Micro-foaming injection molding method and device for remote controller shell

Abstract

The invention relates to the field of injection molding technology forming, and discloses a micro-foaming injection molding method and device for a remote controller shell. Vibration sensing data generated by impact of waste particles in a hopper are collected, virtual waste blocks are recognized, degradation degree information of the virtual waste blocks is quantified, and asymmetric modulation is carried out on the rotating speed of a screw in a charging barrel based on the information; a homogeneous and stable melt is obtained; micro pressure sensors are arranged on the periphery of a mold key hole to form an annular monitoring net, the melt flowing front edge arrival time is recorded, and a cell growth competition index and a partition cell growth competition index are calculated; according to the indexes, overall compensation and partition enhancement compensation are carried out on the injection process, and differential pressure maintaining control is carried out on the pressure maintaining stage; by dynamically monitoring the waste mixing state and the melt flow characteristic, accurate regulation and control of the cell growth competition effect are achieved, the stability of cells around key holes of the remote controller shell is remarkably improved, and the asymmetrical collapse defect caused by waste heat historical difference is eliminated.

Description

Technical Field

[0001] This invention relates to the field of injection molding technology, and more specifically, to a micro-foaming injection molding method and apparatus for remote control housings. Background Technology

[0002] Microcellular injection molding technology is widely used in the manufacturing of precision electronic components such as remote control housings due to its advantages of lightweight products and high dimensional stability. However, during the waste recycling process, differences in the thermal history and uneven degradation of different batches of gate material and scrap can easily lead to a gradient in the distribution of melt molecular chain lengths, resulting in spatial runaway problems in cell growth rates. Although existing technologies have optimized the microcellular process in multiple dimensions, there is still a lack of effective solutions for cell collapse defects caused by the dynamic instability of waste material mixing ratios.

[0003] Chinese patent CN101746014B, authorized by the Ministry of Industry and Information Technology, proposes a micro-foaming injection molding machine and its molding process. It utilizes a booster gear pump and a controllable opening and closing nozzle to achieve cyclical drive of the melt / gas homogeneous system, improving cell uniformity and molding efficiency. While this solution optimizes the cell nucleation process, it does not address the dynamic monitoring of the waste material mixing state or the control of melt homogenization. Chinese patent application CN110480919A discloses a microporous foam plastic molding device and its method, which improves the demolding performance of the product through an anti-sticking block structure. Its core lies in mold structure optimization, but it also fails to address the melt support force gradient problem caused by differences in the thermal history of the waste material. The above technologies all focus on improving molding equipment or mold structure, neglecting the intrinsic correlation mechanism between waste material mixing ratio instability and cell collapse.

[0004] In the micro-foaming injection molding of remote control housings, when waste containing materials with a history of multiple reuses is mixed with waste materials from the first reuse, the spatial gradient of melt strength leads to an imbalance in the competition effect of cell growth. Specifically, in areas with highly degraded waste, the melt strength drops sharply due to significant molecular chain breakage, failing to provide sufficient support to maintain cell stability; while in areas with low degradation, the excessively high melt strength inhibits the uniform expansion of cells in adjacent areas. This asymmetric collapse defect is essentially caused by the difference in the advance rate of the melt flow front and the mismatch of the cell growth time window. Existing technologies lack spatial positioning and dynamic compensation strategies for the degree of melt degradation, resulting in the inability to quantify the cell growth competition index in real time, ultimately leading to visible depressions around the button holes. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, this invention provides a micro-foaming injection molding method and apparatus for remote control housings. By identifying virtual waste clumps and quantifying their degradation degree through vibration sensing data, and combining an annular pressure sensor network with flow front time monitoring, the invention enables real-time calculation of the cell growth competition index and differentiated compensation control during the injection / holding pressure stages, thereby fundamentally eliminating asymmetric collapse defects caused by differences in the thermal history of waste materials.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] The micro-foaming injection molding method for remote control housing includes:

[0008] Vibration sensing data generated by waste particles impacting the inner wall of the hopper is collected in the hopper. Virtual waste clumps are identified based on the vibration sensing data. When the waste particles enter the barrel from the hopper, the degradation degree information of the virtual waste clumps is quantified. Based on the degradation degree information, the screw speed in the barrel is asymmetrically modulated to obtain a homogeneous and stable melt.

[0009] Miniature pressure sensors are arranged around each button hole in the remote control housing mold to form a ring monitoring network. The time it takes for the melt flow front to reach each miniature pressure sensor is recorded as the flow front arrival time. Based on the flow front arrival time, the cell growth competition index and the zoned cell growth competition index are calculated.

[0010] Based on the cell growth competition index and the zoned cell growth competition index, overall compensation and zoned enhancement compensation are implemented in the injection process, and differentiated pressure holding control for cell stabilization is implemented in the pressure holding stage.

[0011] The method for collecting vibration sensing data is as follows: m vibration sensing points are set on the inner wall of the hopper, and the vibration frequency generated by the impact of waste particles on the inner wall of the hopper is collected at each vibration sensing point. The vibration frequency is used as vibration sensing data.

[0012] The method for identifying virtual waste clumps includes: calculating the vibration frequency change gradient between adjacent vibration sensing points based on the vibration frequencies collected at adjacent vibration sensing points, and defining the instantaneous waste aggregation area formed when the vibration frequency change gradient is greater than a preset gradient threshold as a virtual waste clump.

[0013] The waste particles carry thermally sensitive micropowder for marking thermal history characteristics. The thermally sensitive micropowder carried by the waste particles is a corresponding level of thermally sensitive micropowder sprayed according to the different reuse times of the waste particles.

[0014] The method for quantifying the degradation degree information of virtual waste clumps includes:

[0015] An optical observation window for a multispectral camera is installed inside the feed cylinder. When waste particles enter the feed cylinder from the hopper, the multispectral camera acquires multi-band near-infrared spectral images of virtual waste clumps passing through the optical observation window inside the feed cylinder. These images are recorded as virtual waste clump images. The virtual waste clump images are processed to obtain the weighted average number of times the virtual waste clumps are reused. The weighted average number of times the virtual waste clumps are reused is then quantified as the degradation degree value of the virtual waste clumps.

[0016] The method for processing the image of virtual waste clumps to obtain the weighted average number of reuses of the virtual waste clumps includes:

[0017] The virtual waste blob image is grayscale corrected to obtain a grayscale corrected virtual waste blob image. The reflected wavelength of each pixel in the grayscale corrected virtual waste blob image is extracted, and the number of times the virtual waste blob is reused at the corresponding pixel is determined based on the reflected wavelength. Based on the number of times the virtual waste blob is reused at the corresponding pixel, the percentage of pixels with different reuse times in the virtual waste blob image area is calculated. The percentage of pixels with different reuse times in the virtual waste blob image area is used as the percentage of waste mass with different reuse times in the virtual waste blob. The weighted average number of times the virtual waste blob is calculated based on the percentage of waste mass with different reuse times in the virtual waste blob.

[0018] The method for performing asymmetric modulation of the screw speed inside the barrel includes:

[0019] Mark the center point of the virtual waste agglomeration and track the center point of the virtual waste agglomeration to form a predicted settlement trajectory;

[0020] By associating the degradation degree value of the virtual waste clump with the predicted sedimentation trajectory, a labeled waste clump carrying spatial location and degradation degree information is obtained.

[0021] When the degradation degree value is greater than the degradation threshold, a high degradation waste label is added to the labeled waste agglomerate;

[0022] A three-stage asymmetric screw speed modulation (slow-fast-slow) is applied to the area where the tagged waste clumps carrying highly degradable waste labels are located.

[0023] The method for calculating the cell growth competition index includes:

[0024] The measurement point pairs with non-uniform flow phenomena are determined based on the arrival time of the flow front.

[0025] Count the number N of measuring point pairs exhibiting uneven flow. uneq The total number of measurement points N total , will N uneq Divide by N total The cell growth competition index was obtained.

[0026] The method for determining the pair of measuring points exhibiting non-uniform flow includes:

[0027] Calculate the time difference of arrival of the flow front between adjacent miniature pressure sensors based on the arrival time of the flow front;

[0028] If the arrival time difference of the flow front between adjacent micro pressure sensors is greater than a preset time threshold, then the adjacent micro pressure sensors are determined to be a pair of measuring points with uneven flow.

[0029] The method for implementing overall compensation and zoned enhancement compensation during the injection process includes:

[0030] When the cell growth competition index is greater than the preset competition threshold, the injection process is compensated as a whole; when the cell growth competition index of a region is greater than the preset competition index threshold, the injection process is compensated for by enhancement of that region.

[0031] A micro-foaming injection molding apparatus for a remote control housing, used to implement the aforementioned micro-foaming injection molding method for a remote control housing, the apparatus comprising:

[0032] Melt homogenization control module: It is used to collect vibration sensing data generated by waste particles hitting the inner wall of the hopper, identify virtual waste clumps based on the vibration sensing data, quantify the degradation degree information of virtual waste clumps after the waste particles enter the barrel from the hopper, and perform asymmetric modulation of the screw speed in the barrel based on the degradation degree information to obtain a homogeneous and stable melt.

[0033] Competition Index Calculation Module: This module is used to deploy miniature pressure sensors around each button hole in the remote control housing mold to form a ring monitoring network. It records the time when the melt flow front reaches each miniature pressure sensor, which is recorded as the flow front arrival time. Based on the flow front arrival time, it calculates the cell growth competition index and the zoned cell growth competition index.

[0034] Injection control module: Based on the cell growth competition index and the zoned cell growth competition index, it implements overall compensation and zoned enhancement compensation for the injection process;

[0035] Pressure holding control module: Implements differentiated pressure holding control to stabilize the bubble structure during the pressure holding stage.

[0036] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0037] This invention solves the problem of melt homogenization caused by mixing different batches of waste by integrating vibration sensing data acquisition, virtual waste agglomeration identification, melt degradation degree quantification, and screw speed asymmetric modulation. It effectively suppresses the melt strength gradient change caused by uneven molecular chain length distribution. Combined with the annular pressure sensor network in the mold and flow front time monitoring, it realizes real-time quantification of the bubble growth competition index. Then, through injection compensation and differentiated holding pressure strategy, it dynamically balances the matching relationship between melt flow rate and bubble growth rate. Finally, it controls the spatial consistency of bubble nucleation and stable growth at the microscale, which significantly improves the structural stability and surface quality of the button hole area of ​​the remote control shell and fundamentally eliminates the bubble collapse defect caused by thermal history differences during waste recycling. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 A flowchart illustrating the micro-foaming injection molding method for a remote control housing provided in this embodiment of the invention;

[0040] Figure 2 This is a schematic diagram illustrating the principle of acquiring virtual waste clump images through an optical observation window inside the feed cylinder, as provided in an embodiment of the present invention.

[0041] Figure 3 A flowchart of a method for determining a pair of measuring points exhibiting non-uniform flow, provided in an embodiment of the present invention;

[0042] Figure 4 A functional block diagram of a micro-foaming injection molding device for a remote control housing provided in an embodiment of the present invention. Detailed Implementation

[0043] 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.

[0044] Example 1

[0045] Please see Figure 1 As shown, this embodiment provides a micro-foaming injection molding method for a remote control housing, including:

[0046] Step S10: Collect vibration sensing data generated by waste particles impacting the inner wall of the hopper. Identify virtual waste clumps based on the vibration sensing data. After the waste particles enter the barrel from the hopper, quantify the degradation degree information of the virtual waste clumps. Perform asymmetric modulation on the screw speed in the barrel based on the degradation degree information to obtain a homogeneous and stable melt.

[0047] Step S10 aims to solve the problem of uneven melt caused by the formation of "thermal history islands" by mixing waste particles in the hopper, quantitatively identifying the degree of degradation in the barrel, and adjusting the screw speed accordingly, thereby laying the foundation for uniform growth of cells in the subsequent injection molding process.

[0048] Further, step S10 includes:

[0049] Step S11: Set m vibration sensing points on the inner wall of the hopper, collect the vibration frequency generated by the impact of waste particles on the inner wall of the hopper at each vibration sensing point, use the vibration frequency as vibration sensing data, identify virtual waste clumps based on the vibration frequency collected at each vibration sensing point, mark the center point of the virtual waste clumps, and track the center point of the virtual waste clumps to form a predicted settling trajectory; the waste particles carry thermally sensitive micro powder for thermal history feature marking, and the thermally sensitive micro powder carried by the waste particles is a corresponding level of thermally sensitive micro powder sprayed according to the different reuse times of the waste particles.

[0050] The method for identifying virtual waste clumps includes: calculating the vibration frequency change gradient between adjacent vibration sensing points based on the vibration frequencies collected at adjacent vibration sensing points, and defining the instantaneous waste aggregation area formed when the vibration frequency change gradient is greater than a preset gradient threshold as a virtual waste clump.

[0051] Step S11 collects vibration data by deploying vibration sensing points on the inner wall of the hopper. Combined with the thermal history marking function of the thermally sensitive micropowder, it identifies virtual waste clumps and tracks their settling trajectory, solving the technical problem of spatially locating "thermal history islands" in traditional waste mixing processes. The number of vibration sensing points, m, is set to an even number between 12 and 24. This range is determined based on the inner diameter and height of the hopper: the larger the inner diameter and the higher the height, the more sensing points need to be deployed to ensure comprehensive monitoring coverage and avoid missed detections in localized clusters due to sparse sensing points. Choosing an even number ensures symmetrical distribution in the circumferential and vertical directions, making the physical distance between adjacent sensing points uniform and ensuring the accuracy of vibration frequency gradient calculation. Each vibration sensing point P... iAll sensors integrate high-frequency vibration sensors. The sensor's response frequency must match the frequency range of the waste particle impact to avoid vibration data distortion due to frequency mismatch. The installation position of the sensing point must avoid welds, protrusions, and other structures on the inner wall of the hopper to prevent these structures from interfering with the particle impact trajectory and ensuring the vibration sensing point P... i The collected vibration frequency f i It can accurately reflect the impact intensity and density of particles, where i is the index variable of the vibration sensing point.

[0052] The thermal powder carried by the waste particles is pre-coated, and the coating process must be linked to the number of times the waste is reused: each batch of waste is coated with a corresponding level of thermal powder based on its number of reuses, k, where k is a positive integer. For the first reuse, k=1; for the second reuse, k=2; and so on. Different levels of thermal powder have different particle sizes and infrared reflection wavelengths. For example, waste with k=1 is coated with thermal powder with a particle size of 5μm and a reflection wavelength of 800nm, while waste with k=2 is coated with thermal powder with a particle size of 8μm and a reflection wavelength of 900nm. This differentiated design ensures that the subsequent multispectral camera can distinguish waste with different reuse counts through its reflective properties.

[0053] The identification of virtual waste clumps is based on the vibration frequency change gradient between adjacent vibration sensing points. The calculation formula is as follows: , where f i with f i+1 These are two adjacent vibration sensing points P. i With P i+1 The collected vibration frequency, L is P i With P i+1 The physical distance between them. The design logic of this formula is: when waste particles are uniformly distributed in the hopper, the impact intensity received by adjacent sensing points is similar, f i with f i+1 The difference is small. At a low level; when particles aggregate in a local area, the impact frequency of the sensing points in the aggregated area increases significantly, creating a large frequency difference with the sensing points in adjacent non-aggregated areas, leading to... Increase the gradient threshold. The setting method is as follows: Waste particles of known uniformity are loaded into the hopper, and vibration frequency variation gradient data under different uniformities are collected. The minimum vibration frequency variation gradient value that can accurately distinguish between "uniform distribution" and "local aggregation" is taken as... ;like Greater than If the condition is met, it is determined that there is a momentary accumulation of waste in the area, that is, the formation of a virtual waste clumping.

[0054] Virtual waste clumping center point C jThe marking adopts a three-dimensional coordinate interpolation method, where j is the index variable of the virtual waste agglomerate: a three-dimensional rectangular coordinate system is established with the center of the hopper outlet as the origin O, the vertical upward direction as the positive Z-axis, and the horizontal circumferential direction as the XY plane. Each vibration sensing point P i coordinates (x) i ,y i ,z i Pre-calibrated using a laser rangefinder. When detected... Greater than At that time, select the adjacent sensing point P that forms the gradient. i P i+1 And one or two adjacent sensing points. Based on the coordinates of these sensing points and their corresponding vibration frequencies, the geometric center of the clustering area is calculated using the inverse distance weighted interpolation method, and the coordinates of this geometric center are denoted as C. j coordinates (x') j ,y' j ,z' j Trajectory tracking is achieved by continuously collecting C data. j The coordinates are connected to form the predicted settlement trajectory T. j .

[0055] Step S11, through the synergy of vibration sensing and thermal powder, achieves spatial positioning of the "thermal history islands" in waste materials, solving the problem in traditional processes where only melt inhomogeneity is known but the source of inhomogeneity cannot be located. Predicting the settling trajectory provides spatial guidance for subsequent degradation detection within the feed hopper, enabling the multispectral camera to pre-align with the movement path of the clumps and avoid detection delays. Real-time acquisition of vibration sensing data provides a basis for optimizing the hopper feed rate. When the system detects the simultaneous formation of multiple virtual waste clumps, it can determine that the current feed rate is too fast and needs to be reduced to decrease aggregation, further improving the uniformity of waste material mixing. Without step S11, the subsequent step S12 would be unable to determine the waste area to be detected, causing the multispectral camera to only perform random detection and failing to accurately capture the "thermal history islands." Consequently, the screw speed modulation in step S13 would lose its target and fail to achieve targeted homogenization.

[0056] Step S12: An optical observation window of a multispectral camera is installed inside the material cylinder. After the waste particles enter the material cylinder from the hopper, the degradation degree value of the virtual waste agglomerate is quantified by the multispectral camera. The degradation degree value is associated with the predicted sedimentation trajectory to obtain a marked waste agglomerate carrying spatial location and degradation degree information. When the degradation degree value is greater than the degradation threshold Dth, a high degradation waste label is added to the marked waste agglomerate.

[0057] Step S12 uses a multispectral camera inside the barrel to identify the reflective properties of the thermosensitive micropowder in the virtual waste clumps, quantifies the degree of degradation, and correlates it with the settling trajectory. This solves the problem of quantifying the degree of degradation in "thermal history islands," providing data support for the precise control in step S13. The multispectral camera is positioned at the feeding section and compression section of the barrel. The feeding section is where the waste particles first enter the barrel, before they are completely melted, and the thermosensitive micropowder still retains its complete reflective properties, making it easy to identify the number of times it can be reused. The compression section is the key area for particle melting and mixing. Monitoring at this point can capture changes in the degradation state of the clumps during the melting process, preventing the thermosensitive micropowder properties from disappearing due to over-melting after entering the homogenization section.

[0058] like Figure 2 As shown, different colored dots within the virtual waste agglomerate represent waste particles with different reuse counts. The method for quantifying the degradation degree of the virtual waste agglomerate includes: acquiring multi-band near-infrared spectral images of the virtual waste agglomerate through the optical observation window inside the barrel using a multispectral camera, denoted as virtual waste agglomerate images; processing the virtual waste agglomerate images to obtain the weighted average reuse count of the virtual waste agglomerate; and quantifying the weighted average reuse count of the virtual waste agglomerate as the degradation degree value of the virtual waste agglomerate.

[0059] The method for processing virtual waste clump images to obtain the weighted average reuse count of virtual waste clumps includes: performing grayscale correction on the virtual waste clump image to obtain a grayscale-corrected virtual waste clump image; extracting the reflection wavelength of each pixel in the grayscale-corrected virtual waste clump image, and determining the reuse count at the corresponding pixel of the virtual waste clump based on the reflection wavelength; statistically analyzing the pixel proportion of different reuse counts within the virtual waste clump image region based on the reuse count at the corresponding pixel of the virtual waste clump, using the pixel proportion of different reuse counts within the virtual waste clump image region as the waste quality proportion of different reuse counts within the virtual waste clump, and calculating the weighted average reuse count of the virtual waste clump based on the waste quality proportion of different reuse counts within the virtual waste clump.

[0060] The multispectral camera's operating band needs to cover the reflection wavelengths of different levels of thermally sensitive micropowders, such as the near-infrared band from 700nm to 1100nm, with a bandwidth of 20nm for each band, to ensure accurate differentiation of reflection wavelength differences corresponding to different reuse cycles. The camera's image acquisition frequency needs to match the movement speed of the virtual waste clumps, typically set at 20 frames per second. When the clumps move faster, the acquisition frequency needs to be increased to 30 frames per second to avoid blurry images due to insufficient frame rate. The processing of virtual waste agglomerate images consists of three steps: The first step is image preprocessing, which eliminates the interference of reflection from the optical observation window through grayscale correction and removes image noise using Gaussian filtering; the second step is reflective characteristic extraction, which extracts the reflection wavelength of each pixel in the image using spectral analysis software and determines the corresponding reuse count k based on the reflection wavelength. For example, a reflection wavelength of 800nm ​​corresponds to k=1, and 900nm corresponds to k=2; the third step is agglomerate reuse count statistics, which counts the percentage of pixels with different reuse counts k in the virtual waste agglomerate image area. This percentage is the mass percentage of the waste with the corresponding reuse count in the virtual waste agglomerate (because the amount of thermal powder spraying is proportional to the waste mass).

[0061] The quantification of degradation level is based on the weighted average number of reuses (navg), calculated using the following formula: Where K is the total number of reuse categories in the virtual waste cluster, and m k This represents the percentage of waste material in a virtual waste agglomerate that has been reused for the kth time. The design logic of this formula is that waste material reused more times has a higher degree of degradation and a greater impact on melt strength. By using mass weighting, the overall degradation level of the agglomerate can be accurately reflected. For example, if the mass percentage of waste material with k=1 in a virtual waste agglomerate is 60% and the mass percentage of waste material with k=2 is 40%, then navg = (1×0.6 + 2×0.4) / (0.6 + 0.4) = 1.4, indicating that the overall degradation level of the agglomerate is close to 1.5 times reused.

[0062] Based on the weighted average number of reuses of virtual waste clumps, the degradation degree of virtual waste clumps is quantified: The formula for calculating the degradation degree value D is: D=k1×exp(navg / n0), where k1 is a degradation constant, the value of which is determined according to the material type of the waste. For example, the k1 value of polypropylene (PP) waste is 0.8, and the k1 value of acrylonitrile-butadiene-styrene copolymer (ABS) waste is 1.2. The k1 value is obtained through material aging experiments, that is, by measuring the melt flow rate (MFR) of the material under different reuse times, establishing the relationship between MFR and navg, and then inversely deducing the k1 value. is an exponential function with base e of the natural logarithm; n0 is a normalization constant. The core function of n0 is to indirectly control the output range of D by adjusting the input range of the exponential function, so as to avoid the problem of "insufficient discrimination" or "difficulty in setting the threshold" caused by D being too large or too small. For example, n0 is set to 5.

[0063] The correlation between the degradation degree value and the predicted sedimentation trajectory is achieved through coordinate matching: the center point C of the virtual waste agglomeration in step S11 is... j Predicted subsidence trajectory T j The (3D coordinate sequence) is mapped to the axial coordinates of the barrel to determine the time when the agglomerate reaches the observation window of each multispectral camera; when the agglomerate reaches the optical observation window, the degradation degree value of the j-th virtual waste agglomerate calculated at this time is compared with the virtual waste agglomerate center point C. j Coordinate binding, generating a dataset containing "spatial location (x...)" j ,y j ,z j The data for marked waste agglomerates are defined as follows: "Degradation degree D - Arrival time t'". The determination of highly degradable waste is based on the degradation threshold Dth. The method for setting Dth is as follows: the strength of the melt under different degradation degrees is determined by melt tensile test, and the critical degradation degree value when the melt strength drops to the point where it can no longer support the stable growth of cells is taken as Dth. For example, the Dth of PP waste can be set to 3.0. If the degradation degree value of a marked waste agglomerate is greater than 3.0, the marked waste agglomerate is determined to be a highly degradable waste agglomerate and needs to be controlled in subsequent steps.

[0064] Step S12 transforms the thermal history (number of reuses) of waste into a quantifiable degradation value for the first time, solving the problem that traditional processes can only qualitatively determine waste degradation but cannot quantify it. The generation of marked waste clumps achieves a dual binding of "spatial location" and "degradation degree," enabling subsequent control to target high-degradation clumps at specific locations rather than the entire melt, significantly improving control accuracy. The reflective properties monitoring of the thermal powder can also reflect the melting state of the waste. When the reflective intensity of a certain area suddenly decreases, it can be determined that the waste in that area has completely melted, providing data support for optimizing the barrel temperature. The predicted sedimentation trajectory in step S11 provides temporal and spatial prediction for the multispectral detection in step S12, enabling the camera to accurately capture clumps and avoid missed detections. The quantification of the degradation degree in step S12 transforms the "thermal history island" located in step S11 into specific parameters that can be used for regulation, providing direct input for the screw speed modulation in step S13. Without step S12, step S13 would not be able to determine the area to be regulated and the regulation range, and could only use a fixed speed, making it impossible to achieve differentiated homogenization.

[0065] Step S13: When the marked waste agglomerates carrying the high-degradability waste markers enter the compression section of the barrel, a three-stage slow-fast-slow screw speed asymmetric modulation is performed on the area where the marked waste agglomerates carrying the high-degradability waste markers are located to obtain a homogeneous and stable melt.

[0066] Step S13 involves performing three-stage asymmetric screw speed modulation on the labeled waste agglomerates carrying highly degradable markers in the barrel compression section. This solves the problem of highly degradable waste agglomerates being difficult to homogenize and prone to accelerated degradation due to excessive shearing, ultimately obtaining a homogeneous and stable melt. The regional positioning of the screw speed modulation is based on the spatial location of the labeled waste agglomerates: according to the arrival time t' of the labeled waste agglomerates and the axial length of the barrel compression section, the starting position of the agglomerates entering the compression section and the ending position of the agglomerates leaving the compression section are determined, and this interval is the target area for speed modulation.

[0067] The three-stage asymmetric screw speed modulation (slow-fast-slow) includes a first slow stage, a fast stage, and a second slow stage. The first slow stage ensures gradual melting of the waste while preventing excessive chain breakage. The speed range of the first slow stage is determined based on the melting characteristics of highly degradable waste: the molecular chains of highly degradable waste are relatively short, and if a high speed is used, the intense shearing action will further break the molecular chains, leading to a further decrease in melt strength. For example, the speed of the first slow stage is 60% to 70% of the base speed v0. The fast stage ensures that the clumps have sufficient time to be broken up and mixed with the surrounding melt. The speed range of the fast stage must meet two conditions: first, it must generate sufficient shear force to break up the highly degradable clumps and break the "thermal history islands"; second, it must not cause excessive increase in melt temperature to avoid premature decomposition of the foaming agent. For example, the speed of the fast stage is 120% to 140% of v0. The second slow section is used to ensure that the flow state of the melt is stable before it leaves the compression section and enters the homogenization section. The rotation speed of the second slow section is lower than v0, which aims to reduce the flow speed of the melt, so that the mixed melt can return to a stable flow state and reduce the impact of speed fluctuations on the subsequent dispersion of the foaming agent. For example, the rotation speed of the second slow section is 80% to 90% of v0.

[0068] When multiple marked waste clumps are detected simultaneously within the compression section, the weighted average degradation degree Davg of the melt needs to be calculated. The calculation formula is as follows: , where D j V represents the degradation level of the j-th labeled waste agglomerate, M is the total number of labeled waste agglomerates, and V is the degradation level of the j-th labeled waste agglomerate. j V represents the volume of the j-th labeled waste agglomerate. j The equivalent diameter of the virtual waste agglomerate is estimated based on the coordinates of the virtual waste agglomerate in step S11, according to the number n1 of consecutive adjacent sensing points with vibration frequency change gradients exceeding the threshold associated with the same virtual waste agglomerate and their spatial distribution. Where S is the monitoring area of ​​a single sensing point, S = total area of ​​the vibration sensing area on the inner wall of the hopper / m, k3 is the shape coefficient, adapted according to the actual shape of the agglomerate: 0.8 for spherical agglomerates (particles tightly aggregated, no obvious stretching), 1.0 for ellipsoidal agglomerates (slightly stretched along the hopper axis), and 1.2 for irregular blocky agglomerates (particles loosely aggregated, irregular shape). The shape is determined by the spatial distribution density of the virtual waste agglomerate center point in step S11. If the axial coordinate span is greater than the radial coordinate span, it is determined to be ellipsoidal; the agglomerate volume V j =π×de 3 / 6. This calculation method is based on the positive correlation between the vibration influence range and the agglomerate size; the more vibration sensing points, the larger the agglomerate. The design logic of the Davg calculation formula is: the larger the agglomerate, the greater its impact on the overall degradation level of the melt. Volume weighting can accurately reflect the average degradation state of the melt within a region; for example, in a certain compression section region, there are two marked waste agglomerates, D1=4.0 and V1=5cm. 3 D2=2.0, V2=10cm 3 Therefore, Davg = (4.0 × 5 + 2.0 × 10) / (5 + 10) = 2.67. The rotation speed is fine-tuned based on the weighted average degradation degree Davg: when Davg is greater than Dth, the upper limit of the rotation speed in the rapid phase is increased by 5% to 10% to enhance the mixing effect; when Davg is less than 0.5 × Dth, the lower limit of the rotation speed in the rapid phase is decreased by 5% to 10% to avoid excessive shearing that could break the molecular chains of the normal melt; when Davg is between 0.5 × Dth and Dth, the original rotation speed parameters remain unchanged.

[0069] Step S13's three-stage asymmetric modulation achieves a synergistic effect of "protection-disruption-stabilization": the first slow stage protects the molecular chains of highly degradable waste, preventing accelerated degradation; the fast stage breaks down "thermal history islands," achieving homogenization; the second slow stage stabilizes melt flow, laying the foundation for foaming agent dispersion, thus resolving the contradiction that traditional single-speed adjustments cannot simultaneously address "anti-degradation" and "mixing promotion." The Davg-based fine-tuning mechanism allows speed modulation to adapt to melts with different degradation levels, avoiding insufficient mixing or excessive shearing in certain areas due to single-parameter control, thereby improving the adaptability of melt homogenization. Speed ​​modulation targets highly degradable clumps in specific areas, significantly reducing energy consumption compared to traditional whole-tube speed adjustment. The marked waste clumps in step S12 provide the control targets (location, degree of degradation) for step S13. The rotation speed modulation in step S13 directly solves the high degradation problem identified in step S12. The combination of the two realizes the closed loop of "identification-control". Without step S13, the high degradation clumps identified in step S12 cannot be homogenized. These clumps will still cause uncontrolled cell growth after entering the mold, making the previous monitoring meaningless and unable to solve the final cell collapse problem.

[0070] Step S10, through the synergistic effect of S11, S12, and S13, fundamentally solves the problem of "uneven melt caused by mixing waste materials with different thermal histories" in the micro-foaming injection molding of remote control shells. In traditional processes, the differences in thermal histories of waste materials can only be judged by experience, making it impossible to locate specific areas and quantify the degree of degradation, resulting in an inability to accurately solve the problem of uneven melt. Step S11 achieves spatial positioning of "thermal history islands" through vibration sensing and thermally sensitive micropowder, while step S12 quantifies the degree of degradation through multispectral recognition and formula calculation. The combination of these two steps transforms the abstract "thermal history difference" into concrete "spatial coordinates-degradation value" data for the first time, making the source of uneven melt from "invisible" to "monitorable and quantifiable," providing a precise basis for subsequent control. Traditional screw speed control uses fixed parameters, which either fails to mix highly degradable waste materials due to insufficient shear or exacerbates degradation due to excessive shear. Step S13's three-stage asymmetric modulation targets the characteristics of highly degraded agglomerates, employing differentiated rotation speeds at different stages. This achieves both the dispersal and mixing of "thermal history islands" and avoids excessive molecular chain breakage, thus protecting melt strength. Simultaneously, the Davg-based fine-tuning mechanism further enhances the adaptability of the control, ensuring that melts with different degrees of degradation reach a homogeneous state. The collapse of bubbles around the button holes in the remote control housing is essentially due to uncontrolled bubble growth rate caused by melt heterogeneity and insufficient melt support. Step S10, through melt homogenization, eliminates the root cause of uncontrolled bubble growth. The spatial gradient change in melt strength allows bubbles to grow in a uniform melt environment during subsequent injection molding, avoiding asymmetric collapse caused by insufficient local support. Furthermore, the labeled waste agglomerate data (degradation degree, location) generated in step S10 provides feedforward information for bubble growth monitoring and pressure holding control in subsequent step S20, achieving coordinated control throughout the entire process.

[0071] Step S20: Miniature pressure sensors are deployed around each button hole of the remote control housing mold to form a ring monitoring network; the cell growth competition index and the zoned cell growth competition index are calculated; based on the cell growth competition index and the zoned cell growth competition index, overall compensation and zoned enhancement compensation are implemented for the injection process, and differentiated holding pressure control for cell stabilization is implemented for the holding pressure stage.

[0072] Further, step S20 includes:

[0073] Step S21: Miniature pressure sensors are arranged around each button hole of the remote control housing mold to form a ring monitoring network; the time when the melt flow front reaches each miniature pressure sensor is recorded as the flow front arrival time; the measurement point pairs with uneven flow are determined based on the flow front arrival time.

[0074] See Figure 3 Furthermore, step S21 includes:

[0075] Step S211: Miniature pressure sensors are arranged around each button hole of the remote control housing mold to form a ring monitoring network;

[0076] Step S212: Record the time when the melt flow front arrives at each micro pressure sensor, and record it as the flow front arrival time. Calculate the flow front arrival time difference between adjacent micro pressure sensors.

[0077] Step S213: If the arrival time difference of the flow front between adjacent micro pressure sensors is greater than a preset time threshold, then the adjacent micro pressure sensors are determined to be a pair of measuring points with uneven flow.

[0078] Specifically, step S21 involves forming a ring-shaped monitoring network by arranging miniature pressure sensors around the button holes of the remote control housing mold. This network records the arrival time of the melt flow front and identifies non-uniform flow measurement points. The core solution addresses the technical challenge of "the inability to locate residual melt non-uniformity in real-time during the cavity filling stage after homogenization in step S10," providing spatial and real-time foundational data for subsequent bubble growth risk quantification and precise compensation. The melt flow front refers to the dynamic interface at the forefront of the molten plastic's diffusion within the cavity during the remote control housing mold cavity filling process. This interface marks the boundary between the "filled melt area" and the "unfilled cavity area" within the cavity. Its propagation speed and uniformity directly determine the spatial distribution of bubble growth in the early stages—differences in propagation speed lead to different growth times and melt support forces for bubbles in different areas, creating potential risks for subsequent bubble growth competition and collapse.

[0079] The number of miniature pressure sensors, p, is set based on the nominal diameter D1 of the keyhole. For example, when D1 ≤ 5mm (small keyhole), p = 6 is sufficient for coverage; when D1 > 5mm (large keyhole) or higher monitoring accuracy is required, p = 8 to avoid missing local flow conditions due to excessively large sensor spacing. The ring monitoring network is laid out in a polar coordinate system (r, θ) with the center of the keyhole as the pole (r=0, θ=0), where r is the polar radius and θ is the polar angle. Compared to traditional linear or single-point sensor arrangements, the ring distribution achieves 360-degree monitoring around the keyhole without blind spots, avoiding missed detections due to uneven flow caused by local monitoring blind areas; the polar coordinate system layout fixes the relative relationship between the sensor position and the keyhole, and the data has spatial correlation, facilitating subsequent location of areas with uneven flow.

[0080] Step S212 records the arrival time of the melt flow front by identifying pressure signal abrupt changes and calculates the time difference between adjacent micro-pressure sensors, converting flow uniformity into a quantifiable time parameter. The determination of the melt flow front arrival time t is based on pressure signal characteristics: in the initial stage of cavity filling, the pressure detected by the sensor is a baseline value close to atmospheric pressure; when the melt flow front reaches the sensor detection area, the melt generates an instantaneous pressure impact on the sensor probe, and the pressure signal jumps from the baseline value in a short time; the starting time of this pressure jump is t. Pressure signals from each micro-pressure sensor are acquired in real time using a data acquisition card, and t is automatically identified using the first derivative threshold method. This involves calculating the first derivative of the pressure signal reflecting the rate of pressure change; when the first derivative is greater than a preset threshold, this moment is recorded as the melt flow front arrival time t. The threshold is calibrated through a new material filling experiment to ensure that it only responds to effective jumps in melt arrival, for example, 50 MPa / s. The arrival time difference Δt between adjacent micro-pressure sensors is also considered. i*j* The calculation formula is Δt i*j* =|t i* -t j* | where i* and j* are the indices of adjacent miniature pressure sensors, numbered clockwise according to angle θ, j*=i*+1, when i*=p j*=1, ensuring the closed relationship of adjacent sensors under the ring structure, t i* With t j* Both refer to the time it takes for the melt flow front to reach the corresponding micro pressure sensor. The design logic of this formula is: in uniform flow, the distance between adjacent micro pressure sensors and the melt flow front is similar, t i* With t j* Small difference, Δt i*j* At a low level; when the flow is uneven, the melt propagates faster on one side, t i* With t j* The difference increases, Δt i*j* As it rises, through Δt i*j* It can directly reflect local flow velocity differences. The melt flow front arrival time identification method based on pressure abrupt changes more directly reflects the melt front position compared to other methods such as displacement sensors, because pressure is generated immediately upon melt arrival, eliminating the need for indirect calculations and resulting in higher detection accuracy; Δt i*j* The calculation transforms flow uniformity into an intuitive time difference value, providing a comparable quantitative indicator for subsequent judgment of flow non-uniformity, and solving the problem that "flow non-uniformity can only be subjectively observed" in traditional processes.

[0081] Step S213 involves setting a time threshold t. th This identifies measurement point pairs exhibiting uneven flow, enabling spatial localization of areas with uneven flow. thThe settings need to be based on the flow characteristics of the melt under homogeneous conditions: Under the condition of using only virgin material (no waste material reuse, optimal melt homogeneity) and adopting the target production parameters (injection speed, barrel temperature, mold temperature), more than 30 repeated filling experiments are conducted, and the arrival time difference of the flow front of all adjacent micro pressure sensor pairs in each experiment is recorded. The maximum value of these flow front arrival time differences Δt is calculated. max , Δt max The maximum time difference under uniform flow is reflected, and then a safety margin Δts is added to compensate for minor fluctuations in parameters such as injection speed and temperature during production. Δt is typically taken as... max 20%-30%, that is, t th =Δt max +Δts. The time difference Δt between the arrival times of the flow fronts of adjacent miniature pressure sensor pairs. i*j* >t th When a sensor pair is identified as a measuring point pair exhibiting uneven flow, it means that the difference in melt propagation speed in that area exceeds the normal fluctuation range of uniform flow, indicating abnormalities in melt flow resistance or the melt's own uniformity, requiring close monitoring. By locating the measuring point pair, uneven flow is broken down from a "global phenomenon" to a "local area," providing a clear target for subsequent zoned control.

[0082] Step S21, through the technical link of ring monitoring network deployment, arrival time recording, and non-uniformity measurement point judgment, fundamentally solves the problem of the inability to monitor the non-uniform flow in the cavity filling stage of traditional micro-foaming injection molding in real time and spatially. Traditional processes can only infer the non-uniform flow through the concave defects around the button holes after the product is formed, which is a "post-event remedy" and cannot intervene in the cell growth process. Step S21 collects the arrival time t of the melt flow front and the arrival time difference Δt between the flow front and the adjacent micro pressure sensor in real time. i*j* This allows for the identification of uneven flow during the filling process, providing a time window for subsequent real-time compensation and preventing defects. The combination of a ring-shaped monitoring network and measuring point pair determination can accurately pinpoint which adjacent sets of micro pressure sensors exhibit uneven flow. For example, when p=6, if the Δt of sensors 1 and 2... i*j* >t th This method can directly determine the uneven flow in the θ=0°-60° region around the button hole, solving the problem of traditional processes that "only know the unevenness but not the location," and providing a spatial basis for zonal compensation. If step S21 is missing, the subsequent step S22 will not be able to obtain the number of measurement point pairs with uneven flow, resulting in the loss of calculation basis for the bubble growth competition index and the zonal bubble growth competition index; the compensation control in step S23 will not be able to determine "whether compensation is needed" and "which area to compensate," and can only use traditional fixed parameter control, which cannot solve the problem of bubble collapse caused by the unevenness of residual melt.

[0083] Step S22: Count the number of measuring point pairs with uneven flow and calculate the bubble growth competition index; divide the area around the key hole into multiple fan-shaped zones and calculate the zone bubble growth competition index of each fan-shaped zone.

[0084] Step S22 calculates the bubble growth competition index CI and the zoned bubble growth competition index CIq to quantify the bubble growth competition effect, identifying high-risk areas. This core solution addresses the technical challenge of "the inability to quantify uneven flow as a basis for control," transforming physical phenomena into quantitative indicators that can guide compensation operations, providing decision support for subsequent differentiated control. The bubble growth competition effect refers to the difference in bubble growth rates in different regions of the mold cavity due to uneven melt flow—faster flow regions experience faster melt renewal, allowing bubbles to obtain more gas and growth space, resulting in faster growth rates; slower flow regions have longer melt residence times, leading to easier gas escape and slower bubble growth rates. Fast-growing bubbles compete for gas and space from surrounding melts, inhibiting slower-growing bubbles, leading to significant differences in bubble size. When this difference exceeds the melt's support capacity, slow-growing bubbles collapse asymmetrically due to force imbalance.

[0085] The calculation method for the cell growth competition index (CI) includes: counting the number N of measuring points exhibiting non-uniform flow. uneq (i.e., the number of measurement point pairs with uneven flow) and the total number of measurement point pairs N total N total The calculation is based on the number of sensors p in the ring-shaped monitoring network: Since the sensors are distributed in a ring, the number of adjacent pairs of miniature pressure sensors is equal to the number of sensors. The adjacent relationships in the ring structure are closed, with no repetition or omission, i.e., N total =p, such as when p=6, N total When p=6, N total =8. N uneq The statistical method is as follows: traverse all N total Groups of adjacent miniature pressure sensor pairs, determine their Δt values ​​one by one. i*j* Is it greater than t? th If the conditions are met, it will be included in N. uneq The entire process is automated, avoiding errors from manual statistics. The formula for calculating the cell growth competition index is: CI = N uneq / N total The design logic of this formula is as follows: the CI value directly reflects the degree of overall flow unevenness around the keyhole. When CI=0, all measuring points affect Δt. i*j* ≤t th When the flow is completely uniform, there is no risk of competition for bubble growth. As CI increases, the proportion of uneven flow regions increases, and the risk of competition for bubble growth increases accordingly. CI transforms the number of measurement point pairs with uneven flow into a normalized index in the 0-1 range, eliminating statistical differences caused by different numbers of sensors p, and making risk assessment more universal.

[0086] The sector division must ensure that each sector contains a sufficient number of measurement point pairs to guarantee the statistical significance of the CIq calculation. Using the center of the keyhole as the pole, the coverage area of ​​the ring monitoring network is uniformly divided into q sector sectors at an angle θ. The setting of q is based on N. total Each partition must contain at least two adjacent miniature pressure sensors. If there is only one measurement point pair, CIq is only 0 or 1, which lacks statistical representativeness. For example, p=6 (N total When p=6, q=3, each sector contains 2 pairs of measurement points; p=8 (N total When q=4 (=8), each sector contains 2 pairs of measuring points. The calculation of the zoned bubble growth competition index CIq: For the k*th sector, count the number N pairs of measuring points for non-uniform flow within that sector. uneq,k* The total number of measurement points N in this zone total,k* N total,k* =N total / q, the formula for calculating CIq is CIq=N uneq,k* / N total,k* The sector-shaped partitioning refines risk assessment from the "overall level" to the "local partitioning," solving the problem that traditional overall control cannot target local high-risk areas; CIq calculation makes the risk of each partition comparable, and can accurately identify even if the risk of different partitions around the same keyhole is significantly different.

[0087] Step S23, as shown in Table 1, when the cell growth competition index is greater than the preset competition threshold, overall compensation is performed on the injection process; when the zoned cell growth competition index is greater than the preset competition index threshold, zoned enhancement compensation is performed on the injection process; the competition index threshold is greater than the competition threshold.

[0088] Table 1 Comparison of Injection Process Compensation Methods

[0089] Compensation type Triggering conditions Regulation period Overall compensation <![CDATA[CI>THERE th ]]> Injection mid-segment Zone Enhancement Compensation <![CDATA[CIq>CIq th ]]> The time period during which the melt fills the target sector.

[0090] Step S23, based on the bubble growth competition index CI and the regional bubble growth competition index CIq quantified in step S22, solves the technical challenge of "inability to precisely intervene in bubble growth competition induced by uneven melt flow during the injection stage" through overall compensation and regional enhanced compensation synergy. The core is to transform the risk index into a controllable injection speed regulation parameter, breaking the uncontrolled mode of local bubble growth without disrupting the overall melt flow, thus laying the foundation for bubble stability in the subsequent holding pressure stage. During injection, the melt flow speed directly determines the "time window" and "melt supply" for bubble growth. Too high a speed will cause local bubbles to receive too much gas and expand rapidly, while too low a speed will cause gas to escape from the bubbles. Traditional fixed injection speeds cannot adapt to the risk differences in different areas. Step S23, through multi-dimensional compensation, achieves "overall stability + local precision" speed regulation.

[0091] Competition threshold CI th The setting method is as follows: produce samples at different CI values, count the defect rate of depressions around the keyhole, and take the CI value corresponding to the defect rate rising to the preset acceptable upper limit (e.g., ≤1%) as CI. th For example, when CI ≤ 0.2, the defect rate is ≤ 1%, then CI th =0.2; if CI>CI th This indicates a significant risk of unstable bubble growth around the button hole, necessitating overall compensation. Implementing overall compensation requires determining the control period and parameters. The injection process is divided into the initial injection stage (melt filling the cavity inlet section), the middle injection stage (melt filling the main body of the cavity and the area surrounding the button hole), and the final injection stage (melt filling the cavity end). Overall compensation only applies to the middle injection stage, as this is the critical period for initial bubble growth as the melt just contacts the button hole area. Controlling the initial and final stages can easily lead to melt accumulation at the inlet or material shortage at the end. Overall compensation adjusts the baseline injection speed v during the middle injection stage. z Superimposed with a sinusoidal oscillation, the formula is v1(t'')=v z +A×sin(ωt''). Where v1(t'') is the injection velocity after overall compensation, t'' is the time variable, A is the fluctuation amplitude, and ω is the fluctuation frequency. z Based on the pre-set product thickness and material flowability, such as polypropylene material for remote control housings, v zThe speed is typically set to 50 mm / s to 100 mm / s. The fluctuation amplitude A needs to be positively correlated with the cell growth competition index CI. The method for determining it is as follows: establish the correlation curve between CI and A through experiments, test the compensation effect of different fluctuation amplitudes at different CI values, record the minimum fluctuation amplitude value that can make the cell uniformity meet the standard (cell size difference ≤10%), and use the minimum fluctuation amplitude value as the appropriate amplitude for the corresponding CI. The fluctuation frequency ω needs to be adjusted according to the weighted average degradation degree Davg in step S13. The design logic is: the higher the degradation degree of the melt, the lower the viscosity, and the lower the resonance frequency of cell growth (resonance will cause the cells to vibrate violently and break). Therefore, ω is negatively correlated with Davg. The method for determining ω is as follows: take melts with different weighted average degradation degrees Davg, make them into cylindrical samples, and pre-fabricate a single uniform cell inside to simulate the initial cells of micro-foamed injection molding; fix the sample in a melt tensile tester, apply sinusoidal vibration, and record the cell morphology with a high-speed camera. When the cells exhibit periodic violent expansion and contraction synchronized with the applied vibration frequency and with a diameter change of ≥20%, the corresponding vibration frequency is the cell resonance frequency ω under that Davg. res Set ω to ω res 80% to 90% of the time should be used to avoid resonance.

[0092] Competition Index Threshold CIq th The setting needs to be higher than CI. th Because zone-level control requires higher precision, the impact of zone defects on the product's appearance is more localized, and compensation needs to be more accurate to avoid over-intervention, the setting method is CIq. th =CI th +0.1 (for example, CI) th CIq = 0.2 th =0.3); if CIq>CIq th The sector is identified as a high-risk area, indicating that uneven flow in this region is sufficient to cause uncontrolled local bubble competition, requiring enhanced compensation. Enhanced compensation requires determining the compensation period and pulse parameters. The compensation period is the time it takes for the melt to fill the sector, calculated from the arrival time t of the flow front in step S21. During the corresponding period when the melt fills the sector, a high-frequency, small-amplitude "double pulse" wave is superimposed on the aforementioned sinusoidal wave to further disrupt the competitive growth pattern of existing bubbles within the region. The total injection rate is expressed as v2(t'') = v z +A×sin(ωt'')+Ap×[sin(ω p t'')+ sin(2ω p t'')], where v2(t'') is the injection rate after partition enhancement compensation, and the parameter settings for the "dual pulse" fluctuation must meet the requirement of "high frequency and small amplitude": pulse base frequency ω pThe pulse amplitude Ap is 3 to 5 times that of A to avoid resonance with the overall fluctuation. This avoids excessive disturbance leading to melt stratification. The core reason for this design is that a single pulse has a short disturbance duration, making it difficult to break the established bubble competition pattern. Dual pulses can create continuous micro-perturbations, breaking up fast-growing bubbles while promoting gas and space acquisition in slow-growing bubbles, thus achieving bubble homogenization. The dual pulses utilize the fundamental frequency ω... p and frequency multiplication 2ω p The superposition of these factors creates asymmetric disturbances, which more effectively disrupts the regular competitive growth of bubbles.

[0093] Compared to traditional step velocity adjustment, the sinusoidal fluctuation of overall compensation can continuously and gently disturb the melt, avoiding new flow unevenness caused by sudden velocity changes, and resolving the contradiction of traditional compensation "curing unevenness but causing more unevenness." The dual pulse of zoned enhanced compensation only acts on high-risk areas, significantly reducing energy consumption compared to overall enhanced compensation. If step S23 is missing, the risk index quantified in step S22 cannot be translated into actual control actions, and the competitive growth of bubbles in high-risk areas will continue to intensify. Even if high pressure is applied in the subsequent holding stage, the differences in bubble size that have already formed cannot be reversed, ultimately leading to depressions around the button holes.

[0094] Step S24: Real-time acquisition of pressure signals from each micro pressure sensor during the filling process; based on the zoned cell growth competition index and pressure signals, differentiated pressure holding control for cell stabilization is implemented during the pressure holding stage.

[0095] Step S24 solves the technical problem of "the inability to adapt pressure to different risk areas during the pressure holding stage, resulting in insufficient support in high-risk areas or excessive compression in low-risk areas" by constructing a cell collapse risk level assessment model and differentiated step-by-step pressure holding control. The core is to accurately match the zone risk with the pressure holding parameters, provide dynamic support before the cell cools and solidifies, avoid asymmetric collapse, and prevent cell rupture or internal stress concentration caused by excessive pressure holding.

[0096] Further, step S24 includes:

[0097] Step S241: Based on the cell growth competition index, pressure signal and flow front arrival time difference, establish a comprehensive risk coefficient for each sector, establish a cell collapse risk level assessment model for each sector based on the comprehensive risk coefficient, and determine the collapse risk level of each sector.

[0098] The method for establishing the bubble collapse risk level assessment model includes: establishing a comprehensive risk coefficient Rq for each sector, Rq=α×CIq+β×ΔTq+γ×Pqvar, where CIq is the bubble growth competition index of each sector, directly using the calculation result of step S22. CIq itself is a dimensionless ratio parameter with a value range of [0,1], and has been naturally dimensionless through the "quantity ratio" form, reflecting the overall level of bubble competition within the sector; ΔTq is the arrival of the flow front of the sector. The mean time difference is calculated as follows: Traverse all adjacent pairs of miniature pressure sensors within the sector, collect the arrival time difference ΔT of the flow front for each sensor pair, sum all ΔT values, and divide by the total number of adjacent pairs of miniature pressure sensors within the sector to obtain the original dimensional mean arrival time difference of the flow front. ΔTq is the parameter after dimensionless processing of the original dimensional mean arrival time difference of the flow front. Specifically, the processing method is: divide the original dimensional mean arrival time difference of the flow front by the preset time threshold t in step S213. th The influence of time dimension is eliminated by the "ratio of similar quantities", so that ΔTq is transformed into a dimensionless parameter. ΔTq characterizes the average time difference of melt propagation within the sector. The larger ΔTq is, the more significant the overall flow unevenness of the sector and the greater the difference in the initial formation of bubbles. Pqvar is the pressure fluctuation variance of the sector during the filling process. The calculation of Pqvar is based on the pressure signals of the micro pressure sensors: First, the pressure signal acquisition period of all micro pressure sensors in the sector is determined (from the melt flow front reaching the first sensor in the sector to the melt completely filling the sector). Pressure data of each sensor is collected during this period. The variance of the pressure data of each sensor is calculated. The variance reflects the degree of pressure fluctuation of a single sensor. Then, the pressure variances of all sensors in the sector are summed and divided by the number of sensors to obtain the original dimensional pressure fluctuation variance. Pqvar is the parameter after dimensionless processing of the original dimensional pressure fluctuation variance. The specific processing method is: the original dimensional pressure fluctuation variance is divided by the baseline pressure variance P0var, and the influence of pressure dimension is eliminated by the "ratio of similar quantities". The value of P0var is based on: using only fresh material (the best state of melt uniformity) to conduct more than 30 repeated filling experiments, and statistically analyzing the pressure fluctuation variance of each experiment in the sector, taking the average value as P0var. Pqvar characterizes the pressure stability of melt flow within a zone. The larger the Pqvar, the more severe the pressure fluctuation of the melt within that zone, the more unstable the melt environment for bubble growth, and the higher the risk of collapse.

[0099] α, β, and γ are the weight coefficients of CIq, ΔTq, and Pqvar respectively. The setting of α, β, and γ must strictly follow the "risk impact degree" ranking: The cell growth competition index CIq directly determines the non-uniformity of cell growth and is the core inducement of the collapse risk. Therefore, α is set as the maximum weight; The mean value of the arrival time difference of the flow front ΔTq affects the uniformity of the initial cell formation and is the secondary inducement of the collapse risk. Therefore, β is set as the second largest weight; The pressure fluctuation variance Pqvar reflects the flow stability and has a relatively indirect impact on the collapse risk. Therefore, γ is set as the minimum weight. The three weight coefficients satisfy the normalization condition α + β + γ = 1, and the determination method is as follows: Through orthogonal experiments, test the correlation degree between Rq and the actual collapse defect rate under different weight combinations, and select the weight combination with the highest correlation degree; Exemplarily, α = 0.6, β = 0.3, γ = 0.1.

[0100] Set the high-risk threshold Rh and the low-risk threshold Rg. The setting of the high-risk threshold Rh and the low-risk threshold Rg needs to be based on the extreme state and normal state of the actual working conditions: The setting method of Rh is as follows: First, determine the critical value of each parameter. The critical value of CIq is CIq th , the critical value of ΔTq is the time threshold t th , and the critical value of Pqvar is the maximum value of the pressure fluctuation variance of this partition when only using new materials, which is determined by more than 30 new material filling experiments. Substitute the three critical values into the Rq formula, and the calculated result is Rh; Its physical meaning is: The comprehensive risk when the cell competition, flow non-uniformity, and pressure fluctuation in this partition all reach the critical state. If it exceeds this value, the collapse risk is extremely high. The setting method of Rg is as follows: Under the working condition of only using new materials and the optimal melt uniformity, conduct more than 30 repeated filling experiments, calculate the Rq value of each partition in each experiment, and take the average value of all Rq values as Rg; Its physical meaning is: The basic risk under normal flow and stable pressure. If it is lower than this value, the collapse risk is extremely low.

[0101] The cell collapse risk level assessment model is as follows: When Rq > Rh, it is determined as the high collapse risk area. In the high collapse risk area, the cells are already in a severe competition state, and both the flow and pressure are unstable, and high-intensity pressure holding support is required; When Rg < Rq ≤ Rh, it is determined as the medium collapse risk area. In the medium collapse risk area, there is a certain cell competition and flow non-uniformity, but it does not reach the limit, and medium-pressure holding is required; When Rq ≤ Rg, it is determined as the low collapse risk area. In the low collapse risk area, the flow is uniform, the pressure is stable, and there is no significant competition in cell growth, and only basic pressure holding is required. Compared with the traditional single pressure threshold determination, Rq integrates the three-dimensional information of cell competition, flow, and pressure, and the risk assessment is more comprehensive, avoiding misjudgment due to a single parameter.

[0102] Step S242, for the fan-shaped partitions with different collapse risk levels, perform differential stepped pressure holding curve regulation.

[0103] As shown in Table 2, for high collapse risk areas, a three-stage pressure holding mode of "high-medium-low" is implemented. The first stage maintains a pressure of 1.2 times the baseline pressure Pb to ensure sufficient pressure support for the cells and prevent rapid initial expansion. This stage lasts for 40% of the total pressure holding time, ensuring continuous support for the cells during the critical growth period. The second stage reduces the pressure to 0.9 times the baseline pressure to avoid excessive compression that could cause cell rupture. This stage lasts for 30% of the time, ensuring that the cells have enough time to adapt to pressure changes. The third stage further reduces the pressure to 0.7 times the baseline pressure, allowing the cells to moderately rebound to release internal stress. This stage lasts for 30% of the time, allowing the cells to moderately rebound to a stable state. The baseline holding pressure (Pb) is set based on the material type and product thickness: the volume shrinkage rate of the material under different pressures is determined through melt compression experiments. The minimum pressure that allows the volume shrinkage rate around the button holes in the remote control housing to meet the standard is selected as Pb. For example, for polypropylene material around a 1mm thick button hole, Pb is usually set to 20MPa to 30MPa. The baseline holding pressure is the basis for calculating the holding pressure parameters in all risk areas, ensuring consistency in holding pressure control. For medium collapse risk areas, a two-stage holding pressure mode of "medium-low" is implemented. The first stage maintains 1.0 times the baseline holding pressure for 60% of the time, as the cell competition in this area is weak, requiring a longer time to ensure cell stability. The second stage reduces to 0.8 times the baseline holding pressure, avoiding both over-compression and excessive internal stress, and lasts for 40% of the time, balancing stability and stress release requirements. For low collapse risk areas, a constant 0.9 times the baseline holding pressure is maintained, slightly lower than Pb to suppress shrinkage and avoid excessive holding pressure leading to excessive product density and increased weight. Differentiated pressure holding breaks away from the traditional "one-size-fits-all" approach, providing appropriate pressure for different risk areas. High-risk areas receive sufficient support, while low-risk areas avoid excessive intervention, reducing the collapse rate of bubbles around button holes. Compared to constant pressure holding, stepped pressure holding can balance "bubble support" and "stress release," reducing warping defects after product cooling.

[0104] Table 2. Risk Levels of Cell Collapse and Pressure Holding Modes

[0105] Risk level Judgment conditions Pressure holding mode High collapse risk area Rq > Rh High-Medium-Low Three-Tier Central subsidence risk area Rg<Rq≤Rh Medium-low two-tier ladder Low subsidence risk area Rq≤Rg Constant pressure

[0106] In traditional microfoam injection molding processes, even after melt homogenization, two major unresolved issues remain: first, uneven flow during cavity filling cannot be located in real time, and can only be inferred from finished product defects, which is a "post-treatment" measure; second, the control of uneven flow lacks a "risk-oriented" approach, with compensation and holding pressure parameters relying on experience and unable to adapt to local risk differences. Step S20, through the synergistic collaboration of the entire technology chain of "flow monitoring - risk quantification - dynamic compensation - differentiated holding pressure," fundamentally solves the problem of "competitive cell growth and asymmetric collapse caused by uneven residual melt after homogenization in step S10." Its core logic is to transform "invisible melt unevenness" into "monitorable flow data," then into "quantifiable risk indices," and finally into "precisely controllable process parameters," forming a closed-loop control covering cavity filling to holding pressure. Step S21 captures the flow-induced factors in the early stage of bubble growth, step S23 intervenes in competition during the middle stage of growth, and step S24 stabilizes the structure at the end of growth. These three stages form a temporal synergy. Compared with the traditional method that only controls the pressure during the holding stage, this invention can suppress the risk of collapse from the source to the end, reducing the rate of dent defects around the button holes of the remote control shell. The core advantage of this temporal synergy is that early intervention can reduce the control pressure of later holding, avoiding defects that cannot be reversed by excessive competition in the early stage. For example, the compensation in step S23 can reduce the difference in bubble size in high-risk areas, and subsequent stabilization can be achieved with only conventional holding.

[0107] Example 2

[0108] This embodiment, based on Embodiment 1, provides a micro-foaming injection molding device for a remote control housing, such as... Figure 4 As shown, it includes:

[0109] Melt homogenization control module: It is used to collect vibration sensing data generated by waste particles hitting the inner wall of the hopper, identify virtual waste clumps based on the vibration sensing data, quantify the degradation degree information of virtual waste clumps after the waste particles enter the barrel from the hopper, and perform asymmetric modulation of the screw speed in the barrel based on the degradation degree information to obtain a homogeneous and stable melt.

[0110] Competition Index Calculation Module: This module is used to deploy miniature pressure sensors around each button hole in the remote control housing mold to form a ring monitoring network. It records the time when the melt flow front reaches each miniature pressure sensor, which is recorded as the flow front arrival time. Based on the flow front arrival time, it calculates the cell growth competition index and the zoned cell growth competition index.

[0111] Injection control module: Based on the cell growth competition index and the zoned cell growth competition index, it implements overall compensation and zoned enhancement compensation for the injection process;

[0112] Pressure holding control module: Implements differentiated pressure holding control to stabilize the bubble structure during the pressure holding stage.

[0113] Furthermore, in the melt homogenization control module, the method for quantifying the degradation degree information of the virtual waste agglomerates includes: acquiring multi-band near-infrared spectral images of the virtual waste agglomerates through an optical observation window in the barrel using a multispectral camera, recording them as virtual waste agglomerate images; processing the virtual waste agglomerate images to obtain the weighted average number of times the virtual waste agglomerates are reused; and quantifying the weighted average number of times the virtual waste agglomerates are reused as the degradation degree value of the virtual waste agglomerates.

[0114] The method for processing virtual waste clump images to obtain the weighted average reuse count of virtual waste clumps includes: performing grayscale correction on the virtual waste clump image to obtain a grayscale-corrected virtual waste clump image; extracting the reflection wavelength of each pixel in the grayscale-corrected virtual waste clump image, and determining the reuse count at the corresponding pixel of the virtual waste clump based on the reflection wavelength; statistically analyzing the pixel proportion of different reuse counts within the virtual waste clump image region based on the reuse count at the corresponding pixel of the virtual waste clump, using the pixel proportion of different reuse counts within the virtual waste clump image region as the waste quality proportion of different reuse counts within the virtual waste clump, and calculating the weighted average reuse count of the virtual waste clump based on the waste quality proportion of different reuse counts within the virtual waste clump.

[0115] Furthermore, in the competition index calculation module, the method for calculating the cell growth competition index and the partitioned cell growth competition index includes:

[0116] Miniature pressure sensors are arranged around each button hole in the remote control housing mold to form a ring monitoring network; the time when the melt flow front reaches each miniature pressure sensor is recorded as the flow front arrival time, and the measurement point pairs with uneven flow are determined based on the flow front arrival time.

[0117] The number of measuring point pairs exhibiting uneven flow was counted, and the bubble growth competition index was calculated. The area around the keyhole was divided into multiple sector-shaped zones, and the zone bubble growth competition index for each sector was calculated.

[0118] The methods and apparatus of this application may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the method is for illustrative purposes only, and the steps of the method of this application are not limited to the order specifically described above, unless otherwise specifically stated.

[0119] In addition, the parts of the technical solutions provided in the embodiments of this application that are consistent with the implementation principles of the corresponding technical solutions in the prior art have not been described in detail, so as to avoid excessive elaboration.

[0120] 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 invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A micro-foaming injection molding method for a remote control housing, characterized in that, The method includes: Vibration sensing data generated by waste particles impacting the inner wall of the hopper is collected in the hopper. Virtual waste clumps are identified based on the vibration sensing data. When the waste particles enter the barrel from the hopper, the degradation degree information of the virtual waste clumps is quantified. Based on the degradation degree information, the screw speed in the barrel is asymmetrically modulated to obtain a homogeneous and stable melt. Miniature pressure sensors are arranged around each button hole in the remote control housing mold to form a ring monitoring network. The time it takes for the melt flow front to reach each miniature pressure sensor is recorded as the flow front arrival time. Based on the flow front arrival time, the cell growth competition index and the zoned cell growth competition index are calculated. Based on the cell growth competition index and the zoned cell growth competition index, overall compensation and zoned enhancement compensation are implemented in the injection process, and differentiated pressure holding control for cell stabilization is implemented in the pressure holding stage.

2. The micro-foaming injection molding method for the remote control housing according to claim 1, characterized in that, The method for collecting vibration sensing data is as follows: m vibration sensing points are set on the inner wall of the hopper, and the vibration frequency generated by the impact of waste particles on the inner wall of the hopper is collected at each vibration sensing point. The vibration frequency is used as vibration sensing data. The method for identifying virtual waste clumps includes: calculating the vibration frequency change gradient between adjacent vibration sensing points based on the vibration frequencies collected at adjacent vibration sensing points, and defining the instantaneous waste aggregation area formed when the vibration frequency change gradient is greater than a preset gradient threshold as a virtual waste clump.

3. The micro-foaming injection molding method for the remote control housing according to claim 2, characterized in that, The waste particles carry thermally sensitive micropowder for marking thermal history characteristics. The thermally sensitive micropowder carried by the waste particles is a corresponding level of thermally sensitive micropowder sprayed according to the different reuse times of the waste particles.

4. The micro-foaming injection molding method for the remote control housing according to claim 3, characterized in that, The method for quantifying the degradation degree information of virtual waste clumps includes: An optical observation window for a multispectral camera is installed inside the feed cylinder. When waste particles enter the feed cylinder from the hopper, the multispectral camera acquires multi-band near-infrared spectral images of virtual waste clumps passing through the optical observation window inside the feed cylinder. These images are recorded as virtual waste clump images. The virtual waste clump images are processed to obtain the weighted average number of times the virtual waste clumps are reused. The weighted average number of times the virtual waste clumps are reused is then quantified as the degradation degree value of the virtual waste clumps.

5. The micro-foaming injection molding method for the remote control housing according to claim 4, characterized in that, The method for processing the image of virtual waste clumps to obtain the weighted average number of reuses of the virtual waste clumps includes: The virtual waste blob image is grayscale corrected to obtain a grayscale corrected virtual waste blob image. The reflected wavelength of each pixel in the grayscale corrected virtual waste blob image is extracted, and the number of times the virtual waste blob is reused at the corresponding pixel is determined based on the reflected wavelength. Based on the number of times the virtual waste blob is reused at the corresponding pixel, the percentage of pixels with different reuse times in the virtual waste blob image area is calculated. The percentage of pixels with different reuse times in the virtual waste blob image area is used as the percentage of waste mass with different reuse times in the virtual waste blob. The weighted average number of times the virtual waste blob is calculated based on the percentage of waste mass with different reuse times in the virtual waste blob.

6. The micro-foaming injection molding method for the remote control housing according to claim 5, characterized in that, The method for performing asymmetric modulation of the screw speed inside the barrel includes: Mark the center point of the virtual waste agglomeration and track the center point of the virtual waste agglomeration to form a predicted settlement trajectory; By associating the degradation degree value of the virtual waste clump with the predicted sedimentation trajectory, a labeled waste clump carrying spatial location and degradation degree information is obtained. When the degradation degree value is greater than the degradation threshold, a high degradation waste label is added to the labeled waste agglomerate; A three-stage asymmetric screw speed modulation (slow-fast-slow) is applied to the area where the tagged waste clumps carrying highly degradable waste labels are located.

7. The micro-foaming injection molding method for the remote control housing according to claim 6, characterized in that, The method for calculating the cell growth competition index includes: The measurement point pairs with non-uniform flow phenomena are determined based on the arrival time of the flow front. Count the number N of measuring point pairs exhibiting uneven flow. uneq The total number of measurement points N total , will N uneq Divide by N total The cell growth competition index was obtained.

8. The micro-foaming injection molding method for the remote control housing according to claim 7, characterized in that, The method for determining the pair of measuring points exhibiting non-uniform flow includes: Calculate the time difference of arrival of the flow front between adjacent miniature pressure sensors based on the arrival time of the flow front; If the arrival time difference of the flow front between adjacent micro pressure sensors is greater than a preset time threshold, then the adjacent micro pressure sensors are determined to be a pair of measuring points with uneven flow.

9. The micro-foaming injection molding method for the remote control housing according to claim 8, characterized in that, The method for implementing overall compensation and zoned enhancement compensation during the injection process includes: When the cell growth competition index is greater than the preset competition threshold, the injection process is compensated as a whole; when the cell growth competition index of a region is greater than the preset competition index threshold, the injection process is compensated for by enhancement of that region.

10. A micro-foaming injection molding apparatus for a remote control housing, used to implement the micro-foaming injection molding method for a remote control housing according to any one of claims 1-9, characterized in that, The device includes: Melt homogenization control module: It is used to collect vibration sensing data generated by waste particles hitting the inner wall of the hopper, identify virtual waste clumps based on the vibration sensing data, quantify the degradation degree information of virtual waste clumps after the waste particles enter the barrel from the hopper, and perform asymmetric modulation of the screw speed in the barrel based on the degradation degree information to obtain a homogeneous and stable melt. Competition Index Calculation Module: This module is used to deploy miniature pressure sensors around each button hole in the remote control housing mold to form a ring monitoring network. It records the time when the melt flow front reaches each miniature pressure sensor, which is recorded as the flow front arrival time. Based on the flow front arrival time, it calculates the cell growth competition index and the zoned cell growth competition index. Injection control module: Based on the cell growth competition index and the zoned cell growth competition index, it implements overall compensation and zoned enhancement compensation for the injection process; Pressure holding control module: Implements differentiated pressure holding control to stabilize the bubble structure during the pressure holding stage.

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