An automobile pipe clamp injection molding production process optimization method and system, and a storage medium

Abstract

The present application relates to the technical field of automobile production process optimization, in particular to a kind of automobile pipe clamp injection molding production process optimization method, system and storage medium.The present application extracts pipe clamp key structure features, carries out two-dimensional partitioning in combination with wall thickness difference and functional requirements, ensures that partitioning fits the actual structure and functional requirements of pipe clamp, calculates the adaptive partition temperature control and injection molding parameters in advance through multiple rounds of filling, cooling, stress simulation and quantitative evaluation, significantly reduces the number of test molds and development cycle, simultaneously verifies parameter stability by simulating cooling system temperature fluctuation, compares CAE simulation results and analyzes deviation causes by real-time detection of production data, solvent soaking method for stress detection, adjusts process parameters accordingly to avoid batch quality problems caused by traditional parameter fluctuations, and improves production process stability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automobile production process optimization, in particular to an automobile pipe clamp injection molding production process optimization method and system and a storage medium. BACKGROUND

[0002] The automobile pipe clamp is an important functional part for fixing the pipeline in the key areas such as the engine compartment and chassis, and has very high requirements for its dimensional accuracy, mechanical strength and long-term reliability. Such parts usually have complex structure, uneven wall thickness distribution, and are usually made of glass fiber reinforced engineering plastics, preferably using automobile grade raw materials (such as PA66, PP), and after drying and color matching, they are sent to the injection molding machine; then the mold is designed and processed and the trial mold is standardized; then the injection molding machine plasticizes the raw material, high-pressure injection into the mold, and the blank is obtained after cooling and demolding; then the burrs are removed and secondary processing is performed as needed; then the appearance, size and performance are detected, and the qualified products are labeled with batches; finally, the products are packaged and protected, and are stored in batches, all of which meet the standards of the automobile industry.

[0003] In traditional injection molding production, uneven cooling shrinkage can easily cause internal stress concentration, warping and other defects, which seriously affect the product qualification rate and assembly reliability, especially for pipe clamps with complex structure, which may not be filled and may have burrs in some areas. Therefore, we propose an automobile pipe clamp injection molding production process optimization method, system and storage medium. SUMMARY

[0004] The purpose of the present application is to provide an automobile pipe clamp injection molding production process optimization method, system and storage medium, which can solve any of the technical problems proposed in the background art.

[0005] To solve the above technical problems, the present application provides an automobile pipe clamp injection molding production process optimization method, which comprises the following steps:

[0006] S10, obtaining a three-dimensional model of the automobile pipe clamp, extracting key structural features in the three-dimensional model, dividing the pipe clamp model into functional regions based on the key structural features, i.e. buckle region, main thick wall region and transition region, statistically analyzing the wall thickness values of the automobile pipe clamp, determining the wall thickness threshold value according to the functional region division, and dividing the regions into buckle thin wall region, connection transition region and main thick wall region according to the wall thickness threshold value;

[0007] S20, inputting the automobile pipe clamp three-dimensional model containing the partition data into the CAE analysis model of the pipe clamp injection molding, taking the partition geometric data of the pipe clamp three-dimensional model, the raw material characteristic data and the mold process data as inputs, and carrying out multiple rounds of simulation around the three major targets of cooling uniformity, defect elimination and stress control, and determining the partition temperature control parameters through quantitative evaluation results;

[0008] S30, input the determined partition temperature control parameters to the production equipment, perform injection molding production, perform stress detection on the product by using a solvent soaking method, compare the actually detected stress distribution map with the stress nephogram predicted by the CAE analysis model, select a comparison point, calculate a deviation rate, determine the cause of stress according to the deviation rate, if the actual stress concentration area is highly consistent with the prediction result of the CAE analysis model, then adjust the partition temperature of the pipe clamp model according to the stress position, if there is a deviation between the actual stress concentration area and the prediction of the CAE analysis model, then adjust the parameters of the CAE analysis model.

[0009] In an embodiment of the present application, the S10 is a specific step of dividing the pipe clamp model into multiple structure regions, and the specific steps are as follows:

[0010] S11, preliminarily divide the pipe clamp model according to the key structure function of the pipe clamp model, including a buckle region, a main body region and a transition region;

[0011] S12, use the wall thickness analysis tool of SolidWorks to perform global scanning on the pipe clamp model, generate a wall thickness distribution nephogram, and intuitively display the wall thickness values of each position of the model, and on the basis of the wall thickness distribution nephogram, sample and measure the buckle region, the main body region and the transition region of the pipe clamp model;

[0012] S13, statistically analyze the wall thickness values of each region, determine the minimum wall thickness value, the maximum wall thickness value and the common wall thickness interval of the pipe clamp, determine the division threshold values of the thin wall region, the transition region and the thick wall region by using the natural division point method, and divide the pipe clamp model into regions.

[0013] In an embodiment of the present application, when the S13 determines the division threshold values of each region, the division threshold values are adjusted according to the core function priority principle, the wall thickness threshold values that meet the corresponding core functions are solved according to the corresponding core functions of each region, and when the results of the function region division conflict with the results of the wall thickness threshold division, the threshold values are adjusted according to the function requirements, the final region division is determined according to the final wall thickness threshold values, the buckle thin wall region, the connection transition region and the main body thick wall region.

[0014] In an embodiment of the present application, the specific steps of the S20 for determining the partition temperature control parameters are as follows:

[0015] S21, input the automobile pipe clamp three-dimensional model containing the partition data to the CAE analysis model of the pipe clamp injection molding, use the partition geometric data, the raw material characteristic data and the mold process data of the pipe clamp three-dimensional model as inputs, obtain the raw material characteristic data and the mold process data by using the material physical property table provided by the raw material supplier;

[0016] S22, based on the input data, setting the initial injection pressure, speed, simulating the melt filling process, performing filling simulation, setting the initial temperature gradient in the order of thin wall area to transition area to thick wall area, performing cooling simulation, taking cooling uniformity, defect elimination and stress control as the target, determining the initial interval of the partition temperature control parameter;

[0017] S23, based on the initial interval, using L9 orthogonal table, designing multiple simulation parameter combinations, quantifying the score of each simulation result according to cooling uniformity, defect elimination and stress control, selecting the combination with the highest score as the optimal parameter of partition temperature control.

[0018] In an embodiment of the application, the S2.1.2, after performing the filling simulation and the cooling simulation, calculates the residual stress distribution of the pipe clamp after forming based on the filling and cooling simulation results, performs stress simulation, and generates a stress nephogram of the pipe clamp model.

[0019] In an embodiment of the application, the S2.1.3, after obtaining the optimal parameter of partition temperature control, adjusts the optimal parameter of partition temperature control by fine-tuning to verify the fluctuation and improve the anti-interference ability of the optimal parameter of partition temperature control in the case of temperature fluctuation of the cooling system.

[0020] In an embodiment of the application, the S30 according to the stress detection, the specific steps of feeding back the partition temperature of the pipe clamp model and the parameters of the CAE analysis model are as follows:

[0021] S31, deploying temperature detection elements in the cavity of the pipe clamp mold, real-time collecting the actual temperature of each temperature control area cavity wall, dynamically adjusting the actual temperature according to the partition temperature control parameter;

[0022] S32, using the solvent immersion method to detect the stress of the product, placing the actual detected stress distribution map and the stress nephogram predicted by the CAE analysis model in a unified coordinate system, selecting comparison points, calculating the deviation rate, determining the cause of stress according to the deviation rate, and feeding back and adjusting the production process.

[0023] In an embodiment of the application, when the S32 determines the cause of stress according to the deviation rate, the deviation rate is divided into slight deviation, moderate deviation and severe deviation according to the size and distribution characteristics of the deviation rate, and adjustment measures are taken according to the deviation level.

[0024] In an embodiment of the application, the system comprises:

[0025] The structure division module acquires the three-dimensional model of the automobile pipe clamp, extracts the key structural features in the three-dimensional model, and divides the pipe clamp model into multiple structural regions according to the wall thickness difference.

[0026] A CAE simulation module inputs a three-dimensional model of the automobile pipe clamp into a CAE analysis model of pipe clamp injection molding, takes collective data of the three-dimensional model of the pipe clamp, material characteristic data and mold process data as input, takes uniformity of cooling, defect elimination and stress control as targets, performs multiple rounds of simulation, and quantitatively evaluates simulation results to determine partition temperature control parameters;

[0027] A feedback adjustment module inputs the determined partition temperature control parameters into production equipment for injection molding production, performs stress detection on the product by using a solvent soaking method, compares an actually detected stress distribution map with a stress cloud map predicted by the CAE analysis model, calculates a deviation rate, determines a cause of stress generation according to the deviation rate, and feeds back and adjusts a production process.

[0028] In an embodiment of the present application, the storage medium stores a computer program, and the computer program, when executed by a processor, implements all steps of the automobile pipe clamp injection molding production process optimization method according to any one of claims 1-8, and specifically includes:

[0029] reading basic data of pipe clamp structure size, material characteristics and initial mold cooling conditions;

[0030] calling a mold flow analysis CAE algorithm, constructing an analysis model and calculating initial process parameters, the initial process parameters including partition temperature control parameters, injection pressure gradient parameters and injection speed gradient parameters;

[0031] receiving real-time data transmitted by a temperature detection element and executing parameter self-adaptive adjustment logic;

[0032] storing full-process data, the full-process data including simulation parameters, production parameters and quality data, and generating a parameter iteration optimization report.

[0033] The above technical solution of the present application has the following advantages compared with the prior art:

[0034] The automobile pipe clamp injection molding production process optimization method, system and storage medium, the structure division module extracts key structure features of the pipe clamp, performs two-dimensional partitioning in combination with wall thickness differences and functional requirements, ensures that the partitioning is in line with the actual structure and functional requirements of the pipe clamp, solves the problem of traditional single partitioning according to wall thickness only, avoids mismatching of subsequent temperature control parameters and use requirements, and can also adapt to pipe clamp production of different structure types without the need to redesign partitioning logic for a single product;

[0035] The CAE simulation module calculates the adaptive partition temperature control and injection molding parameters in advance through multiple rounds of filling, cooling, stress simulation and quantitative evaluation, greatly reduces the trial mold times and research and development cycle, verifies the parameter stability through the simulation of the temperature fluctuation of the cooling system, ensures that the parameters can still meet the quality requirements under the actual production fluctuation scene, avoids the batch quality problems caused by the traditional parameters due to equipment fluctuation, and improves the production process stability;

[0036] The feedback adjustment module detects the production data and stress through the solvent immersion method in real time, compares the CAE simulation results and analyzes the deviation reasons, adjusts the process parameters, can accurately control the residual stress of the pipe clamp, reduces the reliability risk in the use process, realizes the automatic dynamic adjustment of the production parameters, reduces the manual intervention, and at the same time feeds back the quality data to the CAE model iteration optimization. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to make the content of the application more easily understood, the application will be further described in detail below according to specific embodiments of the application and in combination with the drawings.

[0038] Figure 1 is a method flowchart of the application;

[0039] Figure 2 is a system flowchart of the application.

[0040] The meanings of various labels in the drawings are as follows:

[0041] 100, structure division module; 200, CAE simulation module; 300, feedback adjustment module. DETAILED DESCRIPTION

[0042] The application will be further described below in combination with the drawings and specific embodiments, so that those skilled in the art can better understand the application and implement it, but the embodiments are not as a limitation on the application.

[0043] To solve the problems in the background art, in the injection molding production process of the pipe clamp with a complex structure, the traditional single waterway design and fixed temperature control mode cannot adapt to the heat dissipation requirements of different wall thickness areas of the pipe clamp. The thick wall area is prone to shrink marks due to slow cooling rate, and the thin wall area is prone to warping due to fast cooling rate, so the overall defect rate is high. In addition, the thick wall area (such as the pipe clamp main body support area and the positioning hole periphery) has a large cavity volume and a long melt flow path. Before the melt reaches the end of the cavity, the melt flow capacity decreases due to cooling and solidification and pressure loss, so the thick wall area cannot be completely filled. The thin wall area (such as the pipe clamp buckle elastic arm and the transition connection area) is sensitive to mold clamping precision and cavity pressure control due to small cavity gap. If the melt pressure exceeds the mold clamping force, or there is a gap in the mold cavity, the melt will be extruded out of the cavity, resulting in burrs in the thin wall area.

[0044] Based on this, the embodiment of the present application divides the pipe clamp wall thickness and function by the structure division module to provide a structured analysis object for the CAE simulation module. When performing CAE simulation, different parameters are set for different areas. The optimal temperature control parameters and stress prediction results output by the CAE simulation become the comparison benchmark of the feedback adjustment module. The feedback adjustment module accurately locates the production problems through deviation analysis of actual data and simulation benchmark, adjusts the partition temperature control parameters, and updates the parameter library and calculation relationship of the CAE simulation to improve the simulation accuracy.

[0045] To better realize the above technical concept, please refer to Figure 1 The embodiment of the present application provides an automobile pipe clamp injection molding production process optimization method, which comprises the following steps:

[0046] S10, obtaining a three-dimensional model of an automobile pipe clamp, extracting key structural features in the three-dimensional model, and dividing the pipe clamp model into functional areas based on the key structural features, including a buckle area, a main body area, and a transition area;

[0047] Statistical analysis of the wall thickness value of the automobile pipe clamp, determination of the wall thickness threshold value according to the functional area division, and division of the areas into a buckle thin wall area, a connection transition area, and a main body thick wall area according to the wall thickness threshold value;

[0048] The key structural features refer to the specific functions of each part of the pipe clamp to achieve its core purpose (such as fixing the pipe, adapting the assembly, and ensuring the stability of the connection), which is the core attribute that determines whether the pipe clamp can meet the use requirements. The key structural features of the automobile pipe clamp include the buckle, the main body, and the transition structure, and the corresponding key structural functions are as follows:

[0049] The buckle is the core structure of the connection between the pipe clamp and the pipe, or the pipe clamp and other components (such as the vehicle body support), and its key function directly determines whether the pipe clamp can stably fix the pipe and facilitate installation or disassembly.

[0050] The main body is the skeleton of the pipe clamp, is the installation basis of each functional structure, and its key function determines the overall strength, positioning accuracy and carrying capacity of the pipe clamp;

[0051] The transition structure is located between the buckle (thin wall) and the main body (thick wall), and is a bridge to avoid structural mutation, and its key function directly affects the structural reliability and service life of the pipe clamp.

[0052] The specific steps of dividing the pipe clamp model into multiple structure regions are as follows:

[0053] S11, according to the key structure function of the pipe clamp model, the pipe clamp model is preliminarily divided, including: buckle area, main body area and transition area;

[0054] The preliminary division of the pipe clamp model needs to take the function determined region boundary as the core logic, and around the core functions of the buckle area (elastic clamping), the main body area (rigid support) and the transition area (stress dispersion), the division of each area needs to directly correspond to the core use function of the pipe clamp (such as the buckle area must cover all the structures that realize the clamping of the pipe), avoid the function fragmentation caused by only dividing according to the size or appearance, and verify the region boundary combined with the geometric characteristics of the structure (such as protrusion, depression, thickness change), for example, the buckle area is mostly thin-walled protrusion, the main body area is mostly thick-walled base, and the transition area is mostly flat structure connecting the two;

[0055] Among them, the buckle area: taking the outermost edge of the buckle protrusion as the starting point, extending to the place where the wall thickness begins to obviously thicken to the root, forming the boundary of the buckle area (usually at 1.5 times of the buckle wall thickness, such as the buckle wall thickness is 1.5mm, extending to the place where the wall thickness is greater than 2.2mm), if there are multiple buckles (such as symmetrical double buckles), the boundary of each buckle needs to be defined respectively, if the distance between adjacent buckles is small, they can be combined into a buckle set area to avoid region fragmentation;

[0056] Transition area: taking the outer boundary of the buckle area as the starting point, extending to the place where the wall thickness reaches the standard wall thickness of the main body area (such as the main body wall thickness is 3mm, extending to the place where the wall thickness is greater than 3mm), forming the boundary of the transition area, if there are multiple buckles corresponding to multiple transition sections, the boundary of each transition section needs to be defined respectively, if the transition sections overlap, they can be combined into a transition set area;

[0057] Main body area: taking the outermost edge of the main body base as the starting point, excluding the buckle area and the subsequent transition area, the remaining thick-walled base part is the main body area.

[0058] S12, using the wall thickness analysis tool of SolidWorks, globally scanning the tube clamp model, generating a wall thickness distribution cloud chart, and intuitively displaying the wall thickness values of each position of the model. On the basis of the wall thickness distribution cloud chart, the buckle area, main body area and transition area of the tube clamp model are sampled and measured;

[0059] Start the SolidWorks software, select Open through the File menu, and import the 3D model of the tube clamp that has been modeled. Ensure that the model is complete, without missing features or broken surfaces. If there are abnormalities, they need to be repaired in advance to avoid affecting the analysis results;

[0060] In the tool group of the Evaluation tab, click the Thickness Analysis icon. At this time, the Thickness Analysis Property Manager will pop up. In the Analysis Parameters column of the Property Manager, set the target thickness value. This value can refer to the design standard or the expected average wall thickness of the tube clamp. For example, if the main body wall thickness of a general tube clamp is designed to be 3 mm, you can set it to 3 mm first, check the Display Thin Areas box to highlight the areas in the model where the wall thickness is less than the target thickness; At the same time, in the color setting, select the full color range to display different wall thickness intervals with rich color gradients, and intuitively present the wall thickness distribution difference;

[0061] After the analysis is complete, the tube clamp model will be displayed in different colors according to different wall thickness intervals, and the specific wall thickness value of the mouse position will be displayed. Through the cloud chart, you can clearly observe the wall thickness distribution of the buckle area (usually thin-walled, light color, such as yellow representing 1-2 mm wall thickness interval), main body area (thick-walled, dark color, such as blue representing 3-4 mm wall thickness interval), and transition area (wall thickness between the two, color transition natural, such as green representing 2-3 mm wall thickness interval), and intuitively grasp the wall thickness difference of the entire tube clamp;

[0062] Based on the preliminary division of the buckle area, main body area and transition area, set multiple sampling detection points in each area, and arrange the wall thickness data obtained by sampling and measuring each area into a table. Compare the sampling and measuring data with the previously estimated wall thickness range based on the structural function division area (such as 1-2 mm for the buckle area, 3-4 mm for the main body area, and 2-3 mm for the transition area). If the difference is large, you need to recheck whether the region division boundary is accurate.

[0063] S13, statistical analysis of the wall thickness values of each area, determination of the minimum wall thickness value, maximum wall thickness value and common wall thickness interval (arrangement of wall thickness values in order from small to large), and determination of the division threshold of the thin-walled area, transition area and thick-walled area by natural division point method (finding the critical point of frequency drop in wall thickness data distribution as the division boundary of thin-walled area, transition area and thick-walled area);

[0064] The sample measurement data is combined with the global wall thickness cloud information to form a structured data set, the wall thickness values of the full data set are grouped at intervals of 0.1 mm, the frequency (number of occurrences) of each group is counted, the curve of wall thickness value and frequency is drawn, and the natural demarcation point method is used to determine the valley point in the curve where the frequency decreases significantly as the natural demarcation point;

[0065] For example, the statistical data shows that the frequency ratio of the wall thickness of the buckle area being 1.4-1.9 mm is 85% (such as 1.5 mm appearing 3 times, 1.6 mm appearing 4 times, and 1.8 mm appearing 2 times), so the common interval is 1.4-1.9 mm, the frequency ratio of the wall thickness of the transition area being 2.1-2.6 mm is 90% (such as 2.2 mm appearing 3 times, 2.3 mm appearing 3 times, and 2.5 mm appearing 1 time), so the common interval is 2.1-2.6 mm, and the frequency ratio of the wall thickness of the main body area being 2.9-3.4 mm is 88% (such as 3.0 mm appearing 4 times, 3.1 mm appearing 5 times, and 3.2 mm appearing 3 times), so the common interval is 2.9-3.4 mm;

[0066] The first demarcation point (demarcation between the thin-wall area and the transition area), it is found from the observation of the curve that the frequency in the interval of 1.9-2.0 mm decreases from 5 times in the interval of 1.8-1.9 mm to 1 time, and the frequency in the interval of 2.0-2.1 mm rebounds to 4 times, so the first demarcation point is determined to be 2.0 mm (i.e., the thin-wall area is less than 2.0 mm, and the transition area is greater than 2.0 mm);

[0067] The second demarcation point (demarcation between the transition area and the thick-wall area), the frequency in the interval of 2.8-2.9 mm decreases from 3 times in the interval of 2.7-2.8 mm to 0 time, and the frequency in the interval of 2.9-3.0 mm rebounds to 6 times, so the second demarcation point is determined to be 2.8 mm (i.e., the transition area is less than 2.8 mm, and the thick-wall area is greater than 2.8 mm);

[0068] The threshold range of the thin-wall area is (1.4, 2.0) mm, the threshold range of the transition area is (2.0, 2.8) mm, and the threshold range of the thick-wall area is (2.8, 3.4) mm.

[0069] In order to be able to adjust the wall thickness threshold according to the functional area division, the final wall thickness threshold is determined, wherein when the S13 determines the division threshold of each area, the wall thickness threshold that meets the corresponding core function is solved according to the core function priority principle and the core function corresponding to each area, and the division threshold is adjusted, wherein when the result according to the functional area division conflicts with the result according to the wall thickness threshold division, the threshold is adjusted based on the functional requirement, the final area division is determined according to the final wall thickness threshold, the buckle thin-wall area, the connection transition area, and the main body thick-wall area are determined;

[0070] The core function priority principle refers to taking the core function of each functional area (buckle, main body, transition) as the first priority to determine the unbreakable threshold range of the wall thickness of the area. The core function of the buckle area is elastic clamping (which requires to ensure the pull-out force and not to break), so the wall thickness cannot be too thick (otherwise the elasticity is lost) or too thin (otherwise the strength is insufficient). This range is the bottom threshold, i.e., the core function threshold, which cannot be compromised for other needs.

[0071] When checking the core functionality of each area, first, the core function of the buckle area is elastic clamping, the core function of the main body thick wall area is rigid support, and the core function of the transition area is force transmission.

[0072] The buckle is the locking structure of the pipe clamp and needs to meet the elastic deformation capability. The buckle needs to be elastically deformed (such as the cantilever end being pressed down) during assembly. If the wall thickness is too thin, it will cause permanent deformation, and if it is too thick, it cannot be assembled. The wall thickness threshold that meets the core function of the buckle is solved by the elastic deformation limited wall thickness formula, which is as follows:

[0073] ;

[0074] wherein, is the wall thickness of the buckle, in mm, is the minimum clamping force of the buckle, in N, is the cantilever length of the buckle, in mm, is the safety factor, unitless, is the material elastic modulus, in MPa, i.e. , converted to mm as , is the width of the buckle, in mm, is the maximum allowable elastic deformation of the buckle, in mm.

[0075] wherein, the minimum clamping force of the buckle , the material elastic modulus , the maximum allowable elastic deformation of the buckle , the safety factor , which can be obtained from the material manufacturer's manual, the cantilever length of the buckle , the width of the buckle , which is obtained by actual measurement, and the wall thickness threshold x that meets the core function of the buckle is calculated by substituting the above formula;

[0076] The main body is the skeleton of the pipe clamp and needs to withstand the weight of the pipeline, vibration load and environmental stress (such as temperature change), and needs to meet the static strength requirement. The wall thickness threshold that meets the core function of the main body thick wall area is solved by the static load wall thickness calculation formula, which is as follows:

[0077] ;

[0078] wherein, is the main body wall thickness, in mm, is the dynamic load coefficient, unitless, is the total weight of the pipe clamp, in N, is the main body support arm length, in mm, is the safety factor, unitless, is the main body width, in mm, is the maximum bending stress, in MPa, i.e. , converted to mm is .

[0079] wherein, the dynamic load coefficient , the safety factor , the maximum bending stress , the total weight of the pipe clamp , the main body support arm length , the main body width , the wall thickness threshold value y that meets the core function of the main body is calculated by substituting the actual measurement into the above formula;

[0080] The wall thickness threshold value that meets the core function calculated according to the functional calculation of each region is compared with the threshold range based on the wall thickness threshold value to determine the final regional division, i.e. the buckle thin-walled region is (1.4, x) mm, the connection transition region (x, y) mm and the main thick-walled region (y, 3.4) mm, wherein 1.4 mm, 3.4 mm come from the region range determined by the above demarcation point;

[0081] When the statistical threshold value conflicts with the functional threshold value range, the final regional division boundary is adjusted based on the functional threshold value range to ensure that the partition can reflect the structural characteristics and meet the core performance requirements.

[0082] In addition, S20, input the automobile pipe clamp three-dimensional model containing the partition information into the CAE analysis model of the pipe clamp injection molding, take the partition geometric data of the pipe clamp three-dimensional model, the raw material characteristic data and the mold process data as the input, carry out multiple rounds of simulation around the three targets of cooling uniformity, defect elimination and stress control, determine the partition temperature control parameters through quantitative evaluation results.

[0083] wherein, the specific steps of S20 for determining the partition temperature control parameters are as follows:

[0084] S21, input the automobile pipe clamp three-dimensional model containing partition data into the CAE analysis model of pipe clamp injection molding, use the partition geometry data of the pipe clamp three-dimensional model, the material characteristic data and the mold process data as input, obtain the material characteristic data and the mold process data (wall thickness distribution data, cavity geometry parameters and flow path data) by using the material physical property table provided by the material supplier, obtain the material characteristic data (thermal physical parameters, rheological parameters and mechanical parameters) and the mold process data (cooling system parameters and injection molding machine and gate parameters) by using the material physical property table provided by the material supplier;

[0085] S22, based on the input data, set the initial injection pressure, speed, simulate the melt filling process, perform filling simulation, set the initial temperature gradient in the order of thin wall area to transition area to thick wall area, perform cooling simulation, and determine the initial interval of partition temperature control parameters, taking uniformity, defect elimination and stress control as the target;

[0086] The core of filling simulation is to simulate the flow process of the melt in the cavity, and the initial injection pressure and speed need to be set in combination with three types of input data including cavity geometry, material rheological characteristics and mold gate parameters to avoid underfilling or overfilling;

[0087] Based on the Hele-Shaw flow model, the theoretical filling pressure is calculated in combination with the input data, and the formula is as follows:

[0088] ;

[0089] Wherein, The theoretical filling pressure is , The melt viscosity of the material at the set temperature is , The maximum flow distance is , The preset injection speed is , The average width of the cavity is , The average wall thickness of the cavity is .

[0090] The initial injection speed is set based on the shear characteristics of the material, and the formula is as follows:

[0091] ;

[0092] Wherein, The shear rate is , The preset injection speed is , Average wall thickness of the cavity , Local wall thickness .

[0093] Import the above pressure, velocity parameters in CAE software (such as Moldflow), select the filling analysis sequence, enable shear rate, filling time, pressure distribution output options, after simulation is completed, target inspection is carried out;

[0094] The filling time difference of each area is less than 0.3s (such as 1.2s in the main thick wall area, 1.4s in the buckle area), whether the filling balance is qualified is determined, the maximum shear rate is less than 5000s (to avoid material degradation, the simulation result shows that the maximum is 3500s) to determine whether the shear rate is qualified, if it does not meet the standard (such as filling time difference 0.5s), the speed needs to be adjusted (such as increasing the speed of the buckle area to 90mm / s), and the injection pressure and speed of the pipe clamp mold during injection molding are obtained by re-simulation.

[0095] Among them, the cooling simulation needs to be based on the wall thickness difference of each area, the thermal physical properties of the raw material, and the cooling system parameters, and the temperature gradient is set according to the area. The core is thin wall fast cooling and thick wall slow cooling to ensure cooling uniformity;

[0096] The core of partition temperature control is the temperature difference of the cooling medium (such as water) in each area, so the medium temperature of the buckle thin wall area, the connection transition area and the main thick wall area is set as the key variable, and other parameters (such as water flow, cavity temperature) are temporarily set as the basic value (flow ensures turbulence, cavity temperature is 5-10℃ higher than medium temperature), avoid multi-variable interference interval judgment, with cooling uniformity and defect elimination as quantitative threshold target (for example, cooling uniformity: the final temperature difference of each area is less than 8℃, the internal temperature difference of single area is less than 5℃, defect elimination: the shrinkage depth of the main thick wall area is less than 0.03mm, the warpage of the buckle thin wall area is less than 0.12mm, and there is no flash);

[0097] According to the principle that the temperature of the buckle thin wall area is lower than that of the connection transition area, and the temperature of the connection transition area is lower than that of the main thick wall area (in line with the wall thickness heat dissipation characteristics), the first group of basic temperature parameters are set (such as 45℃ for the buckle thin wall area, 55℃ for the connection transition area, and 65℃ for the main thick wall area), and the cooling simulation is run. If the cooling uniformity is qualified (temperature difference 7℃) and there is no defect, but the cooling time is 18s (exceeding the upper limit of 15s), it means that the overall temperature is too high, and the temperature of each area needs to be reduced. If the warpage of the buckle thin wall area is 0.15mm (exceeding the standard) and the shrinkage of the main thick wall area is 0.02mm (qualified), it means that the temperature of the buckle thin wall area is too low (cooling too fast), and the temperature of the buckle thin wall area needs to be increased;

[0098] Based on the results of the basic simulation, the adjustment direction of the temperature of each region is preliminarily judged (such as 45-55 DEG C for the buckle thin wall region, 60-70 DEG C for the main thick wall region), and in view of the deviation of the basic simulation, a plurality of simulations (such as 5-8 groups) are carried out in a single region fine tuning and other region fixed manner, and the matching condition of each group temperature and target is recorded, as shown in the following table:

[0099]

[0100] Among them, the cooling uniformity is calculated by the maximum temperature difference method, a plurality of feature points (such as the buckle thin wall region, the main thick wall region, the typical position of the connecting transition region) in the forming region are selected, and the difference between the highest temperature and the lowest temperature of these points in the cooling process is calculated. The smaller the difference, the better the cooling uniformity;

[0101] The defect situation is shrinkage and warpage. The shrinkage is caused by the surface depression due to insufficient feeding when the melt cools and shrinks. In the simulation, the volume shrinkage rate is calculated to calculate the volume change ratio of the melt from the molten state to the cooled and solidified state, and the formula is:

[0102]

[0103] Warpage is the deformation of the plastic part caused by uneven internal stress (such as cooling rate difference, uneven shrinkage). In the simulation, the deformation amount (such as the maximum deformation amount of the whole, the deformation amount in a specific direction) is quantified, the maximum deformation distance of the plastic part in the three-dimensional space is calculated, and the deformation amount is calculated separately for the main direction of warpage (such as the length direction, the width direction). The value of the warpage (such as 0.10 / 0.14) in the table corresponds to such deformation amount (unit: mm);

[0104] Only when all key indicators such as cooling uniformity, shrinkage and warpage meet the preset threshold, it is determined to be up to standard; if any indicator does not meet the standard (such as warpage deformation amount exceeds the standard), it is determined to be warpage and shrinkage.

[0105] For the group number (such as 1, 4, 5, 7) that meets the standard in the second step, the temperature is further fine tuned to the boundary, and the interval limit is verified;

[0106] Among them, the buckle thin wall region boundary: the connecting transition region is fixed at 55 DEG C, the main thick wall region is fixed at 62 DEG C, the temperature of the buckle thin wall region is reduced to 47 DEG C (simulation 8), if the warpage amount is 0.13 mm (slightly exceeds the standard), it is increased to 51 DEG C (simulation 9), if the cooling time is 15.5 s (slightly exceeds the standard), the buckle thin wall region boundary is 48-50 DEG C;

[0107] ​The main body thick wall region boundary: the fixed buckle thin wall region 48°C, the connection transition region 55°C, the main body thick wall region is reduced to 60°C (simulation 10), if the shrinkage mark is 0.035mm (slightly over standard), it is increased to 66°C (simulation 11), if the cooling time is 15.8s (slightly over standard), the main body thick wall region boundary is 62-65°C;

[0108] The connection transition region boundary: the fixed buckle thin wall region 48°C, the main body thick wall region 62°C, the connection transition region is reduced to 53°C (simulation 12), if the temperature difference is 8°C (up to standard), it is increased to 57°C (simulation 13), if the temperature difference is 7°C (up to standard), the connection transition region boundary is 53-57°C.

[0109] In order to obtain the internal stress nephogram of the pipe clamp model after simulation, S2.1.2 calculates the residual stress distribution of the pipe clamp after forming based on the filling and cooling simulation results after filling simulation and cooling simulation, performs stress simulation, and generates a stress nephogram of the pipe clamp model;

[0110] The initial shear stress field exported by the filling simulation is directly loaded into the stress simulation model as the starting point of the calculation, ensuring that the shear stress in the filling stage is completely frozen into the pipe clamp, and the temperature load is applied to each unit in time steps according to the temperature field time sequence of the cooling simulation (such as 240°C at 1s, 120°C at 10s, and 80°C at 20s), simulating the temperature change in the actual cooling process and triggering the calculation of thermal shrinkage stress;

[0111] Based on the temperature and time curve of each unit and the thermal expansion coefficient, the theoretical free shrinkage amount of each time step is calculated (shrinkage amount = thermal expansion coefficient x temperature change amount of this time step); , The temperature change amount of this time step is The thermal expansion coefficient is added to the initial shear stress to obtain the instantaneous total stress of this time step; as the cooling process progresses, the temperature decreases, the material changes from a molten state to a solid state, and the stress gradually freezes, finally forming residual stress at the demolding moment;

[0112] After the simulation is completed, the residual stress nephogram of the pipe clamp is generated in the result processing module of the CAE software, and the key data is extracted, the continuous color gradient is selected (such as from blue (low stress, 0MPa) to red (high stress, 80MPa)), and the stress difference is ensured to be intuitive and distinguishable; the stress range threshold (such as 0-60MPa) is set, the area exceeding the safety threshold (such as 60MPa) is highlighted, and the risk points are quickly identified.

[0113] In addition, S2.1.3, based on the initial interval, adopts L9 orthogonal table to design multiple sets of simulation parameter combinations, for each simulation result, quantitatively scores according to cooling uniformity, defect elimination and stress control, selects the highest score combination as the optimal parameter of partition temperature control;

[0114] After determining the initial interval of partition temperature control (such as the buckle thin wall area 50-52℃, the connection transition area 55-59℃, and the main body thick wall area 62-66℃), L9 orthogonal table (with the least experiment covering key variable combinations, and with multi-target scoring to lock the optimal solution) can efficiently design a small number of simulation parameter combinations;

[0115] For example, the selected factors and their levels are as follows:

[0116]

[0117] According to the standard structure of L9 orthogonal table (3 factors and 3 levels), the 3 levels of each variable correspond to the 1, 2 and 3 levels in the table, generating 9 sets of non-repeated and evenly distributed parameter combinations. For the 9 sets of parameter combinations, in addition to the temperature control variables, other parameters that affect the simulation results (such as water flow, injection pressure, time step, mesh accuracy) need to be kept completely consistent to avoid irrelevant variable interference and ensure that the result difference is only due to the change of temperature control parameters. According to the actual quality requirements of the pipe clamp, weights are allocated for the three targets (total 100%), which need to reflect the importance of the target (such as defect elimination directly determines whether the product is qualified or not, which can be allocated a higher weight; cooling uniformity and stress control are allocated the remaining weight according to the degree of influence on reliability), avoiding the disconnection between weight and actual demand;

[0118] For each combination, first calculate the single score of each target according to the scoring standard, and then sum the total score by weighting (or directly summing because the weight has been integrated into the single total score) to ensure that the total score can comprehensively reflect the overall quality performance of the combination. The total scores of the 9 combinations are sorted from high to low, and the combination with the highest score is selected as the optimal parameter.

[0119] In order to solve the temperature fluctuation (cooling medium temperature fluctuation and cooling water flow fluctuation), S23, after obtaining the optimal parameters of partition temperature control, verifies the fluctuation by fine-tuning the optimal parameters of partition temperature control in the case of temperature fluctuation of the cooling system, and improves the anti-interference ability of the optimal parameters of partition temperature control;

[0120] Among them, the cooling medium temperature fluctuation refers to ±1-2℃ instantaneous fluctuation caused by device response delay, and ±1℃ continuous offset caused by load change. The fluctuation range is based on the device manual and historical data, and the fluctuation range is not more than 95% of the range.

[0121] Cooling water flow fluctuation refers to the flow fluctuation of ±10% caused by pipeline leakage and water pump pressure change, and attention should be paid to the coupling effect with temperature fluctuation (such as passive rise of water temperature caused by flow drop);

[0122] A verification group is set for each of the above fluctuations, only one fluctuation is introduced in each group, and other parameters remain consistent with the optimal working condition, the tolerance of test parameters to single disturbance is tested, and whether the core verification cooling uniformity, defect elimination and stress control still meet the standards is tested. When the instantaneous fluctuation occurs, the principle of leaving a margin upward for low temperature fluctuation and balancing the stress downward for high temperature fluctuation is adopted, and the single fine adjustment amplitude is less than 1℃; When the continuous deviation occurs, the target temperature is adjusted synchronously to adapt to the system deviation, and the long-term stability is ensured; When the flow decreases, the water temperature is lowered to compensate for the loss of heat exchange efficiency, and when the flow rises, the water temperature is slightly raised to avoid excessive cooling; When the conduction fluctuation (thermal resistance increases), the water temperature is lowered to maintain the stability of the cavity temperature.

[0123] In addition, S30 inputs the determined partition temperature control parameters to the production equipment for injection molding production, uses the solvent soaking method to detect the stress of the product, compares the actual detected stress distribution map with the stress cloud map predicted by the CAE analysis model, selects the comparison points, calculates the deviation rate, determines the cause of the stress according to the deviation rate, if the actual stress concentration area is highly consistent with the prediction result of the CAE analysis model, adjusts the partition temperature of the pipe clamp model according to the stress position, if there is deviation between the actual stress concentration area and the prediction of the CAE analysis model, adjusts the parameters of the CAE analysis model.

[0124] The specific steps of S30 feeding back the partition temperature of the pipe clamp model and the parameters of the CAE analysis model according to the stress detection are as follows:

[0125] S31, in the cavity of the pipe clamp mold, deploy temperature detection elements, real-time collect the actual temperature of each temperature control area cavity wall, and dynamically adjust the actual temperature with the partition temperature control parameters as the target;

[0126] Each temperature control area (buckle thin-walled area, connection transition area, main body thick-walled area) is provided with at least 2 to 3 detection points to ensure that the temperature data in the area can be calculated on average, avoid misjudgment caused by single point data deviation, and preferentially arrange points at functional sensitive positions of each area (such as the buckle root of the buckle thin-walled area, the positioning hole periphery of the main body thick-walled area, and the wall thickness connection position of the connection transition area), which have the greatest impact on product quality;

[0127] When the deviation between the actual temperature and the target temperature of a certain area reaches ±0.5℃, the system automatically starts the adjustment program to avoid the deviation from expanding. For the adjustment of the cooling water temperature, if the actual temperature of a certain area is higher than the target temperature (for example, the actual temperature of the main thick-walled area is 67℃, and the target temperature is 65.5℃), the cooling water supply of the water path of the area is increased through the temperature control valve to reduce the water temperature of the water path (for example, from 65.5℃ to 64.5℃), so as to speed up the heat dissipation. If the actual temperature is lower than the target temperature (for example, the actual temperature of the buckle thin-walled area is 50℃, and the target temperature is 51.5℃), the cooling water supply is reduced to increase the water temperature of the water path (for example, from 51.5℃ to 52.5℃), so as to slow down the cooling speed.

[0128] For the adjustment of the cooling flow, if the deviation still exists after the water temperature adjustment (for example, the actual temperature of the connecting transition area is too high, and the water temperature has been reduced to the lower limit of the target), the water path flow of the area is adjusted through the variable frequency water pump (for example, the flow is increased from 10L / min to 12L / min) to improve the heat exchange efficiency. If the actual temperature is too low and the water temperature has been increased to the upper limit of the target, the flow can be reduced (for example, from 10L / min to 8L / min) to reduce the heat removal.

[0129] Among them, S32, using solvent soaking method to detect the stress of the product, the actual detected stress distribution map and the stress nephogram predicted by CAE analysis model are placed in the same coordinate system, the comparison points are selected, the deviation rate is calculated, the causes of stress are judged according to the deviation rate, and the production process is adjusted.

[0130] Randomly select 3 to 5 qualified pipe clamps (avoid appearance defect samples) from the production batch, immerse the pretreated pipe clamps completely in the solvent (the solvent used is 60% by volume of glacial acetic acid aqueous solution, the water bath temperature is constant at 23±2℃, for PA66 material, the soaking time is 18 minutes), keep the water bath temperature stable, and the soaking time is set according to the material characteristics;

[0131] Take out the pipe clamp, rinse the surface residual solvent with distilled water, and naturally air dry, then take pictures of the pipe clamp surface erosion marks from multiple angles through a high-resolution camera, import the photographed images into image analysis software, establish a two-dimensional coordinate system according to the actual size of the pipe clamp (take the center of the pipe clamp body as the origin, the X axis along the length direction of the body, and the Y axis along the thickness direction), label the stress grade of each erosion area, and generate the actual stress distribution map of the pipe clamp;

[0132] Select the fixed geometric features on the pipe clamp as the coordinate reference (such as the center of the main body positioning hole and the opening end point of the buckle), ensure that the features can be accurately positioned in the actual sample and the CAE analysis model, and read the stress values of each preset comparison point (such as the buckle root and the midpoint of the connecting transition area) in the stress nephogram of the CAE software;

[0133] Subtract the CAE simulation predicted stress value from the actual detection stress value of each comparison point, take the absolute value of the result (avoid positive and negative deviation offset, only reflect the difference size), divide the absolute value by the CAE simulation predicted stress value, and multiply by 100% to get the deviation rate of the comparison point (expressed in percentage, easy to intuitively judge the difference degree).

[0134] In order to be able to take adjustment measures quickly for deviation levels, wherein S3.1.2 determines the cause of stress according to the deviation rate, according to the size and distribution characteristics of the deviation rate, it is divided into slight deviation, moderate deviation and serious deviation, and adjustment measures are taken according to the deviation level;

[0135] According to the size of the deviation rate, the results are divided into three levels to quickly judge the severity of the difference:

[0136] Low deviation: the deviation rate is less than 5%, indicating that the actual and simulation results are consistent, and the production process and simulation setting in this area are matched;

[0137] Moderate deviation: 5% is greater than the deviation rate and less than 10%, there is a slight difference, and short-term fluctuations in production (such as cooling parameters, raw material batch differences) need to be checked;

[0138] High deviation: the deviation rate is greater than 10%, the difference is significant, and the key production links (such as temperature control parameters, mold state, demolding process) need to be checked;

[0139] The position of stress concentration (such as the root of the buckle), the stress value range and the CAE simulation cloud diagram are completely corresponding, and the deviation rate of each key comparison point is in the low deviation interval, indicating that the CAE model can accurately predict the stress distribution, and the production process parameter direction is correct;

[0140] For the stress concentration area (such as the root of the buckle), on the basis of the existing partition temperature, adjust according to the principle of small adjustment and directional optimization (such as the current temperature is 51.5℃, if the actual stress is close to the material safety threshold, it can be reduced by 0.3-0.5℃, to speed up the cooling of the area and reduce the stress residue), After adjusting, a small batch of trial production (such as 20) is carried out again, and the stress is detected again to verify the adjustment effect. If the stress value decreases and there is no new defect (such as warping), the adjusted parameters are solidified as the new production standard; if the stress does not change significantly after adjustment, stop fine-tuning and maintain the original parameters to avoid over-adjustment leading to other quality problems;

[0141] The actual stress concentration position (such as the transition area near the main body end) is inconsistent with the CAE prediction position (such as the transition area near the buckle end), or the stress value of the key comparison point is significantly deviated, indicating that the parameter setting of the CAE model is out of touch with the actual production, and the CAE model needs to be corrected first;

[0142] In combination with production data (such as actual cooling water temperature fluctuation records, raw material batch test reports), it is judged whether the deviation is caused by the fact that the material parameters (such as thermal expansion coefficient, elastic modulus) and boundary conditions (such as cooling water path heat exchange efficiency) of the CAE model do not conform to the actual situation. If the deviation is caused by the difference between the raw material batches (such as the actual raw material melt index is lower than the simulation input value), the material rheological parameters in the CAE model are updated; if the heat exchange efficiency is reduced due to mold water path fouling, the thermal conductivity coefficient of the corresponding area in the CAE model should be adjusted, and the stress simulation is re-run to generate a new stress cloud map.

[0143] In subsequent production, the actual temperature of each area is continuously monitored through the cavity temperature detection element, samples are taken for stress detection every batch, and the comparison data between actual and simulation are summarized regularly (such as every month) to track the change trend of deviation rate.

[0144] As shown in Figure 2 An automobile pipe clamp injection molding production process optimization system includes:

[0145] A structure division module 100 acquires a three-dimensional model of an automobile pipe clamp, extracts key structural features in the three-dimensional model, and divides the pipe clamp model into multiple structural regions according to wall thickness differences;

[0146] A CAE simulation module 200 inputs the three-dimensional model of the automobile pipe clamp into a CAE analysis model of pipe clamp injection molding, takes the collective data of the pipe clamp three-dimensional model, raw material characteristic data, and mold process data as inputs, takes cooling uniformity, defect elimination, and stress control as targets, performs multiple rounds of simulation, and quantitatively evaluates the simulation results to determine partition temperature control parameters;

[0147] A feedback adjustment module 300 inputs the determined partition temperature control parameters into production equipment for injection molding production, performs stress detection on the product using a solvent soaking method, compares the actual detected stress distribution map with the stress cloud map predicted by the CAE analysis model, calculates the deviation rate, determines the cause of stress according to the deviation rate, and feeds back and adjusts the production process.

[0148] Among them, a computer storage medium specifically includes:

[0149] Read the basic data of pipe clamp structure size, raw material characteristics, and initial mold cooling conditions;

[0150] Call a mold flow analysis CAE algorithm, build an analysis model, and calculate initial process parameters, including partition temperature control parameters, injection pressure gradient parameters, and injection speed gradient parameters;

[0151] Receive real-time data transmitted by temperature detection elements, and execute parameter self-adaptive adjustment logic;

[0152] Store full-process data including simulation parameters, production parameters, quality data, and generate parameter iteration optimization report;

[0153] The computer readable storage medium is specifically selected from a flash memory (such as a NAND flash memory), a solid state disk (SSD) or an industrial hard disk (HDD), and the selection reasons are as follows:

[0154] Adapt to data requirements: CAE simulation data (stress nephogram, temperature control parameter interval), real-time temperature data, stress detection data are required for pipe clamp production, and about 10-50 MB of single batch can store 1000+ batch data in 128 GB and above capacity.

[0155] Adapt to production environment: industrial-grade medium (such as wide-temperature SSD, -40℃ to 85℃) is resistant to high temperature and vibration in the workshop, the read-write speed is greater than 100 MB / s, and the real-time data storage and fast calling requirements are met.

[0156] Obviously, the above embodiments are only examples for clearly illustrating, and are not limitation to the embodiments. For ordinary skilled in the art, other different forms of changes or variations can be made on the basis of the above description. Here, all the embodiments are not required to be exhausted, and the obvious changes or variations derived therefrom are still within the protection scope of the present application.

Claims

1. A method for optimizing the injection molding production flow of automotive tube clamp, characterized in that, The method comprises the following steps: S10, obtaining a three-dimensional model of the automobile pipe clamp, extracting key structural features in the three-dimensional model, dividing the pipe clamp model into functional regions based on the key structural features, i.e., a buckle region, a main body region and a transition region, statistically analyzing wall thickness values of the automobile pipe clamp, determining a wall thickness threshold value according to the functional region division, and dividing the regions into a buckle thin-wall region, a connection transition region and a main body thick-wall region according to the wall thickness threshold value; S20, inputting the automobile pipe clamp three-dimensional model containing the partition data into a CAE analysis model of pipe clamp injection molding, taking the partition geometric data of the pipe clamp three-dimensional model, the raw material characteristic data and the mold process data as inputs, and carrying out multiple rounds of simulation around three major targets of cooling uniformity, defect elimination and stress control, determining the partition temperature control parameters through quantitative evaluation results; S30, inputting the determined partition temperature control parameters into a production equipment for injection molding production, adopting a solvent soaking method to detect the stress of the product, comparing the actually detected stress distribution map with the stress nephogram predicted by the CAE analysis model, selecting comparison points, calculating a deviation rate, determining the cause of stress generation according to the deviation rate, if the actual stress concentration region is highly consistent with the prediction result of the CAE analysis model, adjusting the partition temperature of the pipe clamp model according to the stress position, and if there is a deviation between the actual stress concentration region and the prediction of the CAE analysis model, adjusting the parameters of the CAE analysis model; The specific steps of the S10 for dividing multiple structural regions of the pipe clamp model are as follows: S11, preliminarily dividing the pipe clamp model according to key structural functions of the pipe clamp model, including a buckle region, a main body region and a transition region; S12, using a wall thickness analysis tool of SolidWorks to perform global scanning on the pipe clamp model, generating a wall thickness distribution nephogram, and directly displaying wall thickness values of each position of the model, and sampling and measuring the buckle region, the main body region and the transition region of the pipe clamp model based on the wall thickness distribution nephogram; S13, statistically analyzing the wall thickness values of each region, determining the minimum wall thickness value, the maximum wall thickness value and the common wall thickness interval of the pipe clamp, determining the division threshold values of the thin-wall region, the transition region and the thick-wall region by using a natural demarcation point method, and dividing the regions of the pipe clamp model.

2. The method of claim 1, wherein: When the S13 determines the division threshold values of each region, the division threshold values are adjusted according to the core function priority principle and the corresponding core functions of each region, wherein when the results of the functional region division and the results of the wall thickness threshold value division conflict, the threshold values are adjusted according to the functional requirements, the final region division is determined according to the final wall thickness threshold value, and the buckle thin-wall region, the connection transition region and the main body thick-wall region are obtained.

3. The method of claim 1, wherein: The specific steps of the S2 for determining the partition temperature control parameters are as follows: S21, inputting the automobile pipe clamp three-dimensional model containing the partition data into a CAE analysis model of pipe clamp injection molding, taking the partition geometric data of the pipe clamp three-dimensional model, the raw material characteristic data and the mold process data as inputs, and obtaining the raw material characteristic data and the mold process data by using a material physical property table provided by a raw material supplier; S22, based on the input data, set the initial injection pressure, speed, simulate the melt filling process, perform filling simulation, set the initial temperature gradient in the order of thin wall area to transition area to thick wall area, perform cooling simulation, and determine the initial interval of the partition temperature control parameter for the purpose of cooling uniformity, defect elimination and stress control; S23, based on the initial interval, use L9 orthogonal table to design multiple simulation parameter combinations, for each simulation result, quantitatively score according to cooling uniformity, defect elimination and stress control, and select the combination with the highest score as the optimal parameter of partition temperature control.

4. The method of claim 3, wherein: The S22 after the filling simulation and the cooling simulation, based on the filling and cooling simulation results, calculates the residual stress distribution of the pipe clamp after forming, performs stress simulation, and generates a stress cloud map of the pipe clamp model.

5. The method of claim 3, wherein: The S23 after obtaining the optimal parameter of the partition temperature control, in the case of temperature fluctuation of the cooling system, the optimal parameter of the partition temperature control is adjusted, the fluctuation verification is performed, and the anti-interference ability of the optimal parameter of the partition temperature control is improved.

6. The method of claim 1, wherein: The specific steps of the S3 for feeding back the partition temperature of the pipe clamp model and the parameters of the CAE analysis model according to stress detection are as follows: S31, deploy temperature detection elements in the cavity of the pipe clamp mold, real-time collect the actual temperature of each temperature control area cavity wall, and dynamically adjust the actual temperature according to the partition temperature control parameter; S32, perform stress detection on the product by using the solvent immersion method, map the actually detected stress distribution map and the stress cloud map predicted by the CAE analysis model in a unified coordinate system, select comparison points, calculate the deviation rate, determine the cause of stress according to the deviation rate, and feed back and adjust the production process.

7. The method of claim 6, wherein: When the S32 determines the cause of stress according to the deviation rate, the deviation rate is divided into slight deviation, moderate deviation and severe deviation according to the size and distribution characteristics of the deviation rate, and adjustment measures are taken according to the deviation level.

8. A system for optimizing the injection molding production flow of automotive tube clamps, for implementing the method according to any one of claims 1 to 7, characterized in that, The system comprises: A structure division module (100) acquires a three-dimensional model of an automobile pipe clamp, extracts key structural features in the three-dimensional model, and divides the pipe clamp model into multiple structural regions according to wall thickness differences; A CAE simulation module (200) inputs the three-dimensional model of the automobile pipe clamp into a CAE analysis model of pipe clamp injection molding, takes the collective data of the pipe clamp three-dimensional model, the raw material characteristic data and the mold process data as inputs, performs multiple rounds of simulation for the purpose of cooling uniformity, defect elimination and stress control, and quantitatively evaluates the simulation results to determine partition temperature control parameters; A feedback adjustment module (300) inputs the determined partition temperature control parameters into production equipment for injection molding, performs stress detection on the product by using the solvent immersion method, compares the actually detected stress distribution map with the stress cloud map predicted by the CAE analysis model, calculates the deviation rate, determines the cause of stress according to the deviation rate, and feeds back and adjusts the production process.

9. A computer storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by the processor, all steps of the automobile pipe clamp injection production process optimization method of any one of claims 1-7 are implemented, specifically including: Reading the basic data of pipe clamp structure size, raw material characteristics and initial mold cooling conditions; Call the mold flow analysis CAE algorithm, build an analysis model and calculate initial process parameters, the initial process parameters including partition temperature control parameters, injection pressure gradient parameters, injection speed gradient parameters; Receive real-time data transmitted by the temperature detection element, and execute parameter self-adaptive adjustment logic; Store full-process data, including simulation parameters, production parameters, and quality data, and generate a parameter iteration optimization report.

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