Machining control method based on electric control permanent magnet quick die changing system

Abstract

The invention discloses a machining control method based on an electric control permanent magnet quick die changing system. The machining control method comprises the steps that S1, pre-detection and parameter pre-configuration are conducted before die changing; s2, monitoring and adjusting in real time in the die changing process; s3, processing parameters are automatically optimized after die replacement; s4, dynamically monitoring the machining process and adjusting parameters in real time; s5, multi-mold switching processing intelligent management is carried out; through pre-detection and fine adjustment before mold replacement and real-time monitoring and adjustment in the mold replacement process, the accuracy and stability of mold installation are ensured, the mold replacement time is shortened, the mold replacement error is reduced, and the mold replacement efficiency and precision are improved; machining parameters are automatically optimized according to the actual installation state after die replacement, the machining quality and efficiency are improved, and the machining parameters are optimized; in the machining process, parameters are monitored and adjusted in real time, and intelligent management of multi-mold switching is achieved, so that the whole machining process is more intelligent and automatic; the block chain technology is utilized to construct a mold processing information management system, it is ensured that data cannot be tampered and can be traced, and a reliable basis is provided for production management.

Description

Technical Field

[0001] This invention relates to the field of mold processing control technology, specifically to a processing control method based on an electronically controlled permanent magnet rapid mold changing system. Background Technology

[0002] Existing electro-magnetic rapid mold change systems have many problems when processing after mold replacement. On the one hand, there is a lack of a precise detection and adjustment mechanism for the coordinated state of the mold and equipment after mold replacement, making it difficult to match processing parameters with the characteristics of the new mold. For example, in injection molding production, different molds have different runner designs and cavity dimensions. Traditional processing control methods cannot adjust parameters such as injection pressure, temperature, and time in a timely manner according to these characteristics of the new mold, which can easily cause quality problems such as product size deviation and surface defects, reducing production efficiency and product qualification rate.

[0003] On the other hand, during the processing, existing systems cannot detect and dynamically adjust the processing control strategy in real time for changes in the performance of the mold caused by long-term operation or external factors. For example, in punching, the mold may wear under high-frequency stamping, resulting in a decrease in the accuracy of the stamped parts. However, traditional control methods cannot detect the wear of the mold in time and adjust parameters such as stamping pressure and speed, which increases the scrap rate and accelerates the damage of the mold, thus increasing production costs.

[0004] Furthermore, existing machining control methods require manual setting of numerous machining parameters when switching between multiple molds, which is cumbersome and prone to errors, failing to meet the demands for rapid switching and efficient machining in small-batch, multi-variety production modes. Therefore, a new machining control method based on an electronically controlled permanent magnet rapid mold changing system is urgently needed to solve the above problems. Summary of the Invention

[0005] The purpose of this invention is to provide a processing control method based on an electronically controlled permanent magnet rapid mold changing system. By intelligently detecting, analyzing, and controlling the mold state and processing process before and after mold changing, the method can achieve automatic matching and dynamic adjustment of processing parameters, improve processing accuracy and product quality, reduce production costs, meet the needs of rapid switching between multiple molds, and improve production efficiency and equipment automation level.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0007] A machining control method based on an electronically controlled permanent magnet rapid mold changing system includes the following steps:

[0008] S1, Pre-testing and parameter pre-configuration before mold change: Using a sensor array installed on the equipment workbench and mold, the compatibility status of the equipment and mold is pre-tested, and the test data is transmitted to the control unit. The control unit analyzes the data through a preset compatibility algorithm to determine whether it is compatible. If it is not compatible, an adjustment command is generated for fine-tuning. At the same time, the control unit retrieves the pre-configuration scheme of processing parameters from the database based on the mold information.

[0009] S2, Real-time monitoring and adjustment of mold changing process: During the mold changing process, displacement sensors and pressure sensors installed on the electro-controlled permanent magnet template monitor the movement trajectory and force of the mold in real time. The control unit analyzes the sensor data in real time. If displacement deviation or uneven force occurs, adjustment commands are immediately issued for correction and adjustment.

[0010] S3, Automatic optimization of processing parameters after mold change: After the mold is replaced, the control unit re-detects the actual installation status of the mold, compares and analyzes the detection results with the preset ideal installation status, and automatically optimizes the pre-configured processing parameters using optimization algorithms in combination with mold characteristics and processing requirements.

[0011] S4, Dynamic monitoring and real-time parameter adjustment during processing: During the processing, sensors distributed in key parts of the mold and equipment monitor the working status of the mold and processing parameters in real time. The control unit analyzes the sensor data in real time and establishes a dynamic model. When the data exceeds the normal range, the processing parameters are adjusted in real time according to the type and degree of the anomaly using an adaptive control algorithm.

[0012] S5, Intelligent Management of Multi-Mold Switching Processing: The control unit establishes a mold processing information management system to record historical mold processing data. When switching molds, it automatically generates a suitable processing sequence and parameter adjustment plan based on the current historical mold processing data and the characteristics of the next mold to be processed, combined with the production task requirements.

[0013] In a preferred embodiment, the sensor array includes one or more combinations of distance sensors, angle sensors, and pressure sensors.

[0014] In one preferred embodiment, the processing parameters include one or more of the following: injection pressure, temperature, holding time, and cooling time of the injection molding machine; and stamping speed, stamping force, and stamping stroke of the punch press.

[0015] In a preferred embodiment, the control unit fine-tunes the installation position and angle of the equipment worktable or mold by controlling the actuator installed on the equipment.

[0016] In a preferred embodiment, the optimization algorithm adjusts the processing parameters based on the difference between the actual installation state of the mold and the preset ideal installation state, combined with the mold characteristics and processing requirements.

[0017] In a preferred embodiment, the adaptive control algorithm adjusts the processing parameters in real time based on the type and severity of mold anomalies, combined with a dynamic model.

[0018] In a preferred embodiment, in step S1, the sensor array further includes a vibration sensor and a magnetic field sensor. The adaptation algorithm is a multi-dimensional adaptation algorithm, which performs adaptation judgment by constructing a mold-equipment adaptation digital twin model. The adjustment command is generated based on a PID control algorithm. The pre-configuration scheme for processing parameters is retrieved from the database through a deep learning algorithm and a parameter confidence interval is generated.

[0019] In a preferred embodiment, in step S2, the displacement sensor and the pressure sensor form a distributed fiber optic grating sensor network, and wavelength division multiplexing and time division multiplexing technologies are used to achieve millimeter-level resolution monitoring. The control unit uses a Kalman filter algorithm to process and fuse the sensor data. The adjustment command is generated by an adaptive fuzzy control algorithm. The correction and adjustment are achieved by adjusting the magnetic field distribution of the piezoelectric ceramic micro-drive array and the magnetorheological fluid.

[0020] In a preferred embodiment, in step S3, the re-detection employs multi-sensor fusion technology, including a laser displacement sensor, a 3D scanner, and a stress sensor; the comparative analysis employs a multi-scale comparative analysis method; the optimization algorithm is a quantum behavior particle swarm optimization (QPSO) algorithm; the automatic optimization considers the coupling effect between parameters and constraints; and the robustness of the optimization results is verified through Monte Carlo simulation.

[0021] In a preferred embodiment, in step S5, the mold processing information management system is built on blockchain technology, and adopts a consortium blockchain architecture and asymmetric encryption algorithm to ensure that the data is tamper-proof and traceable. The automatic generation of appropriate processing sequence and parameter adjustment scheme is implemented based on graph neural network algorithm and genetic algorithm, and is virtually verified through digital twin technology.

[0022] Due to the application of the above technical solution, the beneficial effects of this application compared with the prior art are as follows:

[0023] This application provides a machining control method based on an electrically controlled permanent magnet rapid mold changing system. Through pre-detection and fine-tuning before mold changing, and real-time monitoring and adjustment during the mold changing process, it ensures the accuracy and stability of mold installation, reduces mold changing time and errors, and improves mold changing efficiency and precision. After mold changing, it automatically optimizes machining parameters based on the actual installation status, improving machining quality and efficiency. Real-time monitoring and adjustment of parameters during machining, along with intelligent management of multi-mold switching, make the entire machining process more intelligent and automated. Finally, it utilizes blockchain technology to construct a mold machining information management system, ensuring data immutability and traceability, providing a reliable basis for production management. Attached Figure Description

[0024] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0025] Figure 1 This is a schematic flowchart of a machining control method based on an electronically controlled permanent magnet rapid mold changing system according to the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] In this application, the terms "upper," "lower," "left," "right," "front," "rear," "top," "bottom," "inner," "outer," "middle," "vertical," "horizontal," "lateral," and "longitudinal" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are primarily for the purpose of better describing the invention and its embodiments, and are not intended to limit the indicated device, element, or component to having a specific orientation, or to be constructed and operated in a specific orientation.

[0029] Furthermore, in addition to indicating direction or positional relationship, some of the aforementioned terms may also have other meanings. For example, the term "above" may also be used in certain situations to indicate a dependency or connection. Those skilled in the art can understand the specific meaning of these terms in this invention based on the specific circumstances.

[0030] Furthermore, the terms "installation," "setup," "equipped with," "connection," "linking," and "socketing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral structure; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium, or an internal connection between two devices, components, or parts. Those skilled in the art can understand the specific meaning of these terms in this invention based on the specific circumstances.

[0031] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0032] See Figure 1 This application provides a machining control method based on an electronically controlled permanent magnet rapid mold changing system, comprising the following steps:

[0033] S1, Pre-testing and parameter pre-configuration before mold change: Using a sensor array installed on the equipment workbench and mold, the compatibility status of the equipment and mold is pre-tested, and the test data is transmitted to the control unit. The control unit analyzes the data through a preset compatibility algorithm to determine whether it is compatible. If it is not compatible, an adjustment command is generated for fine-tuning. At the same time, the control unit retrieves the pre-configuration scheme of processing parameters from the database based on the mold information.

[0034] Specifically, this includes:

[0035] S1.1, Pre-test of equipment and mold compatibility:

[0036] S1.1.1, Sensor array arrangement and data acquisition:

[0037] Distance sensors (such as laser rangefinders), angle sensors (such as high-precision tilt sensors), pressure sensors (such as thin-film pressure sensors), vibration sensors, and magnetic field sensors are installed at the four corners and center of the equipment's workbench, as well as on the corresponding mounting surfaces of the molds. When the mold is hoisted to the vicinity of the equipment's workbench for installation, the sensor array begins to operate. The distance sensors emit laser beams to measure the distance between the mold mounting holes and the workbench mounting holes, collecting data every 0.1 seconds. The angle sensors detect the angular deviation between the mold mounting surface and the horizontal plane of the workbench in real time. The pressure sensors acquire the initial pressure distribution data of various parts of the mold when the mold makes slight contact with the workbench. The vibration sensors monitor the initial vibration state of the equipment and the mold. The magnetic field sensors detect the initial magnetic field environment of the electrically controlled permanent magnet system.

[0038] S1.1.2, Data Transmission and Analysis:

[0039] The data collected by the sensors is transmitted to the control unit in real time via industrial Ethernet. After receiving the data, the control unit first filters the data to remove noise data caused by environmental interference. Then, it uses a preset multi-dimensional adaptation algorithm to make an adaptation judgment by constructing a digital twin model of mold-equipment compatibility. The algorithm compares and analyzes the actual detection data with the standard adaptation parameters of the equipment and mold. For example, if the distance deviation between the mold mounting hole and the worktable mounting hole exceeds ±0.5mm, or the angle deviation exceeds ±0.3°, or the vibration frequency is abnormal, or the magnetic field distribution is uneven, it is determined that the equipment and mold are not compatible.

[0040] S1.2, Adaptability Adjustment:

[0041] S1.2.1, Adjustment command generation:

[0042] When the control unit determines that there is a mismatch, it generates an adjustment command based on the deviation data using a PID control algorithm. If there is a deviation in the mold installation angle, the control unit calculates the angle value and direction that need to be adjusted and generates the corresponding angle adjustment command. If there is a deviation in the distance, it calculates the displacement adjustment amount in each direction and generates a displacement adjustment command.

[0043] S1.2.2, Actuator Operation:

[0044] The actuators include an electric adjustment mechanism (such as an electric push rod) and a rotary adjustment mechanism (such as a servo motor-driven rotary platform) installed under the equipment's worktable. The electric adjustment mechanism, based on the displacement adjustment command, drives the worktable to perform micro-nano-level precision displacement adjustment in the X, Y, and Z axes via a lead screw and nut transmission. The rotary adjustment mechanism, based on the angle adjustment command, drives the worktable to rotate around a specific axis until the mold and equipment meet the adaptation requirements. During the adjustment process, sensors provide real-time feedback of the adjusted status data, and the control unit optimizes the adjustment command in real-time based on the feedback data, forming a closed-loop control.

[0045] S1.3, Pre-configuration of processing parameters:

[0046] S1.3.1, Mold Information Identification and Retrieval:

[0047] The control unit obtains information such as the mold's model, specifications, material, and historical processing data through RFID tags or QR codes installed on the mold. Then, based on deep learning algorithms, it searches the database. The database stores a large number of processing parameters corresponding to different molds, which have been verified through multiple experiments and actual production. For example, for a certain type of injection mold, the database stores combinations of parameters such as injection pressure, temperature, holding time, and cooling time under different plastic materials and product requirements.

[0048] S1.3.2, Pre-configuration scheme generation:

[0049] The control unit selects the most matching pre-configuration scheme for processing parameters from the database based on the mold information, and generates parameter confidence intervals through deep learning algorithms, such as the confidence interval for injection pressure being [80MPa, 120MPa]. If there is no perfectly matching scheme in the database, the control unit will generate an initial pre-configuration scheme based on the parameters of similar molds through machine learning algorithms for prediction and optimization.

[0050] S2, Real-time monitoring and adjustment of mold changing process: During the mold changing process, displacement sensors and pressure sensors installed on the electro-controlled permanent magnet template monitor the movement trajectory and force of the mold in real time. The control unit analyzes the sensor data in real time. If displacement deviation or uneven force occurs, adjustment commands are immediately issued for correction and adjustment.

[0051] Specifically, this includes:

[0052] S2.1, Real-time monitoring of mold status:

[0053] S2.1.1, Working principle of sensor networks:

[0054] A distributed fiber optic grating sensor network is laid on the surface of the electrically controlled permanent magnet template. This network consists of displacement sensors and pressure sensors. Using wavelength division multiplexing and time division multiplexing technology, it can monitor the three-dimensional spatial movement trajectory, stress distribution cloud map and magnetic field gradient changes of the mold in real time, achieving millimeter-level resolution monitoring. The fiber optic grating sensors convert the changes in the physical quantity of the mold into changes in the wavelength of the optical signal. Through optical time domain reflection technology, data is collected every 0.01 seconds and transmitted to the control unit.

[0055] S2.1.2, Data Processing and Model Building:

[0056] After receiving the optical signal data, the control unit converts it into an electrical signal through the photoelectric conversion module, and then uses the Kalman filter algorithm to process and fuse the data. This algorithm can effectively remove noise and improve data accuracy. Based on the processed data, the control unit constructs a real-time physical field model of the dynamic installation process of the mold, which intuitively displays the changes in displacement, stress, magnetic field and other factors of the mold during the installation process.

[0057] S2.2, Real-time adjustment operation:

[0058] S2.2.1, Exception detection and instruction generation:

[0059] When the control unit detects that the displacement deviation of the mold exceeds ±0.1mm, or the stress distribution non-uniformity exceeds 10%, or the magnetic field gradient changes abnormally, it determines that an installation abnormality has occurred. At this time, the control unit generates adjustment instructions based on the type and degree of the abnormality through an adaptive fuzzy control algorithm. For example, if the mold has a large displacement deviation in the X-axis direction, the control unit generates a displacement adjustment instruction in the X-axis direction and determines the adjustment speed and force.

[0060] S2.2.2, Micro-drive and magnetic field adjustment:

[0061] A piezoelectric ceramic micro-drive array and a magnetorheological fluid device are integrated on an electrically controlled permanent magnet template. The piezoelectric ceramic micro-drive array generates submicron-level displacement according to the displacement adjustment command to accurately correct the position of the mold. The magnetorheological fluid device adjusts the magnetic field distribution by changing the current magnitude to make the mold subjected to uniform force. During the adjustment process, the sensor network continuously monitors the mold status, and the control unit continuously optimizes the adjustment command based on the feedback data until the mold installation reaches the ideal state.

[0062] S3, Automatic optimization of processing parameters after mold change: After the mold is replaced, the control unit re-detects the actual installation status of the mold, compares and analyzes the detection results with the preset ideal installation status, and automatically optimizes the pre-configured processing parameters using optimization algorithms in combination with mold characteristics and processing requirements.

[0063] Specifically, this includes:

[0064] S3.1, Mold installation status re-inspection:

[0065] S3.1.1, Multi-sensor fusion detection:

[0066] After the mold is replaced, a multi-sensor fusion technology is used, including a laser displacement sensor, a 3D scanner, and a stress sensor, to perform nanometer-level precision detection on the actual installation state of the mold. The laser displacement sensor detects the positional accuracy of key parts of the mold; the 3D scanner acquires the overall shape contour data of the mold; the stress sensor detects the residual stress distribution after the mold is installed; the data from each sensor are fused through a federated learning architecture to improve detection accuracy while ensuring data privacy.

[0067] S3.1.2, Comparative Analysis and Model Building:

[0068] The control unit performs multi-scale comparative analysis of the detection results with the preset ideal installation state to construct a residual stress field model of the mold installation state; through comparison, it determines the deviation parameters of the mold installation, such as position deviation, angle deviation, stress concentration area, etc.

[0069] S3.2, Optimization of machining parameters:

[0070] S3.2.1, Optimize algorithm execution:

[0071] The control unit initiates the Quantum Behavior Particle Swarm Optimization (QPSO) algorithm, using machining accuracy, production efficiency, and mold life as optimization objectives. It combines mold installation deviations and mold characteristics to automatically optimize the pre-configured machining parameters across multiple objectives. The algorithm searches in a high-dimensional parameter space, considering coupling effects and constraints between parameters, such as the relationship between injection temperature and pressure, and limitations on stamping speed and stamping force.

[0072] S3.2.2, Result Verification and Confirmation:

[0073] After multiple rounds of iterative calculations, the algorithm generates a Pareto optimal solution set. The control unit performs 1000 simulations on each solution using Monte Carlo simulation to evaluate its stability and reliability under different operating conditions, and finally determines the optimal combination of processing parameters.

[0074] S4, Dynamic monitoring and real-time parameter adjustment during processing: During the processing, sensors distributed in key parts of the mold and equipment monitor the working status of the mold and processing parameters in real time. The control unit analyzes the sensor data in real time and establishes a dynamic model. When the data exceeds the normal range, the processing parameters are adjusted in real time according to the type and degree of the anomaly using an adaptive control algorithm.

[0075] Specifically, this includes:

[0076] S4.1, Mold and Machining Status Monitoring:

[0077] S4.1.1, MEMS sensor array operation:

[0078] MEMS temperature sensors, strain sensors, vibration sensors, and force sensors are installed on key parts such as the mold cavity surface, gate location, equipment spindle, and guide rails. These sensors collect the mold's temperature field, strain field, vibration mode, and processing force field data in real time at a frequency of 100Hz and transmit them to the control unit via a wireless communication module.

[0079] S4.1.2, Data Processing and Model Building:

[0080] The control unit uses wavelet transform and Hilbert-Huang transform to perform time-frequency domain analysis on sensor data and extract data features. Then, a dynamic service behavior prediction model of the mold based on physical-data fusion is established, and machine learning algorithms are used to learn the state change law of the mold under different working conditions.

[0081] S4.2, parameters are adjusted in real time:

[0082] S4.2.1, Anomaly Detection and Assessment:

[0083] The control unit sets a dynamic threshold range based on statistical process control methods; when sensor data exceeds the threshold, it determines that the mold is abnormal; through an adaptive neural fuzzy inference system, combined with the characteristics of abnormal data, it determines the type (such as mold wear, overheating, abnormal vibration, etc.) and degree of abnormality, and uses Bayesian network to trace the source of abnormality and assess risk.

[0084] S4.2.1, Parameter adjustment execution:

[0085] Depending on the type and severity of the anomaly, the control unit uses an adaptive neuro-fuzzy inference system to adjust the processing parameters in real time. For example, if local overheating of the mold is detected, the control unit increases the cooling time and reduces the processing speed; if it is determined that the mold has slight wear, the processing path is adjusted and the processing pressure is reduced. During the adjustment process, the control unit continuously monitors the mold status and optimizes the adjustment strategy based on the feedback data.

[0086] S5, Intelligent Management of Multi-Mold Switching Processing: The control unit establishes a mold processing information management system to record historical mold processing data. When switching molds, it automatically generates a suitable processing sequence and parameter adjustment plan based on the current historical mold processing data and the characteristics of the next mold to be processed, combined with the production task requirements.

[0087] Specifically, this includes:

[0088] S5.1, Mold Lifecycle Data Management:

[0089] S5.1.1, Blockchain System Architecture:

[0090] The control unit establishes a mold processing information management system based on blockchain technology, adopting a consortium blockchain architecture. The system assigns a unique digital identity to each mold and records the mold's processing history data (including the time, parameters, and product quality inspection results of each processing), maintenance records (maintenance time, maintenance content, and maintenance personnel), performance degradation curves (obtained by fitting sensor data), and energy consumption data (power consumption during processing). The data is encrypted and stored using an asymmetric encryption algorithm to ensure the data's immutability and traceability.

[0091] S5.1.2, Smart Contract Applications:

[0092] The system supports smart contract functionality, allowing for pre-setting mold maintenance reminder rules. For example, when the number of mold processing cycles reaches 1000 or the performance degradation index exceeds the threshold, a maintenance reminder notification will be automatically sent. It can also automatically perform performance evaluation and lifespan prediction tasks based on the mold's historical data and current status through smart contracts.

[0093] S5.2, Processing sequence and parameter optimization:

[0094] S5.2.1, Graph Neural Network Algorithm Execution:

[0095] When switching molds, the control unit starts a graph neural network algorithm (using a graph attention network architecture). The algorithm treats the molds as nodes and the process similarity and equipment compatibility relationships between molds as edges. Through node embedding and graph convolution operations, the mold features are mapped to a low-dimensional vector space, and the similarity scores between molds are calculated.

[0096] S5.2.2, Scheme Generation and Verification:

[0097] The control unit combines the time constraints, quality requirements, and energy consumption targets of the production task, and uses a genetic algorithm based on mold similarity scores to automatically generate the optimal processing sequence and parameter adjustment scheme. Then, through digital twin technology, the scheme is simulated and verified in a virtual environment to evaluate its feasibility and optimization effect, and finally the processing sequence and parameter adjustment scheme for actual production are determined.

[0098] To further enhance the intelligence of production, step S6, energy consumption optimization of the processing, can also be included:

[0099] By monitoring the power consumption of the equipment in real time using power sensors installed on the equipment, and combining the thermal conductivity characteristics of the mold (determined by the mold material properties and structural parameters) and processing parameters (such as injection speed and stamping frequency), an energy consumption prediction model based on the first law of thermodynamics is established. The control unit uses reinforcement learning algorithms to dynamically adjust processing parameters with the goal of minimizing energy consumption while ensuring processing quality. For example, during the injection molding process, when the equipment power consumption is found to be too high, the reinforcement learning algorithm tries to reduce the injection speed and observes whether the product quality is affected. If the quality still meets the requirements, the parameter is continuously optimized until the optimal balance between energy consumption and quality is found, thereby achieving green and intelligent processing.

[0100] Example 2

[0101] A specific example of the application of a machining control method based on an electronically controlled permanent magnet rapid mold changing system in an injection molding machine:

[0102] S1, Pre-detection and parameter pre-configuration before mold change:

[0103] Before changing the mold on the injection molding machine, distance sensors on the worktable detect the distance between each mounting hole of the mold and the corresponding mounting part on the worktable, angle sensors detect the angular deviation between the mold mounting reference surface and the worktable, and pressure sensors detect the force on the mold when it is placed on the worktable. These data are transmitted to the control unit, which uses an adaptation algorithm to determine whether the mold and the equipment are compatible. If there is a deviation, the control unit controls the electric adjustment mechanism installed under the worktable to fine-tune the worktable to ensure accurate mold installation. At the same time, the control unit retrieves a pre-configured scheme of processing parameters such as injection pressure, temperature, holding time, and cooling time from the database according to the mold model.

[0104] S2, Real-time monitoring and adjustment during mold changing process:

[0105] During mold changing, the displacement sensor on the electro-magnetic template monitors the movement trajectory of the mold in real time during the magnetization and adsorption process, and the pressure sensor monitors the pressure between the mold and the template. When the displacement sensor detects that the mold movement deviates from the predetermined trajectory, the control unit controls the miniature electric push rod on the template to correct the mold position. When the pressure sensor detects uneven pressure distribution, the control unit adjusts the magnetic field strength of different areas of the electro-magnetic template to make the mold uniformly stressed and ensure that the mold is accurately installed on the injection molding machine.

[0106] S3, Automatic optimization of machining parameters after mold change:

[0107] After the mold is replaced, the control unit uses high-precision displacement and angle sensors to detect the actual installation position and angle of the mold again and compares it with the preset value. If there is a deviation, the control unit uses optimization algorithms to automatically optimize and adjust parameters such as injection pressure, injection speed, and injection time according to the mold cavity structure and injection process requirements. For example, if there is a slight deviation in the mold installation angle, the injection direction is adjusted appropriately so that the plastic melt can fill the cavity more evenly.

[0108] S4, Dynamic monitoring of the processing process and real-time parameter adjustment:

[0109] During the injection molding process, temperature sensors installed on the mold cavity surface monitor the mold temperature in real time, vibration sensors detect mold vibration, and stress sensors monitor the stress on the mold. When the temperature sensor detects that the local temperature of the mold is too high, the control unit increases the flow rate of the cooling water circuit and extends the cooling time. When the vibration sensor detects abnormal mold vibration and determines that there may be poor melt flow, the control unit reduces the injection speed and adjusts the injection pressure to ensure the injection process is stable and improve product quality.

[0110] S5, intelligent management of multi-mold switching processing:

[0111] When an injection molding machine needs to switch between multiple molds, the control unit automatically generates the optimal processing sequence and parameter adjustment plan based on the processing history data of the current mold, such as the number of products processed, product quality inspection results, mold wear, etc., as well as the characteristics of the next mold to be processed. If the injection temperature required by the next mold is significantly different from that of the current mold, the control unit prioritizes the processing of a transitional mold, gradually adjusting the temperature of the injection molding machine to reduce the impact of temperature changes on the equipment and molds, while improving production efficiency and mold lifespan.

[0112] Example 3

[0113] A specific example of the application of a machining control method based on an electronically controlled permanent magnet rapid die-changing system in a punch press:

[0114] S1, Pre-detection and parameter pre-configuration before mold change:

[0115] Before changing the die on the punch press, distance sensors, angle sensors, and pressure sensors are used to detect the compatibility between the die and the punch press worktable. The control unit determines whether the die installation is accurate based on the detection data. If it is not accurate, the control unit controls the adjustment mechanism of the punch press worktable to make fine adjustments. At the same time, the control unit retrieves the pre-configured processing parameters such as stamping speed, stamping force, and stamping stroke from the database based on the die specifications and material.

[0116] S2, Real-time monitoring and adjustment during mold changing process:

[0117] During the mold changing process, the displacement sensor monitors the displacement of the mold during the magnetization and adsorption process of the electro-controlled permanent magnet template, and the pressure sensor monitors the pressure between the mold and the template. When the mold displacement deviation or uneven pressure is detected, the control unit adjusts the mold position and the magnetic field strength of the template in a timely manner to ensure that the mold is accurately installed on the punch press.

[0118] S3, automatic optimization of machining parameters after mold change:

[0119] After the mold is installed, the control unit checks the actual installation status of the mold again. Based on the detection results and mold characteristics, it uses an optimization algorithm to automatically optimize the stamping parameters. For example, if there is a slight deviation in the mold installation height, the stamping stroke is adjusted appropriately to ensure the dimensional accuracy of the stamped parts.

[0120] S4, Dynamic monitoring of the processing process and real-time parameter adjustment:

[0121] During the stamping process, vibration sensors monitor the vibration of the die, stress sensors monitor the force on the die, and displacement sensors monitor the minute displacement of the die. When the stress sensor detects that the local stress on the die is too large and wear may occur, the control unit reduces the stamping speed and the stamping force. When the vibration sensor detects abnormal die vibration and determines that there may be deformation of the stamped part, the control unit adjusts the clearance of the stamping die and the stamping sequence to ensure the stability of the stamping process and improve the quality of the stamped part.

[0122] S5, intelligent management of multi-mold switching processing:

[0123] For multi-die switching processing of punch presses, the control unit automatically plans the processing sequence and adjusts the processing parameters based on the die processing history data and the characteristics of the die to be processed; according to the wear condition of the die, it rationally arranges the order of die use, giving priority to using dies with less wear for high-precision stamping, extending the die service life, and improving the production efficiency and economic benefits of punch presses.

[0124] This application provides a machining control method based on an electrically controlled permanent magnet rapid mold changing system. Through pre-detection and fine-tuning before mold changing, and real-time monitoring and adjustment during the mold changing process, it ensures the accuracy and stability of mold installation, reduces mold changing time and errors, and improves mold changing efficiency and precision. After mold changing, it automatically optimizes machining parameters based on the actual installation status, improving machining quality and efficiency. Real-time monitoring and adjustment of parameters during machining, along with intelligent management of multi-mold switching, make the entire machining process more intelligent and automated. Finally, it utilizes blockchain technology to construct a mold machining information management system, ensuring data immutability and traceability, providing a reliable basis for production management.

[0125] The processing control method of this application integrates IoT, AI and blockchain technologies to provide a complete intelligent solution for the mold processing industry. It is applicable to the entire industrial chain from traditional manufacturing to high-end equipment manufacturing, and has significant advantages, especially in production environments that require high precision, high efficiency and high intelligence.

[0126] Finally, it should be noted that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A machining control method based on an electronically controlled permanent magnet rapid mold changing system, characterized in that, Includes the following steps: S1, Pre-testing and parameter pre-configuration before mold change: Using a sensor array installed on the equipment workbench and mold, the compatibility status of the equipment and mold is pre-tested, and the test data is transmitted to the control unit. The control unit analyzes the data through a preset compatibility algorithm to determine whether it is compatible. If it is not compatible, an adjustment command is generated for fine-tuning. At the same time, the control unit retrieves the pre-configuration scheme of processing parameters from the database based on the mold information. S2, Real-time monitoring and adjustment of mold changing process: During the mold changing process, displacement sensors and pressure sensors installed on the electro-controlled permanent magnet template monitor the movement trajectory and force of the mold in real time. The control unit analyzes the sensor data in real time. If displacement deviation or uneven force occurs, adjustment commands are immediately issued for correction and adjustment. S3, Automatic optimization of processing parameters after mold change: After the mold is replaced, the control unit re-detects the actual installation status of the mold, compares and analyzes the detection results with the preset ideal installation status, and automatically optimizes the pre-configured processing parameters using optimization algorithms in combination with mold characteristics and processing requirements. S4, Dynamic monitoring and real-time parameter adjustment during processing: During the processing, sensors distributed in key parts of the mold and equipment monitor the working status of the mold and processing parameters in real time. The control unit analyzes the sensor data in real time and establishes a dynamic model. When the data exceeds the normal range, the processing parameters are adjusted in real time according to the type and degree of the anomaly using an adaptive control algorithm. S5, Intelligent Management of Multi-Mold Switching Processing: The control unit establishes a mold processing information management system to record historical mold processing data. When switching molds, it automatically generates a suitable processing sequence and parameter adjustment plan based on the current historical mold processing data and the characteristics of the next mold to be processed, combined with the production task requirements.

2. The machining control method based on an electronically controlled permanent magnet rapid mold changing system as described in claim 1, characterized in that, The sensor array includes one or more combinations of distance sensors, angle sensors, and pressure sensors.

3. The machining control method based on the electro-controlled permanent magnet rapid mold changing system as described in claim 1, characterized in that, The processing parameters include one or more of the following: injection pressure, temperature, holding time, and cooling time of the injection molding machine; and stamping speed, stamping force, and stamping stroke of the punch press.

4. The machining control method based on the electro-controlled permanent magnet rapid mold changing system as described in claim 1, characterized in that, The control unit fine-tunes the installation position and angle of the equipment's worktable or mold by controlling the actuators installed on the equipment.

5. The machining control method based on an electronically controlled permanent magnet rapid mold changing system as described in claim 1, characterized in that, The optimization algorithm adjusts the processing parameters based on the difference between the actual installation state of the mold and the preset ideal installation state, combined with the mold characteristics and processing requirements.

6. The machining control method based on an electronically controlled permanent magnet rapid mold changing system as described in claim 1, characterized in that, The adaptive control algorithm adjusts the processing parameters in real time based on the type and severity of mold anomalies, combined with a dynamic model.

7. The machining control method based on an electronically controlled permanent magnet rapid mold changing system as described in claim 1, characterized in that, In step S1, the sensor array further includes a vibration sensor and a magnetic field sensor. The adaptation algorithm is a multi-dimensional adaptation algorithm, which performs adaptation judgment by constructing a mold-equipment adaptation digital twin model. The adjustment command is generated based on the PID control algorithm. The pre-configuration scheme of processing parameters is retrieved from the database through a deep learning algorithm and a parameter confidence interval is generated.

8. The machining control method based on the electro-controlled permanent magnet rapid mold changing system as described in claim 1, characterized in that, In step S2, the displacement sensor and pressure sensor form a distributed fiber optic grating sensor network, and wavelength division multiplexing and time division multiplexing technologies are used to achieve millimeter-level resolution monitoring. The control unit uses a Kalman filter algorithm to process and fuse the sensor data. The adjustment command is generated by an adaptive fuzzy control algorithm. The correction and adjustment are achieved by adjusting the magnetic field distribution of the piezoelectric ceramic micro-drive array and the magnetorheological fluid.

9. The machining control method based on an electronically controlled permanent magnet rapid mold changing system as described in claim 1, characterized in that, In step S3, the re-detection employs multi-sensor fusion technology, including a laser displacement sensor, a 3D scanner, and a stress sensor. The comparative analysis employs a multi-scale comparative analysis method. The optimization algorithm is the quantum behavior particle swarm optimization (QPSO) algorithm. The automatic optimization considers the coupling effect between parameters and constraints, and the robustness of the optimization results is verified through Monte Carlo simulation.

10. The machining control method based on an electronically controlled permanent magnet rapid mold changing system as described in claim 1, characterized in that, In step S5, the mold processing information management system is built on blockchain technology, and adopts a consortium blockchain architecture and asymmetric encryption algorithm to ensure that the data is tamper-proof and traceable. The automatic generation of appropriate processing sequence and parameter adjustment scheme is implemented based on graph neural network algorithm and genetic algorithm, and is virtually verified through digital twin technology.

Images