An automatic compound molding control system for micro screw production

Abstract

The application discloses an automatic composite molding control system for micro screw production and relates to the technical field of mechanical engineering. The exciting module is used for emitting a probe vibration signal of a preset frequency to a key component of a composite molding machine. The collecting module is used for collecting a response signal fed back by the key component after the probe vibration signal is transmitted. The control module is used for analyzing the relevant characteristic changes of the response signal, judging the health state of the key component according to the relevant characteristic changes, and generating and outputting a control instruction for adjusting molding process parameters. The application forms a closed-loop linkage between the health state of the key component and the dynamic optimization of the molding process parameters by actively emitting the probe vibration signal and collecting and analyzing the response signal of the key component. Different process parameter combinations are simulated and optimized in a virtual environment, and the best process scheme is actively found by taking into account the optimal product quality and the minimum equipment stress. The application effectively reduces the waste rate and the risk of sudden failure and improves the overall production efficiency.

Description

Technical Field

[0001] This invention relates to the field of mechanical engineering technology, and in particular to an automated composite molding control system for the production of miniature screws. Background Technology

[0002] In recent years, the industry has generally relied on methods such as regular manual inspections, shutdown disassembly inspections, or simple vibration monitoring to assess equipment status. These traditional methods not only suffer from monitoring lag and low diagnostic accuracy, but also fail to capture the gradual degradation of component performance in real time. This often leads to unplanned downtime, increased defect rates, or even sudden component damage due to failure to provide timely warnings. Most existing technologies are limited to fixed program control of molding process parameters or post-production adjustments based on product quality. There is a lack of a closed-loop control mechanism that can dynamically and intelligently correlate the real-time health status of key components with molding process parameters, which restricts the development of production systems towards intelligent and predictive maintenance.

[0003] In existing technologies, monitoring methods mostly rely on macroscopic parameters, making it difficult to deeply assess the structural health status of key components, detect potential wear or fatigue damage in a timely manner, and have limited early warning capabilities. Process parameter adjustments are often based on empirical formulas or fixed values ​​set offline, lacking the ability to dynamically optimize the molding process according to the actual health status of the equipment. This results in the inability to adaptively adjust pressure and speed to compensate for processing errors when component performance degrades, affecting the stability of product quality. Existing technologies have a closed-loop linkage mechanism between component health monitoring and process parameter optimization, which disconnects equipment operating status from production process control, making it difficult to achieve intelligent and adaptive high-efficiency production. Summary of the Invention

[0004] The technical problem solved by this invention is that: monitoring methods mostly rely on macroscopic parameters, making it difficult to deeply assess the structural health status of key components, and thus unable to detect potential wear or fatigue damage in a timely manner, resulting in limited early warning capabilities. Process parameter adjustments are often based on empirical formulas or fixed values ​​set offline, lacking the ability to dynamically optimize the molding process according to the actual health status of the equipment. This leads to the inability to adaptively adjust pressure and speed to compensate for processing errors when component performance degrades, affecting the stability of product quality. Existing technologies have a closed-loop linkage mechanism between component health monitoring and process parameter optimization, which disconnects equipment operating status from production process control, making it difficult to achieve intelligent and adaptive high-efficiency production.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an automated composite molding control system for the production of miniature screws, comprising an excitation module, a data acquisition module, and a control module:

[0006] The excitation module is used to transmit probe vibration signals of a preset frequency to key components of the composite molding machine;

[0007] The acquisition module is used to acquire the response signal transmitted and fed back by the probe vibration signal through the key component;

[0008] The control module is used to analyze the relevant feature changes of the response signal, determine the health status of the key component based on the relevant feature changes, and generate and output control commands to adjust the molding process parameters based on the determined health status.

[0009] As a preferred embodiment of the automated composite molding control system for micro screw production described in this invention, the excitation module includes a signal generation unit, a power amplification unit, and a piezoelectric ceramic actuator.

[0010] The signal generating unit is used to generate a probe vibration signal at a preset frequency;

[0011] The power amplification unit is used to electrically connect with the signal generation unit and amplify the power of the probe vibration signal;

[0012] The piezoelectric ceramic actuator is used to be electrically connected to the power amplification unit and to convert the probe vibration signal after amplification of the power meter into mechanical vibration;

[0013] The key components include the mold, punch, and spindle of the composite molding machine.

[0014] As a preferred embodiment of the automated composite molding control system for micro screw production described in this invention, the vibration module further includes a vibration head;

[0015] The excitation head is fixedly connected to the vibration output end of the piezoelectric ceramic actuator to apply the mechanical vibration to the preset detection area of ​​the key component.

[0016] As a preferred embodiment of the automated composite molding control system for micro screw production described in this invention, the acquisition module is used to acquire response signals of the preset detection area through different sensors fixed on key components;

[0017] The different sensors include piezoelectric accelerometers, laser Doppler vibration meters, and acoustic emission sensors.

[0018] As a preferred embodiment of the automated composite molding control system for miniature screw production described in this invention, the acquisition module further includes a signal conditioning circuit.

[0019] The signal conditioning circuit is used to filter and amplify the response signal.

[0020] As a preferred embodiment of the automated composite molding control system for miniature screw production described in this invention, the control module includes an analysis unit, a judgment unit, and an execution unit.

[0021] The analysis unit is used to receive the response signal and extract the frequency domain features of the response signal through a fast Fourier transform.

[0022] The judgment unit is used to compare the key parameters of the frequency domain features with a preset benchmark threshold range, and judge the health status of the key components based on the comparison results.

[0023] The execution unit is used to generate and output control instructions for adjusting the molding process parameters of the micro screw production based on the health status of the key components. The molding process parameters include molding pressure, molding temperature, processing speed, die gap, and feed rate.

[0024] In a preferred embodiment of the automated composite molding control system for micro screw production described in this invention, the logic for determining the health status of the key components based on the comparison results includes:

[0025] Extract key parameters of the frequency domain features, including peak amplitude at a specific frequency point, main resonance frequency offset, and quality factor. The specific frequency point includes the main resonance frequency point and harmonic frequency point of the key component.

[0026] The key parameters are compared with a preset benchmark threshold range;

[0027] If the key parameter exceeds the preset benchmark threshold range, the health status of the key component is determined to be abnormal.

[0028] If the key parameters do not exceed the preset benchmark threshold range, the health status of the key component is determined to be healthy.

[0029] In a preferred embodiment of the automated composite molding control system for miniature screw production described in this invention, the logic for generating and outputting control commands to adjust molding process parameters includes:

[0030] If the health status of the key component is healthy, the automated composite molding process of micro screw production is optimized under normal space constraints using a preset finite element analysis model to obtain the optimal combination of molding process parameters, and the optimal combination of molding process parameters is converted into control instructions for adjusting the molding process parameters.

[0031] The automated composite molding process for optimizing the production of micro screws includes:

[0032] Obtain the normal spatial dimensions, maximum allowable deformation, and allowable material stress of key components;

[0033] In the finite element analysis model, the normal spatial dimension of the key component is defined as the design variable. Within the preset molding process parameter range, the molding process parameters of each dimension are randomly selected, and the random selections of the molding process parameters of each dimension are combined to obtain different combinations of process parameters. For different combinations of process parameters, virtual molding experiments are conducted to obtain the optimal combination of molding process parameters.

[0034] In a preferred embodiment of the automated composite molding control system for miniature screw production described in this invention, the logic for conducting virtual molding experiments includes:

[0035] The process parameters are configured onto the corresponding boundary conditions in the finite element analysis model. Using a structural statics simulation tool, the displacement changes of key components under the action of the process parameters during the composite molding process are simulated to obtain the deformation distribution. Simultaneously, the internal stresses generated by various forces and loads acting on the key components during the composite molding process of micro screws are calculated to obtain the stress distribution. While calculating the deformation distribution and stress distribution, the molding process of micro screws is simulated to obtain simulation results. The simulation results include the maximum deformation of key components, the maximum equivalent stress, and product quality indicators.

[0036] The normal spatial dimensions of key components are used as design variables. The optimization objectives are to optimize product quality indicators, minimize the maximum deformation of key components, and minimize the maximum equivalent stress. The normal spatial dimensions of key components, the maximum allowable deformation, and the allowable stress of materials are used as constraints. A multi-objective optimization algorithm is used to iteratively find the optimal combination of molding process parameters.

[0037] As a preferred embodiment of the automated composite molding control system for miniature screw production described in this invention, the control module further includes an early warning unit.

[0038] The early warning unit is used to determine the wear level of the critical component when the health status of the critical component is determined to be abnormal, and to issue an early warning based on the wear level.

[0039] The wear levels include primary wear, intermediate wear, and severe wear;

[0040] The determination of the wear condition of the key components includes:

[0041] When the main resonant frequency offset is greater than the first threshold and less than the second threshold, the wear level is determined to be primary wear.

[0042] When the main resonance frequency offset is greater than or equal to the second threshold, and the peak amplitude of the specific frequency point is greater than the third threshold, the wear level is determined to be medium wear.

[0043] When the main resonant frequency offset is greater than or equal to the fourth threshold or the decrease in the quality factor is greater than the fifth threshold, the wear level is determined to be severe wear.

[0044] The early warning based on the wear level includes:

[0045] When the wear is determined to be primary, a first-level warning is triggered. The first-level warning is used to display a prompt message on the human-machine interface indicating a decline in component performance.

[0046] When the wear is determined to be moderate, a second-level warning is triggered. The second-level warning is used to activate the audible and visual alarms at the equipment site and send an alarm email to the designated management terminal.

[0047] When severe wear is detected, a Level 3 warning is triggered. The Level 3 warning is an emergency warning displayed on the human-machine interface in a flashing red light, indicating that the component is about to fail.

[0048] The beneficial effects of this invention are as follows: By actively emitting probe vibration signals and collecting and analyzing the response signals of key components, this invention can deeply assess the internal structural health status of key components. It can promptly detect early wear and fatigue damage that are difficult to capture with traditional macroscopic parameter monitoring, significantly improving the accuracy and timeliness of early warning. It forms a closed-loop linkage between the health status of key components and the dynamic optimization of molding process parameters. When component performance degradation is detected, it can actively find the optimal process solution that balances optimal product quality and minimum equipment stress without interrupting production or consuming actual materials, based on finite element analysis models and multi-objective optimization algorithms, through simulation and optimization of different combinations of process parameters in a virtual environment. This achieves the dual maximization of production efficiency and equipment lifespan, ensuring the continuous stability of product quality. This mechanism, which deeply integrates equipment health monitoring with production process control, realizes a leap from passive maintenance to active control and from experience-based parameter tuning to intelligent optimization, ultimately achieving intelligent and adaptive high-efficiency production, effectively reducing scrap rates and the risk of sudden failures, and improving overall production efficiency. Attached Figure Description

[0049] Figure 1 This is a basic flowchart of an automated composite molding control system for the production of miniature screws, provided as an embodiment of the present invention. Detailed Implementation

[0050] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0051] Example, refer to Figure 1 As one embodiment of the present invention, an automated composite molding control system for the production of miniature screws is provided, including an excitation module, a data acquisition module, and a control module:

[0052] The excitation module is used to transmit probe vibration signals of a preset frequency to key components of the composite molding machine.

[0053] The acquisition module is used to acquire the response signal transmitted and fed back from the probe vibration signal through key components.

[0054] The control module is used to analyze the relevant characteristic changes of the response signal, and judge the health status of key components based on the relevant characteristic changes. Based on the judged health status, it generates and outputs control commands to adjust the molding process parameters.

[0055] It enables proactive online monitoring and diagnosis of the health status of key components of composite molding machines. It can capture dynamic characteristic changes of components caused by wear, fatigue, etc. in real time, and automatically adjust molding process parameters according to their health status. This tightly integrates equipment status monitoring with production process control, effectively compensates for processing errors caused by component performance degradation, and significantly improves the quality stability of micro screw production and the reliability of equipment operation.

[0056] The excitation module includes a signal generation unit, a power amplification unit, and a piezoelectric ceramic actuator.

[0057] The signal generation unit is used to generate probe vibration signals at a preset frequency.

[0058] The signal generation unit, as the source, can generate probe vibration signals whose frequency, waveform, and amplitude can be precisely programmed and controlled. This active excitation method is different from the random vibration during operation of passive acquisition equipment, ensuring the consistency and specificity of the excitation signal.

[0059] The power amplifier unit is used to electrically connect with the signal generation unit and amplify the power of the probe vibration signal.

[0060] The power amplification unit amplifies the weak electrical signal to provide sufficient driving force for subsequent electromechanical conversion. This is especially important for key components such as molds, punches and spindles with high structural rigidity, ensuring that vibration can effectively penetrate and excite their inherent modes.

[0061] The piezoelectric ceramic actuator is used to electrically connect to the power amplifier unit and convert the probe vibration signal after amplification of the power meter into mechanical vibration.

[0062] Key components include the mold, punch, and spindle of the composite molding machine.

[0063] As the end of electromechanical conversion, piezoelectric ceramic actuators have fast response speed, high displacement resolution and large output force. They can accurately convert amplified electrical signals into high-frequency micron-level mechanical vibrations. This high-precision vibration is necessary for detecting early microscopic damage to components.

[0064] Through the coordinated operation of the signal generation unit, power amplification unit, and piezoelectric ceramic actuator, high-frequency, controllable probe vibrations can be generated to actively examine the internal state of components. This is completely different from the passive monitoring method that relies on the vibration of the equipment itself. Its excitation signal has concentrated energy and a high signal-to-noise ratio, which can penetrate the surface and accurately excite weak features that reflect early wear or fatigue, greatly improving the sensitivity and depth of fault diagnosis.

[0065] The excitation module also includes an excitation head.

[0066] The excitation head is fixedly connected to the vibration output end of the piezoelectric ceramic actuator to apply mechanical vibration to the preset detection area of ​​the key component.

[0067] The preset detection area is the range where the stress is greater than the median stress and the range where the vibration response is the largest, obtained by performing finite element analysis on key components.

[0068] As a precision mechanical interface, the excitation head accurately applies the micron-level vibration of the piezoelectric ceramic actuator to the detection area, avoiding energy dissipation and signal distortion. The system can obtain the most realistic component response with minimal energy input, achieving high-precision health monitoring without affecting normal production or damaging components. This is a key technological breakthrough for realizing online, non-destructive testing in industrial environments.

[0069] The acquisition module is used to acquire response signals from a preset detection area using different sensors fixed to key components.

[0070] Different sensors include piezoelectric accelerometers, laser Doppler vibration meters, and acoustic emission sensors.

[0071] These three sensors are not simply redundant configurations, but rather an organic whole with complementary functions. The piezoelectric accelerometer is good at capturing high-frequency vibration signals and is very sensitive to the overall dynamic response of the structure. The laser Doppler vibrometer can accurately measure vibration velocity and displacement non-contactly and is suitable for areas where it is inconvenient to install contact sensors or for measuring minute displacements. The acoustic emission sensor is specifically designed to capture stress waves released instantaneously by crack propagation and plastic deformation inside the material. When used together, it can simultaneously obtain response information of the preset detection area from multiple physical dimensions of vibration, displacement and acoustics, constructing a dataset that is much richer and more three-dimensional than a single sensor. This allows for a more comprehensive and accurate assessment of the health status of key components, effectively avoiding missed detections or misjudgments caused by the limitations of a single sensor.

[0072] By integrating piezoelectric accelerometers, laser Doppler vibration meters, and acoustic emission sensors, information can be acquired simultaneously from three different dimensions: mechanical vibration, optical measurement, and acoustic emission. This integrated sensing mode can comprehensively and three-dimensionally depict its health status, effectively avoiding blind spots and misjudgments that may be caused by a single sensor.

[0073] The acquisition module also includes signal conditioning circuitry.

[0074] Signal conditioning circuits are used to filter and amplify response signals.

[0075] The signal conditioning circuit filters and amplifies the original response signal, accurately removing irrelevant environmental noise and electromagnetic interference, and amplifying useful weak feature signals. This ensures that the data processed by the subsequent analysis unit is pure and of high quality, thereby avoiding erroneous decisions caused by signal contamination. It is the cornerstone of the stable operation of the entire intelligent control system.

[0076] The control module includes an analysis unit, a judgment unit, and an execution unit.

[0077] The analysis unit is used to receive the response signal and extract the frequency domain features of the response signal through a fast Fourier transform.

[0078] The judgment unit is used to compare the key parameters of the frequency domain features with the preset benchmark threshold range, and judge the health status of the key components based on the comparison results.

[0079] The execution unit is used to generate and output control instructions to adjust the molding process parameters of micro screw production based on the health status of key components. The molding process parameters include molding pressure, molding temperature, processing speed, die clearance, and feed rate.

[0080] The collaboration of the analysis unit, judgment unit, and execution unit not only solves the problem of diagnosing whether the equipment is good or bad, but also answers the question of how to produce the best results under the current conditions. It takes the health status of the equipment as input and directly outputs control instructions to adjust the specific process parameters of molding pressure, temperature, and speed, enabling the production line to have the ability to self-regulate and continuously optimize according to its own conditions. This is the core manifestation of realizing true intelligent manufacturing.

[0081] The logic for determining the health status of key components based on the comparison results includes:

[0082] Key parameters of frequency domain features are extracted, including peak amplitude at a specific frequency point, main resonant frequency offset, and quality factor. The specific frequency points include the main resonant frequency point and harmonic frequency point of the key components.

[0083] Compare key parameters with preset benchmark threshold ranges;

[0084] If a key parameter exceeds the preset baseline threshold range, the health status of the key component is determined to be abnormal.

[0085] If the key parameters do not exceed the preset benchmark threshold range, the health status of the key components is determined to be healthy.

[0086] The preset benchmark threshold range is set by iteratively and repeatedly testing new key components under standard operating conditions, collecting key parameters in the frequency domain, and analyzing the vibration response signal of the key components through fast Fourier transform. The indicators used to quantitatively judge their health status are extracted from these parameters, including peak amplitude at a specific frequency point, main resonant frequency offset, and quality factor. The statistical average and standard deviation of the key parameters in the frequency domain are calculated. Combined with the factory safety margin coefficient of the key components and the parameter boundaries of the key components before failure in the historical fault data collected from big data, a numerical range is finally determined that is centered on the statistical average and expanded based on the standard deviation, without exceeding the historical failure boundary.

[0087] The quality factor is a physical quantity that measures how quickly the vibration energy of a system decays. A higher value indicates less energy loss and purer vibration, while a lower value indicates increased damping or damage.

[0088] By extracting frequency domain parameters that are highly sensitive to structural damage, such as the main resonant frequency offset, peak amplitude, and quality factor, and comparing them with preset benchmark thresholds, the health status of components can be judged with extremely high accuracy and objectivity. This method transforms fuzzy, empirical judgments into precise, data-driven scientific diagnoses, enabling reliable identification of early and minor damage.

[0089] The logic for generating and outputting control commands to adjust molding process parameters includes:

[0090] If the health status of the key components is healthy, the automated composite molding process for micro screw production is optimized under normal space constraints using a preset finite element analysis model to obtain the optimal combination of molding process parameters. The optimal combination of molding process parameters is then converted into control commands to adjust the molding process parameters.

[0091] Optimizing the automated composite molding process for miniature screw production includes:

[0092] Obtain the normal spatial dimensions, maximum allowable deformation, and allowable material stress of key components.

[0093] In the finite element analysis model, the normal spatial dimensions of key components are defined as design variables. Within the range of preset molding process parameters, the molding process parameters of each dimension are randomly selected, and the random selections of the molding process parameters of each dimension are combined to obtain different combinations of process parameters. For different combinations of process parameters, virtual molding experiments are conducted to obtain the optimal combination of molding process parameters.

[0094] The preset finite element analysis model is a highly realistic computer simulation program that accurately simulates the molding process by discretizing physical entities into tiny units. This allows for the rapid prediction and evaluation of the impact of different combinations of process parameters on product quality without consuming actual resources, in order to find the optimal solution for real-world production in the virtual world.

[0095] Normal space dimension refers to the precise three-dimensional spatial area that a component's functional surface must occupy, perpendicular to the contact surface, when the component is assembled or in operation. It defines the physical contour and positional boundary of the component in critical directions and is a rigid geometric constraint that ensures that it can be correctly installed and achieve its intended function.

[0096] When the equipment is healthy, instead of sticking to the existing process, we proactively use finite element analysis models to quickly simulate and optimize thousands of process parameter combinations in virtual space. This approach can find the optimal process solution that balances product quality and equipment protection without consuming any actual materials or interrupting production, thus maximizing both production efficiency and equipment lifespan. It is a typical combination of predictive maintenance and proactive optimization.

[0097] The logic for conducting virtual molding experiments includes:

[0098] The process parameters are configured onto the corresponding boundary conditions in the finite element analysis model. Using structural statics simulation tools, the displacement changes of key components under the action of the process parameter combination during the composite molding process are simulated to obtain the deformation distribution. Simultaneously, the internal stress generated by various forces and loads acting on the key components during the composite molding process of micro screws is calculated to obtain the stress distribution. While calculating the deformation distribution and stress distribution, the molding process of micro screws is simulated to obtain simulation results. The simulation results include the maximum deformation of key components, the maximum equivalent stress, and product quality indicators.

[0099] The normal spatial dimensions of key components are used as design variables. The optimization objectives are to optimize product quality indicators, minimize the maximum deformation of key components, and minimize the maximum equivalent stress. The normal spatial dimensions of key components, the maximum allowable deformation, and the allowable stress of materials are used as constraints. A multi-objective optimization algorithm is used to iteratively find the optimal combination of molding process parameters.

[0100] Boundary conditions are mathematical rules defined on the finite element analysis model to simulate various external actions and internal constraints in the real physical world. Boundary conditions are divided into loads, constraints, and contacts. Loads include the pressure applied by the punch and the temperature transmitted by the die. Constraints include completely fixing the equipment base and restricting its movement in all directions. Contacts include defining the interaction between the punch and the screw blank under extrusion and friction. These conditions together constitute the physical laws that drive the simulation model.

[0101] Configuring the process parameter combination to the corresponding boundary conditions in the finite element analysis model is an operation that precisely maps the abstract process parameters to the aforementioned boundary conditions. The operator selects a specific geometric region of the model in the software, then creates the corresponding boundary condition type, and inputs the specific values ​​of the process parameter combination. By repeating the above steps, all process parameters are applied as corresponding boundary conditions to the correct positions of the model, thereby transforming the static digital model into a virtual experimental system with a complete physical context that can be started for calculation.

[0102] Deformation distribution is a color cloud map that visually displays the changes in shape and position of key components under forming force. Its core significance lies in quickly locating structurally weak and dangerous areas and comparing them with preset normal space dimensions to predict whether excessive deformation of components will affect assembly accuracy or lead to product scrap. It is a key basis for ensuring the functional integrity of equipment.

[0103] Various forces and loads, including externally applied process parameters and internally generated reaction forces, are crucial in virtual experiments. Accurately configuring these forces and loads is a prerequisite for establishing a virtual environment consistent with the real world. They are the root causes of subsequent deformation and stress, and their accuracy directly determines the credibility of the simulation results.

[0104] Stress distribution is a cloud map that shows the distribution of internal forces generated inside a component to resist external forces. It is a core indicator for assessing the health status of a component. By comparing it with the allowable stress of the material, it can predict whether the component is at risk of plastic deformation or fracture. It is also a direct basis for structural optimization, helping engineers to find stress concentration areas and make improvements to enhance the durability of the mold.

[0105] Product quality indicators are a series of quantitative standards for measuring whether the simulated miniature screw products are qualified. They are the final output of virtual experiments and cover geometric dimensions, physical properties, and functional parameters. By directly measuring these indicators from the simulation model, accurate predictions of product quality can be made without producing physical samples, thereby judging the merits of the combination of process parameters.

[0106] The core of using multi-objective optimization algorithms for iterative optimization is to establish an automatic trial-and-error and intelligent evolution loop. A batch of initial process parameter combinations are randomly generated, and their performance is evaluated one by one through virtual experiments. The algorithm selects the high-performing combinations and uses their characteristics to generate a new generation of better parameters. This cycle of generation, evaluation, selection, and re-evolution is repeated continuously, so that the parameter combinations gradually approach the ideal state. When the iteration meets the termination condition, a set of non-dominant Pareto optimal solutions is finally output, providing decision-makers with a variety of high-quality trade-offs.

[0107] When conducting virtual experiments, the optimization objectives are to simultaneously achieve the best product quality indicators, the least component deformation, and the least equivalent stress. This is equivalent to finding an optimal solution. It ensures that the combination of process parameters given by the system not only makes the product qualified, but also maximizes the service life of expensive components such as molds and punches. This realizes the upgrade of the lean production concept from pursuing output to pursuing the maximization of comprehensive benefits.

[0108] The control module also includes an early warning unit.

[0109] The early warning unit is used to determine the wear level of critical components when the health status of critical components is determined to be abnormal, and to issue an early warning based on the wear level.

[0110] Wear levels include primary wear, intermediate wear, and severe wear.

[0111] Assessing the wear condition of critical components includes:

[0112] When the main resonant frequency offset is greater than the first threshold and less than the second threshold, the wear level is judged to be primary wear.

[0113] When the main resonance frequency offset is greater than or equal to the second threshold, and the peak amplitude at a specific frequency point is greater than the third threshold, the wear level is determined to be medium wear.

[0114] When the main resonant frequency offset is greater than or equal to the fourth threshold or the decrease in the quality factor is greater than the fifth threshold, the wear level is determined to be severe wear.

[0115] Early warning based on wear level includes:

[0116] When the wear is determined to be primary, a first-level warning is triggered. The first-level warning is used to display a prompt message on the human-machine interface indicating that the component's performance has deteriorated.

[0117] When the wear level is determined to be moderate, a second-level warning is triggered. The second-level warning is used to activate the audible and visual alarms at the equipment site and send an alarm email to the designated management terminal.

[0118] When severe wear is detected, a Level 3 warning is triggered. The Level 3 warning is an emergency warning displayed on the human-machine interface in a flashing red light, indicating that the component is about to fail.

[0119] The first threshold is set based on the reference value of the main resonant frequency of the key component in a brand-new healthy state. The main resonant frequency in the healthy state is determined by experimental modal analysis. The first threshold is set as a deviation of one to two percent from the reference frequency value. This small deviation indicates that the stiffness of the component begins to decrease slightly due to initial wear or surface fatigue, which is the initial signal of performance degradation.

[0120] The second threshold is also set based on the main resonant frequency reference value under healthy conditions. It is set to a deviation of three to five percent from the reference frequency value. When the frequency deviation reaches or exceeds this range, it indicates that the stiffness of the component has decreased significantly and the performance has deteriorated significantly. However, it has not yet entered the stage of rapid failure and is one of the criteria for judging intermediate wear.

[0121] The third threshold is set based on the peak amplitude reference value of a component at a specific frequency point when the component is in a healthy state. After obtaining the reference amplitude value through vibration testing, the third threshold is set to 1.5 to 2 times the reference value. When the component experiences mid-term wear, its damping characteristics will change, resulting in a significant increase in amplitude. Exceeding this threshold means that the vibration energy absorption capacity has deteriorated, which is a clear signal of accelerated wear.

[0122] The fourth threshold is one of the extreme indicators for determining that a component is about to fail severely. It is also based on the reference value of the main resonant frequency under healthy conditions. It is set that if the reference frequency value deviates by 8% to 10%, there may be macroscopic cracks or structural damage, which is one of the criteria for determining severe wear.

[0123] The system doesn't simply issue alarms; instead, it precisely categorizes wear into three levels—primary, intermediate, and severe—based on subtle changes in parameters such as the main resonant frequency offset. It then matches these levels with a progressive response strategy, from interface prompts and audible / visual alarms to emergency warnings. This refined management approach allows maintenance to be carried out calmly and purposefully, avoiding unnecessary disruptions to production while ensuring timely intervention in major risks. This elevates equipment management from a passive to the highest level of proactivity.

[0124] This invention actively emits probe vibration signals and collects and analyzes the response signals of key components to deeply assess the internal structural health status of these components. It can promptly detect early wear and fatigue damage that traditional macroscopic parameter monitoring struggles to capture, significantly improving the accuracy and timeliness of early warnings. It establishes a closed-loop linkage between the health status of key components and the dynamic optimization of molding process parameters. When component performance degradation is detected, it can proactively find the optimal process solution that balances optimal product quality and minimum equipment stress without interrupting production or consuming actual materials, based on finite element analysis models and multi-objective optimization algorithms, through simulation and optimization of different process parameter combinations in a virtual environment. This achieves the dual maximization of production efficiency and equipment lifespan, ensuring continuous and stable product quality. This mechanism, which deeply integrates equipment health monitoring with production process control, represents a leap from passive maintenance to proactive control and from experience-based parameter tuning to intelligent optimization, ultimately achieving intelligent and adaptive high-efficiency production. It effectively reduces scrap rates and the risk of sudden failures, improving overall production efficiency.

[0125] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0126] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An automated composite molding control system for the production of miniature screws, characterized in that, It includes an excitation module, a data acquisition module, and a control module: The excitation module is used to transmit probe vibration signals of a preset frequency to key components of the composite molding machine; The acquisition module is used to acquire the response signal transmitted and fed back by the probe vibration signal through the key component; The control module is used to analyze the relevant feature changes of the response signal, and determine the health status of the key component based on the relevant feature changes, and generate and output control commands to adjust the molding process parameters based on the determined health status. The logic for generating and outputting control commands to adjust molding process parameters includes: If the health status of the key component is healthy, the automated composite molding process of micro screw production is optimized under normal space constraints using a preset finite element analysis model to obtain the optimal combination of molding process parameters, and the optimal combination of molding process parameters is converted into control instructions for adjusting the molding process parameters. The automated composite molding process for optimizing the production of micro screws includes: Obtain the normal spatial dimensions, maximum allowable deformation, and allowable material stress of key components; In the finite element analysis model, the normal spatial dimension of the key component is defined as the design variable. Within the preset molding process parameter range, the molding process parameters of each dimension are randomly selected, and the random selections of the molding process parameters of each dimension are combined to obtain different combinations of process parameters. For different combinations of process parameters, virtual molding experiments are conducted to obtain the optimal combination of molding process parameters. The logic for conducting virtual molding tests includes: The process parameters are configured onto the corresponding boundary conditions in the finite element analysis model. Using a structural statics simulation tool, the displacement changes of key components under the action of the process parameters during the composite molding process are simulated to obtain the deformation distribution. Simultaneously, the internal stresses generated by various forces and loads acting on the key components during the composite molding process of micro screws are calculated to obtain the stress distribution. While calculating the deformation distribution and stress distribution, the molding process of micro screws is simulated to obtain simulation results. The simulation results include the maximum deformation of key components, the maximum equivalent stress, and product quality indicators. The normal spatial dimensions of key components are used as design variables. The optimization objectives are to optimize product quality indicators, minimize the maximum deformation of key components, and minimize the maximum equivalent stress. The normal spatial dimensions of key components, the maximum allowable deformation, and the allowable stress of materials are used as constraints. A multi-objective optimization algorithm is used to iteratively find the optimal combination of molding process parameters.

2. The automated composite molding control system for miniature screw production as described in claim 1, characterized in that, The excitation module includes a signal generation unit, a power amplification unit, and a piezoelectric ceramic actuator; The signal generating unit is used to generate a probe vibration signal at a preset frequency; The power amplification unit is used to electrically connect with the signal generation unit and amplify the power of the probe vibration signal; The piezoelectric ceramic actuator is used to be electrically connected to the power amplification unit and to convert the probe vibration signal after amplification of the power meter into mechanical vibration; The key components include the mold, punch, and spindle of the composite molding machine.

3. The automated composite molding control system for miniature screw production as described in claim 2, characterized in that, The excitation module also includes an excitation head; The excitation head is fixedly connected to the vibration output end of the piezoelectric ceramic actuator to apply the mechanical vibration to the preset detection area of ​​the key component.

4. The automated composite molding control system for miniature screw production as described in claim 3, characterized in that, The acquisition module is used to acquire response signals from the preset detection area through different sensors fixed on key components; The different sensors include piezoelectric accelerometers, laser Doppler vibration meters, and acoustic emission sensors.

5. The automated composite molding control system for miniature screw production as described in claim 4, characterized in that, The acquisition module also includes a signal conditioning circuit; The signal conditioning circuit is used to filter and amplify the response signal.

6. The automated composite molding control system for miniature screw production as described in claim 5, characterized in that, The control module includes an analysis unit, a judgment unit, and an execution unit; The analysis unit is used to receive the response signal and extract the frequency domain features of the response signal through a fast Fourier transform. The judgment unit is used to compare the key parameters of the frequency domain features with a preset benchmark threshold range, and judge the health status of the key components based on the comparison results. The execution unit is used to generate and output control instructions for adjusting the molding process parameters of the micro screw production based on the health status of the key components. The molding process parameters include molding pressure, molding temperature, processing speed, die gap, and feed rate.

7. The automated composite molding control system for miniature screw production as described in claim 6, characterized in that, The logic for determining the health status of the key components based on the comparison results includes: Extract key parameters of the frequency domain features, including peak amplitude at a specific frequency point, main resonance frequency offset, and quality factor. The specific frequency point includes the main resonance frequency point and harmonic frequency point of the key component. The key parameters are compared with a preset benchmark threshold range; If the key parameter exceeds the preset benchmark threshold range, the health status of the key component is determined to be abnormal. If the key parameters do not exceed the preset benchmark threshold range, the health status of the key component is determined to be healthy.

8. The automated composite molding control system for miniature screw production as described in claim 7, characterized in that, The control module also includes an early warning unit; The early warning unit is used to determine the wear level of the critical component when the health status of the critical component is determined to be abnormal, and to issue an early warning based on the wear level. The wear levels include primary wear, intermediate wear, and severe wear; The determination of the wear level of the key component includes: When the main resonant frequency offset is greater than the first threshold and less than the second threshold, the wear level is determined to be primary wear. When the main resonance frequency offset is greater than or equal to the second threshold, and the peak amplitude of the specific frequency point is greater than the third threshold, the wear level is determined to be medium wear. When the main resonant frequency offset is greater than or equal to the fourth threshold or the decrease in the quality factor is greater than the fifth threshold, the wear level is determined to be severe wear. The early warning based on the wear level includes: When the wear is determined to be primary, a first-level warning is triggered. The first-level warning is used to display a prompt message on the human-machine interface indicating a decline in component performance. When the wear is determined to be moderate, a second-level warning is triggered. The second-level warning is used to activate the audible and visual alarms at the equipment site and send an alarm email to the designated management terminal. When severe wear is detected, a Level 3 warning is triggered. The Level 3 warning is an emergency warning displayed on the human-machine interface in a flashing red light, indicating that the component is about to fail.

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