2382 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 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 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 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 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 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 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 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.

The invention relates to the technical field of injection molding production process monitoring, and discloses an injection molding production monitoring method, device and system.The injection molding production monitoring method comprises the steps that multi-source sensing data is collected, and pressure time sequence data and material spectrum data in the injection molding process are collected; extracting pressure waveform features to obtain a complete pressure waveform feature set; performing material characteristic analysis, and forming a material characteristic fingerprint in combination with the pressure waveform characteristics and the spectral data; material batch similarity calculation: constructing a material batch fingerprint database, and calculating the similarity between the new batch of materials and the historical batch of materials; process parameter optimization: continuously improving the accuracy of parameter optimization through multi-objective tradeoff optimization and parameter verification and iterative optimization; quality problem root causes are analyzed, and material problems and process parameter problems are accurately distinguished; through fusion of pressure waveform analysis and a material characteristic monitoring technology, comprehensive monitoring and intelligent regulation and control of an injection molding production process are realized.

The invention relates to the technical field of artificial intelligence, in particular to an injection molding process parameter optimization method and system based on hybrid algorithm and model fusion, and the method comprises the steps: optimizing a parameter combination of a support vector regression model through a simulated annealing algorithm, building a weighted fusion model based on the optimized support vector regression model and a random forest, and optimizing the model; constructing a hybrid model of an adaptive selection weighted fusion model and an optimized support vector regression model; constructing a three-objective optimization model including buckling deformation, volume shrinkage and production energy consumption, and searching a Pareto optimal solution set in a process parameter space by adopting a multi-objective genetic algorithm by taking the hybrid model as a target value evaluation tool; carrying out local correction on the key process parameters by adopting a gradient descent method until the deviation falls back to be within a preset threshold value, and obtaining optimized process parameters; the defect rate of products can be reduced, and meanwhile production energy consumption is reduced.

The invention discloses a temperature and pressure sensor and a manufacturing method thereof, the temperature and pressure sensor comprises a support assembly formed by an inner support and a thermistor through an injection molding process, two pin copper wires of the thermistor are welded to an insertion piece of the inner support, and a plastic supporting structure wrapping the thermistor and the inner support is formed through injection molding; one end of a PIN needle of the terminal button is connected with an external plugging port; one end of a conductive circuit of the flexible wire board is respectively welded with the PIN of the ceramic capacitor and the insertion piece of the inner bracket, and the other end of the conductive circuit of the flexible wire board is welded with the other end of the PIN of the terminal button; a through hole is formed between the support assembly and the shell, a medium makes contact with the ceramic capacitor through the through hole, the ceramic capacitor transmits a pressure signal to the outside through the flexible wire board and the end button, the thermistor senses the temperature of the medium through the plastic supporting structure, and a temperature signal is transmitted to the outside through the inserting piece, the flexible wire board and the end button. The thermistor is effectively prevented from losing efficacy due to medium impact, and meanwhile, the structural process is relatively simple and easy to assemble.

The embodiment of the invention discloses a harmonic reducer, a mechanical arm and cleaning equipment, the harmonic reducer comprises a flexible gear, a connecting part and an output part, in the manufacturing process of the harmonic reducer, the protruding connecting part is formed on the surface of the flexible gear, production and machining of the flexible gear and the connecting part are more convenient, the machining precision can be improved, and the machining efficiency is improved. For example, the flexible gear and the connecting part which are integrally formed can be manufactured through an injection molding process, a connecting hole process in the traditional technology is replaced, the probability of deformation of the connecting part can be reduced or eradicated, and then the transmission precision of the harmonic reducer is guaranteed. In the assembling process, the output part is connected with the flexible gear through the connecting part, the connecting part protrudes out of the surface of the flexible gear, at least part of the connecting part can stretch into the output part, or at least part of components of the output part are arranged on the connecting part in a sleeving mode, and therefore rapid assembling of the output part and the connecting part can be facilitated; and the output part is conveniently connected with the flexible gear.

The invention discloses a plastic fireproof material for extrusion. The invention belongs to the technical field of plastic particles capable of reaching the fireproof grade A (non-combustible grade), magnesium hydroxide, aluminum hydroxide and other additives are reasonably added into the PE plastic particles, the fireproof grade A non-combustible grade standard is successfully reached, compared with existing plastic particles only capable of reaching the fireproof grade B, the fireproof safety is remarkably improved, and the service life of the plastic particles is prolonged. The cable can be widely applied to places with strict fireproof requirements, and the fire risk is effectively reduced; the raw materials are subjected to constant-temperature drying treatment in the pretreatment process, so that the raw materials flow and fill a mold more easily in the injection molding process, the production efficiency is improved, the quality and performance of a final product can be ensured, the defects of yellowing, silver wires, bubbles, shrinkage cavities and the like of the product are avoided, and the yield of the product is improved.

The invention discloses an injection molding process parameter multi-objective optimization method based on a high-precision prediction model, and the method comprises the steps: carrying out the enhancement of data through MIC and GAN based on a simulation test result, so as to improve the quality of simulation data; then carrying out hyper-parameter tuning by using a KOA and IVYA mixed framework; then, an SHAP method is adopted to enhance the interpretability of the XGBoost model, and an optimized process parameter search range is provided; and finally, searching a Pareto leading edge of a quality target by applying MOCGO, and determining an optimal process parameter combination. Test results show that the buckling deformation and the volume shrinkage are respectively reduced by 35.7% and 10.1% by using the technological parameter combination optimized by the method disclosed by the invention, the buckling deformation and the volume shrinkage of the molded plastic part can be effectively reduced, so that a high-quality plastic part product is obtained, and the product quality is improved. And a theoretical basis and data support can be provided for obtaining an optimal process parameter combination of injection molding.

The invention provides a process parameter optimization method and equipment in an injection molding process and a medium, and belongs to the technical field of plastic product production. The process parameter optimization method in the injection molding process comprises the following steps: collecting production data of the injection molding process, wherein the production data comprises a production sample and a plurality of process parameters; extracting a principal component corresponding to the production sample by using a PCA method, and projecting the production data to the principal component for dimensionality reduction to obtain a principal component matrix; calculating the comprehensive contribution score of each process parameter in the multiple process parameters on the principal component by using the principal component matrix, and screening the process parameters according to the comprehensive contribution scores to obtain a contribution process parameter matrix; inputting the contribution process parameter matrix into a quality prediction model constructed based on XGBoost, and predicting and outputting a corresponding prediction quality index; the process parameters are optimized using the predicted quality indicators. According to the method and the device, the problem that the XGBoost modeling effect is influenced by information redundancy and variable collinearity caused by over-high dimensionality of process parameters in the prior art can be solved.

The embodiment of the utility model discloses a harmonic reducer, a mechanical arm and cleaning equipment, the harmonic reducer comprises a flexible gear, a connecting part and an output part, in the manufacturing process of the harmonic reducer, the protruding connecting part is formed on the surface of the flexible gear, production and machining of the flexible gear and the connecting part are more convenient, the machining precision can be improved, and the machining efficiency is improved. For example, the flexible gear and the connecting part which are integrally formed can be manufactured through an injection molding process, a connecting hole process in the traditional technology is replaced, the probability of deformation of the connecting part can be reduced or eradicated, and then the transmission precision of the harmonic reducer is guaranteed. In the assembling process, the output part is connected with the flexible gear through the connecting part, the connecting part protrudes out of the surface of the flexible gear, at least part of the connecting part can stretch into the output part, or at least part of components of the output part are arranged on the connecting part in a sleeving mode, and therefore rapid assembling of the output part and the connecting part can be facilitated; and the output part is conveniently connected with the flexible gear.

The invention discloses an injection mold based on waste recovery, and relates to the technical field of injection molds for waste recovery, the injection mold based on waste recovery comprises a mold base and a mold extrusion disc, and a sliding groove is formed in the side wall of the mold base. According to the injection mold based on waste recovery, before injection molding, thrust is applied to the shoveling plate through the fixed mold disc, so that the thrust spring is compressed, the shoveling plate is pushed to reset through the elastic force of the thrust spring after the injection molding work is completed, leftover materials existing in a gap between the mold disc and the mold base are shoveled, and the injection molding efficiency is improved. The waste leftover materials can be conveniently collected, and the problems that the leftover materials remain in the gaps of the mold for a long time and are difficult to remove, and the mold cannot be closed easily after being accumulated for a long time are solved. The problem that leftover materials generated by matching gaps in the injection molding process of injection molding products are difficult to remove is solved.

There is provided a molding management system including a server communicably connected to a terminal device, the molding management system managing production of a product in a production process including an injection molding process for the product by an injection molding device. The server includes a storage unit configured to store a plurality of pieces of lot information in which lot identification information for identifying a lot of the product and lot related information including one or more pieces of information concerning the lot of the product are associated with each other and a control unit configured to cause the terminal device to display a lot movement image for receiving lot moving operation for moving the product between a selected pair of the lots among the lots indicated by the plurality of pieces of lot information stored in the storage unit.

The invention discloses a part batch injection molding device and an injection molding process thereof, and relates to the technical field of injection molding, the part batch injection molding device comprises an injection mold, the injection mold comprises a movable mold, a fixed mold and a stripping mechanism, the movable mold is provided with a plurality of mold cores, the fixed mold is provided with mold cavities matched with the mold cores, and a plurality of injection molding cavities are formed after the movable mold and the fixed mold are closed; a cutting mechanism is arranged on the surface of a movable mold plate of the movable mold and comprises a cutter part and a transmission part, and the cutter part comprises a sliding groove, a sliding block, a sliding base and a self-adaptive cutter. The problems that in the prior art, in the demolding process, connection between a waste rod and a formed product needs to be separated through manpower or special equipment subsequently, the complexity of the production process is increased, and the labor cost is increased are solved. The die has the advantages that the cutting process is embedded into the demolding action through the cutting design of the internal structure of the die, and in-die cutting is achieved, so that the production process is simplified, and the comprehensive cost is reduced.

The invention discloses a workpiece local heat control method based on deep learning, and the method comprises the following steps: 1, preparing neural network data: employing a thermal imager to shoot a detected object, and obtaining an overall temperature distribution diagram of a system; step 2, training a neural network: training a dual-output deep learning model by taking the control parameter v as input and taking the labels (Ts1, Ts2) as output; according to the scheme, a deep learning model with a seven-layer network structure is adopted; and step 3, randomly generating control parameters, and selecting optimal parameters according to a prediction result of the neural network: during small-range temperature regulation and control, firstly recording the temperatures Ts1, pre and Ts2, pre of the s1 region and the s2 region at the moment. According to the workpiece local heat control method based on deep learning, an innovative control strategy is provided, control parameters are optimized through a neural network, precise regulation and control of the temperature of a specific area are achieved, meanwhile, the temperature stability of other areas is effectively maintained, and therefore the requirement for high-precision temperature control in the die casting and injection molding process is met.