3104 results about "Injection molding process" patented technology

The invention provides an intelligent toy injection molding process optimization method which comprises the following steps: aiming at a key temperature control area, calculating an optimized cooling time value and a cooling water temperature set value through a correlation model of thermodynamic attribute parameters of toy materials, cooling time and cooling water temperature, and issuing the cooling time value and the cooling water temperature set value to an injection molding machine control system, meanwhile, according to the temperature fluctuation amplitude of the key area, the forming pressure value is finely adjusted, so that the temperature fluctuation of the key area is reduced; in the injection molding process, temperature data of multiple positions on the surface of the mold are collected in real time and transmitted to the central processing unit, the central processing unit conducts filtering and denoising on the collected temperature data and then judges whether the temperature data exceed the optimized temperature control range or not, if yes, an alarm mechanism is triggered, and meanwhile operation parameters of the injection molding machine are automatically adjusted. And the time for deviating from the temperature control range is shortened to the minimum.

The invention belongs to the field of artificial intelligence, particularly relates to an intelligent regulation and control system for an injection molding process of an industrial control system, and aims to solve the problem that high-precision cooperative regulation and control are difficult under material batch fluctuation, mold state change and environmental disturbance. The system comprises a multi-source sensing module, a dynamic modeling module, a self-adaptive decision-making module, an execution feedback module and a knowledge evolution module, and high-stability and high-adaptability intelligent regulation and control of the injection molding process are achieved through a mixed digital twin model integrating a physical mechanism and data driving, confidence-guided multi-objective optimization and continuous evolution of a process knowledge graph.

The invention relates to the technical field of precise injection molding control, and discloses an injection molding process parameter self-adaptive compensation system which comprises a multi-source sensing module used for collecting mold cavity pressure, melt temperature and screw displacement information in the injection molding process in real time; the data fusion module is used for performing time sequence synchronization, space registration and noise suppression on the multi-source sensing data; the dynamic modeling module is used for constructing a dynamic model of an injection molding process state based on the fused process data and outputting a state estimation value; and the stability analysis module is used for carrying out system stability analysis based on the dynamic model and generating a control compensation strategy. Cooperative control of precise positioning and high-pressure response is realized through a composite driving framework and a dynamic disturbance compensation technology; an incremental learning algorithm and a multi-dimensional quality evaluation system are combined, a process parameter self-evolution mechanism is constructed, material characteristic drift and complex working condition disturbance are effectively dealt with, and closed-loop optimization from process control to quality feedback is formed.

The invention relates to an injection mold cavity state monitoring method, system and device and a medium, and belongs to the technical field of mold state monitoring. The monitoring method comprises the steps that cavity state data of a to-be-detected injection mold is received, and clock synchronization and preprocessing are conducted; dynamically segmenting the time sequence data according to the injection molding process cycle, generating a sliding window for each process stage of the injection molding process cycle, and marking a key point in each sliding window; extracting statistical features and difference features between cavities, calculating trend features of adjacent data key points in each sliding window, combining to obtain high-dimensional feature vectors, inputting the high-dimensional feature vectors into a normal process reference model for comparison, and calculating to obtain a feature difference degree of each feature; performing anomaly detection according to the feature difference degree, positioning an anomaly time period and an anomaly space region, and obtaining an anomaly detection result; and generating maintenance prompt information according to the anomaly detection result and sending the maintenance prompt information to the management terminal. According to the invention, timely early warning of the abnormal state of the mold can be realized.

The invention relates to a full-automatic injection molding optimization method, device and equipment based on digital twinning, and belongs to the technical field of intelligent manufacturing equipment. Inputting the sensor data into a digital twin model to construct equipment dynamic response simulation, and synchronously comparing the actual mode locking force with the offset of a preset pressure threshold; generating compensation data of mold locking pressure according to the offset, and dynamically correcting an acceleration curve of a servo motor in a glue injection stage on the basis of the displacement of a screw rod; in hot runner control, a thermal interference compensation coefficient is calculated according to a temperature difference value of adjacent temperature zones, and an output power gradient signal of each heating ring is adjusted; the corrected parameter information is transmitted to an equipment PLC through an industrial bus to execute control, and a compensation coefficient threshold value is iteratively updated according to a product weight fluctuation value in the next forming period; closed-loop data interaction of an equipment control system is achieved, and the injection molding process is optimized.

The invention relates to the technical field of intelligent control of injection molding machines, in particular to a self-adaptive full-automatic spline injection molding system. Comprising an injection molding data acquisition module, a spline model generation module, a self-adaptive control module, a finished product quality detection module and a monitoring and early warning module. The data acquisition module acquires multi-dimensional data in the injection molding process in real time, the spline model generation module adopts deep learning to construct a nonlinear spline model, and the adaptive control module adjusts equipment parameters in real time based on the model. The finished product quality detection module analyzes quality features such as sizes and defects through machine vision and generates a detection report. And the monitoring and early warning module monitors the running state of the equipment in real time, identifies fault features and provides adjustment suggestions. The spline injection molding process is comprehensively optimized, and the production efficiency, the finished product quality and the safety and reliability of equipment operation are remarkably improved.

The invention discloses an intelligent control system and method for gas-liquid photoelectric slip ring injection molding machining, and relates to the technical field of injection molding process control. The data acquisition module is used for acquiring multi-dimensional production data by using a sensor array and an advanced optical technology; the data analysis and processing module fuses and optimizes data through deep learning; the decision-making module combines reinforcement learning and a knowledge graph to generate a strategy; the execution control module adopts an advanced control algorithm to realize accurate control; the monitoring and early warning module guarantees production by using technologies such as digital twinning; the man-machine interaction module applies a leading-edge interaction technology; the communication module adopts a high-speed communication architecture; all the modules cooperate to achieve intellectualization and precision of injection molding machining. The gas-liquid photoelectric slip ring injection molding processing level is greatly improved, the product percent of pass is improved, the production efficiency is improved, the unit product energy consumption is reduced, meanwhile, intelligent interaction and remote management are achieved, and the production benefit and quality are improved.

The invention relates to the technical field of injection molding process control, and discloses an injection molding parameter self-learning collaborative regulation and control system which is characterized in that multi-source parameters such as temperature and pressure are acquired in real time through a data acquisition module, and the multi-source parameters are mapped to a low-dimensional manifold space through a dynamic manifold construction module to decouple a nonlinear coupling relationship; the chaos synchronization control module suppresses external disturbance based on master-slave system design, and the distributed optimization module realizes multi-target real-time decision by adopting a split Bregman algorithm; the execution module converts the optimization instruction into a control signal and triggers closed-loop feedback; and the federated learning architecture realizes cross-device strategy migration by extracting continuous homology features of a manifold space and combining a Hausdorff distance and homeomorphic mapping, and drives a global model to update. According to the invention, the technical problems of difficult dynamic regulation and control of nonlinear parameters, insufficient real-time performance of multi-constraint optimization, low cross-scene repeated parameter adjustment efficiency and lack of self-adaptability of long-term process drift in the injection molding process are solved.

The invention discloses a process parameter optimization control method and system for an automobile injection molding part, and belongs to the technical field of data processing, and the method comprises the steps: collecting sample data to train a feedforward neural network based on the historical processing record of a target injection molding part, and constructing a product quality prediction plug-in; performing parameter fluctuation influence analysis according to the historical processing record, and constructing a fluctuation influence analyzer; determining product demand characteristics of the target injection molded part in combination with the automobile use scene; and by taking the product demand characteristics as expectations, optimizing the process parameters by using the product quality prediction plug-in and the fluctuation influence analyzer, and outputting the optimal process parameters for processing control. The problems of large product quality fluctuation and low production efficiency caused by insufficient injection molding process parameter control precision in the prior art are solved. By combining historical records, product quality prediction plug-ins and fluctuation analysis, precise optimization of process parameters is realized, the product quality is stabilized, and the controllability and efficiency of the production process are improved.

The invention discloses an injection molding process for a plastic part of an air purifier, and relates to the technical field of injection molding processes. Multi-area dynamic mold temperature adjustment is performed, a plurality of temperature control areas are divided according to the thin wall and curved surface structure characteristics of the plastic part, the temperature is adjusted in real time, stress concentration and deformation are remarkably reduced, and the service life of the plastic part is prolonged. The problem of shrinkage stress of a thin-wall area is solved in a targeted mode, cooling of a thick area is accelerated, meanwhile, real-time temperature control and deformation compensation are controlled in a linkage mode, the temperature and the cooling rate are dynamically adjusted according to deformation data monitored in real time, and the deformation trend is predicted, so that pre-deformation compensation is provided, warping of the thin-wall area and the curved surface area is controlled, and deformation of the thin-wall area and the curved surface area is controlled. The stability of the plastic part in the forming process is kept, and the product precision is improved; in addition, simulation prediction and mold parameter adjustment are carried out, cooling requirements of different areas are adjusted, the temperature difference between the thin wall and the curved surface area is reduced, and the whole cooling process is more efficient.

The invention discloses an injection molding process fault diagnosis model training method and system based on a large language model and a fault diagnosis method. The model training method comprises the following steps: collecting and cleaning process parameters under the fault working condition of the injection molding machine, converting the process parameters into a natural language text, combining the natural language text with a fault label to construct a textualized data set, and dividing the textualized data set into a training set and a verification set according to a proportion; and in combination with the text data dimension and the fault category number, loading the pre-trained large language model and configuring a diagnosis model structure in a quantitative mode. And inputting the training set into a model to extract semantic features, processing the semantic features by a feature conversion module to generate a high-order feature vector, and inputting a classification head to output a fault category probability. And back propagation is carried out by using a loss function, and model parameters are efficiently and finely tuned in combination with low-rank adaptation and a layered freezing strategy. And repeating training until the performance reaches the standard, and outputting a final diagnosis model. The method is efficient in training, and can effectively reduce the maintenance and use cost of the model.

The invention relates to the technical field of injection molding machine temperature control, and particularly discloses a real-time temperature control method applied to an injection molding machine, which uses a PID (Proportion Integration Differentiation) controller to carry out temperature control in an injection molding process. Firstly, a target temperature value is set, an automatic tuning algorithm is used for determining a preliminary PI D parameter of a real-time temperature PID controller, then an artificial intelligence technology based on deep learning is used for carrying out time sequence analysis on real-time temperature data in the injection molding process so as to reveal the effectiveness of the current PID parameter on temperature control, and the accuracy of temperature control is improved. Therefore, the self-adaptive optimization of the initial PID parameters is carried out based on the time sequence change mode of the temperature, so that the accuracy and the response speed of temperature control are improved. According to the invention, the temperature change can be quickly responded, and more efficient and more stable temperature management is realized, so that overshoot and oscillation phenomena are reduced, and more stable and accurate temperature control in the injection molding process is ensured.

The invention provides a cooling adjusting method and system for an injection mold, and relates to the technical field of injection molds. The method comprises the following steps: acquiring real-time temperature data in the injection molding process of the mold by utilizing temperature sensors arranged at a plurality of preset measuring points of a mold cavity of the injection mold; based on the real-time temperature data, a mold cavity heat distribution model representing mold cavity temperature distribution information is generated; according to the mold cavity heat distribution model, the mold cavity is divided into a plurality of sub-cooling areas, and each sub-cooling area corresponds to at least one cooling loop; determining the temperature state of each sub-cooling area based on the mold cavity heat distribution model, and generating a flow regulation instruction of the corresponding cooling loop according to the temperature state; and controlling an electric control valve and a variable frequency water pump of each cooling loop according to the flow regulation instruction so as to regulate the flow of the cooling fluid flowing through each sub-cooling area. According to the technical scheme, partition precise adjustment of the cooling process of the injection mold can be achieved.

The invention provides an injection molding process quality prediction and process parameter sliding window online optimization method and system. The method comprises the steps of obtaining time sequence process parameter data and batch statistical data; constructing and training a CNN-PCLSTM quality prediction model to output a product quality prediction value; dividing a sliding window in the time sequence process parameter data; constructing a loss function based on a deviation between a product quality prediction value predicted by the CNN-PCLSTM quality prediction model and a preset expected product quality target value, performing iterative optimization calculation based on the loss function, and adjusting a time sequence process parameter set value in the sliding window; updating the process parameter set value in the sliding window obtained through iterative optimization calculation to a control system in the injection molding process; and moving the sliding window and repeating the steps. According to the method, the problems of low quality prediction precision and insufficient optimization efficiency in the prior art are effectively solved.

The invention relates to the technical field of deep learning, and discloses an earphone mold injection molding parameter optimization method, device and equipment, and the method comprises the steps: collecting temperature distribution data and mold cavity pressure data in an earphone mold cavity, and carrying out earphone shell injection molding dynamic analysis to obtain an earphone shell material flow curve; carrying out topological characteristic analysis on the earphone shell material flow curve, and constructing an earphone shell injection molding process parameter model; based on the earphone shell injection molding process parameter model, the mold cavity temperature and the mold cavity pressure serve as state variables, the heating power and the injection pressure serve as control variables, and an earphone shell injection molding optimization controller is constructed; the earphone shell injection molding optimization controller is connected with the injection molding machine control system, the heating power and the injection pressure of the earphone mold are adjusted in real time, the optimal technological parameters of the earphone mold are solved, a real-time feedback and parameter fine adjustment mechanism is adopted, the injection molding machine control system can quickly respond to technological fluctuation, and the consistency of product quality is guaranteed.

The invention discloses a multi-objective optimization method for double-material injection molding process parameters. The multi-objective optimization method comprises the following steps: firstly, obtaining a simulation result data set based on Moldflow simulation analysis and a Taguchi test; then, on the basis of the simulation test data set, a GAM model is constructed and then trained, super-parameters of GAM are optimized through a GTO algorithm, and a GTO-GAM model between injection molding process parameters and a quality optimization target is established; then, performing multi-target optimization on the GTO-GAM model through an MODA algorithm to obtain a Pareto leading edge of a quality optimization target; and finally, determining an optimal combination of process parameters in the obtained Pareto leading edge of the quality optimization target through a CRITIC-TOPSIS evaluation method. According to the method, the buckling deformation and the volume shrinkage rate of the double-material injection molding plastic part can be effectively reduced, and a theoretical basis and data support can be provided for obtaining the optimal technological parameter combination of double-material injection molding.

The invention specifically relates to the technical field of intelligent control, and discloses an intelligent control-based mold forming optimization system, which comprises a defective finished product image acquisition module, a defect intelligent identification module, a defect score quantification module, a process parameter synchronous acquisition module, a process risk dynamic analysis module and a process association closed-loop optimization module, the method comprises the following steps of: calculating a single-parameter risk index and a comprehensive risk index based on a process risk dynamic analysis module through a process parameter synchronous acquisition module in real time of full-flow process parameters of an injection molding process, identifying abnormity when the parameters deviate from an optimal target value, immediately triggering early warning, quickly positioning core cause parameters through a defect-process incidence matrix, and quickly positioning the core cause parameters through the defect-process incidence matrix. The influence of adjustment of different parameters on comprehensive defect scores is simulated within an equipment constraint range through a reverse optimization algorithm, a reverse optimization scheme is generated, a full-link traceable data link is formed through timestamps, stop loss time and production cost are greatly reduced, and accurate assessment and prediction of risks are achieved.

The invention provides an intelligent control method for improving the forming efficiency of an injection mold, and relates to the technical field of injection molding control, and the intelligent control method comprises the following steps: performing mold flow analysis through initial experiment parameters, and configuring an initial experiment parameter range; the quality index factor parameters are executed in an experiment to obtain quality index experiment response, and an optimal experiment parameter combination is obtained; constructing a quality index prediction model; constructing an initial population to perform explosion search; acquiring a quality index production response according to the optimal process parameter combination, performing deviation analysis on the quality index production response and the quality index prediction response, and adjusting the optimal process parameter combination; and injection mold forming is controlled based on the actual process parameter combination. By means of the method and device, the technical problems that in the prior art, parameter setting depends on artificial experience, and interaction of multiple parameters is difficult to effectively optimize can be solved, the technical purposes of optimizing the injection molding process parameters and dynamically adjusting the parameters are achieved, and the technical effect of improving the production efficiency is achieved.

An apparatus for instantaneous generation of a pin placement quote in an injection molding process, the apparatus comprising at least a processor and a memory containing instructions configuring the at least a processor to receive part data related to at least a part from an entity, generate a pin placement schema as a function of the part data, provide a pin placement quote based on the pin placement schema, wherein providing the pin placement quote includes generating a projected part data by overlaying the pin placement schema onto the part data, determining a list of quote parameters, and formulating the pin placement quote by associating the list of quote parameters with the projected part data, and display the pin placement quote to the entity using a user interface at a display device.

The invention discloses an electric toothbrush head plastic mold forming control device and method, and relates to the technical field of mold forming control. The device comprises the steps that the production requirement of an electric toothbrush head is obtained, plastic mold design is conducted, and brush head plastic mold design parameters are obtained; according to the brush head plastic mold design parameters, a plastic mold production line is activated, and a target brush head plastic mold is obtained; constructing a brush head molding process control space to obtain a brush head injection molding process solution set; carrying out traversal search optimization by taking the production demand of the brush head of the electric toothbrush as a constraint parameter, and outputting a forming process control parameter; and performing a brush head pre-forming test to obtain brush head forming test parameters, and performing brush head production optimization control. The technical problem that in the prior art, the product quality and the production efficiency are low in the whole brush head production process is solved, and the technical effects that precise control and optimization from the production requirement to the whole brush head production process are achieved, and the product quality and the production efficiency are improved are achieved.

The invention discloses a multi-objective optimization method for injection molding process parameters. The method comprises the following steps: firstly, obtaining a simulation result data set based on Moldflow simulation analysis and CCF; then, an NGO-XGBoost is adopted to construct an injection molding quality optimization model; then explaining and analyzing the NGO-XGBoost model by using an SHAP method to obtain importance and positive and negative influences of each optimization design variable on a prediction quality optimization target, and performing multi-target optimization through MOMVO to obtain a Pareto leading edge; and finally, determining an optimal combination of process parameters through a CRITIC-TOPSIS evaluation method. According to the method, the buckling deformation and the volume shrinkage of the ABS plastic injection molding plastic part can be effectively reduced, so that a high-quality ABS plastic part product is obtained, and a theoretical basis and data support can be provided for obtaining an optimal technological parameter combination of ABS plastic injection molding.

The invention belongs to the technical field of PBT plastic particle injection molding, and particularly relates to a precise injection molding process control system for PBT plastic particles. The control system is composed of the following modules: a material characteristic sensing and modeling module, a multi-physical field simulation and parameter generation module, a distributed optical fiber global sensing module, a predictive closed-loop control module, and a digital twinning and full-period optimization module. The material characteristic sensing and modeling module is used for acquiring microscopic parameters of PBT raw materials through a near infrared spectrum sensor and constructing a material characteristic dynamic model; raw material characteristic fluctuation is pre-judged through near infrared spectrum dynamic modeling, technological parameter drift is predicted in advance in combination with an LSTM neural network, and feed-forward compensation is started before deviation is formed; compared with post correction in the prior art, the mechanism has the advantages that the parameter fluctuation control precision is improved, and the prevention efficiency for the problems of hydrolytic degradation, internal stress concentration and the like which are prone to occurring in PBT is improved.

The invention belongs to the technical field of flame retardant materials, and provides a polymer additive and a preparation method thereof, and a polymer composition, the polymer additive comprises the following components by mass: 95-99.88 parts of diethyl phosphinate, 0.1-3 parts of ethyl butyl phosphinate, 0.01-1 part of ethyl phosphonate, and 0.01-1 part of monoethyl phosphinate; the water-soluble acid value of the polymer additive is 0.1-0.3 mg KOH / g, and the polymer additive has low acidity, and when the polymer additive is used as a flame retardant for a polymer composition, the generation of high-temperature decomposition of dialkyl phosphinic acid can be slowed down, so that the polymer composition has lower water boiling precipitate content, the use stability of the polymer composition under damp and hot conditions is improved, the service life of the polymer composition is prolonged, and the service life of the polymer composition is prolonged. And moreover, the amount of mold scale generated in the injection molding process is smaller, the injection molding production efficiency can be improved, and the product appearance is improved.

The invention relates to the technical field of material forming, in particular to a flexible leather injection molding method, a mold and an injection molding part. The flexible leather injection molding method comprises the steps that flexible leather is put into an injection mold, and the mold is closed; the flexible leather is attached and fixed to a front mold body of the injection mold through negative pressure; injection molding raw materials are injected into a cavity of the injection mold, so that the injection molding raw materials are combined with the flexible leather; and opening the film after the plastic is cooled and cured. According to the method, the fitting process and the injection molding process of the flexible leather are combined, the flexible leather is placed in the injection mold in advance, the flexible leather is precisely attached to the surface of the mold through the negative pressure technology, then the injection molding raw material in the hot melting state is directly injected, and integrated forming of the flexible leather and the injection molding raw material is achieved; the manufacturing requirements of products with complex designs such as grooves, craters and button positions can be better met, the attaching technology after injection molding of a traditional technology is completed is omitted, and the design cost and the overall product cost are reduced.

The invention provides a polyamide composite material and application thereof. The polyamide composite material comprises the following components in parts by mass: 35-82 parts of polyamide resin, 5-45 parts of a reinforcing material and 5-25 parts of an organic phosphonate flame retardant, the water-soluble acid value of the organic phosphonate flame retardant is 0.1 to 0.3 mg KOH / g. By adopting the organic phosphonate flame retardant with a low acid value, the generation of dialkyl phosphinic acid decomposed in a high-temperature process can be slowed down, so that the amount of mold scale generated in an injection molding process of the polyamide composite material is obviously reduced, and the injection molding production efficiency is improved; meanwhile, the polyamide composite material has the advantages that the content of water boiling precipitates is obviously reduced, the damp-heat-resistant stability is excellent, and the service life of the polyamide composite material under damp-heat conditions is prolonged.

The invention relates to the technical field of mold machining optimization, and discloses a mold machining optimization method, system and device and a storage medium. The method comprises the steps that design parameters of a mold are obtained and input into simulation software, and a mold product model is generated; performing parameter optimization through mold flow analysis to obtain optimized design parameters; actual production parameters are calculated according to the optimized parameter values, and key parameter values in the production process are recorded; and finally exporting and displaying an optimization result, and determining an optimal design value. The method has the following effects that through simulation analysis and multiple iterative optimization, the efficiency of mold design and the injection molding process is effectively improved, the precision and quality of product forming are ensured, and intelligent monitoring and optimized display of the production process are achieved.

The invention belongs to the technical field of underground gas storage, and particularly relates to a long-term efficient hydrogen storage composite lining structure for an underground rock cavern and a preparation method of the long-term efficient hydrogen storage composite lining structure. The long-term efficient hydrogen storage composite lining structure for the underground rock cavern comprises surrounding rock and an internal composite structure of the surrounding rock. The surrounding rock reinforcing layer is formed by pressure pre-grouting in surrounding rock and can resist tensile stress transmitted to the surrounding rock by internal air pressure and enhance air tightness; the permeable layer is arranged on the inner side of the surrounding rock reinforcing layer and can discharge underground water to prevent hydrostatic pressure from causing extrusion deformation damage to the concrete bearing layer during construction and causing instability of the sealing steel lining; the reinforced concrete bearing layer is arranged on the inner side of the permeable layer and the outer side of the slip layer, and micro self-healing capsules capable of repairing cracks and enhancing sealing performance are evenly added in the injection molding process; the anti-drag sliding layer is arranged between the steel lining and the bearing layer and inhibits shear damage of the sealing steel lining to the bearing layer under high internal pressure; the steel lining sealing layer is arranged on the inner side of the sliding layer to bear part of gas load and seal gas.

The invention relates to the technical field of automatic control, and discloses a control method and system for an injection molding process of automotive trim. The method comprises the following steps: synchronously acquiring a pressure pulsation signal, flow velocity distribution data and flow front position information of a plastic melt through a high-frequency pressure pulsation sensor array and a melt flow velocity sensor, and performing fast Fourier transform processing on the pressure pulsation signal to obtain a characteristic frequency component; a flow impedance coefficient and a shear stress distribution parameter are calculated in combination with flow velocity data, the impedance coefficient is compared with a reference value to identify an abnormal type, a pressure compensation amount is calculated through a classification compensation algorithm, and finally a servo hydraulic execution system carries out rapid pressure adjustment. The technical problem that the flow impedance characteristic change of the plastic melt cannot be recognized in real time and the injection pressure cannot be dynamically compensated in the existing automotive trim injection molding process is solved.

The invention discloses an intelligent pressure maintaining control method for a thin-wall product of an injection molding machine based on mold pressing feedback. The intelligent pressure maintaining control method comprises the following steps: (1) constructing a hardware layer; (2) configuring a software layer; (3) a mold cavity pressure acquisition module acquires original pressure data in the injection mold cavity in real time through a pressure sensor; (4) the data preprocessing module carries out filtering processing on pressure signals generated by the pressure data, and (5) the dynamic pressure maintaining control module dynamically adjusts pressure parameters and pressure maintaining time by receiving the real-time pressure data, product wall thickness parameters, mold structure parameters and injection molding process parameters, generates a pressure maintaining control instruction and sends the pressure maintaining control instruction to an injection molding machine control system; driving the injection molding machine to execute pressure maintaining action; (6) the dynamic pressure maintaining control module associates and stores the pressure change curve and the parameter adjustment record in the pressure maintaining process in a feedback learning module, and optimization calculation is carried out; and (7) the man-machine interaction interface displays the pressure change curve, the pressure maintaining control parameters and the forming state of the injection mold cavity in real time.

The invention discloses a medical developing marker and a manufacturing method thereof, and relates to the field of medical instruments, the medical developing marker comprises a closed tube, the outer wall of the closed tube is provided with a bionic barb structure, the bionic barb structure is distributed in a flexible zigzag convex manner, and the bionic barb structure and the closed tube are integrally formed through an injection molding process; a closed cavity is arranged in the closed tube, four developing strips are symmetrically arranged in the tube wall of the closed cavity, the tube wall of the region where the developing strips are located is etched to form a weak band, and a developing material layer and a sustained-release medicine layer are sequentially deposited at the weak band. The developing material layer and the sustained-release drug and / or marked nano-particle layer are alternately deposited to form a functional composite layer, and the functional composite layer can directionally rupture to release the therapeutic drug under the action of implantation pressure. The marker integrates the functions of precise development, tissue anchoring and drug sustained release, is adaptive to multi-mode imaging such as ultrasonic imaging, X-ray imaging, CT imaging, MRI imaging and PET-CT imaging, and has remarkable clinical value in precise positioning treatment of solid tumors such as breast cancer and liver cancer.