Rheological-magnetic field coupling-based injection molding magnetic powder orientation optimization method and system

Abstract

This invention relates to the field of magnetic material injection molding technology, and discloses a method and system for optimizing the orientation of injection-molded magnetic powder based on rheology-magnetic field coupling. The method involves deploying a distributed sensor network at key locations in the mold runner and cavity to synchronously and in real-time capture the dynamic viscosity gradient of the melt and the spatial distribution of magnetic field strength during the filling process. The captured data is input into a multiphysics coupling simulation engine based on micromechanics to calculate the instantaneous orientation vector field of the magnetic powder within the cavity. Then, an orientation order topology map is constructed using tensor analysis and image algorithms to accurately identify weakly oriented regions and their boundaries. Finally, a composite control command for the magnetic field and injection parameters is generated based on the identification results. This method enables online diagnosis and active compensation control of the magnetic powder orientation state, improving the uniformity and consistency of the injection-molded magnet orientation.

Description

Technical Field

[0001] This invention relates to the field of magnetic material injection molding technology, specifically to a method and system for optimizing the orientation of injection-molded magnetic powder based on rheological-magnetic field coupling. Background Technology

[0002] In the process of preparing anisotropic bonded magnets using injection molding technology, the orientation distribution of magnetic powder particles within the mold cavity during the filling stage is crucial in determining the final magnetic properties of the product. Higher orientation and more uniform distribution result in superior core performance indicators such as remanence and coercivity. Current technologies generally employ a pre-set static or time-controlled magnetic field around the mold cavity to guide the orientation of the magnetic powder.

[0003] Existing technical solutions have shortcomings. The setting of the magnetic field typically relies on prior predictions from CAE simulations or past process experience, representing an open-loop control strategy. Injection molding is a highly dynamic process; the viscosity of the polymer melt changes in real time with shear rate, temperature, and filler concentration, directly affecting the fluid shear force and viscous resistance acting on the magnetic powder particles. Fixed magnetic field parameters cannot respond to these dynamic fluctuations in rheological state, leading to a deviation between the actual orientation state of the magnetic powder under the coupling effect of the complex flow field and the preset magnetic field and the simulation prediction. This makes it difficult to obtain a consistent and highly ordered orientation structure throughout the entire cavity.

[0004] Current orientation optimization methods lack effective utilization of on-site data from the filling process. Conventional methods cannot perceive the true distribution of melt rheological state and magnetic field strength at different locations within the mold cavity in real time. For localized poor orientation areas caused by flow front effects, fountain flow, or weld seams, existing technologies lack precise online identification and diagnostic capabilities. Their control measures are relatively macroscopic and singular, often only able to adjust the magnetic field or injection parameters as a whole, unable to provide localized and precise compensation intervention for the spatial geometric characteristics and boundary evolution dynamics of specific weak orientation areas within the mold cavity. This limits the consistency and yield rate of high-performance complex structure injection-molded magnets. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for optimizing the orientation of injection molding magnetic powder based on rheological-magnetic field coupling, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, this invention provides a method for optimizing the orientation of injection molding magnetic powder based on rheological-magnetic field coupling, the method comprising:

[0007] During the injection filling stage, a distributed sensor network arranged at key locations in the mold flow channel and cavity is used to simultaneously capture the dynamic viscosity change gradient and the spatial distribution of magnetic field strength during the melt flow process.

[0008] The captured dynamic viscosity change gradient and magnetic field intensity spatial distribution data are input into a multi-physics coupling simulation engine based on micromechanics. This simulation engine iteratively calculates the angular momentum conservation equation of magnetic powder under the combined action of viscoelastic fluid and time-varying magnetic field, and outputs the instantaneous orientation vector field of magnetic powder on discrete grid nodes in the mold cavity.

[0009] Tensor invariant analysis is performed on the instantaneous orientation vector field of the magnetic powder to extract the orientation orderliness parameter of each grid node, and an orientation orderliness topology map reflecting the overall orientation state of the cavity is constructed based on the orientation orderliness parameter.

[0010] On the orientation order topology map, a region growing algorithm is applied to identify weak orientation regions with orientation order below a set threshold, and morphological operations are used to detect transition boundary regions with drastic changes in orientation order gradient.

[0011] Based on the spatial geometric characteristics of the weakly oriented region and the evolution dynamics of the transition boundary region, a composite control instruction set containing magnetic field parameter adjustment and injection parameter compensation is generated.

[0012] Preferably, the step of performing tensor invariant analysis on the instantaneous orientation vector field of the magnetic powder, extracting the orientation orderliness parameter of each grid node, and constructing an orientation orderliness topology map reflecting the overall orientation state of the cavity based on the orientation orderliness parameter includes:

[0013] The instantaneous orientation vector of the magnetic powder for each grid node is decomposed into eigenvalues. The ratio of the eigenvalues ​​in the principal axis direction to the eigenvalues ​​in the secondary direction is calculated. The normalized ratio of the eigenvalues ​​is defined as the orientation orderliness parameter of the node.

[0014] The spatial coordinates of all grid nodes in the cavity are associated with the corresponding orientation order parameters, and a continuous spatial orientation order distribution surface is generated by using a scattered data interpolation method.

[0015] Contour lines are extracted from the spatial orientation order distribution surface, and grid nodes with the same orientation order parameters are connected to form a multi-level contour map.

[0016] The multi-level contour map is superimposed and fused with the three-dimensional geometric model of the cavity to generate the orientation order topology map, which has high information and can intuitively display the spatial changes of orientation order.

[0017] Preferably, the step of applying a region growing algorithm to identify weakly oriented regions with an orientation orderliness below a set threshold on the orientation orderliness topology map, and simultaneously detecting transitional boundary regions with drastic changes in orientation orderliness gradient through morphological operations, includes:

[0018] An orientation orderliness threshold is set on the orientation orderliness topology map, and this threshold is used as the seed point selection criterion.

[0019] Starting from seed points below the orientation orderliness threshold, a region growing algorithm is applied to expand the region based on the similarity of orientation orderliness between adjacent grid nodes until a boundary node is encountered, thereby marking the connected weak orientation regions.

[0020] Morphological gradient operations are performed on the complete orientation order topology graph to calculate the magnitude of the difference in orientation order between each grid node and its neighboring nodes, thereby generating an orientation order gradient graph.

[0021] A gradient threshold is set on the orientation order gradient map, and the set of grid nodes whose gradient values ​​exceed the threshold is defined as the transition boundary region, and the contour coordinates of the transition boundary region are recorded.

[0022] Preferably, the step of generating a composite control instruction set including magnetic field parameter adjustment and injection parameter compensation based on the spatial geometric characteristics of the weak orientation region and the evolution dynamics of the transition boundary region includes:

[0023] The geometric characteristics of the weakly oriented region are analyzed, including the region area, shape factor, and positional relationship relative to the gate. Based on these characteristics, the required magnetic field enhancement intensity and range are calculated.

[0024] Monitor the movement speed and direction of the transition boundary region in consecutive time steps to predict the possible orientation defect propagation path.

[0025] Based on the prediction of the magnetic field enhancement intensity and range, as well as the orientation defect propagation path, the frequency modulation depth and current amplitude increment of the magnetic field generator are determined, and the speed correction curve and pressure holding strategy of the injection molding machine screw are also determined.

[0026] The adjustment parameters of the magnetic field generator and the adjustment parameters of the injection molding machine are encapsulated and synchronized in a timing sequence to form the composite control instruction set.

[0027] Preferably, the process of encapsulating and synchronizing the adjustment parameters of the magnetic field generator and the adjustment parameters of the injection molding machine to form the composite control instruction set includes:

[0028] Add timestamps to the magnetic field parameter adjustment amount and the injection parameter compensation amount to ensure that the magnetic field change is accurately matched with the melt flow stage;

[0029] Establish a constraint relationship library between adjustment parameters to prevent conflicting adjustment commands from being issued simultaneously;

[0030] The time-synchronized and conflict-free magnetic field parameter adjustment amount and injection parameter compensation amount are packaged into data frames according to a preset communication protocol to form the composite control instruction set that can be directly sent to the execution device.

[0031] Preferably, the captured dynamic viscosity change gradient and magnetic field strength spatial distribution data are input into a multiphysics coupling simulation engine based on micromechanics. This simulation engine iteratively calculates the angular momentum conservation equation of magnetic powder under the combined action of viscoelastic fluid and time-varying magnetic field, and outputs the instantaneous orientation vector field of magnetic powder on discrete grid nodes within the mold cavity, including:

[0032] The multiphysics coupling simulation engine based on micromechanics includes a balance relationship describing the rotational inertia of magnetic powder, fluid viscous resistance, and magnetic field torque.

[0033] At each computation time step, the dynamic viscosity change gradient at the current moment is used as the input of fluid viscous drag, and the spatial distribution of magnetic field strength at the current moment is used as the input of magnetic field torque. The equilibrium relationship is solved by implicit time integration method.

[0034] The process of solving the equilibrium relationship outputs a vector with magnitude and direction at each discrete grid node. This vector represents the average orientation state of the magnetic powder aggregate corresponding to the discrete grid node, i.e., the instantaneous orientation vector field of the magnetic powder.

[0035] Preferably, during the injection filling stage, a distributed sensor network arranged at key locations in the mold runner and cavity is used to simultaneously capture the dynamic viscosity change gradient and the spatial distribution of magnetic field strength during the melt flow process, including:

[0036] The distributed sensing network includes a micro differential pressure sensor array and a three-dimensional fluxgate sensor. The micro differential pressure sensor array is arranged along the melt flow path, and the dynamic viscosity change gradient is obtained by measuring the pressure difference between adjacent points.

[0037] The three-dimensional fluxgate sensor is embedded in the mold in the form of a spatial dot matrix to directly measure the magnetic field vector at the point where it is located, and obtains the spatial distribution of the magnetic field intensity through time series acquisition.

[0038] Preferably, after generating the composite control instruction set including magnetic field parameter adjustment and injection parameter compensation, the method further includes:

[0039] The magnetic field parameter adjustment portion of the composite control instruction set is sent to the high-frequency power amplifier to drive the solenoid assembly to generate a corrected magnetic field with specific waveform parameters.

[0040] The injection parameter compensation portion of the composite control instruction set is sent to the injection molding machine motion controller to adjust the axial movement law of the screw;

[0041] After one control cycle of instruction execution, new process data is collected again through the distributed sensor network to start a new round of simulation calculation and instruction generation process.

[0042] Preferably, the step of collecting new process data again through the distributed sensor network after the instruction has been executed for one control cycle, and initiating a new round of simulation calculation and optimization instruction generation process, includes:

[0043] Compare the differences between the orientation order topology graph obtained in the new round of calculation and the previous round to evaluate the effectiveness of the composite control instruction set;

[0044] If the area reduction of the weakly oriented region does not reach the expected target, the threshold in the region growing algorithm or the size of the structural element in the morphological operation is adaptively updated based on historical control records and effects.

[0045] Repeated execution of the entire process from data acquisition to instruction generation enables progressive optimization of the magnetic powder orientation state during injection molding filling.

[0046] Preferably, the present invention also includes an injection molding magnetic powder orientation optimization system based on rheological-magnetic field coupling. The system includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the injection molding magnetic powder orientation optimization method based on rheological-magnetic field coupling as described above.

[0047] Compared with the prior art, the beneficial effects of the present invention are:

[0048] By deploying a distributed sensor network at key locations in the mold flow channel and cavity, the dynamic viscosity gradient and spatial distribution of magnetic field intensity during melt flow are simultaneously captured. This technical approach transforms the filling process from a black box to a real-time perceptible state, providing a direct data foundation for subsequent precise control. Directly acquiring real data from the coupled rheological and magnetic fields eliminates reliance on potentially unrealistic offline simulations or empirical estimates for determining the orientation process, thus improving the system's responsiveness and control accuracy in complex dynamic processes.

[0049] Real-time sensor data is input into a multiphysics-coupled simulation engine based on micromechanics. The instantaneous magnetic powder orientation vector field is output by iteratively calculating the angular momentum conservation equation of the magnetic powder. Tensor invariant analysis is then performed on the vector field to construct an orientation order topology map. Region growing algorithms and morphological operations are applied to identify weakly oriented regions and their transition boundaries. This method achieves microscopic mechanism modeling and quantitative, visual diagnosis of the macroscopic spatial distribution of orientation states. It can accurately pinpoint the spatial location and morphological characteristics of problem areas, surpassing traditional coarse evaluation methods based on macroscopic assumptions or single-node data.

[0050] Based on the spatial geometric characteristics of the identified weak orientation regions and the evolutionary dynamics of the transition boundary regions, a composite control command set is generated that simultaneously includes magnetic field parameter adjustments and injection parameter compensation. This control strategy is no longer a single-variable adjustment, but rather a multi-parameter synergistic intervention based on a deep understanding of the orientation formation mechanism. Attached Figure Description

[0051] Figure 1 This is a schematic diagram illustrating the working principle of the injection molding magnetic powder orientation optimization method based on rheology-magnetic field coupling described in this invention.

[0052] Figure 2 A flowchart for constructing an orientation-ordered topology graph;

[0053] Figure 3 Flowchart for generating a composite control instruction set;

[0054] Figure 4 This is a time series distribution diagram;

[0055] Figure 5 This is a diagram showing the distribution of the orientation vector field. Detailed Implementation

[0056] 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.

[0057] Please see Figure 1This invention provides a method for optimizing the orientation of magnetic powder in injection molding based on rheology-magnetic field coupling. The method includes: during the injection filling process, using a distributed sensor network arranged at key locations in the mold flow channel and cavity, synchronously collecting dynamic viscosity gradient and spatial distribution data of magnetic field intensity during melt flow; these data are input in real time into a multiphysics coupling simulation engine based on micromechanics, which iteratively solves the angular momentum conservation equation of magnetic powder under the combined action of viscoelastic fluid and time-varying magnetic field, and outputs the instantaneous orientation vector field of magnetic powder on discrete grid nodes in the mold cavity; performing tensor invariant analysis on the orientation vector field, extracting the orientation orderliness parameter of each grid node, and constructing an orientation orderliness topology map reflecting the overall orientation state of the cavity; applying a region growing algorithm on the topology map to identify weak orientation regions with orientation orderliness below a set threshold, and simultaneously detecting transition boundary regions with drastic changes in orientation orderliness gradient through morphological operations; and generating a composite control instruction set including magnetic field parameter adjustment and injection parameter compensation based on the spatial geometric characteristics of the weak orientation regions and the evolution dynamics of the transition boundary regions to optimize the orientation process in real time.

[0058] Example 1: See Figure 2 In practical implementation, the rheology-magnetic field coupling-based injection molding magnetic powder orientation optimization method, after calculating the instantaneous orientation vector field of magnetic powder on discrete grid nodes within the mold cavity, enters the stage of performing tensor invariant analysis on the instantaneous orientation vector field of magnetic powder and constructing an orientation order topology map. The instantaneous orientation vector field of magnetic powder is a spatial distribution data set containing vector information at each grid node, with each vector representing the average orientation state of the magnetic powder aggregate at the corresponding spatial point. The core purpose of tensor invariant analysis of the instantaneous orientation vector field of magnetic powder is to transform the vector information into scalar parameters that can quantify the degree of orientation. Specifically, this involves performing eigenvalue decomposition on the instantaneous orientation vector of magnetic powder at each grid node. The eigenvalue decomposition process constructs a second-order tensor for the orientation vector of each node, and obtains its eigenvalues ​​and eigenvectors by solving the characteristic equation of this tensor. The ratio of the eigenvalues ​​in the principal axis direction to the eigenvalues ​​in the secondary direction is calculated; this ratio effectively reflects the concentration or orderliness of the magnetic powder orientation at that node. After normalizing this ratio, it is defined as the orientation orderliness parameter of the grid nodes. The normalization process uses a linear scaling method to map the ratio to a closed interval of 0 to 1, where 0 represents a completely disordered state and 1 represents a completely ordered ideal orientation state.

[0059] In practical implementation, after extracting the orientation orderliness parameters of all grid nodes, it is necessary to construct an orientation orderliness topology map reflecting the overall orientation state of the cavity. The construction process begins by associating the spatial coordinates of all grid nodes in the cavity with their corresponding orientation orderliness parameters, forming a scattered dataset containing three-dimensional spatial locations and orderliness values. Since the grid nodes are discretely distributed, a scattered data interpolation method is used to generate a continuous spatial orientation orderliness distribution surface to obtain continuous spatial distribution information. The scattered data interpolation method can be either Kriging interpolation or radial basis function interpolation. These methods can predict the orientation orderliness values ​​of unsampled areas based on known node data and spatial correlations, thus forming a continuous scalar field covering the entire cavity space. The spatial orientation orderliness distribution surface is a mathematical surface with spatial coordinates as the independent variable and orientation orderliness parameters as the dependent variable; it smoothly describes the variation of orientation orderliness within the cavity.

[0060] In practical implementation, after generating the spatial orientation order distribution surface, contour extraction is required to enhance the readability and analyzability of the topology map. Contour extraction is the process of connecting grid nodes with the same orientation order parameter. By setting a series of equally spaced orientation order thresholds, multiple contour lines can be extracted. Each contour line represents a set of points with equal orientation order within the cavity, and multiple contour lines together constitute a multi-level contour map. Contour extraction algorithms can employ moving cube algorithms or traveling block algorithms. These algorithms can efficiently construct isosurfaces in a three-dimensional scalar field and project them onto a two-dimensional plane to form contour lines. The multi-level contour map clearly displays the spatial distribution pattern of orientation order, such as the boundary positions of high-orientation and low-orientation regions and the direction of gradient changes.

[0061] In practice, after obtaining the multi-level contour maps, they need to be overlaid and fused with the three-dimensional geometric model of the cavity to ultimately generate an orientation-order topology map that provides height information and visually displays the spatial changes in orientation order. The overlay and fusion process maps the two-dimensional multi-level contour maps onto the surface or interior space of the three-dimensional geometric model of the cavity, achieving precise registration between the contour lines and the geometric model through 3D rendering technology. The fused orientation-order topology map is a composite model containing three-dimensional geometric information and orientation order distribution information. Visualization techniques such as color coding, transparency adjustment, or height mapping can be used to enhance the intuitive display of spatial changes in orientation order. For example, warm colors can represent areas with high orientation order, and cool colors can represent areas with low orientation order; the depth of the color represents the magnitude of the order value, making the spatial distribution of orientation states immediately apparent.

[0062] In practical implementation, a region growing algorithm is applied to identify weakly oriented regions with orientation orderliness below a set threshold on the generated orientation orderliness topology map. The region growing algorithm is a region-based segmentation method. Its implementation requires setting an orientation orderliness threshold on the orientation orderliness topology map. This threshold serves as the standard for selecting seed points; only grid nodes with orientation orderliness parameters below this threshold are eligible for selection as seed points. Starting from these seed points below the orientation orderliness threshold, the region growing algorithm expands the regions based on the similarity of orientation orderliness between adjacent grid nodes. Similarity judgment is typically achieved by calculating the difference in orientation orderliness parameters between adjacent nodes and comparing it with a tolerance range. If the difference is within the tolerance range, the nodes are considered similar and assigned to the same region. The region expansion process continues until boundary nodes are encountered. Boundary nodes are those nodes whose adjacent nodes do not meet the similarity condition, thus marking connected weakly oriented regions. The region growing algorithm can effectively aggregate discrete low-orientation-orderliness points into continuous and meaningful regions, providing clear target regions for subsequent regulation.

[0063] In practical implementation, morphological operations are simultaneously used to detect transitional boundary regions with drastic changes in orientation orderliness gradient. Morphological operations are mathematical methods used in image processing to analyze shape and structure. When applied to orientation orderliness topology maps, morphological gradient operations are first performed on the complete orientation orderliness topology map. Morphological gradient operations can highlight regions in the image with drastic changes in grayscale values, which correspond to regions with drastic changes in orientation orderliness gradient in the orientation orderliness topology map. The magnitude of the difference in orientation orderliness between each grid node and its neighboring nodes is calculated to generate the orientation orderliness gradient map. The neighborhood can be defined as a four-connected neighborhood or an eight-connected neighborhood, and the magnitude calculation reflects the average degree of difference between the central node and its surrounding nodes. The orientation orderliness gradient map is a new scalar field, where each pixel value represents the local gradient intensity of the original topology map at that point.

[0064] In practical implementation, a gradient threshold is set on the orientation order gradient map, and the set of grid nodes with gradient values ​​exceeding this threshold is defined as the transition boundary region. The choice of the gradient threshold depends on the process requirements for orientation uniformity. The lower the threshold, the more sensitive the detected transition boundary region, potentially including more subtle gradient changes; the higher the threshold, the more significant gradient jump regions are detected. Transition boundary regions typically correspond to interfaces where the orientation state changes drastically, and these regions may be the origin or extension path of orientation defects. Recording the contour coordinates of the transition boundary region is a crucial step. Contour coordinates can be obtained through edge tracking algorithms such as chain code algorithms or boundary following algorithms. The recorded contour coordinates accurately describe the geometry and location of the transition boundary region, providing a geometric basis for analyzing the evolution dynamics of orientation defects. The combined application of region growing algorithms and morphological operations enables automatic identification and quantitative description of different feature regions in the orientation order topology map, providing a basis for generating precise composite control instruction sets.

[0065] Example 2: See Figure 3 In practical implementation, the process of generating a composite control instruction set based on the spatial geometric characteristics of the weakly oriented region and the evolutionary dynamics of the transition boundary region is a decision-making process based on quantitative analysis and prediction. Analyzing the geometric characteristics of the weakly oriented region is the primary step. These characteristics include the region area, shape factor, and positional relationship relative to the gate. The region area is obtained by calculating the total number of grid cells contained in the weakly oriented region and multiplying it by the representative physical area of ​​a single cell. The shape factor is quantified by calculating the ratio of the square of the region's perimeter to its area; circular regions have the smallest shape factor, while complex and irregular regions have larger shape factors. The positional relationship of the weakly oriented region relative to the gate is determined by calculating the length and direction of the line connecting the region's centroid and the gate's center. Based on these characteristics—region area, shape factor, and positional relationship relative to the gate—the required magnetic field enhancement intensity and range are calculated. An exemplary calculation relationship can be expressed by the following formula:

[0066]

[0067] in: This represents the calculated relative increase in the required magnetic field strength. This represents the area of ​​the weakly oriented region. Indicates the total area of ​​the cavity. The shape factor representing the weakly oriented region. This represents the distance from the centroid of the weakly oriented region to the gate. Indicates the characteristic length of the cavity (e.g., the length of the cavity diagonal). These are weighting coefficients, calibrated using material properties and historical process data. The range of the magnetic field is set based on the size of the circumcircle or minimum bounding box of the weakly oriented region.

[0068] In practical implementation, monitoring the movement speed and direction of the transition boundary region in consecutive time steps is crucial for predicting the orientation defect propagation path. The movement speed is obtained by calculating the displacement of the centroid of the transition boundary region between two consecutive sampling time points, expressed as a time interval. The movement direction is determined by the angle of the centroid displacement vector. The predicted orientation defect propagation path is based on the current movement speed and direction, assuming uniform linear motion over a short period, thus extrapolating the possible positions the transition boundary region may reach in the next few time steps. This prediction is dynamic and is updated and corrected with each new time step's data acquisition. Combining the calculated magnetic field enhancement intensity and range with the predicted orientation defect propagation path, the frequency modulation depth and current amplitude increment of the magnetic field generator are determined. The frequency modulation depth is adjusted based on the relaxation time of the material along the predicted defect propagation path, while the current amplitude increment is directly related to the calculated relative enhancement of the required magnetic field strength. Proportional. At the same time, the speed correction curve and pressure holding strategy of the injection molding machine screw are determined. The speed correction curve aims to affect the shear field by adjusting the melt front speed, thereby cooperating with the magnetic field. The pressure holding strategy focuses on maintaining or changing the cavity pressure at a specific stage to stabilize the orientation structure.

[0069] In some embodiments, the adjustment parameters of the magnetic field generator and the adjustment parameters of the injection molding machine are encapsulated and synchronized in a timing sequence to form a final composite control command set. The adjustment parameters of the magnetic field generator mainly include the frequency modulation depth. and current amplitude increment The main adjustment parameters for injection molding machines include the screw speed correction curve. And pressure maintenance parameters. Optionally, the encapsulation process will mark the start time of the effective date for each set of adjustment parameters. and duration This ensures that changes in the magnetic field precisely align with specific stages of the melt flow. Synchronous timing requires checking for overlapping or conflicting time stamps for different adjustment parameters and sorting them chronologically to form a sequence of instructions to be executed along a timeline.

[0070] It can be understood that the composite control instruction set is a structured data set that ensures the temporal coordination between magnetic field control and injection parameter control. In some embodiments, the data structure of the instruction set may include an instruction header, a timestamp field, a magnetic field parameter segment, and an injection parameter segment. The instruction header identifies the version and type of the instruction set; the timestamp field indicates the generation time and execution time window of the instruction set; the magnetic field parameter segment contains the frequency modulation depth. and current amplitude increment The specific values; the injection parameter segment includes a discrete data point sequence of the screw speed correction curve and the values ​​of the pressure holding parameters. Optionally, the packaged composite control instruction set is sent to its respective control execution unit via industrial fieldbus or industrial Ethernet protocol, with the magnetic field parameter part sent to the controller of the high-frequency power amplifier and the injection parameter part sent to the main controller of the injection molding machine.

[0071] Example 3: In specific implementation, during the injection filling stage, a distributed sensor network arranged at key locations in the mold runner and cavity synchronously captures the dynamic viscosity gradient and spatial distribution of magnetic field strength during the melt flow process. The distributed sensor network includes a micro-differential pressure sensor array and a three-dimensional fluxgate sensor. The micro-differential pressure sensor array consists of a series of miniature differential pressure sensing units. These sensing units are embedded in the mold wall at specific intervals along the melt flow path, arranged in a linear array or grid. By measuring the real-time pressure difference between two adjacent measuring points and combining it with the geometric dimensions of the runner or cavity, the micro-differential pressure sensor array inverts the local shear stress and shear rate of the melt based on the principles of viscous fluid dynamics, thereby calculating the dynamic viscosity gradient. The calculation of the dynamic viscosity gradient can be based on the following relationship:

[0072]

[0073] in: This represents the calculated gradient of dynamic viscosity change. This represents the pressure difference between adjacent measuring points measured by a micro differential pressure sensor array. Indicates the length of the flow path between adjacent measuring points. This indicates the hydraulic radius of the flow channel or cavity in that section. This represents the volumetric flow rate through this section. The volumetric flow rate is derived from the screw motion parameters provided by the injection molding machine control system. Three-dimensional fluxgate sensors are embedded inside the mold in a spatial array, typically positioned at the corners of the cavity, the center of the wall, and in areas where complex flow may occur. The three-dimensional fluxgate sensors can directly measure the components of the magnetic field vector at their location in three orthogonal directions. By continuously acquiring time-series data at a sampling rate higher than the characteristic frequency of the filling process, complete information on the spatial distribution of magnetic field strength at key locations in the cavity over time can be obtained. Data acquisition from the micro-differential pressure sensor array and the three-dimensional fluxgate sensors is triggered by a synchronous clock signal to ensure that the dynamic viscosity gradient data and the spatial distribution data of the magnetic field strength have a consistent time reference.

[0074] In practical implementation, the adjustment parameters of the magnetic field generator and the injection molding machine are encapsulated and synchronized in a timing sequence to form a composite control instruction set. This process is the core of achieving precise and coordinated control. Adding timestamps to the magnetic field parameter adjustment and injection parameter compensation amounts is the foundation of synchronous control. These timestamps are generated based on a high-precision central controller clock and include not only the absolute time of instruction generation but also the delay time of the planned instruction execution or the time offset relative to the start of the filling process, ensuring that changes in magnetic field parameters precisely match specific stages of melt flow. Establishing a constraint relationship library between adjustment parameters is a crucial step in preventing instruction conflicts. This constraint relationship library is defined in the form of rules. For example, a rule can stipulate that when the injection parameter compensation amount includes a high holding pressure setting, the current amplitude increment in the magnetic field parameter adjustment amount must not exceed a certain safety limit to prevent excessive magnetic field torque from damaging the material structure under high pressure. Another example is that a rule can constrain the increase in injection speed to match the decrease in magnetic field frequency to avoid excessively rapid flow damaging the forming orientation structure. The rules in the constraint relationship library are predefined based on materials science principles, electromagnetic field theory, and injection molding process knowledge, and can be optimized during actual operation.

[0075] In some embodiments, time-synchronized and conflict-free magnetic field parameter adjustments and injection parameter compensations are packaged into data frames according to a preset communication protocol. The communication protocol can be the industry-standard Modbus TCP protocol. Each data frame includes a frame header, device address code, function code, data field, and checksum. The frame header identifies the start of the data frame; the device address code specifies the device receiving this instruction, such as a magnetic field generator power amplifier or an injection molding machine motion controller; the function code defines the type of instruction, such as writing to a register or performing a specific action; the data field carries the specific adjustment parameters; for example, for magnetic field parameter adjustments, the data field may include frequency modulation values, current amplitude increments, and duration of action; for injection parameter compensations, the data field may include a set of discrete data points of the screw position-speed curve or pressure setpoints; the checksum is used to ensure the integrity of data transmission.

[0076] It is understandable that the encapsulation and synchronization timing orchestration process ultimately produces a composite control instruction set with a clear structure, well-defined timing, and no logical conflicts. Essentially, the composite control instruction set is a list containing time-series control commands, each command explicitly specifying the time point, the device being adjusted, and the parameters to be adjusted. In some embodiments, the composite control instruction set can be viewed as a script file generated by a higher-level optimization algorithm, then parsed by an interpreter and distributed to the corresponding lower-level controller for execution. Optionally, the composite control instruction set undergoes a simulation verification process before formal deployment. The execution effect of the instruction set is rapidly simulated in a simplified process model to predict whether it can effectively improve the orientation state and avoid potential process oscillations or instability. The composite control instruction set is sent to the execution device via an industrial network, driving a high-frequency power amplifier to adjust the output of the magnetic field generator and commanding the injection molding machine's motion controller to change the screw's motion pattern, thereby achieving real-time optimized control based on rheological-magnetic field coupling.

[0077] See Figure 4 This paper demonstrates the generation process of composite control commands based on rheological-magnetic field coupling. By synchronously capturing dynamic viscosity gradient and spatial distribution data of magnetic field strength during melt flow through a distributed sensor network, the system encapsulates and synchronizes the adjustment parameters of the magnetic field generator and the injection molding machine, forming a composite control command set. This process involves key steps such as timestamp addition, constraint verification, and parameter packaging to ensure precise matching between magnetic field changes and melt flow stages, achieving accurate and coordinated control of the magnetic powder orientation state. The time-series distribution diagram shows the variation of each parameter in the composite control command set over time. The diagram includes four key parameter curves: magnetic field frequency, magnetic field current, injection speed, and holding pressure, reflecting the coordinated changes of various control parameters during injection molding. The magnetic field frequency curve shows the system's dynamic adjustment of the magnetic field strength; the magnetic field current curve characterizes the power output change of the magnetic field generator; the injection speed curve describes the flow control during melt filling; and the holding pressure curve reflects the pressure maintenance strategy in the later stages of molding. The coordinated changes of these parameters aim to optimize the orientation behavior of the magnetic powder under the combined action of the flow field and magnetic field, improving the functional performance of the product.

[0078] Example 4: In specific implementation, inputting the captured dynamic viscosity change gradient and magnetic field strength spatial distribution data into a micromechanical-based multiphysics coupling simulation engine is the core step in calculating the instantaneous orientation vector field of the magnetic powder. The micromechanical-based multiphysics coupling simulation engine contains a balance equation describing the rotational inertia of the magnetic powder, fluid viscous drag, and magnetic field torque. This balance equation is the physical basis for the simulation engine's numerical solution. The balance equation describing the rotational inertia of the magnetic powder, fluid viscous drag, and magnetic field torque can be expressed in the following form:

[0079]

[0080] in: The tensor representing the moment of inertia of a single magnetic powder particle. Represents the angular velocity vector of magnetic powder Regarding time The derivative, The tensor representing the fluid viscous drag coefficient. This represents the local vorticity vector of the fluid at the location of the magnetic powder. This represents the magnetic moment vector of the magnetic powder. The vector representing the external magnetic flux density at the location of the magnetic powder, with the symbol... This represents the vector cross product. This equilibrium equation shows that the sum of the rotational inertia effect of the magnetic powder and the viscous damping effect applied by the fluid equals the torque effect applied by the magnetic field. A multiphysics-based simulation engine based on micromechanics iteratively calculates the angular momentum conservation equation of the magnetic powder under the combined action of a viscoelastic fluid and a time-varying magnetic field, i.e., the above equilibrium equation, to simulate the orientation dynamics of the magnetic powder.

[0081] In practice, at each computation time step, the simulation engine uses the dynamic viscosity change gradient captured by the distributed sensor network at the current moment as the input to the fluid viscous drag coefficient tensor. Specifically, the dynamic viscosity change gradient is used to calculate the fluid viscous drag coefficient tensor. and local vorticity vector Simultaneously, the spatial distribution data of the magnetic field strength captured by the distributed sensor network at the current moment is converted into a magnetic induction intensity vector. As the input of the magnetic field torque, the equilibrium relationship is solved using an implicit time integration method. Implicit time integration methods, such as the backward Euler method or the Crank-Nicholson method, provide better numerical stability and are particularly suitable for handling rigid problems involving different time scales. The solution process involves linearizing the nonlinear differential equations in each time step and solving the resulting linear equations to obtain the angular velocity vector of the magnetic powder at each discrete grid node. The value at the new time step.

[0082] It is understandable that the angular velocity vector... The rotational state of the magnetic powder is described, but the final output needs to be orientation information. At each discrete grid node, a multiphysics-coupled simulation engine based on micromechanics outputs a vector with magnitude and direction. This vector characterizes the average orientation state of the magnetic powder assembly at that location, i.e., the instantaneous orientation vector field of the magnetic powder. The instantaneous orientation vector of the magnetic powder is typically composed of the angular velocity vector. The orientation angle is defined by time integration, or the local average orientation direction is obtained by solving orientation-related evolution equations (such as the Fock-Planck equation considering rotational diffusion). The instantaneous orientation vector field of the magnetic powder is the direct data source for subsequent tensor invariant analysis and construction of the orientation order topology. To more clearly illustrate the key physical quantities involved in the multiphysics coupled simulation engine and their roles in the calculation, please refer to Table 1, which provides a brief summary.

[0083] Table 1: Key Physical Quantities of the Multiphysics Coupled Simulation Engine

[0084]

[0085] In some embodiments, the solution domain of the multiphysics coupling simulation engine based on micromechanics is discretized into finite grid cells, and the equilibrium relation is established and solved at each grid node. Optionally, for cavities with complex geometries, unstructured grids can be used for discretization to better fit the cavity boundaries. The density of the grid can be adjusted as needed, with a denser grid in regions with large gradients, such as the flow front and cavity corners, and a sparser grid in regions with gentler changes to improve computational efficiency. The simulation engine iterates until the solution converges or reaches a preset number of iterations, and finally outputs the instantaneous magnetic powder orientation vector field covering all grid nodes of the entire cavity solution domain at the current time step. It can be understood that the instantaneous magnetic powder orientation vector field is a transient field that evolves over time, and the simulation engine needs to repeat the above calculation process at every important moment in the injection molding filling process to capture the dynamic evolution of the orientation state.

[0086] See Figure 5This paper demonstrates the process of calculating the instantaneous orientation vector field of magnetic powder using a multiphysics-coupled simulation engine based on micromechanics. The simulation engine iteratively calculates the angular momentum conservation equation of magnetic powder under the combined action of a viscoelastic fluid and a time-varying magnetic field, comprehensively considering the balance between the powder's rotational inertia, fluid viscous drag, and magnetic field torque. At each calculation time step, the dynamic viscosity gradient is used as the fluid viscous drag input, and the spatial distribution of the magnetic field intensity is used as the magnetic field torque input. An implicit time integration method is employed to solve the balance equation, ultimately outputting the instantaneous orientation vector field of the magnetic powder on discrete mesh nodes within the mold cavity. The orientation vector field distribution diagram visually illustrates the spatial distribution characteristics of the magnetic powder orientation state within the mold cavity. The background color in the diagram represents the degree of orientation order at each location, with a color gradient from blue to yellow reflecting the change in orientation order from low to high. The red arrows indicate the instantaneous orientation direction of the magnetic powder, and the length and direction of the arrows characterize the orientation intensity and anisotropy, respectively. It can be observed that a higher degree of orientation order is formed in the central region of the mold, while weaker orientation regions exist at the edges and corners. The annular distribution pattern of the vector field reflects the complex dynamic behavior of the coupling between the melt flow field and the magnetic field, providing important spatial distribution information for subsequent orientation optimization control.

[0087] Example 5: In specific implementation, after generating a composite control instruction set containing magnetic field parameter adjustment and injection parameter compensation, the execution phase begins. The magnetic field parameter adjustment portion of the composite control instruction set is sent to a high-frequency power amplifier. The high-frequency power amplifier receives digital instructions containing specific waveform parameters and, through its internal digital-to-analog conversion module and power amplification circuit, drives the connected solenoid assembly to generate a corrected magnetic field with corresponding waveform parameters. The spatiotemporal distribution characteristics of the corrected magnetic field are precisely controlled by the composite control instruction set. For example, increasing the current amplitude can enhance the magnetic field strength, and frequency modulation can change the magnetic field penetration depth and torque action mode. Simultaneously, the injection parameter compensation portion of the composite control instruction set is sent to the injection molding machine motion controller. The injection parameter compensation portion typically includes the screw's axial position-speed curve correction value or pressure setpoint adjustment value. Based on the received compensation instructions, the injection molding machine motion controller adjusts the output of the servo motor or hydraulic system in real time, thereby changing the screw's axial motion pattern, including injection speed, holding pressure switching point, and pressure holding value. The adjustment of the screw's motion pattern aims to change the shear flow field and pressure distribution inside the melt, making them work synergistically with the corrected magnetic field.

[0088] In practical implementation, after one control cycle of command execution, the system enters a feedback and optimization loop. New process data is collected again through a distributed sensor network. This network, including a micro-differential pressure sensor array and a three-dimensional fluxgate sensor, resynchronizes the measurement of dynamic viscosity gradients and spatial distribution of magnetic field strength at key locations in the mold flow channel and cavity. The newly collected process data is immediately input into a multiphysics-coupled simulation engine based on micromechanics, initiating a new round of simulation calculations and optimization command generation. This new round of simulation calculations re-solves the angular momentum conservation equation of the magnetic powder under the updated rheological and magnetic fields, outputting a new instantaneous orientation vector field of the magnetic powder on the discrete grid nodes within the mold cavity, and generating a new orientation order topology map accordingly. Comparing the orientation order topology map obtained in the new round of calculations with the previous round is crucial for evaluating the control effect. This comparison can be achieved by calculating the difference matrix of orientation order parameters on corresponding grid nodes in the two topology maps. Global differences can be quantified using mean absolute error or root mean square error, while local differences focus on specific regions, such as previously identified weak orientation regions.

[0089] It is understandable that comparing the differences in orientation-ordered topology graphs can quantitatively assess the effectiveness of the composite control instruction set. If the area reduction of weakly oriented regions does not meet the expected target, for example, if the reduction ratio of the area of ​​weakly oriented regions in the new round of topology graphs compared to the previous round is lower than the preset optimization threshold, the system initiates an adaptive adjustment mechanism. Based on historical control records and effects, the orientation-ordered threshold in the region growing algorithm or the size of the structuring element used in morphological operations is adaptively updated. Historical control records include the thresholds used in previous control cycles, the sizes of structuring elements, and the corresponding rate of change of the area of ​​weakly oriented regions. The adaptive update process can be described as an optimization search. For example, if the shrinkage of weakly oriented regions is found to be slow continuously, it may mean that the initially set orientation-ordered threshold is too strict, and the system will reduce the orientation-ordered threshold in the region growing algorithm by a certain step size. ,in: It's a new threshold. It's the old threshold. The step size is adjusted based on historical results to identify more core weakly oriented regions for targeted control; or, if the transition boundary regions are identified as too fragmented, the size of the structural elements in the morphological operation may be increased to obtain a more coherent boundary profile.

[0090] In some embodiments, the entire process from data acquisition to instruction generation is repeatedly executed to achieve progressive optimization of the magnetic powder orientation state during injection molding. This closed-loop control cycle continues until the entire filling process is completed, or until the evaluation indicators of the orientation order topology (such as the global average orientation order and the total area of ​​weakly oriented regions) reach a preset qualification standard. Optionally, the duration of each control cycle can be dynamically adjusted according to the speed of the filling process. Shorter control cycles are used in stages of rapid flow changes to achieve fast response, while slightly longer control cycles can be used in stages of relatively stable flow to reduce computational load. It can be understood that this progressive optimization method based on real-time feedback and model prediction can dynamically compensate for process fluctuations and model errors, gradually pushing the magnetic powder orientation state towards a more uniform and ordered target. In some embodiments, to improve the convergence speed, more advanced optimization algorithms, such as model-based predictive control algorithms, can be used to generate a composite control instruction set. This algorithm not only considers the optimization at the current moment but also predicts the system behavior within a finite future time and generates a corresponding feedforward control sequence. Optionally, all historical data from the entire incremental optimization process, including process data for each round, simulation results, generated instruction sets, and effect evaluations, is fully recorded for subsequent process analysis and model calibration, thereby continuously improving the performance of the optimization system. Through this iterative execution, measurement, comparison, and adjustment, the injection molding magnetic powder orientation optimization method can achieve online assurance of orientation quality under uncertain process environments.

[0091] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for injection molding magnetic powder orientation optimization based on rheology-magnetic field coupling, characterized in that, The method comprises the following steps: During the injection filling stage, the dynamic viscosity variation gradient and the spatial distribution of magnetic field intensity during the melt flow process are synchronously captured through a distributed sensor network arranged at key positions of the mold runner and the mold cavity; The captured dynamic viscosity variation gradient and the spatial distribution of magnetic field intensity data are input into a multi-physical field coupling simulation engine based on micro-mechanics, which outputs the instantaneous orientation vector field of the magnetic powder at the discrete grid nodes in the mold cavity by iteratively calculating the angular momentum conservation equation of the magnetic powder under the combined action of viscoelastic fluid and time-varying magnetic field; Tensor invariant analysis is performed on the instantaneous orientation vector field of the magnetic powder to extract the orientation order parameter of each grid node, and an orientation order topology reflecting the overall orientation state of the cavity is constructed based on the orientation order parameter; On the orientation order topology, a region growing algorithm is applied to identify the weak orientation region with an orientation order lower than a set threshold, and a morphological operation is performed to detect the transition boundary region with a sharp orientation order gradient variation; According to the spatial geometric characteristics of the weak orientation region and the evolution dynamics of the transition boundary region, a composite control instruction set containing magnetic field parameter adjustment and injection parameter compensation is generated; The tensor invariant analysis of the instantaneous orientation vector field of the magnetic powder, the extraction of the orientation order parameter of each grid node, and the construction of the orientation order topology reflecting the overall orientation state of the cavity based on the orientation order parameter comprise: Eigenvalue decomposition is performed on the instantaneous orientation vector of each grid node to calculate the ratio of the eigenvalues of the principal axis direction and the secondary direction, and the ratio is normalized to define the orientation order parameter of the node; The spatial coordinates of all grid nodes in the cavity are associated with the corresponding orientation order parameters, and a scattered data interpolation method is used to generate a continuous spatial orientation order distribution surface; Contour lines are extracted from the spatial orientation order distribution surface, and grid nodes with the same orientation order parameter are connected to form a multi-level contour map; The multi-level contour map is superimposed and fused with the three-dimensional geometric model of the cavity to generate the orientation order topology with height information and intuitive display of the spatial variation of the orientation order.

2. The rheometric-magnetic field coupling based injection molding magnetic powder orientation optimization method according to claim 1, characterized in that, The region growing algorithm is applied to identify the weak orientation region with an orientation order lower than a set threshold, and a morphological operation is performed to detect the transition boundary region with a sharp orientation order gradient variation on the orientation order topology, which comprises: An orientation order threshold is set on the orientation order topology, and the threshold is used as the seed point selection standard; Starting from the seed point with an orientation order lower than the threshold, the region growing algorithm is applied to expand the region according to the orientation order similarity between adjacent grid nodes until the boundary node is encountered, thereby marking the connected weak orientation region; A morphological gradient operation is performed on the complete orientation order topology to calculate the modulus of the orientation order difference between each grid node and its neighborhood nodes, and an orientation order gradient map is generated. A gradient threshold is set on the orientation order degree gradient map, a transition boundary region is defined as a set of grid nodes whose gradient values exceed the threshold, and the contour coordinates of the transition boundary region are recorded.

3. The method of injection molding magnetic powder orientation optimization based on rheological-magnetic field coupling according to claim 2, characterized in that, The composite control instruction set containing the magnetic field parameter adjustment amount and the injection parameter compensation amount is generated according to the spatial geometric characteristics of the weak orientation region and the evolution dynamics of the transition boundary region, including: The geometric characteristics of the weak orientation region are analyzed, including the area, shape factor, and position relationship relative to the gate, and the required magnetic field enhancement strength and action range are calculated based on these characteristics; The moving speed and direction of the transition boundary region in the continuous time step are monitored, and the orientation defect expansion path that it may cause is predicted; The frequency modulation depth and current amplitude increment of the magnetic field generator are determined in combination with the magnetic field enhancement strength and action range and the prediction of the orientation defect expansion path, and the speed correction curve and pressure retention strategy of the injection molding machine screw are determined; The adjustment parameters of the magnetic field generator and the adjustment parameters of the injection molding machine are packaged and synchronously sequenced to form the composite control instruction set.

4. The method for injection molding magnetic powder orientation optimization based on rheological-magnetic field coupling according to claim 3, characterized in that, The adjustment parameters of the magnetic field generator and the adjustment parameters of the injection molding machine are packaged and synchronously sequenced to form the composite control instruction set, including: Timestamps are added to the magnetic field parameter adjustment amount and the injection parameter compensation amount respectively to ensure accurate matching of magnetic field changes and melt flow stages; A constraint relationship library is established between the adjustment parameters to prevent mutually conflicting adjustment instructions from being issued at the same time; The time-synchronized and conflict-free magnetic field parameter adjustment amount and injection parameter compensation amount are packaged into data frames according to the preset communication protocol to form the composite control instruction set that can be directly issued to the execution device.

5. The rheometric-magnetic field coupled based injection molding magnetic powder orientation optimization method according to claim 1, wherein, The captured dynamic viscosity variation gradient and magnetic field intensity spatial distribution data are input into a multi-physical field coupling simulation engine based on micro-mechanics, which outputs the magnetic powder instantaneous orientation vector field on the discrete grid nodes in the mold cavity by iteratively calculating the angular momentum conservation equation of the magnetic powder under the combined action of viscoelastic fluid and time-varying magnetic field, including: The multi-physical field coupling simulation engine based on micro-mechanics includes a balance relationship formula describing the rotational inertia of the magnetic powder, the fluid viscous resistance, and the magnetic field torque; At each calculation time step, the dynamic viscosity variation gradient at the current time is taken as the input of the fluid viscous resistance, and the magnetic field intensity spatial distribution at the current time is taken as the input of the magnetic field torque, and the balance relationship formula is solved by using the implicit time integration method; The process of solving the balance relationship formula outputs a vector with size and direction at each discrete grid node, which represents the average orientation state of the magnetic powder assembly at the spatial point corresponding to the discrete grid node, i.e. the magnetic powder instantaneous orientation vector field.

6. The rheometric-magnetic field coupled based injection molding magnetic powder orientation optimization method according to claim 1, wherein, During the injection filling stage, the dynamic viscosity variation gradient and the magnetic field intensity spatial distribution during the melt flow process are synchronously captured through the distributed sensor network arranged at key positions of the mold runner and cavity, including: The distributed sensing network comprises a micro pressure difference sensor array and a three-dimensional magnetic flux gate sensor, the micro pressure difference sensor array is arranged along the melt flow path, and the dynamic viscosity change gradient is obtained by measuring the pressure difference of adjacent points; The three-dimensional magnetic flux gate sensor is embedded in the mold in a spatial dot array form, directly measures the magnetic field vector at the point, and obtains the spatial distribution of the magnetic field intensity through time sequence acquisition.

7. The rheometric-magnetic field coupled based injection molding magnetic powder orientation optimization method of claim 1, wherein, After generating the composite control instruction set comprising the magnetic field parameter adjustment amount and the injection parameter compensation amount, the method further comprises: sending the magnetic field parameter adjustment amount part in the composite control instruction set to a high-frequency power amplifier to drive the solenoid assembly to generate a corrected magnetic field with specific waveform parameters; sending the injection parameter compensation amount part in the composite control instruction set to an injection molding machine motion controller to adjust the axial motion law of the screw; After one control cycle of instruction execution, new process data is collected again through the distributed sensing network to start a new round of simulation calculation and optimization instruction generation process.

8. The rheometric-magnetic field coupled based injection molding magnetic powder orientation optimization method according to claim 7, wherein, The method further comprises: comparing the difference between the orientation order degree topological graph obtained in the new round of calculation and the last round to evaluate the effectiveness of the composite control instruction set; if the area reduction of the weak orientation region does not reach the expected target, then based on the historical control records and effects, the threshold value in the region growing algorithm or the structure element size in the morphological operation is adaptively updated; repeating the whole process from data collection to instruction generation to realize the progressive optimization of the magnetic powder orientation state in the injection molding filling process.

9. A rheo-magnetic field coupling based injection molding magnetic powder orientation optimization system comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, The processor, when executing the computer program, realizes the steps of the injection molding magnetic powder orientation optimization method based on rheology-magnetic field coupling according to any one of claims 1 to 8.

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