CAD software SolidWorks, based on the modular design scheme, the system assembly drawing is automatically generated, and the engineering drawing containing dimensioning and tolerance annotation is output, and the association and traceability of parts are realized through the assembly tree structure. In the assembly design stage, virtual assembly technology is applied to check the matching degree of assembly tolerances between modules through assembly simulation, optimize assembly sequence and positioning benchmarks, eliminate interference, and generate assembly process documents. A manufacturing execution system (MES) is established to manage the production processes such as module processing, assembly, and testing in an informationized manner. Visual monitoring and quality traceability of the production process are achieved through barcodes, RFID and other technologies. For key components with assembly accuracy requirements of ±02mm, online measurement and error compensation technology are used to ensure production consistency. Module-based maintenance procedures are formulated, and the fault tree analysis (FTA) method is used to establish a module-level fault diagnosis knowledge base to achieve rapid fault location and isolation. Through the spare parts management system, key module spare parts are reasonably configured to achieve rapid replacement of faulty modules, and the mean time to repair (MTTR) is controlled within 1 hour.

[0028] S104. To ensure one-time precision molding and smooth demoulding of complex internal structures, a precision die-casting mold with multi-directional parting, inclined top and core pulling is designed. At the same time, the surface quality of the mold cavity is required to be high to ensure the surface finish of the aluminum box molded parts and improve the appearance quality.

[0029] S104 includes: obtaining a three-dimensional model of a die-casting mold with a complex internal structure, the three-dimensional model including a multi-directional parting, a bevel top and a core pulling precision structure; according to the three-dimensional model, using a CNC machining center to perform precision machining on the mold cavity surface to obtain a cavity surface with surface roughness that meets the requirements; for the cavity surface, using a special coating technology to deposit a layer of wear-resistant, corrosion-resistant and lubricating coating; obtaining the appearance quality requirements of the aluminum box molded parts, setting cooling water channels and exhaust grooves in the three-dimensional model, and using mold flow analysis software to simulate and optimize the aluminum liquid filling process; obtaining the pressure, speed and displacement parameter signals of the injection system, and establishing a data model of the injection process; for the data model, using a machine learning algorithm to perform intelligent optimization control on the injection parameters; obtaining the temperature and stress state parameters of the die-casting mold, and establishing a preventive maintenance management system for the die-casting mold; using a machine learning algorithm to analyze the state parameters, predict the remaining service life of the mold, and formulate an optimal maintenance plan.

[0030] Specifically, in the process of three-dimensional modeling of die-casting molds, CATIA software is used to perform parametric design on the mold structure, and by setting variable parameters, the mold structure can be quickly modified and optimized. For example, when designing the inclined top structure, by setting parameters such as the inclined top angle and length, the inclined top surface is automatically generated and Boolean operations are performed with the cavity surface to obtain the final cavity structure. For cavity surface processing, a five-axis CNC machining center is used to optimize the tool path and cutting parameters to achieve efficient and precise processing of the cavity surface. For example, when processing the cavity surface, a ball-end milling cutter is used to perform multi-axis linkage processing with an axial cutting depth of 2mm and a radial cutting depth of 1mm, and a constant cutting force control strategy is used to adjust the feed speed in real time to obtain a high-quality surface with a surface roughness Ra less than 8μm. In the design of die-casting molds, AutoCAST software is used to simulate and analyze the filling process, and the smooth filling of aluminum liquid is achieved by optimizing the design of the pouring system and the exhaust system. For example, when designing cooling water channels, the optimal water channel diameter is determined to be 8mm and the spacing is 50mm through fluid mechanics calculations, and a spiral arrangement is used to improve cooling efficiency and uniformity. In mold surface treatment, plasma spraying technology is used to deposit metal-ceramic composite coatings, and by optimizing the spraying process parameters, a coating with uniform thickness and high density is obtained. For example, a plasma spraying system is used to deposit Ni-Cr-Al-Y coatings with a current of 80A, a voltage of 600V and a spraying distance of 100mm to obtain a high-quality coating with a thickness of 3mm, a hardness of HV900, and a porosity of less than 1%. In the optimization of the die-casting production process, an intelligent control algorithm based on deep learning is used. By real-time monitoring and analysis of parameters such as pressure, speed, and displacement during the injection process, the pressure change trend of the aluminum liquid filling process is predicted, and the injection curve is dynamically adjusted to achieve precise control of the injection process. For example, a prediction model for the injection process based on the long short-term memory neural network (LSTM) is established. By training 1,000 sets of historical production data, the prediction accuracy of the injection pressure can reach more than 95%, effectively reducing the occurrence of die-casting defects. In multi-directional parting control, a PLC-based synchronous control system is adopted. The timing and speed curve of each parting action are set by programming to achieve precise synchronous control of multiple hydraulic cylinders. For example, in the six-way parting structure, by setting the action timing of each parting unit, the cavity closing time is less than 5s and the cavity opening time is less than 1s, ensuring efficient coordination of the cavity parting action. In the preventive maintenance of die-casting molds, a health monitoring and life prediction method based on Bayesian networks is adopted. By real-time monitoring of the temperature, stress and other state parameters of the mold, combined with historical maintenance data and failure mechanism knowledge, a probability model of the mold health status is constructed to predict the remaining service life of the mold.For example, the mold temperature is collected in real time through embedded wireless sensors, and the data is updated every 10 minutes. When the temperature exceeds 200°C, a warning signal is triggered. Combined with the Bayesian network model, the remaining number of uses of the mold is predicted, and a maintenance plan is made in advance to avoid sudden failure of the mold.

[0031] S105. During the die-casting process, the aluminum liquid filling and solidification process is simulated by numerical simulation software to optimize the design of the pouring system and cooling system. According to the simulation results, the process parameters of aluminum liquid temperature, injection speed and pressure, and mold temperature are adjusted to obtain high-quality aluminum box molded parts without shrinkage cavities and cracks.

[0032] S105 includes: according to the three-dimensional model of the aluminum box die-casting, establishing a finite element analysis model including aluminum liquid, injection system, pouring system, cooling system and mold components, and setting material properties, boundary conditions and initial conditions; using computational fluid dynamics methods to simulate the filling behavior of the aluminum liquid during the injection process, obtaining the temperature field, velocity field and pressure field distribution of the aluminum liquid during filling, and judging whether there are air entrainment and splashing defects; if there are filling defects, adjusting the structural parameters or process parameters of the injection system until the filling defects are eliminated; using heat transfer methods to simulate the solidification process of the aluminum liquid in the mold cavity, obtaining the temperature field distribution and solidification time during solidification, and judging whether there are shrinkage holes and crack defects; if there are solidification defects, adjusting the structural parameters or process parameters of the cooling system until the solidification defects are eliminated.

[0033] Specifically, based on the three-dimensional model of the aluminum box die-casting, the finite element analysis model including the aluminum liquid, injection system, pouring system, cooling system and mold components was established using ANSYS software, and the material properties of the aluminum alloy and mold steel were set, such as the density was 2700kg / m 3 and 7800kg / m 3 , and the thermal conductivity is 237W / (m·K) and 48W / (m·K) respectively. In the numerical simulation of the injection process, the VOF method is used to track the free surface of the aluminum liquid, and the NS equation is solved in combination with the k-ε turbulence model to obtain the temperature field, velocity field and pressure field distribution during aluminum liquid filling. Through the analysis of the velocity cloud map, it is found that there is an air entrainment defect with a maximum velocity of 50m / s at the gate. In order to eliminate this defect, the gate cross-sectional area is increased from the original 100mm 2 Increased to 150mm 2, and the aluminum liquid temperature was reduced from 720℃ to 700℃, and the filling simulation was repeated. The results showed that the air entrainment defect was effectively suppressed. After the aluminum liquid was filled, the finite difference method was used to solve the heat transfer equation to simulate the solidification process of the aluminum liquid in the cavity. Through the analysis of the temperature cloud map, it was found that there was a shrinkage defect with a minimum temperature of 450℃ at the bottom of the aluminum box. In order to eliminate this defect, a cooling water channel with a diameter of 8mm was added to the bottom of the aluminum box, and the mold opening time was extended from 10s to 15s. The solidification simulation was repeated, and the results showed that the shrinkage defect was effectively eliminated. After the optimization design, 9 groups of orthogonal process parameters were simulated and analyzed to obtain the influence of factors such as aluminum liquid temperature, injection speed and pressure on filling time and solidification time. Based on the gray correlation analysis method, the optimal process parameter combination was determined as: aluminum liquid temperature 705℃, injection speed 2m / s, injection pressure 90MPa, at this time the filling time was 18s, the solidification time was 4s, and the uniformity of cavity temperature distribution was improved by 20%. The optimized pouring system and cooling system design and the best process parameter combination were applied to actual production. The 100 aluminum box die-castings produced were subjected to X-ray non-destructive testing, and no defects such as shrinkage cavities and cracks were found. The product qualification rate reached more than 98%.

[0034] S106. Use online monitoring technology to monitor the die-casting process in real time, including key parameters such as aluminum liquid temperature, injection curve and mold temperature field. Once the monitoring data exceeds the preset threshold range, adjust the process parameters or shut down for maintenance in time to ensure the stability of mass production and the consistency of products.

[0035] S106 includes: obtaining the aluminum liquid temperature at the outlet of the aluminum liquid heating furnace, the injection curve data of the injection device, and the temperature field data of each area inside the mold to obtain three kinds of original data; inputting the comparison result into a pre-built convolutional neural network model, and using the convolutional neural network model to extract features from the comparison result to obtain feature data; judging whether the feature data matches the preset fault mode, and if so, outputting a fault code; using the adjusted process parameters for die-casting production, and using a machine vision system to photograph the die-cast products; extracting product size features through an image recognition algorithm; judging whether the product size features meet the preset tolerance range, and if not, sending a shutdown command.

[0036] Specifically, a thermocouple sensor is deployed to continuously collect the temperature of the molten aluminum at the outlet of the molten aluminum heating furnace at a sampling frequency of 1000 times per second. It is assumed that the temperature of the molten aluminum is stable between 680 degrees Celsius and 720 degrees Celsius under normal working conditions. At the same time, a pressure sensor is deployed to obtain the injection curve data of the injection device at a sampling frequency of 500 times per second. The injection curve contains information such as fast injection speed and boost pressure. For example, the fast injection speed is set to 4 meters per second and the boost pressure is set to 70 MPa. In addition, in the five key areas inside the mold, such as the gate and the exhaust port, a temperature sensor is deployed to obtain the temperature field data at a sampling frequency of 200 times per second. For example, the temperature at the gate is set to 250 degrees Celsius. The three kinds of raw data are transmitted to the data processing center in real time. After receiving the three kinds of raw data, the data processing center performs a fast Fourier transform containing 1024 sampling points on each data, converts the time domain signal into a frequency domain signal, and obtains the spectrum data. Analyze whether there are high-frequency components exceeding 50 Hz in the spectrum data. If a high-frequency component is detected, a Butterworth low-pass filter is applied to filter the raw data, and the cutoff frequency is set to 20 Hz to filter out high-frequency noise, and three standard data are obtained. The three standard data are input into the pre-built support vector machine model as training samples. The support vector machine model uses the radial basis function as the kernel function, and the parameters are set as: the gamma value is 1 and the penalty coefficient is 10. Through training on a large amount of historical data, the support vector machine model can classify the three standard data and determine the normal operating range corresponding to each data. For example, the normal operating range of aluminum liquid temperature is 690 degrees Celsius to 710 degrees Celsius, the normal operating range of fast injection speed is 8 meters per second to 2 meters per second, and the normal operating range of gate temperature is 245 degrees Celsius to 255 degrees Celsius. These normal operating range data are stored in the database. The system extracts the normal operating range data from the database and matches the corresponding threshold data according to the current product brand, such as the aluminum alloy brand 6061. For example, the aluminum liquid temperature threshold corresponding to 6061 aluminum alloy is 700±5 degrees Celsius, the fast injection speed threshold is 4±1 meters per second, and the gate temperature threshold is 250±2 degrees Celsius. The three raw data collected in real time are compared with the three threshold data to obtain the comparison results. For example, the current aluminum liquid temperature is 703 degrees Celsius, which is within the threshold range, and the comparison result is normal; the current fast injection speed is 3 meters per second, which exceeds the threshold range, and the comparison result is abnormal; the current gate temperature is 248 degrees Celsius, which is within the threshold range, and the comparison result is normal. The comparison results are input into the pre-built convolutional neural network model, which contains 3 convolutional layers and 2 fully connected layers, with convolution kernel sizes of 3x3, 5x5 and 3x3 respectively, and the activation function uses the hyperbolic tangent function. The convolutional neural network model extracts features from the comparison results and obtains a feature vector containing 128 eigenvalues. The feature vector is matched with the preset fault mode library.If the Euclidean distance between the feature vector and the feature vector of a certain fault mode is less than the preset value of 5, it is considered that the match is successful, and the fault code corresponding to the fault mode is output, for example, "code 001" represents "fast injection speed is too high". The system extracts the fault code "code 001" from the fault code library, and finds the corresponding solution "reduce the fast injection speed setting value" according to the preset solution library. The system controls the robot arm to adjust the speed regulating valve of the injection device, reduces the fast injection speed setting value from 3 meters per second to 0 meters per second, and adjusts the pressure of the hydraulic system by controlling the solenoid valve to stabilize the actual fast injection speed at 0 meters per second, and obtains the adjusted process parameters. The adjusted process parameters are used for die casting production, and a machine vision system with a resolution of 1920x1080 pixels is used to shoot the die-casting products. Through image processing algorithms, such as edge detection and Hough transform, product dimensional features, such as length, width and key aperture, are extracted. The extracted dimensional features are compared with the preset tolerance range, for example, the tolerance range of a certain key dimension is 100±5 mm. If all dimensional features are within the tolerance range, the product is qualified; if any dimensional feature exceeds the tolerance range, for example, a certain dimension measurement value is 108 mm, the product is unqualified, and the system sends a shutdown command to the control system. After receiving the shutdown command, the control system controls the die-casting machine to stop running, and sends a maintenance signal through the human-machine interface to prompt the operator to perform equipment maintenance.

[0037] S107. After the aluminum box molded parts are demoulded, machine vision technology is used to quickly inspect their appearance quality and remove surface defective products. Qualified products are tested for dimensional accuracy using a three-coordinate measuring machine to ensure that the key dimensions meet the matching requirements of the modular interface.

[0038] S107 includes: obtaining image information of the aluminum box molded part after demoulding, using an image segmentation algorithm to segment the image information to obtain an appearance area image of the aluminum box molded part; for the appearance area image, using a feature extraction algorithm to extract texture features and color features of the appearance area image to obtain an appearance feature vector; inputting the appearance feature vector into a pre-established appearance defect discrimination model to obtain a defect discrimination result, if the defect discrimination result is a defect, the aluminum box molded part is judged as a defective product, otherwise the aluminum box molded part is judged as a qualified product; obtaining three-dimensional point cloud data of the qualified product, using a point cloud segmentation algorithm to extract key dimension point cloud data from the three-dimensional point cloud data; fitting the key dimension point cloud data according to preset key dimension parameters to obtain actual values ​​of the key dimensions; comparing the actual values ​​of the key dimensions with preset modular interface matching dimensions, if the actual values ​​meet the requirements of the matching dimensions, the aluminum box molded part is judged to be a final qualified product; recording and statistically analyzing the detection data of the final qualified product to obtain detection result data, and using the detection result data for subsequent production process optimization.

[0039] Specifically, after the aluminum box molded parts are demoulded, a high-resolution industrial camera is used to obtain its surface image information, and the image resolution is 2048×2048 pixels. Then, the acquired image is segmented using an image segmentation algorithm based on region growth. By setting the seed point and growth criterion of region growth, the appearance region image of the aluminum box molded parts can be accurately segmented. For the segmented appearance region image, the gray level co-occurrence matrix algorithm is used to extract the texture features of the image, and the HSV color space is used to extract the color features of the image. The texture features and color features are combined to form a 36-dimensional appearance feature vector. According to the pre-established appearance defect discrimination model based on support vector machine, the extracted appearance feature vector is input into the model for defect discrimination. By setting a suitable discrimination threshold, the surface defects such as scratches, dents, and peeling of the aluminum box molded parts can be effectively discriminated, and the discrimination accuracy can reach more than 95%. For the aluminum box molded parts judged as qualified products, a structured light 3D scanner is used to obtain their 3D point cloud data, and the point cloud density is 0.5mm. By designing a point cloud segmentation algorithm based on normal vectors and curvature, the point cloud data of key dimensions such as the upper cover, lower cover, and thread of the aluminum box molded parts can be accurately extracted. Then, according to the preset key dimension parameters, the least squares method is used to perform cylindrical fitting, plane fitting, and other processing on the key dimension point cloud data to obtain the actual values ​​of key dimensions such as the inner diameter of the upper cover, the outer diameter of the lower cover, and the outer diameter of the thread, and the fitting accuracy can reach 0.1mm. Finally, the actual value of the key dimension is compared with the matching dimension of the modular interface. If the actual value meets the matching dimension tolerance requirements, the aluminum box molded part is judged to be a final qualified product. The inspection data of the final qualified products are recorded and statistically analyzed. By drawing control charts, calculating process capability indexes, etc., the production quality status of aluminum box molded parts can be monitored in real time, providing data support and decision-making basis for the optimization of aluminum box molding process parameters.

[0040] S108. To verify the rigidity of the socket aluminum box molded parts, a vibration fatigue test is required. Based on the test results, if necessary, further optimize the material selection, heat treatment process or structural design to meet the requirements of long-term reliability. Through the integrated optimization of modular design and precision molding process, the high-strength, lightweight and configurable high-quality production of the socket aluminum box is finally achieved.

[0041] S108 includes: according to the structural characteristics and use requirements of the socket aluminum box molded parts, determine the load spectrum and acceleration level of the vibration fatigue test, and formulate the test plan and evaluation criteria. Obtain the three-dimensional model of the socket aluminum box molded parts, use finite element analysis software to simulate the stress distribution and deformation under the vibration fatigue load, and identify the fatigue vulnerable area. For the fatigue vulnerable area, optimize the material selection and heat treatment process parameters of the socket aluminum box, improve the fatigue strength and vibration resistance of the material, and reduce the risk of stress concentration and fracture. Adopt the modular design concept, decompose the socket aluminum box into standardized functional units, optimize the structural layout and dimensional parameters through parametric modeling and topological optimization, and achieve a balance between lightweight and high strength. Apply precision molding processes, including die casting and extrusion, to manufacture high-precision molded parts of the socket aluminum box, strictly control dimensional tolerances and surface quality, and reduce processing stress and defects. During the vibration fatigue test, strain gauges and acceleration sensors are used to monitor the stress-strain response and vibration characteristic parameters of the socket aluminum box molded parts in real time to obtain fatigue life and failure mode data. Based on the test results and simulation analysis, the fatigue life and reliability level of the socket aluminum box molding are evaluated to determine whether it meets the long-term use requirements, determine the key factors that need to be optimized and improved, and ensure the consistency and reliability of mass production.

[0042] Specifically, according to the structural characteristics and use requirements of the socket aluminum box molding, a combination of random vibration spectrum and sine sweep vibration spectrum is used, and the acceleration level is selected as 5g, 10g and 15g. The frequency range is 10Hz~2000Hz, and the test time is 2 hours / axial. Using 1 / 4 bridge strain gauges and I EPE acceleration sensors, the sampling frequency is set to 10kHz. Through fast Fourier transform and power spectrum density analysis, the stress root mean square value is 35MPa, the displacement root mean square value is 8mm, and the fatigue life is 100,000 times. Using ANSYS finite element software, the socket aluminum box molding is meshed, the unit type is SOLID186, the mesh size is 1mm, and the load application method is base acceleration. Through the analysis of stress cloud map and deformation cloud map, the stress concentration area is identified to be located at the internal thread and external corners. Comprehensive test data and simulation results show that the fatigue life of the socket aluminum box molding meets the use requirements, but the stress concentration problem needs to be further optimized. For vulnerable areas, high-strength aluminum alloy material 7075-T6 is selected, and aging heat treatment is used to increase the yield strength to more than 500MPa, and the fatigue strength is increased by 20%. Through parametric modeling, the socket aluminum box is divided into three parts: box body, flange and thread. Under the premise of ensuring assembly tolerance, the topological optimization algorithm is used to reduce the wall thickness by 20%, increase internal reinforcement ribs and fillet transition, and achieve a 15% weight reduction. High vacuum die-casting technology is used, and the mold is processed by precision electrospark machining. The surface roughness is controlled within Ra8, the dimensional tolerance is controlled within ±05mm, and the dimensional consistency error between production batches is less than 02mm.

[0043] It will be apparent to those skilled in the art that the present application is not limited to the details of the exemplary embodiments described above, and that the present application can be implemented in other specific forms without departing from the spirit or essential features of the present application. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the present application is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present application. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A modular design and precision molding method for a socket aluminum box, characterized in that: The method comprises the following steps: S101. In view of the functional independence and flexible configuration requirements of the socket aluminum box, a topology optimization algorithm is used to optimize the modular design of its internal structure, so as to maximize rapid replacement and reduce costs while ensuring strength and rigidity; S102. Based on the optimized three-dimensional model of the socket aluminum box, perform mechanical performance simulation through finite element analysis software, obtain stress-strain distribution cloud map, determine the stress concentration area, and if the stress exceeds the allowable stress of the material, increase the thickness or design reinforcement ribs for the weak parts of the structure, select high-strength materials, and use heat treatment and surface strengthening processes to improve rigidity; S103. On the basis of meeting the requirements of mechanical properties, the modular design of the internal structure is further refined, and standardized interfaces are adopted to achieve the reusability and easy assembly of functional modules, which can be flexibly configured and easy to maintain; S104. To ensure one-time precision molding and smooth demoulding of complex internal structures, a precision die-casting mold with multi-directional parting, inclined top and core pulling is designed. At the same time, the surface quality of the mold cavity is required to be high to ensure the surface finish of the aluminum box molded parts and improve the appearance quality; S105. During the die-casting process, the aluminum liquid filling and solidification process is simulated by numerical simulation software, the gating system and cooling system design are optimized, and according to the simulation results, the process parameters of the aluminum liquid temperature, injection speed and pressure, and mold temperature are adjusted to obtain high-quality aluminum box molded parts without shrinkage cavities and cracks; S106. Use online monitoring technology to monitor the die-casting process in real time, including key parameters such as aluminum liquid temperature, injection curve and mold temperature field. Once the monitoring data exceeds the preset threshold range, adjust the process parameters or shut down for maintenance in time to ensure the stability of mass production and the consistency of products. S107. After the aluminum box molded parts are demoulded, the machine vision technology is used to quickly inspect their appearance quality, and surface defective products are eliminated. The qualified products are tested for dimensional accuracy using a three-coordinate measuring machine to ensure that the key dimensions meet the matching requirements of the modular interface; S108. In order to verify the rigidity of the socket aluminum box molded parts, a vibration fatigue test is required. According to the test results, if necessary, the material selection, heat treatment process or structural design is further optimized to meet the requirements of long-term reliability. Through the integrated optimization of modular design and precision molding process, the high-strength, lightweight and configurable high-quality production of the socket aluminum box is finally achieved.

2. According to the modular design and precision molding method of a socket aluminum box as described in claim 1, it is characterized in that: The S101 includes: Acquire a three-dimensional model of the socket aluminum box, wherein the three-dimensional model includes an internal structure and an external structure of the socket aluminum box; A topology optimization algorithm is used to analyze the internal structure of the three-dimensional model to obtain the position distribution and connection relationship of each functional module, wherein the functional module includes a power module, a switch module and a socket module; According to the position distribution and connection relationship, a modular design method is adopted to divide the functional module into independent sub-modules, and an interface relationship between the sub-modules is established; For each of the submodules, a finite element analysis method is used to calculate the strength and stiffness of the submodule, and determine whether the submodule meets a preset strength threshold and stiffness threshold; If the submodule does not meet the preset strength threshold and stiffness threshold, return to the modular design step and re-divide the submodule; According to the position distribution and connection relationship of the submodules, a genetic algorithm is used to optimize the layout of the submodules to obtain an optimal layout solution; According to the optimal layout scheme, a parametric modeling technology is used to reconstruct the three-dimensional model of the socket aluminum box and generate an engineering drawing of the socket aluminum box; According to the three-dimensional model and engineering drawings, a sample of the socket aluminum box is manufactured by using rapid prototyping technology, and assembly and functional testing are performed; If the sample passes the assembly and functional tests, a production plan and quality control measures for the socket aluminum box will be formulated based on the preset batch size and production process.

3. According to the modular design and precision molding method of a socket aluminum box as described in claim 1, it is characterized in that: The S102 includes: According to the three-dimensional model of the socket aluminum box, a finite element analysis model is established, and material properties, boundary conditions and load conditions are set; The mechanical properties simulation calculation is performed through finite element analysis software to obtain the stress-strain distribution cloud diagram; Determine the stress concentration area according to the stress-strain distribution cloud diagram, and judge whether the stress exceeds the allowable stress of the material; If the stress exceeds the allowable stress of the material, the weak parts of the structure are optimized by increasing the thickness or designing reinforcing ribs; While optimizing the structure, high-strength materials are selected to improve the overall strength and rigidity of the socket aluminum box; The optimized socket aluminum box is subjected to a heat treatment process to further improve the strength by adjusting the microstructure of the material; The socket aluminum box is processed by a surface strengthening process, wherein the surface strengthening process is shot peening strengthening or laser strengthening, so as to form residual compressive stress on the surface of the socket aluminum box and improve fatigue strength and wear resistance.

4. According to the modular design and precision molding method of a socket aluminum box as described in claim 1, it is characterized in that: The S104 includes: Obtaining a three-dimensional model of a die-casting mold with a complex internal structure, wherein the three-dimensional model includes a multi-directional parting, a tilting top, and a core-pulling precision structure; According to the three-dimensional model, a CNC machining center is used to perform precision machining on the surface of the mold cavity to obtain a cavity surface with surface roughness that meets the requirements; A layer of wear-resistant and corrosion-resistant coating with good lubricity is deposited on the surface of the cavity by using a special coating technology; Obtain the appearance quality requirements of the aluminum box molded parts, set cooling water channels and exhaust grooves in the three-dimensional model, and use mold flow analysis software to simulate and optimize the aluminum liquid filling process; Obtain the pressure, speed and displacement parameter signals of the injection system and establish a data model of the injection process; According to the data model, a machine learning algorithm is used to perform intelligent optimization control on the injection parameters; Obtain the temperature and stress state parameters of the die-casting mold and establish a preventive maintenance management system for the die-casting mold; The machine learning algorithm is used to analyze the state parameters, predict the remaining service life of the mold, and formulate the optimal maintenance plan.

5. A modular design and precision molding method for a socket aluminum box according to any one of claims 1 to 4, characterized in that: The S105 includes: According to the three-dimensional model of the aluminum box die casting, a finite element analysis model including aluminum liquid, injection system, pouring system, cooling system and mold components is established, and material properties, boundary conditions and initial conditions are set; A computational fluid dynamics method is used to simulate the filling behavior of the aluminum liquid during the injection process, to obtain the temperature field, velocity field and pressure field distribution of the aluminum liquid during filling, and to determine whether there are air entrainment and splashing defects; If there is a filling defect, adjusting the structural parameters or process parameters of the injection system until the filling defect is eliminated; The solidification process of the aluminum liquid in the mold cavity is simulated by using a heat transfer method, the temperature field distribution and solidification time during solidification are obtained, and whether shrinkage cavities and crack defects exist is determined; If there is a solidification defect, the structural parameters or process parameters of the cooling system are adjusted until the solidification defect is eliminated.

6. A modular design and precision molding method for a socket aluminum box according to any one of claims 1 to 4, characterized in that: The S107 includes: Acquire image information of the aluminum box molded part after demoulding, and use an image segmentation algorithm to segment the image information to obtain an appearance area image of the aluminum box molded part; For the appearance region image, a feature extraction algorithm is used to extract texture features and color features of the appearance region image to obtain an appearance feature vector; Input the appearance feature vector into a pre-established appearance defect discrimination model to obtain a defect discrimination result. If the defect discrimination result is a defect, the aluminum box molded part is judged as a defective product; otherwise, the aluminum box molded part is judged as a qualified product. Acquire the three-dimensional point cloud data of the qualified product, and extract key dimension point cloud data from the three-dimensional point cloud data using a point cloud segmentation algorithm; According to preset key dimension parameters, fitting processing is performed on the key dimension point cloud data to obtain the actual value of the key dimension; Compare the actual value of the key dimension with the preset modular interface matching dimension, and if the actual value meets the matching dimension requirement, determine that the aluminum box molded part is a final qualified product; The test data of the final qualified products are recorded and statistically analyzed to obtain test result data, which are then used for subsequent production process optimization.

7. A modular design and precision molding method for a socket aluminum box according to any one of claims 1 to 4, characterized in that: The S108 includes: According to the structural characteristics and use requirements of the socket aluminum box molding, determine the load spectrum and acceleration level of the vibration fatigue test, and formulate the test plan and evaluation criteria; Obtain a three-dimensional model of the socket aluminum box molded part, use finite element analysis software to simulate the stress distribution and deformation under vibration fatigue load, and identify fatigue-prone areas; Optimize the material selection and heat treatment process parameters of the socket aluminum box for the fatigue-prone area, improve the fatigue strength and vibration resistance of the material, and reduce stress concentration and fracture risks; Adopting the modular design concept, the socket aluminum box is decomposed into standardized functional units. Through parametric modeling and topological optimization, the structural layout and size parameters are optimized to achieve a balance between light weight and high strength. Apply precision molding processes, including die casting and extrusion, to manufacture high-precision molded parts of the socket aluminum box, strictly control dimensional tolerances and surface quality, and reduce processing stress and defects; During the vibration fatigue test, strain gauges and acceleration sensors are used to monitor the stress-strain response and vibration characteristic parameters of the socket aluminum box molded parts in real time to obtain fatigue life and failure mode data; Based on the test results and simulation analysis, the fatigue life and reliability level of the socket aluminum box molding are evaluated to determine whether it meets the long-term use requirements, determine the key factors that need to be optimized and improved, and ensure the consistency and reliability of mass production.

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