Intelligent control and elimination method and system for stress in injection molded part

Abstract

The application relates to the technical field of injection molding process control, and discloses an intelligent regulation and control elimination method and system for internal stress of an injection molded part. The method is used for monitoring temperature and time data in the forming process of the injection molded part in real time, constructing a stress distribution field in a glass transition temperature interval through numerical simulation, extracting a threshold value in combination with a material characteristic database to determine a stress change turning point time position, comparing stress locking stage values based on the time position, dividing a high-risk area and determining an intervention priority, screening an intervention opportunity window, establishing a dynamic corresponding relationship between time and stress, and determining an optimal intervention time point for internal stress regulation under the condition that stress is not completely locked and elimination efficiency is optimal. The application can accurately position an internal stress intervention opportunity, improve stress elimination efficiency and injection molded part forming quality, effectively avoid problems such as deformation and cracking caused by excessive internal stress, and adapt to the intelligent regulation and control requirements of high-precision injection molding.

Description

Technical Field

[0001] This invention relates to the field of injection molding process control technology, and in particular to an intelligent method and system for regulating and eliminating internal stress in injection molded parts. Background Technology

[0002] Injection molded parts are the most widely used basic structural components in modern manufacturing, occupying a core position in key fields such as automobiles, home appliances, and medical devices. Their molding quality directly determines the assembly accuracy, structural strength, and safety of the final product. Internal stress, an inherent defect that occurs during the injection molding process along with melting, filling, cooling, and solidification, is a major cause of warping, dimensional deviations, surface cracking, and brittle fracture in products. Uncontrolled internal stress can significantly reduce product lifespan and even pose potential safety hazards, becoming a key technical challenge restricting the improvement of the quality of high-end injection molded parts.

[0003] Existing methods for relieving internal stress in injection molded parts largely rely on empirical process adjustments or post-treatment heat treatment. They generally lack real-time perception and quantitative analysis of the dynamic evolution of stress during the molding process. Intervention methods depend on fixed procedures or manual experience, making it difficult to adapt to different material properties and complex working conditions. These methods fail to adequately consider the decisive role of the material's glass transition temperature in stress locking, and cannot accurately identify the critical range and inflection point of stress transition from a relaxed to a locked state. They generally suffer from delayed intervention timing and insufficient targeting of control strategies, resulting in unstable stress relief effects, poor controllability, and an inability to meet the high-quality requirements of precision injection molding.

[0004] This invention addresses the technical shortcomings of existing technologies, such as the inability to accurately identify stress change inflection points, the difficulty in dynamically determining the optimal intervention time, and the low efficiency of stress relief. It solves the core technical problems of traditional methods, such as delayed intervention, poor adaptability, and insufficient control precision. Summary of the Invention

[0005] This invention provides an intelligent control and elimination method and system for internal stress in injection molded parts, which can accurately locate the timing of internal stress intervention, improve stress elimination efficiency and injection molded part molding quality, effectively avoid problems such as deformation and cracking caused by excessive internal stress, and adapt to the intelligent control requirements of high-precision injection molding.

[0006] In a first aspect, to solve the above-mentioned technical problems, the present invention provides an intelligent control and elimination method for internal stress in injection molded parts, comprising: The temperature and time data during the injection molding process are monitored in real time to form monitoring data. Based on the monitoring data, the stress distribution field of the injection molded part in the glass transition temperature range is constructed through preset numerical simulation technology. By combining a pre-set material property database, the glass transition temperature threshold corresponding to the material used in the injection molded part is extracted, and then the time location of the stress change inflection point is determined based on the stress distribution field and the glass transition temperature threshold. Based on the time positioning, the stress values ​​in the stress locking stage of the stress distribution field are compared and analyzed. If the stress value exceeds the critical stress limit corresponding to the glass transition temperature threshold, the injection molded part area corresponding to the stress value is divided into a high-risk area. The high-risk areas are sorted according to the stress exceedance amplitude and influence range of each high-risk area to obtain the intervention priority ranking of the high-risk areas. Based on the prioritization of interventions, intervention window opportunities are selected within a preset molding time range. By integrating the stress change sequence data of the injection molding process, a dynamic correspondence between time and stress is established. The optimal intervention time for stress control in the injection molded part is determined based on the criterion that the stress is not completely locked and the elimination efficiency is the highest.

[0007] In one optional implementation, the real-time monitoring of temperature and time data during the injection molding process generates monitoring data, and based on this monitoring data, a stress distribution field of the injection molded part within the glass transition temperature range is constructed using a preset numerical simulation technique, including: Temperature and time data during the injection molding process are collected in real time, and noise reduction and calibration are performed to form monitoring data; Based on the monitoring data, a preset numerical simulation technique is invoked to simulate the stress generation and distribution process of the injection molded part in the glass transition temperature range, construct a stress distribution model, and thus form the stress distribution field of the injection molded part in each temperature range. The stress distribution model is used to present the stress magnitude and distribution pattern in different areas of the injection molded part.

[0008] In one optional implementation, the step of extracting the glass transition temperature threshold corresponding to the material used in the injection molded part by combining a preset material property database, and then determining the time location of the stress change inflection point based on the stress distribution field and the glass transition temperature threshold, includes: The system calls up a preset material property database and extracts the corresponding glass transition temperature threshold based on the material used in the injection molded part. The stress values ​​and stress variations with temperature and time in each region of the stress distribution field are extracted and compared with the glass transition temperature threshold to define the critical temperature range for stress to transition from a relaxed state to a locked state. Based on the critical temperature range, the stress change trajectory is tracked, key nodes that cause stress abrupt changes and enter a locked state are identified, and the forming time of the key nodes is associated with the time of the stress change inflection point.

[0009] In one optional implementation, based on the time positioning, the stress values ​​in the stress distribution field during the stress-locking stage are compared and analyzed. If the stress value exceeds the critical stress limit corresponding to the glass transition temperature threshold, the injection-molded part area corresponding to the stress value is divided into a high-risk area. The high-risk areas are then ranked according to the stress exceedance amplitude and impact range to obtain an intervention priority ranking for the high-risk areas, including: Based on the time positioning, a comparative analysis is conducted on the stress values ​​in the stress locking stage of the stress distribution field. If the comparative analysis results show that the stress value in the stress locking stage exceeds the critical stress limit corresponding to the glass transition temperature threshold, the injection molded part area corresponding to the stress value is defined as a high-risk area, and the stress exceedance value and influence range of each high-risk area are recorded simultaneously. Based on the stress exceedance amplitude and impact range of each high-risk area, as well as the degree of impact of stress exceedance on the molding accuracy, structural strength and performance of injection molded parts, intervention priority is determined, and a priority ranking of intervention areas for high-risk areas is generated.

[0010] In one optional implementation, the intervention priority ranking is used to select intervention timing windows within a preset molding time range. Stress change sequence data from the injection molding process are integrated to establish a dynamic correlation between time and stress. The optimal intervention time for stress control within the injection molded part is determined based on the criterion that stress is not completely locked and the elimination efficiency is highest. This includes: Based on the aforementioned intervention priority ranking and combined with the preset molding time range, intervention timing windows that meet the stress intervention conditions are selected; Collect stress change sequence data during the injection molding process; By combining the stress-locking characteristics of high-risk areas, the intervention time windows and stress change sequence data obtained from screening are integrated, and the impact of intervention at different time points on stress relief in high-risk areas is analyzed, establishing the correspondence between time and stress relief efficiency. Based on the aforementioned correspondence, the optimal intervention time for stress control in injection molded parts is determined by using the criterion that stress is not completely locked and the elimination efficiency is optimal, combined with the real-time stress state of high-risk areas.

[0011] In one alternative implementation, after determining the optimal intervention time for stress regulation in the injection molded part, the method further includes: When the optimal intervention time point is located before the time positioning of the stress change inflection point, the adjusted cooling parameter configuration is generated by combining the preset cooling curve optimization logic.

[0012] In one alternative implementation, after generating the adjusted cooling parameter configuration, the method further includes: The operating parameters of the injection molding equipment are updated using the adjusted cooling parameters, and the updated stress distribution map analysis data is obtained. If the range of the high-risk area is reduced, it is determined to be an effective adjustment path.

[0013] In one alternative implementation, after determining that the adjustment path is valid, the method further includes: Based on the effective adjustment path, combined with time-stress dynamic matching data and historical stress-related data, the stability of the stress reduction curve is analyzed through information comparison technology to obtain the final stress distribution optimization result.

[0014] In one optional implementation, based on the effective adjustment path, and combining time-stress dynamic matching data and historical stress-related data, the stability of the stress reduction curve is analyzed using information comparison technology to obtain the final stress distribution optimization result, including: Based on the effective adjustment path, extract the time-stress dynamic matching data and the historical stress-related data stored in the material property database; By employing information comparison technology, the stress data corresponding to the effective adjustment path, the dynamic matching data of time stress, and the historical stress-related data are compared. The focus is on the amplitude change, rate of change, and fluctuation frequency of the stress reduction curve. The curve change pattern is analyzed and the curve fluctuation characteristics are captured, and the comparison results are output. Based on the comparison results and the changing trend of the stress reduction curve, the stability of the stress reduction curve is quantitatively analyzed to obtain the optimized stress distribution results.

[0015] Secondly, the present invention also provides an intelligent control and elimination system for internal stress in injection molded parts, comprising: Stress field construction module: Real-time monitoring of temperature and time data during the injection molding process, generating monitoring data, and based on the monitoring data, constructing the stress distribution field of the injection molded part within the glass transition temperature range through preset numerical simulation technology; Turning point positioning module: Combined with a preset material property database, extract the glass transition temperature threshold corresponding to the material used in the injection molded part, and then determine the time positioning of the stress change turning point based on the stress distribution field and the glass transition temperature threshold. High-risk sorting module: Based on the time positioning, the stress values ​​in the stress locking stage of the stress distribution field are compared and analyzed. If the stress value exceeds the critical stress limit corresponding to the glass transition temperature threshold, the injection molded part area corresponding to the stress value is divided into a high-risk area. The high-risk areas are sorted according to the stress exceedance amplitude and influence range of each high-risk area to obtain the intervention priority sorting of the high-risk areas. Optimal Intervention Positioning Module: Based on the intervention priority ranking, the intervention timing window is selected within the preset molding time range. The stress change sequence data of the injection molding process is integrated to establish a dynamic correspondence between time and stress. The optimal intervention time point for stress control in the injection molded part is determined based on the criterion that the stress is not completely locked and the elimination efficiency is the highest.

[0016] Compared with the prior art, the present invention has the following beneficial effects: (1) By monitoring the molding temperature and time data in real time and using numerical simulation technology to construct the stress distribution field in the glass transition temperature range, the generation and distribution state of stress in the whole domain of the injection molded part can be reflected intuitively and accurately, realizing the full visualization and quantitative perception of the stress evolution process, and solving the problem that traditional methods cannot grasp the dynamic changes of stress in real time.

[0017] (2) By combining the material property database to extract the glass transition temperature threshold, the time node of stress change inflection point can be accurately located, and the critical range of stress transition from relaxation to locking can be clearly defined, providing a reliable time benchmark and judgment basis for internal stress intervention, and greatly improving the pertinence and accuracy of stress control.

[0018] (3) By comparing and analyzing the stress values ​​in the locking stage, high-risk areas are automatically divided and intervention priority ranking is generated. This allows for prioritizing areas with severe stress exceeding the standard, avoiding indiscriminate intervention, effectively improving control efficiency, reducing process adjustment costs, and ensuring the molding accuracy and structural stability of injection molded parts.

[0019] (4) Based on priority sorting, the intervention timing window is selected and a dynamic correspondence between time and stress is established. The optimal intervention time point is determined based on the principle that the stress is not completely locked and the elimination efficiency is the highest. This avoids stress solidification from the root, significantly improves the internal stress elimination effect, and effectively reduces molding defects such as product deformation and cracking.

[0020] (5) The overall method relies on data-driven and logical judgment to achieve intelligent control of the whole process. It does not need to rely on human experience, can be adapted to different materials and process conditions, has strong versatility, and can continuously and stably improve the molding quality of injection molded parts to meet the needs of high precision and high reliability injection molding manufacturing. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating an intelligent control and elimination method for internal stress in injection molded parts provided by an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of an intelligent control and elimination system for internal stress in injection molded parts provided in an embodiment of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Reference Figure 1 This invention provides an intelligent method for controlling and eliminating internal stress in injection molded parts, comprising the following steps: S11, Real-time monitoring of temperature and time data during the injection molding process of the injection molded part, forming monitoring data, and based on the monitoring data, constructing the stress distribution field of the injection molded part in the glass transition temperature range through preset numerical simulation technology; S12, Combined with the preset material property database, extract the glass transition temperature threshold corresponding to the material used in the injection molded part, and then determine the time location of the stress change inflection point based on the stress distribution field and the glass transition temperature threshold. S13, based on the time positioning, compare and analyze the stress values ​​in the stress locking stage of the stress distribution field. If the stress value exceeds the critical stress limit corresponding to the glass transition temperature threshold, divide the injection molded part area corresponding to the stress value into a high-risk area, and sort them according to the stress exceedance amplitude and influence range of each high-risk area to obtain the intervention priority ranking of the high-risk areas. S14. Based on the prioritization of intervention, an intervention window is selected within a preset molding time range. The stress change sequence data of the injection molding process is integrated to establish a dynamic correspondence between time and stress. The optimal intervention time for stress control in the injection molded part is determined based on the condition that the stress is not completely locked and the elimination efficiency is the highest.

[0024] This invention focuses on high-precision ABS injection-molded parts for medical device shells. These parts have a wall thickness of 1.5~3.5mm and a molding accuracy requirement of ±0.03mm, necessitating the complete avoidance of warping, cracking, and dimensional deviations caused by internal stress. The hardware system is deeply integrated into the intelligent manufacturing architecture, including a multi-channel high-precision temperature acquisition system (temperature measurement accuracy ±0.5℃, acquisition frequency 100Hz), intelligent injection molding equipment (with real-time parameter control unit, data interaction interface, and support for Profinet bus communication), a finite element thermo-mechanical coupling numerical simulation platform (equipped with an injection stress simulation algorithm developed using Abaqus and connected to the intelligent manufacturing data platform), and a pre-fabricated standardized material property database (built on SQL Server, structurally recording the thermodynamic and mechanical parameters of commonly used injection molding materials such as ABS, PA66, PP, and PC, providing real-time parameter support for full-process control). The software system includes core programs such as stress field construction, inflection point location, high-risk sorting, and optimal intervention location. All programs are developed using Python and achieve data interoperability within the intelligent manufacturing system, adhering to the data-driven logic of intelligent manufacturing throughout the entire process to achieve intelligent control. In this embodiment, the complete molding cycle of the injection molded part is 13 seconds, including 2 seconds of melt filling, 3 seconds of pressure holding, and 8 seconds of cooling. Eight fiber optic temperature sensors are installed on the inner wall of the mold cavity, the gate area, locations of abrupt changes in wall thickness, around the cooling channels, and at the root of the injection molded part's ribs. The sensors and the data acquisition system are connected to the intelligent manufacturing data transmission network via a 485 bus, providing a precise and continuous data source for the entire process of data acquisition and stress analysis. The specific steps are as follows: In step S11, temperature and time data during the injection molding process are monitored in real time to form monitoring data. Based on the monitoring data, the stress distribution field of the injection molded part in the glass transition temperature range is constructed using preset numerical simulation technology.

[0025] In one embodiment, this step first uses an 8-channel fiber optic temperature sensor to synchronously collect temperature data and system-synchronized time data at each monitoring point throughout the entire molding cycle of the injection molded part. After the raw data is transmitted to the data processing module via the acquisition system, noise reduction is performed using a moving average filtering algorithm. Five consecutive data points are used as a filtering window, and the average value of the data within the window is calculated as the effective data. Electromagnetic interference and spike abnormal signals caused by equipment vibration are filtered out. At the same time, system deviation calibration is performed based on the sensor calibration curve to control the temperature acquisition error within ±0.5℃. The time recording deviation is corrected to ±5ms by the pulse signal of the injection molding machine spindle encoder. Finally, standardized monitoring data containing the temperature value of each monitoring point every 10ms, the corresponding timestamp, and the location information of the monitoring point are formed. The data is stored in CSV format and synchronized to the simulation platform in real time to ensure the accuracy, continuity, and integrity of the data. Based on the calibrated temperature and time monitoring data, a preset finite element thermo-mechanical coupling numerical simulation technology is invoked. This technology is based on the injection molding stress simulation algorithm developed in Abaqus. The simulation interface is called via a Python script to achieve real-time linkage between the monitoring data and the simulation model. The core of this technology is the basic thermo-mechanical coupling equations of injection molding, including the heat conduction equation, stress balance equation, and constitutive equation. Real-time temperature and molding time are used as dynamic boundary conditions for the simulation, and the thermodynamic properties of the ABS material (coefficient of thermal expansion 7 × 10⁻⁶) are imported. ⁻5 Based on the parameters of the injection molded part (temperature, elastic modulus 2200 MPa, thermal conductivity 0.25 W / (m·K)) and its three-dimensional structural dimensions, a tetrahedral mesh was created for the injection molded part. The mesh size was 0.5 mm, resulting in 12,000 nodes across the entire region. For stress concentration areas (gate, areas of abrupt wall thickness changes), the mesh was refined to a smaller size of 0.3 mm to ensure computational accuracy. During the simulation, the internal stress amplitude at different temperatures was quantified using the thermal stress calculation formula: In the formula, σ(T) is the internal stress amplitude at the current temperature, E(T) is the elastic modulus of the material at the current temperature (MPa), α is the thermal expansion coefficient of the material ( / ℃), T is the real-time temperature (℃), and T0 is the reference temperature at which stress does not change abruptly (℃, taking the uniform melting temperature of ABS material as 220℃). This formula iteratively calculates the stress values ​​of all nodes in the injection molded part using real-time temperature curves as boundary conditions, simulating the generation, transmission, accumulation, and distribution of thermal stress, shrinkage stress, and flow residual stress within the glass transition temperature range (85~105℃). A stress distribution model is constructed, which visually presents the stress magnitude, direction, gradient, and distribution patterns in different regions of the injection molded part. It clearly distinguishes between stable stress regions, stress-rising stress regions, and critical stress regions. The model outputs stress tensor data for all nodes, which is uploaded to the intelligent manufacturing platform in real time. Finally, based on this stress distribution model, iterative calculations of all nodes in the injection molded part are performed using finite element simulation at 10ms time steps. This outputs the stress amplitude of each region within the glass transition temperature range at different times and temperatures, forming a stress distribution field encompassing spatiotemporal dimensions. This stress distribution field is presented as a cloud map and numerical matrix on the intelligent manufacturing visualization interface, containing stress data for all nodes across the entire injection molded part. It fully recreates the dynamic evolution of stress within the critical temperature range, providing accurate and comprehensive data support for subsequent steps.

[0026] In step S12, the glass transition temperature threshold corresponding to the material used in the injection molded part is extracted by combining the preset material property database, and then the time location of the stress change inflection point is determined according to the stress distribution field and the glass transition temperature threshold.

[0027] In one embodiment, this step first calls a preset material property database. This database is a structured relational database built on SQL Server, categorized and stored by material grade. It pre-includes key thermodynamic and mechanical parameters of commonly used plastic materials, such as glass transition temperature, thermal conductivity, specific heat capacity, elastic modulus, coefficient of thermal expansion, shrinkage rate, and stress relaxation characteristics. It supports precise queries and parameter extraction by material grade and material type. Through the database query interface, based on the ABS material grade (ABS-757K) used in the injection molded part of this embodiment, the glass transition temperature threshold corresponding to this material is extracted to be 95℃. This threshold is determined according to GB / T11998-2018 "Determination of Glass Transition Temperature of Plastics - Thermomechanical Analysis". Other thermodynamic parameters of the material are all from the official technical manual provided by the material supplier (No.: ABS-757K-202403). This threshold is used as the critical temperature criterion for the stress of the injection molded part to enter the locked state and is simultaneously stored in the analysis database. Then, from the stress distribution field constructed in S11, the real-time stress values ​​of the gate area, the location of abrupt changes in wall thickness, the corner structure, the root of the stiffener plate, and the far end area are extracted through the data extraction module. At the same time, the continuous data sequence of stress changes with temperature and molding time in the above areas is extracted. The stress change data is compared and analyzed point by point with the glass transition temperature threshold (95℃). Matplotlib is used to draw the stress change trend curve with temperature as the horizontal axis and stress amplitude as the vertical axis. The stress-temperature change relationship is fitted by the curve fitting algorithm (least square method) to determine the evolution characteristics of the stress state as the temperature decreases. When the first derivative of the stress change rate changes abruptly, the temperature range is defined as the critical range of stress transformation. Finally, the critical temperature range for the stress of the ABS injection molded part to change from a high elastic relaxation state to a rigid locking state in this embodiment is defined as 90~100℃. Finally, using the critical temperature range as the scope and the molding time as the clue, the stress change trajectory of each region is continuously tracked through the stress change trajectory tracking algorithm. The stress amplitude, stress change rate, and stress gradient abrupt change characteristics are monitored in real time. The stress change rate threshold is set as ≤3MPa / s (relaxed state) and ≥15MPa / s (locked state). When the stress change rate abruptly changes from ≤3MPa / s to ≥15MPa / s, the node is determined to be a key node where the stress changes from a controllable relaxation state to an irreversible locked state. The key node is accurately associated with the time data in the molding process through timestamp matching. The time positioning of the stress change inflection point in this embodiment is determined to be 7.24s after the start of molding, that is, 1.24s of the cooling stage. This time positioning data is synchronized to the subsequent analysis module to provide a clear time reference for subsequent steps.

[0028] In step S13, based on the time positioning, the stress values ​​in the stress locking stage of the stress distribution field are compared and analyzed. If the stress value exceeds the critical stress limit corresponding to the glass transition temperature threshold, the injection molded part area corresponding to the stress value is divided into a high-risk area. The high-risk areas are sorted according to the stress exceedance amplitude and influence range of each high-risk area to obtain the intervention priority ranking of the high-risk areas.

[0029] In one embodiment, this step first uses the stress change inflection point time determined in S12 (7.24s after the start of molding) as a benchmark, and defines the stress locking stage as from 7.24s of molding to 13s of cooling end. The stress value of each region node in the stress locking stage in the stress distribution field is quantitatively compared point by point by a stress field point-by-point comparison analysis program. The program compares the real-time stress value of each region with the critical stress limit (30MPa, determined by the allowable stress value of ABS material in the material property database combined with the stress relaxation characteristics at the glass transition temperature) corresponding to the glass transition temperature threshold (95℃) by traversing the numerical matrix of the stress distribution field, and generates a stress exceedance judgment matrix. If the comparison results show that the stress value of a node in a certain area exceeds the critical stress limit, then the node is marked as an out-of-limit node. The connected area formed by all adjacent out-of-limit nodes is defined as a high-risk area. At the same time, the stress exceedance amplitude (actual stress value - critical stress limit, taking the average value within the area) and the influence range (area covered by the out-of-limit stress, calculated by accumulating the mesh node surfaces) of each high-risk area are recorded synchronously through the data statistics module. The detection showed that there were 3 high-risk areas in the ABS injection molded part in this embodiment. Among them, the actual stress near the gate was 42.3 MPa, the exceedance amplitude was 12.3 MPa, and the influence area was 180 mm. 2 The actual stress at the location of the abrupt change in wall thickness (from 1.5mm to 3.5mm) was 38.7MPa, exceeding the standard range by 8.7MPa, with an affected area of ​​110mm². 2 The actual stress at the root of the stiffener was 35.2 MPa, exceeding the standard limit by 5.2 MPa, with an affected area of ​​75 mm². 2Finally, an intervention priority determination algorithm was used to comprehensively assess the stress exceedance amplitude and impact range of each high-risk area, as well as the degree of impact of stress exceedance on the molding accuracy, structural strength, and performance of injection molded parts. This algorithm employs the Analytic Hierarchy Process (AHP), using stress exceedance amplitude, impact range, and overall impact degree as three core evaluation indicators, and assigning corresponding weights and quantification rules. The stress exceedance amplitude has a weight of 0.5, and is divided into five levels based on the exceedance amplitude: Level 1 (≤3MPa) receives 2 points, Level 2 (3~6MPa) receives 4 points, Level 3 (6~9MPa) receives 6 points, Level 4 (9~12MPa) receives 8 points, and Level 5 (>12MPa) receives 10 points. The impact range has a weight of 0.3, and is divided into 4 levels according to the impact area: Level 1 (≤50mm²) gets 2 points, Level 2 (50~100mm²) gets 5 points, Level 3 (100~150mm²) gets 8 points, and Level 4 (>150mm²) gets 10 points. The comprehensive impact degree has a weight of 0.2, which includes three sub-indicators: impact on molding accuracy, impact on structural strength, and impact on performance. The impact on molding accuracy is graded according to the proportion of dimensional deviation exceeding the allowable value (±0.03mm), the impact on structural strength is graded according to the proportion of impact strength reduction, and the impact on performance is graded according to the impact on the service life and assembly adaptation. The highest score of the three sub-indicators is taken as the comprehensive impact degree score. The comprehensive priority score for each high-risk area was calculated using the above quantitative rules. The total score = score for exceeding the standard range × 0.5 + score for the range of influence × 0.3 + score for the degree of comprehensive influence × 0.2. The comprehensive score for the area near the gate was 8.6 points, the comprehensive score for the location of abrupt changes in wall thickness was 6.3 points, and the comprehensive score for the root of the stiffener plate was 4.1 points. The high-risk areas in this embodiment were sorted from highest to lowest score to generate the intervention priority ranking. The first priority was the area near the gate, the second priority was the location of abrupt changes in wall thickness, and the third priority was the root of the stiffener plate. The ranking results were stored in the analysis database to provide a clear focus for subsequent control steps.

[0030] In step S14, based on the priority ranking of interventions, intervention timing windows are selected within a preset molding time range. The stress change sequence data of the injection molding process are integrated to establish a dynamic correspondence between time and stress. The optimal intervention time point for stress control in the injection molded part is determined based on the condition that the stress is not completely locked and the elimination efficiency is the highest.

[0031] In one embodiment, this step first prioritizes interventions for high-risk areas obtained in S13. Combining this with the preset time range of 13s for the total molding cycle of the injection molded part in this embodiment, and centering on the stress change inflection point time (7.24s), it backs 2s and extends 1s backwards. Through the intervention timing window screening program, the intervention timing window that meets the stress intervention conditions is selected as 5.24s to 8.24s after the start of molding. This program verifies the relaxation characteristics of the stress within the window and confirms that the stress within the window has not been completely solidified and is still in a state that can be relaxed and controlled. The stress can be efficiently eliminated by adjusting the process parameters, which is the effective time range for internal stress control. Then, the data acquisition module continuously collects stress change sequence data of the injection molded part from the entire process of melt filling, holding pressure to cooling. The stress value of each time node is recorded at a collection frequency of 100Hz, forming a complete continuous time and stress corresponding dataset containing timestamps, stress amplitudes, and regional locations. Combined with the stress locking characteristics of three high-risk areas near the gate, the location of sudden wall thickness changes, and the root of the stiffener (the stress locking rate of ABS material in the 90~100℃ range increases exponentially with the decrease of temperature, and this characteristic is determined by the stress relaxation test data of ABS material in the material property database), the selected intervention timing window is fused and matched with the stress change sequence data through a data fusion algorithm. This algorithm uses the timestamp as the association key to accurately map the time data and stress data within the window, realizing a precise dynamic correspondence between time and stress. The generated time-stress dataset within the window is a mapping dataset of timestamps (accuracy ±3ms) and the stress value of the entire injection molded part at the corresponding time, containing key information such as stress amplitude and stress direction in each region. Subsequently, the fused time-stress dataset was quantitatively analyzed. An intervention effect simulation analysis program was used to calculate the impact of process parameter adjustments at different time points on stress relief in various high-risk areas. The intervention action was uniformly set as "stepwise adjustment of cooling parameters," with adjustment dimensions including cooling water temperature (±5℃), water flow rate (±0.5m / s), and local flow distribution (±30%). The adjustment range of intervention parameters was consistent at each time point, differing only in the intervention start time. In addition to the core stress relief efficiency (stress reduction per unit time), two auxiliary indicators were simultaneously monitored: stress uniformity (the difference between the maximum and minimum stress within the area) and secondary stress risk (whether new stress concentration areas are generated after intervention). Stress relief efficiency was calculated using the formula: In the formula, R is the stress relief efficiency (MPa / s), σ1 is the average stress amplitude of the high-risk area before intervention (MPa), σ2 is the average stress amplitude of the high-risk area 3s after intervention (MPa), and Δt is the duration of intervention (s).

[0032] For each 10ms time point within the intervention window (5.24s~8.24s), the stress change process within 3s after intervention was simulated. From 5.24s to 6.18s, the stress relief efficiency gradually increased from 5.3MPa / s to 8.2MPa / s, and the stress uniformity difference decreased from 8.7MPa to 4.2MPa, with no secondary stress generation. From 6.18s to 8.24s, the stress relief efficiency rapidly decreased from 8.2MPa / s to 3.1MPa / s, and the stress uniformity difference increased from 4.2MPa to 9.5MPa. After 8.0s, secondary stress concentration (amplitude ≤5MPa) appeared in some areas. Subsequently, with time as the horizontal axis and stress relief efficiency as the vertical axis, a polynomial fitting algorithm (cubic polynomial, R0) was used. 2 The time-stress relief efficiency curve was obtained by fitting a time-intervention effect (≥0.98), and the stress uniformity and secondary stress risk level at each time point were marked to form a complete "time-intervention effect" mapping dataset. Finally, based on this dynamic correspondence, the criteria for judgment were that the stress was not completely locked and the relief efficiency was the highest. Combined with the real-time stress state of the high-risk area, a comprehensive judgment was made. By finding the point with the maximum efficiency in the dynamic correspondence curve, the optimal intervention time for stress control in the injection molded part in this embodiment was determined to be 6.18s after the start of molding, that is, 0.18s in the cooling stage. Intervention at this time can make the overall stress relief efficiency reach 8.2MPa / s, which is the maximum value within the intervention window. Moreover, the stress has not yet entered an irreversible locked state at this time, and the intervention effect is optimal. This optimal intervention time point was synchronized to the parameter control module.

[0033] Step S14 is followed by step S15, where when the optimal intervention time point is located before the time positioning of the stress change inflection point, the stress reduction curve is predicted by a preset heat treatment simulation path technology, and the adjusted cooling parameter configuration is generated by combining the preset cooling curve optimization logic. In one implementation, this step first determines, through time value comparison, that the optimal intervention time point (6.18s) determined in S14 is located before the stress change inflection point time point (7.24s) determined in S12, thus satisfying the execution conditions of this step. Then, a preset heat treatment simulation path technology is invoked. This technology is an intelligent simulation method based on the temperature-stress coupling mechanism, using the stress relaxation equation of viscoelastic mechanics as its core, integrated into a finite element simulation platform. Using the stress distribution data at the optimal intervention time point (6.18s) as initial input, and combining thermodynamic parameters such as the thermal conductivity (0.25W / (m·K)), specific heat capacity (1.9kJ / (kg·K)), and stress relaxation time of the ABS material, three different intervention intensities (high, medium, and low) are set (corresponding to the adjustment range of cooling parameters) to simulate the internal stress of the injection molded part under different heat treatment paths. During the stress reduction process, three stress reduction curves corresponding to different intervention intensities were obtained through iterative calculations. The curves are plotted with time on the horizontal axis and stress amplitude on the vertical axis. The high intervention intensity curve shows that the stress decreased from 42.5 MPa to 28.3 MPa within 3 seconds after intervention, with a stress reduction rate of 4.73 MPa / s; the moderate intervention intensity curve shows that the stress decreased from 42.5 MPa to 30.1 MPa within 3 seconds after intervention, with a stress reduction rate of 4.13 MPa / s; and the low intervention intensity curve shows that the stress decreased from 42.5 MPa to 33.7 MPa within 3 seconds after intervention, with a stress reduction rate of 2.93 MPa / s.Finally, the adjusted cooling parameter configuration is generated by combining the preset cooling curve optimization logic. This optimization logic is a "multi-objective hierarchical optimization system". The core objective is to match a stress reduction curve with moderate intervention intensity (stress decreases from 42.5MPa to 30.1MPa within 3s, with an average reduction rate of 4.13MPa / s) to ensure a smooth stress decrease. At the same time, it also takes into account the auxiliary objectives of no local overcooling (maximum temperature difference on the surface of the injection molded part ≤3℃) and no reduction in molding efficiency (cooling time ≤8s). The constraints include a cooling water temperature adjustment range of 15~35℃, a water flow rate adjustment range of 0.8~2.0m / s, and a local flow distribution adjustment range of 70%~130%. The cooling curve is decomposed into three adjustable variables: basic cooling water temperature (T), water flow rate (v), and local flow coefficient of high-risk area (k, k = high-risk area flow rate / global average flow rate). The parameters are optimized by non-dominated sorting genetic algorithm (NSGA-II). The objective function of this algorithm is min (stress over-limit value), max (stress over-limit value), and max (stress over-limit value). The optimization parameters (stress reduction efficiency) and constraint (molding cycle ≤ 13s) were set to 100 generations with a population size of 50. First, 50 random parameter combinations (T, v, k) satisfying the constraints were generated as the initial population. Then, each parameter combination was substituted into the stress simulation model to calculate the fitness values ​​of three indicators: stress reduction rate, surface temperature difference, and cooling time. After 100 iterations, the optimal parameter combination in the Pareto optimal solution set was selected: cooling water temperature 27℃, water flow rate 1.5m / s, and local flow coefficient 1.25 (i.e., a 25% increase in flow rate in high-risk areas). This optimal parameter combination was substituted into the simulation model for verification. The results showed that the stress decreased from 42.5MPa to 30.3MPa within 3s (deviation from the target curve ≤ 0.7%), the maximum surface temperature difference of the injection molded part was 2.1℃, and the cooling time was 7.5s, fully satisfying all optimization objectives and constraints. Finally, this parameter combination was determined as the adjusted cooling parameter configuration and output to the injection molding equipment parameter control unit in a standardized format.

[0034] After step S15, step S16 is also included, in which the operating parameters of the injection molding equipment are updated using the adjusted cooling parameters, and the updated stress distribution map analysis data is obtained. If the range of the high-risk area is reduced, it is determined to be an effective adjustment path.

[0035] In one embodiment, this step first sends the optimized cooling parameter configuration generated in S15 to the parameter control unit of the intelligent injection molding equipment through the parameter interaction interface of the injection molding equipment. The original operating parameters are replaced by a PLC program, completing a unified update of the equipment's cooling water temperature, water flow rate, cooling sequence, and local flow distribution in the cooling channels, ensuring the accuracy and real-time nature of parameter adjustments. Then, according to the adjusted cooling parameters, three consecutive injection molding tests are completed. The coefficient of variation for the stress exceeding the standard amplitude in the high-risk area in the three sets of tests is 0.04, indicating good process stability after the cooling parameter adjustment with no significant fluctuations. During the tests, other injection molding parameters (injection pressure, injection speed, holding pressure, and holding time) remain unchanged. Under the same acquisition conditions as in S11, temperature and stress distribution data for each molding cycle are re-acquired using an 8-channel fiber optic temperature sensor and a stress simulation platform. After data processing and simulation calculations, updated stress distribution map analysis data is generated, which includes the updated stress distribution field, the location of high-risk areas, and stress values. Finally, the updated stress distribution map was compared with the original stress distribution map using a stress distribution comparison analysis program, and the comparison was performed region by region. The program calculated the area change rate and stress exceedance amplitude reduction rate of the high-risk area by overlaying the cloud map of the two stress distribution maps. It focused on the stress amplitude and distribution range changes of three high-risk areas: near the gate, at the location of sudden wall thickness changes, and at the root of the stiffener. After comparison and adjustment, the stress exceedance amplitude of the three high-risk areas decreased to 5.2MPa, 3.8MPa, and 1.9MPa, respectively, and the corresponding affected areas decreased to 76mm², 42mm², and 31mm², respectively. The overall shrinkage rate of the high-risk area reached more than 65%, and the stress concentration was significantly reduced. Based on the comparison results, it was confirmed that the parameter adjustment could effectively reduce the internal stress level of the injection molded part and reduce the range of the high-risk area, meeting the judgment conditions of the effective adjustment path. The control path corresponding to the optimization of the cooling parameters was determined as the effective adjustment path and stored in the historical process database.

[0036] Step S16 is followed by step S17, which, based on the effective adjustment path, combines time-stress dynamic matching data and historical stress-related data, analyzes the stability of the stress reduction curve through information comparison technology, and obtains the final stress distribution optimization result. In one embodiment, this step first extracts all data on the dynamic matching of time and stress within the current molding cycle from the analysis database through the data extraction module, based on the effective adjustment path determined in S16. This includes timestamps, stress amplitudes, stress relief efficiency, cooling parameters, etc. Simultaneously, it retrieves 20 sets of historical stress-related data from the material property database and historical process database for the same grade of ABS material (ABS-757K), the same structure of injection molded parts, and the same process conditions. These historical data all come from molding records of injection molded parts with the same injection molding equipment, the same material grade, and the same structure. The data acquisition conditions are completely consistent with those of this embodiment, including stress amplitudes, stress change rates, stress reduction curve fluctuation ranges, and stability judgment indicators during the historical molding process, ensuring the reliability and representativeness of the comparative analysis. Then, a preset information comparison technology is used. This technology is a multi-source data comparison method based on spatiotemporal alignment. Through time series alignment algorithm, the real-time stress data, time stress dynamic matching data and historical stress-related data corresponding to the effective adjustment path are compared point by point and segment by segment in spatiotemporal dimension. With a time step of 10ms, the focus is on the amplitude change, rate of change and fluctuation frequency of the stress reduction curve. The overall change law of the curve is analyzed by calculating the root mean square error and coefficient of variation of the curve. The local fluctuation characteristics of the curve are captured by wavelet transform. The comparison results include comparison indicators, curve characteristics and deviation analysis. The comparison shows that the amplitude of the stress reduction curve after the intervention is stable, the rate is stable between 4.0 and 4.5 MPa / s, the fluctuation amplitude is controlled within ±0.3 MPa, there are no obvious sudden changes or abnormal fluctuations, and all indicators are better than the historical average level. Subsequently, based on the above comparison results and the overall trend of the stress reduction curve, the coefficient of variation method was used to quantitatively analyze the stability, consistency and reliability of the stress reduction curve. The coefficient of variation of the stress amplitude at each time point of the stress reduction curve was calculated, and the stability coefficient of the curve was 0.06. The coefficient ≤ 0.1 was set as the stable level. Based on this, it was determined that the stress reduction process was stable and controllable, and there was no risk of secondary stress concentration. Finally, by combining and analyzing real-time data, dynamic matching data, and historical data, the final stress distribution optimization result of this injection molded part internal stress control was formed through the data aggregation and analysis module. In this embodiment, the overall internal stress level of the injection molded part decreased by more than 35%, the original three high-risk areas were basically eliminated, the stress distribution of the entire injection molded part was uniform, and the average stress value was 26.8 MPa, which is far below the critical stress limit of 30 MPa. The molded ABS injection molded medical device shell, when tested by a coordinate measuring machine, showed a warpage deformation of 0.02 mm and a dimensional deviation controlled within ±0.02 mm, which fully meets the molding quality and performance requirements of high-precision medical device shells. The final optimization result and the corresponding process parameters were stored in the historical process database to provide a reference for stress control of similar injection molded parts in the future.

[0037] refer to Figure 2The second embodiment of the invention provides an intelligent control and elimination system for internal stress in injection molded parts, comprising: Stress field construction module: Real-time monitoring of temperature and time data during the injection molding process, generating monitoring data, and based on the monitoring data, constructing the stress distribution field of the injection molded part within the glass transition temperature range through preset numerical simulation technology; Turning point positioning module: Combined with a preset material property database, extract the glass transition temperature threshold corresponding to the material used in the injection molded part, and then determine the time positioning of the stress change turning point based on the stress distribution field and the glass transition temperature threshold. High-risk sorting module: Based on the time positioning, the stress values ​​in the stress locking stage of the stress distribution field are compared and analyzed. If the stress value exceeds the critical stress limit corresponding to the glass transition temperature threshold, the injection molded part area corresponding to the stress value is divided into a high-risk area. The high-risk areas are sorted according to the stress exceedance amplitude and influence range of each high-risk area to obtain the intervention priority sorting of the high-risk areas. Optimal Intervention Positioning Module: Based on the intervention priority ranking, the intervention timing window is selected within the preset molding time range. The stress change sequence data of the injection molding process is integrated to establish a dynamic correspondence between time and stress. The optimal intervention time point for stress control in the injection molded part is determined based on the criterion that the stress is not completely locked and the elimination efficiency is the highest.

[0038] It should be noted that the intelligent control and elimination system for internal stress in injection molded parts provided in this embodiment of the invention is used to execute all the process steps of the intelligent control and elimination method for internal stress in injection molded parts described in the above embodiment. The working principles and beneficial effects of the two are one-to-one, so they will not be described again.

[0039] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for intelligent control and elimination of internal stress in injection molded parts, characterized in that, include: The temperature and time data during the injection molding process are monitored in real time to form monitoring data. Based on the monitoring data, the stress distribution field of the injection molded part in the glass transition temperature range is constructed through preset numerical simulation technology. By combining a pre-set material property database, the glass transition temperature threshold corresponding to the material used in the injection molded part is extracted, and then the time location of the stress change inflection point is determined based on the stress distribution field and the glass transition temperature threshold. Based on the time positioning, the stress values ​​in the stress locking stage of the stress distribution field are compared and analyzed. If the stress value exceeds the critical stress limit corresponding to the glass transition temperature threshold, the injection molded part area corresponding to the stress value is divided into a high-risk area. The high-risk areas are sorted according to the stress exceedance amplitude and influence range of each high-risk area to obtain the intervention priority ranking of the high-risk areas. Based on the prioritization of interventions, intervention window opportunities are selected within a preset molding time range. By integrating the stress change sequence data of the injection molding process, a dynamic correspondence between time and stress is established. The optimal intervention time for stress control in the injection molded part is determined based on the criterion that the stress is not completely locked and the elimination efficiency is the highest.

2. The intelligent control and elimination method for internal stress in injection molded parts according to claim 1, characterized in that, The real-time monitoring of temperature and time data during the injection molding process generates monitoring data. Based on this monitoring data, a stress distribution field of the injection molded part within the glass transition temperature range is constructed using preset numerical simulation technology, including: Temperature and time data during the injection molding process are collected in real time, and noise reduction and calibration are performed to form monitoring data; Based on the monitoring data, a preset numerical simulation technique is invoked to simulate the stress generation and distribution process of the injection molded part in the glass transition temperature range, construct a stress distribution model, and thus form the stress distribution field of the injection molded part in each temperature range. The stress distribution model is used to present the stress magnitude and distribution pattern in different areas of the injection molded part.

3. The intelligent control and elimination method for internal stress in injection molded parts according to claim 1, characterized in that, The process involves combining a pre-set material property database to extract the glass transition temperature threshold corresponding to the material used in the injection molded part, and then determining the time location of the stress change inflection point based on the stress distribution field and the glass transition temperature threshold, including: The system calls up a preset material property database and extracts the corresponding glass transition temperature threshold based on the material used in the injection molded part. The stress values ​​and stress variations with temperature and time in each region of the stress distribution field are extracted and compared with the glass transition temperature threshold to define the critical temperature range for stress to transition from a relaxed state to a locked state. Based on the critical temperature range, the stress change trajectory is tracked, key nodes that cause stress abrupt changes and enter a locked state are identified, and the forming time of the key nodes is associated with the time of the stress change inflection point.

4. The intelligent control and elimination method for internal stress in injection molded parts according to claim 1, characterized in that, Based on the time positioning, the stress values ​​in the stress distribution field during the stress locking stage are compared and analyzed. If the stress value exceeds the critical stress limit corresponding to the glass transition temperature threshold, the injection molded part area corresponding to the stress value is divided into a high-risk area. The high-risk areas are then ranked according to the stress exceedance amplitude and impact range to obtain an intervention priority ranking for the high-risk areas, including: Based on the time positioning, a comparative analysis is conducted on the stress values ​​in the stress locking stage of the stress distribution field. If the comparative analysis results show that the stress value in the stress locking stage exceeds the critical stress limit corresponding to the glass transition temperature threshold, the injection molded part area corresponding to the stress value is defined as a high-risk area, and the stress exceedance value and influence range of each high-risk area are recorded simultaneously. Based on the stress exceedance amplitude and impact range of each high-risk area, as well as the degree of impact of stress exceedance on the molding accuracy, structural strength and performance of injection molded parts, intervention priority is determined, and a priority ranking of intervention areas for high-risk areas is generated.

5. The intelligent control and elimination method for internal stress in injection molded parts according to claim 1, characterized in that, The intervention priority ranking is used to select intervention window opportunities within a preset molding time range. By integrating stress change sequence data from the injection molding process, a dynamic correlation between time and stress is established. The optimal intervention time for stress control within the injection molded part is determined based on the criterion that stress is not completely locked and the elimination efficiency is highest. This includes: Based on the aforementioned intervention priority ranking and combined with the preset molding time range, intervention timing windows that meet the stress intervention conditions are selected; Collect stress change sequence data during the injection molding process; By combining the stress-locking characteristics of high-risk areas, the intervention time windows and stress change sequence data obtained from screening are integrated, and the impact of intervention at different time points on stress relief in high-risk areas is analyzed, establishing the correspondence between time and stress relief efficiency. Based on the aforementioned correspondence, the optimal intervention time for stress control in injection molded parts is determined by using the criterion that stress is not completely locked and the elimination efficiency is optimal, combined with the real-time stress state of high-risk areas.

6. The intelligent control and elimination method for internal stress in injection molded parts according to claim 1, characterized in that, After determining the optimal intervention time for stress control in injection molded parts, the following steps are also included: When the optimal intervention time point is located before the time positioning of the stress change inflection point, the adjusted cooling parameter configuration is generated by combining the preset cooling curve optimization logic.

7. The intelligent control and elimination method for internal stress in injection molded parts according to claim 6, characterized in that, After generating the adjusted cooling parameter configuration, the following is also included: The operating parameters of the injection molding equipment are updated using the adjusted cooling parameters, and the updated stress distribution map analysis data is obtained. If the range of the high-risk area is reduced, it is determined to be an effective adjustment path.

8. The intelligent control and elimination method for internal stress in injection molded parts according to claim 7, characterized in that, After determining that the adjustment path is valid, it also includes: Based on the effective adjustment path, combined with time-stress dynamic matching data and historical stress-related data, the stability of the stress reduction curve is analyzed through information comparison technology to obtain the final stress distribution optimization result.

9. The intelligent control and elimination method for internal stress in injection molded parts according to claim 8, characterized in that, Based on the effective adjustment path, combined with time-stress dynamic matching data and historical stress-related data, the stability of the stress reduction curve is analyzed through information comparison technology to obtain the final stress distribution optimization result, including: Based on the effective adjustment path, extract the time-stress dynamic matching data and the historical stress-related data stored in the material property database; By employing information comparison technology, the stress data corresponding to the effective adjustment path, the dynamic matching data of time stress, and the historical stress-related data are compared. The focus is on the amplitude change, rate of change, and fluctuation frequency of the stress reduction curve. The curve change pattern is analyzed and the curve fluctuation characteristics are captured, and the comparison results are output. Based on the comparison results and the changing trend of the stress reduction curve, the stability of the stress reduction curve is quantitatively analyzed to obtain the optimized stress distribution results.

10. An intelligent system for regulating and eliminating internal stress in injection molded parts, characterized in that, include: Stress field construction module: Real-time monitoring of temperature and time data during the injection molding process, generating monitoring data, and based on the monitoring data, constructing the stress distribution field of the injection molded part within the glass transition temperature range through preset numerical simulation technology; Turning point positioning module: Combined with a preset material property database, extract the glass transition temperature threshold corresponding to the material used in the injection molded part, and then determine the time positioning of the stress change turning point based on the stress distribution field and the glass transition temperature threshold. High-risk sorting module: Based on the time positioning, the stress values ​​in the stress locking stage of the stress distribution field are compared and analyzed. If the stress value exceeds the critical stress limit corresponding to the glass transition temperature threshold, the injection molded part area corresponding to the stress value is divided into a high-risk area. The high-risk areas are sorted according to the stress exceedance amplitude and influence range of each high-risk area to obtain the intervention priority sorting of the high-risk areas. Optimal Intervention Positioning Module: Based on the intervention priority ranking, the intervention timing window is selected within the preset molding time range. The stress change sequence data of the injection molding process is integrated to establish a dynamic correspondence between time and stress. The optimal intervention time point for stress control in the injection molded part is determined based on the criterion that the stress is not completely locked and the elimination efficiency is the highest.

Images