Automobile pipe clamp injection molding production process optimization method and system and storage medium

Abstract

The invention 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. Two-dimensional partitioning is performed by extracting key structural characteristics of the pipe clamp and combining wall thickness difference and functional requirements to ensure that the partitions fit the actual structure and functional requirements of the pipe clamp, adaptive partition temperature control and injection molding parameters are calculated in advance through multiple rounds of filling, cooling, stress simulation and quantitative evaluation, the mold testing frequency and the research and development period are greatly reduced, and the production efficiency is improved. Meanwhile, parameter stability is verified by simulating temperature fluctuation of a cooling system, production data are detected in real time, stress is detected through a solvent soaking method, CAE simulation results are compared, deviation reasons are analyzed, technological parameters are adjusted in a targeted mode, the batch quality problem caused by equipment fluctuation of traditional parameters is avoided, and the stability of the production process is improved.

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 present application aims to provide an automobile pipe clamp injection molding production process optimization method, system and storage medium to solve any of the technical problems raised 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: 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 areas based on the key structural features, i.e. buckle area, main thick wall area and transition area, statistically analyzing the wall thickness values of the automobile pipe clamp, determining the wall thickness threshold value according to the functional area division, and dividing the areas into buckle thin wall area, connection transition area and main thick wall area according to the wall thickness threshold value; 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 simulations around the three targets of cooling uniformity, defect elimination and stress control, and determining the partition temperature control parameters through quantitative evaluation results; 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.

[0006] In an embodiment of the present application, the S10 is a specific step of dividing the pipe clamp model into multiple structure regions as follows: 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; S12, using the wall thickness analysis tool of SolidWorks, the pipe clamp model is scanned globally, a wall thickness distribution nephogram is generated, the wall thickness values of each position of the model are intuitively displayed, and on the basis of the wall thickness distribution nephogram, the buckle area, the main body area and the transition area of the pipe clamp model are sampled and measured; S13, the wall thickness values of each region are statistically analyzed, the minimum wall thickness value, the maximum wall thickness value and the common wall thickness interval of the pipe clamp are determined, the natural demarcation point method is used to determine the division threshold of the thin wall area, the transition area and the thick wall area, and the pipe clamp model is divided into regions.

[0007] In an embodiment of the present application, when the S13 determines the division threshold of each region, according to the core function priority principle, the wall thickness threshold value meeting the corresponding core function is solved according to the core function corresponding to each region, the division threshold is adjusted, wherein when the result of the function region division conflicts with the result of the wall thickness threshold division, the threshold is adjusted according to the function demand, the final region division is determined according to the final wall thickness threshold, the buckle thin wall region, the connection transition region and the main body thick wall region.

[0008] In an embodiment of the present application, the specific steps of S20 for determining the partition temperature control parameters are as follows: 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 input, use the material physical property table provided by the raw material supplier to obtain the raw material characteristic data and the mold process data; 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; 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.

[0009] In an embodiment of the present application, the S2.1.2, after performing filling simulation and cooling simulation, calculates the residual stress distribution of the tube clamp after forming based on the filling and cooling simulation results, performs stress simulation, and generates a stress contour map of the tube clamp model.

[0010] In an embodiment of the present application, the S2.1.3, after obtaining the optimal parameters of the partition temperature control, adjusts the optimal parameters of the partition temperature control by fine-tuning to verify the fluctuation and improve the anti-interference ability of the optimal parameters of the partition temperature control in the case of temperature fluctuation of the cooling system.

[0011] In an embodiment of the present application, the S30 according to the stress detection feedbacks the partition temperature of the tube clamp model and the parameters of the CAE analysis model, and the specific steps are as follows: S31, deploying temperature detection elements in the cavity of the tube clamp mold, collecting the actual temperature of each temperature control area cavity wall in real time, and dynamically adjusting the actual temperature based on the partition temperature control parameters; S32, using the solvent immersion method to detect the stress of the product, mapping the actual detected stress distribution map and the stress contour map 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.

[0012] In an embodiment of the present application, when the S32 determines the cause of stress according to the deviation rate, it 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.

[0013] In an embodiment of the present application, the system comprises: The structure division module acquires the three-dimensional model of the automobile tube clamp, extracts the key structural features in the three-dimensional model, and divides the tube clamp model into multiple structural regions according to the wall thickness difference. 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 the simulation results to determine partition temperature control parameters; 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.

[0014] 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: reading basic data of pipe clamp structure size, material characteristics and initial mold cooling conditions; calling a mold flow analysis CAE algorithm, constructing an analysis model and calculating initial process parameters, wherein the initial process parameters include partition temperature control parameters, injection pressure gradient parameters and injection speed gradient parameters; receiving real-time data transmitted by a temperature detection element and executing parameter self-adaptive adjustment logic; storing full-process data, wherein the full-process data includes simulation parameters, production parameters and quality data, and generating a parameter iteration optimization report.

[0015] The above technical solutions of the present application have the following advantages compared with the prior art: The automobile pipe clamp injection molding production process optimization method, system and storage medium, the structure division module extracts key structural 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; The CAE simulation module calculates adaptive partition temperature control and injection molding parameters in advance through multiple rounds of filling, cooling, stress simulation and quantitative evaluation, greatly reduces the number of mold testing and the research and development cycle, verifies parameter stability by simulating temperature fluctuations of the cooling system, ensures that the parameters can still meet quality requirements under actual production fluctuation scenarios, avoids batch quality problems caused by traditional parameter fluctuations due to equipment fluctuations, and improves production process stability; The feedback adjustment module can accurately control the residual stress of the pipe clamp and reduce the reliability risk in use by detecting the production data and stress by solvent soaking in real time, comparing the CAE simulation results and analyzing the deviation reasons, and adjusting the process parameters, can realize automatic dynamic adjustment of production parameters, reduce manual intervention, and at the same time, feedback the quality data to the CAE model iteration optimization. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to make the content of the present application more easily understood, the present application will be further described in detail below according to specific embodiments of the present application and in conjunction with the drawings.

[0017] Figure 1 is a method flowchart of the present application; Figure 2 is a system flowchart of the present application.

[0018] The meanings of various reference numerals in the drawings are as follows: 100, structure division module; 200, CAE simulation module; 300, feedback adjustment module. DETAILED DESCRIPTION

[0019] The present application will be further described below in conjunction with the drawings and specific embodiments, so that those skilled in the art can better understand the present application and implement it, but the embodiments are not limiting to the present application.

[0020] In view of the problems in the background art, for the injection molding production process of the pipe clamp with complex structure, the traditional single waterway design and fixed temperature control mode cannot adapt to the heat dissipation needs 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, resulting in high overall defect rate. In addition, the thick wall area (such as the pipe clamp main support area and the positioning hole periphery) has large cavity volume and long melt flow path. Before the melt reaches the end of the cavity, the flow capacity decreases due to cooling and solidification and pressure loss, so the cavity cannot be completely filled, resulting in insufficient filling of the thick wall area. 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.

[0021] Based on this, this embodiment of the invention proposes to divide regions according to the pipe clamp wall thickness and function through a structural division module, providing a precise structured analysis object for the CAE simulation module. During CAE simulation, differentiated parameters are set for different regions. The optimal temperature control parameters and stress prediction results output by the CAE simulation become the comparison benchmark for the feedback adjustment module. The feedback adjustment module accurately locates production problems by analyzing the deviation between actual data and simulation benchmarks, adjusts the temperature control parameters of each zone, and updates the parameter library and calculation relationships of the CAE simulation to improve simulation accuracy.

[0022] To better realize the above technical concepts, please refer to Figure 1 As shown, this embodiment of the invention provides a method for optimizing the injection molding production process of automotive pipe clamps, including the following steps: S10. Obtain the three-dimensional model of the car pipe clamp, extract the key structural features in the three-dimensional model, and divide the pipe clamp model into functional areas based on the key structural features, namely the buckling area, the main body area and the transition area. Statistical analysis of the wall thickness values ​​of automotive pipe clamps was conducted, and the wall thickness threshold was determined based on the functional area division. Based on the wall thickness threshold, the area was divided into a snap-fit ​​thin-walled area, a connection transition area, and a main body thick-walled area. Among them, key structural features refer to the specific functions that each part of the pipe clamp undertakes to achieve its core purpose (such as fixing pipes, adapting to assembly, and ensuring connection stability), and are the core attributes that determine whether the pipe clamp can meet the usage requirements. The key structural features of automotive pipe clamps include the snap-fit, the main body, and the transition structure. The specific key structural functions of these three are as follows: The clip is the core structure for connecting pipe clamps to pipes or other components (such as vehicle body brackets). Its key function directly determines whether the pipe clamp can stably fix the pipe and facilitate installation or disassembly. The main body is the skeleton of the pipe clamp and the installation foundation for various functional structures. Its key functions determine the overall strength, positioning accuracy and load-bearing capacity of the pipe clamp. The transition structure is located between the clip (thin-walled) and the main body (thick-walled). It serves as a bridge to avoid structural abrupt changes, and its key functions directly affect the structural reliability and service life of the pipe clamp.

[0023] The specific steps for dividing the pipe clamp model into multiple structural regions in S10 are as follows: S11. Based on the key structural functions of the pipe clamp model, the pipe clamp model is initially divided into: the snap-fit ​​area, the main body area, and the transition area. The preliminary division of the pipe clamp model needs to take the functional determination of the region boundary as the core logic. 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), avoiding the fragmentation of function caused by the division only according to the size or appearance, and verifying the region boundary combined with the geometric characteristics of the structure (such as protrusions, depressions, thickness changes). For example, the buckle area is mostly thin-walled protrusions, the main body area is mostly thick-walled bases, and the transition area is mostly flat structures connecting the two. Among them, the buckle area: taking the outermost edge of the buckle protrusion as the starting point, extending to the root to the place where the wall thickness begins to obviously thicken, forming the boundary of the buckle area (usually at 1.5 times the buckle wall thickness, such as buckle wall thickness 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 fragmentation of the region. The transition area: taking the outer boundary of the buckle area as the starting point, extending to the main body area to the place where the wall thickness reaches the standard wall thickness of the main body area (such as main body wall thickness 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. The main body area: taking the outermost edge of the main body base as the starting point, excluding the buckle area, the subsequent transition area, and the remaining thick-walled base part is the main body area.

[0024] S12, use the wall thickness analysis tool of SolidWorks to scan the pipe clamp model globally, generate a wall thickness distribution cloud map, and intuitively display the wall thickness values of each position of the model. On the basis of the wall thickness distribution cloud map, the buckle area, main body area, and transition area of the pipe clamp model are sampled and measured. Start SolidWorks software, select open through file menu, import the 3D model of the pipe 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. In the tool group of the evaluation tab, click the thickness analysis icon. At this time, the property manager of thickness analysis will pop up. In the analysis parameter bar of the property manager, set the target thickness value. This value can refer to the design standard of the pipe clamp or the expected average wall thickness. For example, if the main body wall thickness of the general pipe clamp is designed to be 3mm, it can be set to 3mm first. Check the display thin area 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 let the model display different wall thickness intervals with rich color gradients, intuitively presenting the wall thickness distribution difference. After the analysis is completed, the pipe 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, the wall thickness distribution of the buckle area (usually thin-walled, light color, such as yellow representing 1-2mm wall thickness interval), the main body area (thick-walled, dark color, such as blue representing 3-4mm wall thickness interval), and the transition area (wall thickness between the two, color transition natural, such as green representing 2-3mm wall thickness interval) can be clearly observed, and the wall thickness difference of the entire pipe clamp can be intuitively mastered. Based on the preliminary division of the buckle area, the main body area and the transition area, a plurality of sampling detection points are set respectively, and the wall thickness data obtained by sampling measurement in each area is arranged into a table. The sampling measurement data is compared with the wall thickness range estimated based on the structural function division area (such as 1-2mm for the buckle area, 3-4mm for the main body area, and 2-3mm for the transition area). If the difference is large, the accuracy of the region division boundary needs to be rechecked.

[0025] S13, statistics analysis of the wall thickness value of each area, determine the minimum wall thickness value, the maximum wall thickness value and the common wall thickness interval (arrange the wall thickness value in order from small to large), use the natural division point method (find the critical point of the frequency drop in the wall thickness data distribution, as the division boundary of the thin-walled area, the transition area and the thick-walled area) to determine the division threshold of the thin-walled area, the transition area and the thick-walled area; Combine the sampling measurement data with the global wall thickness cloud chart information to form a structured data set. Group the wall thickness values of the full data set by 0.1mm intervals, count the frequency (number of occurrences) of each group, draw the curve of wall thickness value and frequency, and use the natural division point method to determine the trough point of the curve where the frequency decreases significantly as the natural division point. For example, the statistical data shows that the frequency of wall thickness of 1.4-1.9mm in the buckle area accounts for 85% (such as 1.5mm appearing 3 times, 1.6mm appearing 4 times, and 1.8mm appearing 2 times), so the common interval is 1.4-1.9mm. The frequency of wall thickness of 2.1-2.6mm in the transition area accounts for 90% (such as 2.2mm appearing 3 times, 2.3mm appearing 3 times, and 2.5mm appearing 1 time), so the common interval is 2.1-2.6mm. The frequency of wall thickness of 2.9-3.4mm in the main body area accounts for 88% (such as 3.0mm appearing 4 times, 3.1mm appearing 5 times, and 3.2mm appearing 3 times), so the common interval is 2.9-3.4mm. The first division point (the division between the thin-walled area and the transition area) is observed. The frequency of the 1.9-2.0mm interval decreases from 5 times in the 1.8-1.9mm interval to 1 time, and the frequency of the 2.0-2.1mm interval increases to 4 times. Therefore, the first division point is determined as 2.0mm (i.e. the thin-walled area is less than 2.0mm, and the transition area is greater than 2.0mm). The second demarcation point (demarcating the transition region and the thick wall region) is 2.8 mm, because 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 times, and then the frequency in the interval of 2.9-3.0 mm increases to 6 times; The threshold range of the thin wall region is (1.4, 2.0) mm, the threshold range of the transition region is (2.0, 2.8) mm, and the threshold range of the thick wall region is (2.8, 3.4) mm.

[0026] In order to adjust the wall thickness threshold according to the functional region division, the final wall thickness threshold is determined. When determining the division threshold of each region, the core function priority principle is followed, the wall thickness threshold that meets the corresponding core function is solved according to the corresponding core function of each region, and the division threshold is adjusted. When the results according to the functional region division and the results according to the wall thickness threshold division conflict, the threshold is adjusted based on the functional requirements, and the final region division is determined according to the final wall thickness threshold, the thin wall region, the connection transition region and the main thick wall region are buckled; The core function priority principle means that the core function of each functional region (buckle, main body, transition) is the first priority, and the unbreakable threshold range of the wall thickness of the region is determined. The core function of the buckle region is elastic clamping (which requires to ensure the pulling 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 line threshold, that is, the core function threshold, which cannot be compromised for other requirements. When checking the core function of each region, first, the core function of the buckle region is elastic clamping, the core function of the main thick wall region is rigid support, and the core function of the transition region is force transmission. The buckle is a locking structure of the pipe clamp, which 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 will not be able to 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: ; Wherein, is the buckle wall thickness, 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, that is , converted to mm is , is the buckle width, in mm, This represents the maximum allowable elastic deformation of the buckle, expressed in mm.

[0027] Among them, the minimum clamping force of the buckle Material elastic modulus The maximum elastic deformation allowed by the buckle Safety factor The length of the clip-on cantilever can be obtained from the material manufacturer's manual. Buckle width The wall thickness threshold x that satisfies the core function of the buckle is obtained by substituting actual measurements into the above formula. The main body is the skeleton of the pipe clamp, which needs to withstand the weight of the pipeline, vibration loads, and environmental stresses (such as temperature changes). It must meet the static strength requirements. The wall thickness threshold that satisfies the core function of the thick-walled area of ​​the main body is solved by the wall thickness calculation formula for static load. The formula is as follows: ; in, The thickness is the main wall thickness, in mm. This is the dynamic load factor, which has no unit. This represents the total weight of the pipe clamps, in N (unit: liters). The length of the main support arm is in mm. For safety margin, no unit is specified. The width of the main body is in mm. The maximum bending stress is expressed in MPa, i.e. Converted to mm .

[0028] Among them, dynamic load coefficient Safety factor Maximum bending stress The total weight of the pipe clamps was obtained from the material manufacturer's manual. Main support arm length Main body width The wall thickness threshold y that satisfies the core function of the main body is obtained by substituting the actual measurement into the above formula. The wall thickness thresholds that meet the core functions are calculated based on the functionality of each region. These thresholds are compared with the threshold ranges for region division based on the wall thickness thresholds to determine the final region division. That is, the snap-fit ​​thin-walled region is (1.4, x) mm, the connecting transition region is (x, y) mm, and the main body thick-walled region is (y, 3.4) mm, where 1.4 mm and 3.4 mm are from the region ranges determined by the above-mentioned dividing points. When the statistical threshold and the functional threshold range conflict, the final region division boundary is adjusted based on the functional threshold range to ensure that the partitioning reflects both structural characteristics and core performance requirements.

[0029] Further, S20, input the automobile pipe clamp three-dimensional model containing partition information to the CAE analysis model of pipe clamp injection molding, take the partition geometric data, raw material characteristic data and mold process data of the pipe clamp three-dimensional model as input, carry out multiple rounds of simulation around the three major targets of cooling uniformity, defect elimination and stress control, determine the partition temperature control parameters through quantitative evaluation results.

[0030] Among them, the specific steps of S20 to determine the partition temperature control parameters are as follows: S21, input the automobile pipe clamp three-dimensional model containing partition data to the CAE analysis model of pipe clamp injection molding, take the partition geometric data, raw material characteristic data and mold process data of the pipe clamp three-dimensional model as input, use the material property table provided by the raw material supplier to obtain the raw material characteristic data and mold process data (wall thickness distribution data, cavity geometric parameters and flow path data), use the material property table provided by the raw material supplier to obtain the raw material characteristic data (thermal physical parameters, rheological parameters and mechanical parameters) and mold process data (cooling system parameters and injection molding machine and gate parameters); 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 with cooling uniformity, defect elimination and stress control as the target; The core of filling simulation is to simulate the flow process of melt in the cavity, the initial injection pressure and speed need to be set in combination with three types of input data of cavity geometry, raw material rheological properties and mold gate parameters to avoid underfilling or overfilling; 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: ; Among them, is the theoretical filling pressure, unit is , is the melt viscosity of the raw material at the set temperature, unit is , is the maximum flow distance, unit is , is the preset injection speed, unit is , is the average width of the cavity, unit is , is the average wall thickness of the cavity, unit is .

[0031] The initial injection speed is set based on the shear characteristics of the raw material, and the formula is as follows: ; wherein, is the shear rate, unit is , is the preset injection speed, unit is , is the average wall thickness of the cavity , is the local wall thickness .

[0032] Import the above pressure, speed 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; The filling time difference of each area is less than 0.3s (such as the main thick wall area 1.2s, the buckle area 1.4s), whether the filling balance is qualified is judged, the maximum shear rate is less than 5000s (to avoid material degradation, the simulation result shows that the maximum is 3500s) whether the shear rate is qualified is judged, if it does not meet the standard (such as the filling time difference is 0.5s), the speed needs to be adjusted (such as the speed of the buckle area is increased to 90mm / s), the injection pressure and speed of the pipe clamp mold during injection are obtained by re-simulation.

[0033] Wherein, the cooling simulation needs to be based on the wall thickness difference of each area, the thermal physical properties of the raw material, the cooling system parameters, the temperature gradient is set according to the area, the core is thin wall fast cooling, thick wall slow cooling, and the cooling uniformity is ensured; The core of partition temperature control is the temperature difference of cooling medium (such as water) in each area, therefore, 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, 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, take 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); 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 the buckle thin wall area 45℃, the connection transition area 55℃, the main thick wall area 65℃), after running the cooling simulation, if the cooling uniformity is qualified (temperature difference 7℃), there is no defect, but the cooling time is 18s (exceeding the upper limit of 15s), it shows that the overall temperature is too high, 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 shows that the temperature of the buckle thin wall area is too low (cooling too fast), the temperature of the buckle thin wall area needs to be increased; Based on the results of the basic simulation, the adjustment direction of the temperature of each region is preliminarily judged (such as 45-55℃ for the buckle thin-wall region and 60-70℃ 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 manner of single-region fine-tuning and fixed other regions, and the matching condition of each group of temperature and target is recorded, as shown in the following table:

[0034] 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 and 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 is, the better the cooling uniformity is; The defect conditions are 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: ; The warpage is the deformation of the plastic part caused by uneven internal stress (such as cooling rate difference and uneven shrinkage). In the simulation, the deformation amount (such as the overall maximum deformation amount and the deformation amount in a specific direction) is quantified to calculate the maximum deformation distance of the plastic part in the three-dimensional space, and the deformation amount is calculated separately for the main direction of warpage (such as the length direction and the width direction). The value of the warpage (such as 0.10 / 0.14) in the table corresponds to such deformation amount (unit: mm); Only when all the key indicators such as cooling uniformity, shrinkage and warpage meet the preset threshold value, it is determined to be up to standard; if any indicator does not meet the standard (such as the warpage deformation amount exceeds the standard), it is determined to be out of standard.

[0035] For the group numbers (such as 1, 4, 5 and 7) that are all up to standard in the second step, the temperature is further fine-tuned to the boundary to verify the interval limit; Among them, the buckle thin-wall region boundary: fixing the connecting transition region at 55℃ and the main thick-wall region at 62℃, the temperature of the buckle thin-wall region is reduced to 47℃ (simulation 8), if the warpage amount is 0.13mm (slightly exceeds the standard), it is increased to 51℃ (simulation 9), if the cooling time is 15.5s (slightly exceeds the standard), then the buckle thin-wall region boundary is 48-50℃; The main thick-wall region boundary: fixing the buckle thin-wall region at 48℃ and the connecting transition region at 55℃, the main thick-wall region is reduced to 60℃ (simulation 10), if the shrinkage is 0.035mm (slightly exceeds the standard), it is increased to 66℃ (simulation 11), if the cooling time is 15.8s (slightly exceeds the standard), then the main thick-wall region boundary is 62-65℃; Connection transition region boundary: the fixed buckle thin-walled region is 48℃, the main body thick-walled region is 62℃, the connection transition region is reduced to 53℃ (simulation 12), if the temperature difference is 8℃ (standard), the connection transition region is increased to 57℃ (simulation 13), if the temperature difference is 7℃ (standard), and the connection transition region boundary is 53-57℃.

[0036] 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; 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 inside 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℃ at the 1st second, 120℃ at the 10th second, and 80℃ at the 20th second), simulating the temperature change in the actual cooling process and triggering the calculation of thermal shrinkage stress; 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), the thermal shrinkage additional stress of each time step is vector superimposed with 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; After the simulation is completed, the pipe clamp residual stress nephogram is generated and the key data is extracted in the result processing module of the CAE software, a continuous color gradient (such as from blue (low stress, 0MPa) to red (high stress, 80MPa)) is selected to ensure that the stress difference is intuitive and distinguishable; the stress range threshold (such as 0-60MPa) is set to highlight the areas exceeding the safety threshold (such as 60MPa) to facilitate quick identification of risk points.

[0037] In addition, S2.1.3, based on the initial interval, uses an L9 orthogonal table to design multiple simulation parameter combinations, quantitatively scores each simulation result according to cooling uniformity, defect elimination, and stress control, and selects the combination with the highest score as the optimal parameters for partition temperature control; After determining the initial interval of the partition temperature control (such as the buckle thin-walled region 50-52℃, the connection transition region 55-59℃, and the main body thick-walled region 62-66℃), an L9 orthogonal table (with the least experiments covering key variable combinations to lock the optimal solution with multi-target scoring) can efficiently design a small number of simulation parameter combinations; For example, the selected factors and their levels are as follows:

[0038] According to the standard structure of L9 orthogonal table (3 factors and 3 levels), 1, 2 and 3 levels of each variable correspond to columns 1, 2 and 3 in the table, 9 groups of non-repeated and evenly distributed parameter combinations are generated. For the 9 groups of parameter combinations, except the temperature control variable, other parameters affecting the simulation results (such as water flow, injection pressure, time step, grid accuracy) need to be kept completely consistent to avoid interference from irrelevant variables and ensure that the result difference is only caused by the change of the temperature control parameter. According to the actual quality requirements of the pipe clamp, weights are allocated to 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), to avoid the weight being out of touch with the actual demand; For each combination, first calculate the individual score of each target according to the scoring standard, then sum the total score by weighting (or directly summing because the weight has been integrated into the total score of each target), to ensure that the total score can comprehensively reflect the overall quality performance of the combination. The total scores of the 9 combinations are ranked from high to low, and the combination with the highest score is selected as the optimal parameter.

[0039] In order to solve the temperature fluctuation (cooling medium temperature fluctuation and cooling water flow fluctuation), S23, after obtaining the optimal parameters of the partition temperature control, adjusts the optimal parameters of the partition temperature control to verify the fluctuation and improve the anti-interference ability of the optimal parameters of the partition temperature control in the case of temperature fluctuation of the cooling system; The cooling medium temperature fluctuation refers to ±1-2℃ instantaneous fluctuation caused by equipment response delay, and ±1℃ continuous offset caused by load change. The fluctuation range is based on the equipment manual and historical data, and the fluctuation range is not more than 95% of the range. The cooling water flow fluctuation refers to the ±10% fluctuation of the flow caused by pipeline leakage and water pump pressure change, which needs to be concerned about the coupling effect with temperature fluctuation (such as the decrease of flow easily causes passive rise of water temperature). For the above fluctuations, a verification group is set up for each fluctuation, only one fluctuation is introduced in each group, and other parameters are kept consistent with the optimal working condition, the tolerance of the test parameters to single interference is tested, and whether the cooling uniformity, defect elimination and stress control still meet the standards is verified. When the instantaneous fluctuation occurs, the single fine-tuning amplitude is less than 1℃ according to the principle of leaving a margin for low temperature fluctuation and balancing the stress for high temperature fluctuation. When the continuous offset occurs, the target temperature is adjusted to adapt to the system offset, to ensure long-term stability. When the flow decreases, the water temperature is lowered to compensate for the loss of heat exchange efficiency, and when the flow increases, the water temperature is slightly raised to avoid excessive cooling. When the conduction fluctuation (thermal resistance increases) occurs, the water temperature is lowered to maintain the stability of the cavity temperature.

[0040] Further, S30, input the determined partition temperature control parameters to the production equipment, carry out injection molding production, use the solvent soaking method to detect the stress of the product, compare the actual detected stress distribution map with the stress nephogram predicted by the CAE analysis model, select the comparison points, calculate the 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 deviation between the actual stress concentration area and the prediction of the CAE analysis model, then adjust the parameters of the CAE analysis model.

[0041] Wherein, 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 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 with the partition temperature control parameters as the target; Each temperature control area (buckle thin-walled area, connection transition area, main body thick-walled area) is equipped 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 deploy 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 of the connection transition area), which have the greatest impact on product quality; 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 cooling water temperature, if the actual temperature of a certain area is higher than the target temperature (such as the actual temperature of the main body thick-walled area is 67℃, and the target temperature is 65.5℃), then increase the cold water supply of the cooling water path of the area through the temperature control valve, reduce the water temperature of the water path (such as from 65.5℃ to 64.5℃), speed up the heat dissipation, if the actual temperature is lower than the target temperature (such as the actual temperature of the buckle thin-walled area is 50℃, and the target temperature is 51.5℃), then reduce the cold water supply, and increase the water temperature of the water path (such as from 51.5℃ to 52.5℃), slow down the cooling speed; For the adjustment of cooling flow, if the deviation still exists after the water temperature adjustment (such as the actual temperature of the connection transition area is too high, and the water temperature has been reduced to the target lower limit), then adjust the water flow of the area through the frequency conversion water pump (such as the flow from 10L / min to 12L / min), increase the heat exchange efficiency, if the actual temperature is too low and the water temperature has been increased to the target upper limit, then reduce the flow (such as from 10L / min to 8L / min), reduce the heat removal.

[0042] S32, stress detection of the product is performed by solvent immersion method, the actually detected stress distribution map and the stress nephogram predicted by the CAE analysis model are located in a unified coordinate system, comparative points are selected, a deviation rate is calculated, the cause of stress is determined according to the deviation rate, and feedback adjustment is performed on the production process; 3 to 5 qualified pipe clamps (avoiding appearance defect samples) are randomly selected from a production batch, the pretreated pipe clamps are completely immersed in a solvent (the solvent used is an ice acetic acid aqueous solution with a volume concentration of 60%, the water bath temperature is constant at 23±2℃, for PA66 material, the immersion time is 18 minutes), the water bath temperature is kept stable, and the immersion time is set according to the material characteristics; The pipe clamps are taken out, the surface residual solvent is washed with distilled water, and after natural air drying, the pipe clamp surface erosion traces are photographed from multiple angles by a high-resolution camera, the photographed images are imported into image analysis software, a two-dimensional coordinate system is established according to the actual size of the pipe clamp (with the center of the main body as the origin, the X axis is along the length direction of the main body, and the Y axis is along the thickness direction), the stress grades of each erosion area are labeled, and an actual stress distribution map of the pipe clamp is generated; Fixed geometric features on the pipe clamp are selected as coordinate references (such as the center of the main body positioning hole and the opening end of the buckle), and it is ensured that the features can be accurately positioned in the actual sample and the CAE analysis model, in the stress nephogram of the CAE software, the stress values of each preset comparative point (such as the root of the buckle and the midpoint of the connection transition area) are read; The actual detection stress value of each comparative point is subtracted from the CAE simulation prediction stress value, the absolute value of the result is taken (to avoid offsetting of positive and negative deviations, and only to reflect the difference size), the absolute value is divided by the CAE simulation prediction stress value, and then multiplied by 100%, to obtain the deviation rate of the comparative point (expressed in percentage, to facilitate intuitive judgment of the difference degree).

[0043] In order to be able to take adjustment measures quickly according to the deviation level, when S3.1.2 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, and adjustment measures are taken according to the deviation level; The results are divided into three levels according to the size of the deviation rate, to quickly judge the severity of the difference: 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 are matched; Moderate deviation: the deviation rate is greater than 5% and less than 10%, there is slight difference, and short-term fluctuations in production (such as cooling parameters and raw material batch differences) need to be checked; 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 and demolding process) need to be checked; The actual stress concentration position (such as the buckle root), the stress value range, and the CAE simulation cloud map correspond completely in actual detection, the deviation rate of each key comparison point is in the low deviation interval, which indicates that the CAE model can accurately predict the stress distribution, and the production process parameter direction is correct. For the stress concentration area (such as the buckle root), on the basis of the existing partition temperature, the adjustment is made according to the principle of small adjustment and directional optimization (for example, if the current temperature is 51.5℃, and the actual stress is close to the material safety threshold, the temperature can be lowered by 0.3-0.5℃ to speed up the cooling of the area and reduce the stress residue), and after adjustment, a small batch of trial production (such as 20 pieces) is carried out again, the stress is detected again, and the adjustment effect is verified. If the stress value decreases and there is no new defect (such as warping), the adjusted parameter is solidified as the new production standard; if the stress does not change significantly after adjustment, the fine-tuning is stopped, and the original parameter is maintained to avoid excessive adjustment leading to other quality problems. If the actual stress concentration position (such as the connection transition area near the main body end) is inconsistent with the CAE prediction position (such as the connection transition area near the buckle end), or the stress value deviation of the key comparison point is significant, it indicates that the parameter setting of the CAE model is disconnected from the actual production, and the CAE model needs to be corrected first. Combined with production data (such as actual cooling water temperature fluctuation records, raw material batch detection reports), it is judged whether the deviation is caused by the disconnection between the CAE model material parameters (such as thermal expansion coefficient, elastic modulus) and the actual boundary conditions (such as cooling water road heat exchange efficiency). 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 waterway scaling, the thermal conductivity coefficient of the corresponding area in the CAE model should be adjusted, and the stress simulation is run again to generate a new stress cloud map.

[0044] 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 actual and simulated comparison data are regularly (such as every month) summarized to track the change trend of the deviation rate.

[0045] As shown in Figure 2 An automobile pipe clamp injection molding production process optimization system includes:

[0046] 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, 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; The feedback adjustment module 300 inputs the determined partition temperature control parameters to the production equipment, performs injection molding production, performs stress detection on the product by using the 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 performs feedback adjustment on the production process.

[0047] Among them, a computer storage medium, specifically includes: Read the basic data of the structure size of the pipe clamp, the characteristics of the raw material, and the initial mold cooling condition; Call the mold flow analysis CAE algorithm, build the analysis model and calculate the initial process parameters, the initial process parameters include 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 the full-process data, the full-process data includes simulation parameters, production parameters, quality data, and generate parameter iteration optimization report; Use computer readable storage medium, specifically can select flash memory (such as NAND flash), solid state disk (SSD) or industrial hard disk (HDD), the reasons for selection: Adapt to data requirements: pipe clamp production needs to store CAE simulation data (stress cloud map, temperature control parameter interval), real-time temperature data, stress detection data, about 10-50MB per batch, 128GB and above capacity can store 1000+ batch data; Adapt to production environment: industrial-grade medium (such as wide-temperature SSD, -40℃ to 85℃) resistant to high temperature and vibration in workshop, read-write speed greater than 100MB / s, meet the real-time data storage and fast calling requirements.

[0048] Obviously, the above embodiments are only examples for clear illustration, and are not limited 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, it is not necessary and impossible to enumerate all the embodiments. 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 three-dimensional model of the automobile pipe clamp containing the partition data into a CAE analysis model of pipe clamp injection molding, taking the partition geometric data of the three-dimensional model of the pipe clamp, 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 the stress 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.

2. The method of claim 1, wherein: The specific steps of the S10 for dividing the pipe clamp model into multiple structural regions are as follows: S11, preliminarily dividing the pipe clamp model according to the key structural functions of the pipe clamp model, including a buckle region, a main body region and a transition region; S12, using the wall thickness analysis tool of SolidWorks to perform global scanning on the pipe clamp model, generating a wall thickness distribution nephogram, and directly displaying the wall thickness values of the model at different positions, 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 the natural demarcation point method, and dividing the regions of the pipe clamp model.

3. The method of claim 2, 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 division according to the wall thickness threshold values 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.

4. 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 three-dimensional model of the automobile pipe clamp containing the partition data into the CAE analysis model of the pipe clamp injection molding, taking the partition geometric data of the three-dimensional model of the pipe clamp, the raw material characteristic data and the mold process data as inputs, obtaining the raw material characteristic data and the mold process data by using the material physical property table provided by the 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.

5. The method of claim 4, wherein: After the S22 performs filling simulation and cooling simulation, based on the filling and cooling simulation results, the residual stress distribution of the tube clamp after forming is calculated, stress simulation is performed, and a stress cloud map of the tube clamp model is generated.

6. The method of claim 4, wherein: After the S23 obtains 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 fine-tuned to verify the fluctuation and improve the anti-interference ability of the optimal parameter of the partition temperature control.

7. The method of claim 1, wherein: The specific steps of the S3 for feeding back the partition temperature of the tube 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 tube 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, use the solvent immersion method to detect the stress of the product, place the actual 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 adjust the production process.

8. The method of claim 7, 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.

9. 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 8, characterized in that, The system comprises: A structure division module (100) acquires a three-dimensional model of an automobile tube clamp, extracts key structural features in the three-dimensional model, and divides the tube clamp model into multiple structural regions according to the wall thickness difference; A CAE simulation module (200) inputs the three-dimensional model of the automobile tube clamp into a CAE analysis model of tube clamp injection molding, takes the collective data of the tube clamp three-dimensional model, the raw material characteristic data and the mold process data as inputs, and performs multiple rounds of simulation for the purpose of cooling uniformity, defect elimination and stress control, and quantitatively evaluates the simulation results to determine the partition temperature control parameters. A feedback adjustment module (300) inputs the determined partition temperature control parameters into production equipment for injection molding, detects the stress of the product using the solvent immersion 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 adjusts the production process.

10. 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 tube clamp injection production process optimization method of any one of claims 1-8 are implemented, specifically including: reading the basic data of tube 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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