High-speed tool changer integrated with energy recovery

Abstract

The application relates to the technical field of machine tool cutters, and discloses a high-speed tool changing system integrated with energy recovery, which comprises the following: a direct drive driving unit is electrically connected with a numerical control machine tool cutter tower through a direct drive motor; a kinetic energy recovery unit switches the working mode of the direct drive motor into a generator mode based on a fuzzy PID algorithm, and converts kinetic energy into electric energy during deceleration or braking through regenerative braking; an energy management unit defines the flow paths of energy input, storage and consumption ends based on an energy flow balance model; an intelligent control unit coordinates and integrates fault diagnosis on the speed regulation of the direct drive driving unit, the mode switching of the kinetic energy recovery unit and the energy distribution of the energy management unit based on a speed regulation algorithm of model predictive control; and an energy release unit judges whether energy release abnormity exists in the stored electric energy based on analysis results, so that the stability and reliability of the tool changing system are ensured.

Description

Technical Field

[0001] This invention relates to the field of machine tool technology, and more specifically, to a high-speed tool changing system with integrated energy recovery. Background Technology

[0002] As a component enabling continuous multi-process machining, the tool changing efficiency of a CNC machine tool directly affects the overall machining cycle time and production efficiency. With the manufacturing industry moving towards precision, the tool changing speed of CNC machine tools faces challenges. However, during frequent tool changes, the tool turret and its related moving parts still possess a certain amount of kinetic energy. Traditional tool changing systems dissipate this kinetic energy directly as heat through mechanical braking or damping, lacking an effective energy recovery mechanism, thus wasting energy. This energy dissipation mode not only reduces energy utilization efficiency but also shortens the service life of the CNC machine tool due to frequent wear of braking components.

[0003] Therefore, it is necessary to design a high-speed tool change system with integrated energy recovery to solve the problems existing in the current technology. Summary of the Invention

[0004] In view of this, the present invention proposes a high-speed tool changing system with integrated energy recovery, which aims to solve the problem of energy waste caused by the lack of an effective energy recovery mechanism in tool changing systems.

[0005] This invention proposes a high-speed tool changing system with integrated energy recovery, comprising: A direct drive unit is configured to be electrically connected to the CNC machine tool turret using a direct drive motor, wherein the direct drive motor is used to drive the CNC machine tool turret to rotate. The kinetic energy recovery unit is configured to switch the working mode of the direct drive motor to generator mode based on a fuzzy PID algorithm when the CNC machine tool turret decelerates or brakes, and convert the kinetic energy during deceleration or braking into electrical energy through regenerative braking. The fuzzy PID algorithm takes the speed deviation as input and the mode switching signal strength and braking force as output. The energy management unit is configured to define the flow paths of energy input, storage, and consumption based on an energy flow balance model, and to store the electrical energy. The intelligent control unit is configured to coordinate and integrate fault diagnosis for the speed regulation of the direct drive unit, the mode switching of the kinetic energy recovery unit, and the energy distribution of the energy management unit, based on the speed regulation algorithm of the model predictive control and combined with the operating parameters of the direct drive motor, the state parameters of the CNC machine tool turret, and the energy storage parameters. The energy release unit is configured to acquire all historical energy release data, parse all historical energy release data, determine whether there is an energy release anomaly after storage based on the parsing results, and adjust the energy release after storage based on all historical energy release data when an energy release anomaly is determined.

[0006] Furthermore, when switching the operating mode of the direct drive motor to generator mode based on the fuzzy PID algorithm, and converting the kinetic energy during deceleration or braking into electrical energy through regenerative braking, the process includes: The kinetic energy recovery unit determines input variables and output variables. The input variable is the speed deviation between the current speed of the CNC machine tool turret and the target deceleration speed. The output variable is the trigger signal strength for mode switching of the direct drive motor and the braking force of regenerative braking. The input variables are fuzzified, and the speed deviation is divided into seven fuzzy sets: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large. The membership degree of the seven fuzzy sets is determined based on the triangular membership function. A fuzzy rule base is established according to the operating characteristics of the CNC machine tool turret. The membership degree and the fuzzy rule base are inferred based on the Mamdani inference method and the center of gravity method. The trigger signal strength for the mode switching of the direct drive motor and the braking force of the regenerative braking are output.

[0007] Furthermore, when defining the flow paths of energy input, storage, and consumption based on the energy flow balance model, it includes: The energy management unit is equipped with a hybrid energy storage component, which consists of a supercapacitor and an energy storage battery. The supercapacitor is used to store the electrical energy converted by regenerative braking when the CNC machine tool turret decelerates or brakes, and the energy storage battery is used to store the electrical energy in the supercapacitor that has not been consumed within a set time period. The kinetic energy recovery unit is used as the energy input end, the direct drive unit is used as the energy consumption end, and the hybrid energy storage component is used as the energy storage end. An energy flow path is established in the order of the energy input end, the energy storage end, and the energy consumption end. Obtain a sample dataset, construct an initial energy flow balance model based on the sample dataset and energy flow path, input the actual operating energy data into the initial energy flow balance model to determine the model prediction result, adjust the initial energy flow balance model based on the energy deviation between the model prediction result and the energy data, and determine the final energy flow balance model.

[0008] Furthermore, the intelligent control unit includes a monitoring module and a processing module; The monitoring module collects the operating parameters of the direct drive motor, the status parameters of the CNC machine tool turret, the conversion parameters of the kinetic energy recovery unit, and the energy parameters of the energy management unit, and constructs a monitoring dataset. The processing module preprocesses the monitoring dataset, including data noise reduction and data standardization.

[0009] Furthermore, the intelligent control unit also includes an output module; The output module establishes a motor speed prediction model based on the preprocessed monitoring dataset. The motor speed prediction model takes the current speed and output torque of the direct drive motor and the load of the CNC machine tool turret as inputs, and the voltage and current of the direct drive motor as outputs.

[0010] Furthermore, the intelligent control unit also includes a diagnostic alarm module; The diagnostic alarm module obtains the balance prediction result of the energy flow balance model and compares it with the preprocessed monitoring dataset. If the parameters in the preprocessed monitoring dataset are not equal to the balance prediction result, fault investigation is initiated; otherwise, fault investigation is not initiated. When troubleshooting is initiated, the corresponding energy flow path is determined based on the unequal parameters and the fault location is located. The fault parameters at the fault location are collected and compared with the normal operating condition parameter range. A fault diagnosis report is generated based on the comparison results.

[0011] Furthermore, when acquiring all historical energy release data, analyzing all historical energy release data, and determining whether there are any energy release anomalies in the stored electrical energy based on the analysis results, this includes: Acquire energy release data for each stage and determine the corresponding historical energy release data. Analyze the corresponding historical energy release data to determine energy overflow behavior, normal release behavior, and energy deficiency behavior. If the energy release data does not belong to the normal release behavior, it is determined that the stored electrical energy has an energy release anomaly; If the energy release data belongs to the normal release behavior, then it is determined that the stored electrical energy does not have any abnormal energy release.

[0012] Furthermore, when an energy release anomaly is detected, adjustments are made to the stored energy release based on all historical energy release data, including: Identify all historical energy release data with energy overflow behavior and construct an energy overflow dataset; identify all historical energy release data with energy shortage behavior and construct an energy shortage dataset. The number of historical energy release data in the energy overflow dataset is counted and recorded as the overflow number; the number of historical energy release data in the energy shortage dataset is counted and recorded as the shortage number. The release of stored electrical energy is adjusted based on the amount of overflow and the amount of shortage.

[0013] Furthermore, when adjusting the release of stored electrical energy based on the overflow and insufficient amounts, the following steps are included: If the overflow amount is greater than the insufficient amount, then the energy released after storage is reduced based on the insufficient amount; If the overflow quantity is less than the insufficient quantity, then the energy released after storing the electrical energy is increased based on the overflow quantity; If the overflow quantity equals the insufficient quantity, then the energy released after storage is adjusted based on the overflow quantity and the insufficient quantity.

[0014] Furthermore, the integrated energy recovery high-speed tool changing system also includes: The CNC machine tool turret is used to adapt to the PSC head tool holder. The PSC head tool holder is provided with several positioning slots, and each positioning slot is used to connect to the CNC machine tool turret. The PSC head tool holder is symmetrically provided with tool body supports, and the tool body supports are fixedly connected to the PSC head tool holder. The tool body supports have PSC interfaces. The PSC headstock is symmetrically provided with a first groove and a second groove.

[0015] Compared with existing technologies, the advantages of this invention are as follows: When the CNC machine tool turret decelerates or brakes, a fuzzy PID algorithm is used to switch the direct drive motor's operating mode to generator mode. Regenerative braking converts kinetic energy into electrical energy, which is then stored by the energy management unit. The fuzzy PID algorithm uses speed deviation as input to precisely control the mode switching signal strength and braking force, ensuring efficient kinetic energy conversion. Simultaneously, consuming kinetic energy through electrical energy conversion reduces wear between mechanical components. The intelligent control unit, based on a model predictive control algorithm and integrating direct drive motor operating parameters, turret status parameters, and energy storage parameters, coordinates the control of each unit. On one hand, the direct drive unit achieves precise drive of the CNC machine tool turret; on the other hand, the modes of the kinetic energy recovery unit and energy release unit ensure smooth speed adjustment and mode switching. The fault diagnosis function integrated into the intelligent control unit monitors the state of the CNC machine tool turret during deceleration or braking in real time, reducing the risk of downtime. The stored electrical energy is managed by the energy release unit, which analyzes historical release data to determine if there are any abnormalities in energy release, achieving intelligent energy management and improving the overall system intelligence. Attached Figure Description

[0016] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A functional block diagram of a high-speed tool changer system with integrated energy recovery provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the PSC head tool holder provided in an embodiment of the present invention.

[0017] The components are: 1. PSC head tool holder; 10. Positioning groove; 11. First groove; 12. Second groove; 2. Tool body support; 20. PSC interface. Detailed Implementation

[0018] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0019] In some embodiments of this application, see Figure 1 As shown, a high-speed tool changer system with integrated energy recovery includes: The direct drive unit is configured to use a direct drive motor electrically connected to the CNC machine tool turret, and the direct drive motor is used to drive the CNC machine tool turret to rotate.

[0020] The kinetic energy recovery unit is configured to switch the working mode of the direct drive motor to generator mode based on the fuzzy PID algorithm when the CNC machine tool turret decelerates or brakes, and convert the kinetic energy during deceleration or braking into electrical energy through regenerative braking. The fuzzy PID algorithm takes the speed deviation as input and the mode switching signal strength and braking force as output.

[0021] The energy management unit is configured to define the flow paths of energy input, storage, and consumption based on an energy flow balance model, and to store electrical energy.

[0022] The intelligent control unit is configured to coordinate and integrate fault diagnosis for speed regulation of the direct drive unit, mode switching of the kinetic energy recovery unit, and energy distribution of the energy management unit, based on a speed regulation algorithm based on model predictive control and combined with the operating parameters of the direct drive motor, the state parameters of the CNC machine tool turret, and energy storage parameters.

[0023] The energy release unit is configured to acquire all historical energy release data, parse all historical energy release data, determine whether there is an energy release anomaly after storage based on the parsing results, and adjust the energy release after storage based on all historical energy release data when an energy release anomaly is determined.

[0024] Specifically, the direct drive unit uses a direct drive motor that is directly electrically connected to the CNC machine tool turret, eliminating intermediate transmission links. It utilizes the high torque and high dynamic response characteristics of the direct drive motor to drive the CNC machine tool turret to rotate, avoiding the problems of large mechanical losses and slow response of traditional transmissions. When the CNC machine tool turret needs to decelerate or brake, the kinetic energy recovery unit activates a fuzzy PID algorithm, using the speed deviation between the current speed of the CNC machine tool turret and the target deceleration speed as input. The target deceleration speed is set according to the needs of the tool changing scenario, such as the positioning speed when the CNC machine tool turret needs to rotate to the target tool position, to ensure that the CNC machine tool turret can be accurately aligned after deceleration to complete the tool change. By establishing fuzzy sets and membership functions, the actual speed deviation is transformed into membership degrees. A fuzzy rule base is then established based on the operating characteristics of the CNC machine tool turret. For example, if the speed deviation is large, the mode switching signal strength and braking force are moderate. Through Mamdani inference and the center-of-gravity method, the fuzzy sets are transformed into actual mode switching signal strength and braking force values. Simultaneously, the direct-drive motor is controlled to switch the operating mode to generator mode. Regenerative braking converts the kinetic energy of the CNC machine tool turret during deceleration or braking into electrical energy. The recovered electrical energy is then used during the next tool change start-up, deceleration, or standby, reducing dependence on the external power grid and thus lowering the energy consumption of the tool changing system. Using a direct-drive motor to drive the rotation of the CNC machine tool turret avoids intermediate transmission links such as gears and belts, thereby improving the response speed and positioning accuracy of the tool changing system and ensuring tool changing efficiency. The energy management unit stores the electrical energy generated by the kinetic energy recovery unit to prevent energy loss and thus preserve energy for a long time. At the same time, it establishes an energy flow path based on an energy flow balance model to ensure the stability and reliability of energy release.

[0025] Understandably, the intelligent control unit uses a model predictive control (MPC)-based speed regulation algorithm and adjusts the speed of the direct drive motor based on parameters such as the motor's physical characteristics (stator resistance, inductance, etc.) and the CNC machine tool turret's mechanical characteristics (moment of inertia, coefficient of friction). This ensures the reliability of the CNC machine tool turret's rotation and precise positioning during deceleration or braking, providing a stable speed basis for energy recovery. This forms a closed-loop control system for the direct drive unit, kinetic energy recovery unit, and energy management unit. Furthermore, by comparing potentially deviating operating parameters, status parameters, and energy storage parameters, fault diagnosis is performed, and early warning information is output, ensuring the reliability of the tool changing system. The energy release unit is the key link between energy storage and reuse. In the energy management unit... When the intelligent control unit uses the recovered electrical energy during the next tool change start, deceleration, or standby, the energy release unit monitors the released energy, such as the released power and the energy supplied to the target, and analyzes historical energy release data to determine the normal energy release range and power fluctuation range of the tool load. By comparing the released energy with the determined benchmarks, it determines whether there is any abnormal energy release, avoiding problems such as unstable start-up of the direct drive motor and fluctuations in the operation of the CNC machine tool turret caused by abnormal energy release. The closed-loop control connected to the intelligent control unit improves the overall operational reliability of the system, ensures the precise matching of electrical energy release with the needs of each consumption end, and guarantees the utilization of kinetic energy while reducing energy waste caused by improper release.

[0026] In some embodiments of this application, when switching the working mode of the direct drive motor to generator mode based on the fuzzy PID algorithm and converting the kinetic energy during deceleration or braking into electrical energy through regenerative braking, the process includes: a kinetic energy recovery unit determining input and output variables. The input variable is the speed deviation between the current speed of the CNC machine tool turret and the target deceleration speed. The output variables are the trigger signal strength for mode switching of the direct drive motor and the braking force of regenerative braking. The input variables are fuzzified, and the speed deviation is divided into seven fuzzy sets: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large. The membership degree of the seven fuzzy sets is determined based on the triangular membership function. A fuzzy rule base is established according to the operating characteristics of the CNC machine tool turret. The membership degree and the fuzzy rule base are inferred based on the Mamdani inference method and the centroid method. The trigger signal strength for mode switching of the direct drive motor and the braking force of regenerative braking are output.

[0027] Specifically, the kinetic energy recovery unit clearly defines input and output variables. The input variable focuses on the speed deviation between the current speed of the CNC machine tool turret and the target deceleration speed. This speed deviation directly reflects the difference between the actual operating state of the CNC machine tool turret and the target deceleration speed. The output variable focuses on the mode switching trigger signal strength of the direct drive motor and the braking force of the regenerative braking. The former determines the timing and smoothness of the transition from the working mode to the generator mode, while the latter regulates the efficiency of kinetic energy conversion into electrical energy and the stability of the CNC machine tool turret deceleration. The fuzzy PID algorithm can accurately capture the state of the speed deviation, thereby achieving flexible control. Based on the fuzzy PID algorithm, the speed deviation is divided into seven fuzzy sets: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large, covering the entire deviation range of the CNC machine tool turret from overspeed to underspeed. The triangular membership function transforms the specific numerical values ​​of the real-time collected speed deviation into the membership degrees of the corresponding fuzzy sets, realizing the transformation of precise data into fuzzy concepts and providing a foundation for subsequent reasoning. A fuzzy rule base is established based on the operating characteristics of the CNC machine tool turret. This fuzzy rule base aligns with the characteristics of the CNC machine tool turret during high-speed rotation: a larger speed deviation requires a timely response, while a gradual change in speed deviation necessitates maintaining stability. For example, concrete rules are established such as adjusting the trigger signal strength and braking force if the speed deviation is large. Based on the Mamdani inference method, the membership degrees of the input variables are matched with the fuzzy rule base. The output of multiple rules is combined to obtain a fuzzy set of output variables. The fuzzy set is then converted into numerical values ​​using the centroid method. The specific output values ​​are used to switch the trigger signal strength and regenerative braking force, thereby controlling the direct drive motor to smoothly switch to generator mode. Regenerative braking efficiently converts the kinetic energy of the turret during deceleration or braking into electrical energy, ensuring minimal vibration or positioning deviation of the CNC machine tool turret. This achieves efficient kinetic energy recovery, reduces energy waste, improves system energy efficiency, and balances high-speed tool changing efficiency with system operational stability.

[0028] In some embodiments of this application, when defining the flow paths of energy input, storage, and consumption based on an energy flow balance model, the process includes: an energy management unit setting up a hybrid energy storage component, which consists of a supercapacitor and an energy storage battery. The supercapacitor stores the electrical energy converted from regenerative braking during deceleration or braking of the CNC machine tool turret, and the energy storage battery stores the electrical energy in the supercapacitor that has not been consumed within a set time period. A kinetic energy recovery unit is used as the energy input end, a direct drive unit as the energy consumption end, and the hybrid energy storage component as the energy storage end. An energy flow path is established in the order of energy input end, energy storage end, and energy consumption end. A sample dataset is obtained. An initial energy flow balance model is constructed based on the sample dataset and the energy flow path. The actual operating energy data is input into the initial energy flow balance model to determine the model prediction result. The initial energy flow balance model is adjusted based on the energy deviation between the model prediction result and the energy data, and the final energy flow balance model is determined.

[0029] Specifically, the hybrid energy storage component is composed of a supercapacitor and an energy storage battery. The supercapacitor is specifically designed to receive the electrical energy converted from regenerative braking when the CNC machine tool turret decelerates or brakes. The supercapacitor can quickly respond to a large amount of electrical energy generated in a short period of time, avoiding the loss of instantaneous energy. The energy storage battery is responsible for receiving the electrical energy that the supercapacitor does not consume within a set time period, realizing the long-term storage of energy and forming a hierarchical storage mechanism of instantaneous capture and long-term storage. The set time period is dynamically set based on the capacity of the supercapacitor; the larger the capacity, the longer the corresponding set time period. The kinetic energy recovery unit is defined as the energy input end, as it is the source of electrical energy generation. The direct drive unit is defined as the energy consumption end, as it consumes electrical energy when driving the CNC machine tool turret to rotate. The hybrid energy storage component is defined as the energy storage end, which undertakes the temporary storage and allocation of electrical energy. The energy flow path is constructed in the order of energy input end, energy storage end, and energy consumption end to sort out the complete transmission chain of electrical energy from generation, storage to utilization, providing a structured framework for model construction. The sample dataset covers information such as the electrical energy output of the energy input end, the charging and discharging status of the energy storage end, and the energy demand of the energy consumption end under different working conditions (tool change frequency, tool load changes). Based on the sample dataset and the established energy flow path, an initial energy flow balance model is constructed. The energy data from the actual system operation is then input into the initial energy flow balance model to obtain the model prediction results. This energy data includes the electrical energy converted during the deceleration or braking process of the CNC machine tool turret (voltage, current, and cumulative energy value), the real-time charge and charging / discharging power of the supercapacitor in the hybrid energy storage component, the rate of change of charge and remaining charge of the energy storage battery, and the real-time energy consumption (power and cumulative power consumption) when driving the CNC machine tool turret rotation. The model prediction results are the predicted values ​​of the system's energy flow state, corresponding one-to-one with the actual operating energy data. By comparing the energy deviation between the model prediction results and the energy data, the parameters of the initial energy flow balance model, such as energy conversion efficiency and loss coefficient, are adjusted. Through repeated iterative optimization, the deviation between the model prediction results and the energy data is stabilized within a fluctuation of 1%-3%, ultimately determining an accurate energy flow balance model. By defining the energy flow path through the energy flow balance model, efficient storage and targeted allocation of recovered electrical energy are achieved, thereby reducing energy loss.

[0030] In some embodiments of this application, the intelligent control unit includes a monitoring module and a processing module. The monitoring module collects the operating parameters of the direct drive motor, the status parameters of the CNC machine tool turret, the conversion parameters of the kinetic energy recovery unit, and the energy parameters of the energy management unit and constructs a monitoring dataset. The processing module preprocesses the monitoring dataset, including data noise reduction and data standardization.

[0031] In some embodiments of this application, the intelligent control unit further includes an output module. The output module establishes a motor speed prediction model based on the preprocessed monitoring dataset. The motor speed prediction model takes the current speed and output torque of the direct drive motor and the load of the CNC machine tool turret as inputs, and the voltage and current of the direct drive motor as outputs.

[0032] Specifically, the monitoring module is responsible for real-time acquisition of operating parameters of the direct drive motor, such as stator current; state parameters of the CNC machine tool turret, such as rotational position, angular velocity, and vibration amplitude; conversion parameters of the kinetic energy recovery unit, such as voltage and current generated by regenerative braking; and energy parameters of the energy management unit, such as real-time charge of the supercapacitor and remaining charge of the energy storage battery. The conversion parameters of the kinetic energy recovery unit and the energy parameters of the energy management unit constitute a portion of the energy data. These scattered data are integrated into a structured monitoring dataset, providing a data foundation for subsequent processing. The processing module preprocesses the monitoring dataset. Since the structured monitoring dataset has data fluctuations, and the model can eliminate the influence of the data through iterative learning, data denoising is used to eliminate electromagnetic interference and abnormal jumps in data caused by mechanical vibration, such as instantaneously increased current values ​​and irregular speed fluctuations, without using model processing, thus preserving the true data. Data standardization transforms parameters of different magnitudes and units, such as speed units (r / min) and torque units (N·m), into standardized data of the same magnitude, thereby eliminating dimensional differences between parameters. Based on the preprocessed monitoring dataset, a motor speed prediction model is constructed. This data-driven model can learn the driving law of the direct-drive motor and output the voltage and current that can maintain the target deceleration speed. This guides the direct-drive motor to adjust its operating state, thereby achieving precise control and smooth tool changing of the CNC machine tool turret and ensuring the reliability of the system's collaborative operation.

[0033] In some embodiments of this application, the intelligent control unit further includes a diagnostic alarm module. The diagnostic alarm module acquires the balance prediction result of the energy flow balance model and compares it with the preprocessed monitoring dataset. If the parameters in the preprocessed monitoring dataset are not equal to the balance prediction result, fault investigation is initiated; otherwise, fault investigation is not initiated. When fault investigation is initiated, the corresponding energy flow path is determined based on the unequal parameters, the fault location is located, the fault parameters at the fault location are collected and compared with the normal operating condition parameter range, and a fault diagnosis report is generated based on the comparison result.

[0034] Specifically, the diagnostic alarm module acquires the balance prediction results output by the energy flow balance model, i.e., the model's predicted values ​​for the energy input, energy storage, and energy consumption ends. Simultaneously, it retrieves the preprocessed monitoring dataset from the processing module and compares the balance prediction results with the corresponding parameters in the monitoring dataset one by one to determine if they match. If parameters are unequal, such as a discrepancy between the predicted supercapacitor charge and the actual monitored value, a fault investigation mechanism is triggered. If all parameters match, the investigation is not initiated. After investigation is initiated, the diagnostic alarm module locates the corresponding energy flow path based on the unequal parameters. If the charge level of the supercapacitor does not match the actual monitored value, the path corresponding to the energy consumption end is traced to the specific fault location, such as the supercapacitor. Fault parameters at that location are collected, such as abnormal charging and discharging current and voltage, and compared with the normal operating condition parameter range. The normal operating condition parameter range is determined based on the actual component's instruction manual, such as the supercapacitor, energy storage battery, and direct drive motor. Finally, a fault diagnosis report containing the fault location and abnormal parameter performance is generated, thereby timely detection of potential faults, prevention of fault spread, and improvement of system reliability and maintenance efficiency.

[0035] In some embodiments of this application, when acquiring all historical energy release data and parsing all historical energy release data, and determining whether there is an energy release anomaly in the stored electrical energy based on the parsing results, the process includes: acquiring energy release data for each stage and determining the corresponding historical energy release data; parsing the corresponding historical energy release data to determine energy overflow behavior, normal release behavior, and energy insufficiency behavior; if the energy release data does not belong to normal release behavior, then it is determined that there is an energy release anomaly in the stored electrical energy; if the energy release data belongs to normal release behavior, then it is determined that there is no energy release anomaly in the stored electrical energy.

[0036] Specifically, the energy release unit determines the energy release data according to different stages of CNC machine tool turret operation, such as tool change start, deceleration, and standby. The energy release data reflects the energy released from the stored electrical energy. It acquires all corresponding historical energy release data and divides this historical energy release data into three behaviors: energy overflow behavior, which indicates that the historical energy release data exceeds the actual demand under the current working condition, resulting in redundant energy waste; normal release behavior, which indicates that the historical energy release data is precisely matched with the real-time energy demand, with no waste or supply gap; and energy insufficiency behavior, which indicates that the historical energy release data cannot meet the equipment operation requirements, which may lead to unstable equipment startup or operation interruption. The system uses the normal release behavior as the criterion. If the current energy release data does not match the parsed normal release behavior (i.e., it is an energy overflow or energy shortage behavior), it is determined that there is an energy release anomaly after storage. If it is consistent with the normal release behavior, it is determined that there is no energy release anomaly. This reduces the waste caused by energy overflow and avoids the impact of insufficient energy on the stability of the CNC machine tool turret. It ensures the coordination between the energy release process and the operation of the CNC machine tool turret, and further improves the energy utilization efficiency and overall reliability of the system.

[0037] In some embodiments of this application, when an energy release anomaly is determined, adjusting the stored energy release based on all historical energy release data includes: determining all historical energy release data with energy overflow behavior and constructing an energy overflow dataset; determining all historical energy release data with energy shortage behavior and constructing an energy shortage dataset; counting the number of historical energy release data in the energy overflow dataset and recording it as the overflow number; counting the number of historical energy release data in the energy shortage dataset and recording it as the shortage number; and adjusting the stored energy release based on the overflow number and the shortage number.

[0038] In some embodiments of this application, when adjusting the release of stored electrical energy based on the overflow quantity and the insufficient quantity, the following steps are taken: if the overflow quantity is greater than the insufficient quantity, the energy released after storage is reduced based on the insufficient quantity; if the overflow quantity is less than the insufficient quantity, the energy released after storage is increased based on the overflow quantity; and if the overflow quantity is equal to the insufficient quantity, the energy released after storage is adjusted based on the overflow quantity and the insufficient quantity.

[0039] Specifically, the number of historical energy release data in the energy overflow dataset is recorded as the overflow quantity, reflecting the extent to which historical energy release exceeded demand. The number of historical energy release data in the energy shortage dataset is recorded as the shortage quantity, reflecting the extent to which historical energy release fell below demand. If the overflow quantity is greater than the shortage quantity, it indicates that historical energy release redundancy was excessive. Therefore, the current energy release is reduced based on the shortage quantity. For example, if there are 15 energy overflow records (overflow quantity 15) and 8 energy shortage records (shortage quantity 8), adjustments are made based on the shortage quantity (8 records). Referring to the operating conditions corresponding to these 8 shortage records, the current energy release is gradually reduced from its current level, with the reduction limited to not triggering new energy shortage behaviors. Similarly, if the overflow quantity is less than the shortage quantity, the released energy is increased based on the overflow quantity, with the increase limited to not triggering new energy overflow behaviors. If the overflow quantity and the shortage quantity are equal, the operating conditions of both types of records are considered, such as the load level during overflow and the peak demand during shortage. The energy with overall deviations is weighted and summed, and this weighted sum is used to adjust the energy released after storage. For example, if the overflow and under-exploitation quantities are 5, the historical energy release data corresponding to the overflow quantity are 6kJ, 7kJ, 10kJ, 7kJ, and 8kJ, and the historical energy release data corresponding to the under-exploitation quantity are 4kJ, 3kJ, 3kJ, 2kJ, and 4kJ. If the historical energy release data for normal release behavior is 5kJ, the energy deviation corresponding to the overflow quantity is the sum of the historical energy release data for each historical energy release data point and the historical energy release data for normal release behavior. The total difference is 13KJ. The energy corresponding to the insufficient quantity is the sum of the differences between each historical energy release data and the historical energy release data of normal release behavior, which is -9KJ. The energy released after storage is adjusted based on the weighted sum of 13KJ and -9KJ. The weight coefficient of the two is (0, 1). Assuming that the weighted result is 1KJ, 1KJ of energy is supplemented by a direct drive motor on the original basis to achieve a balance between the two abnormalities. This allows the adjustment to meet the needs of long-term system operation, thus taking into account both stability and flexibility.

[0040] See Figure 2 As shown, in some embodiments of this application, the high-speed tool changing system with integrated energy recovery further includes: a CNC machine tool turret for adapting to the PSC head tool holder 1, the PSC head tool holder 1 having a plurality of positioning slots 10, each positioning slot 10 for connecting to the CNC machine tool turret, the PSC head tool holder 1 having symmetrically arranged tool body supports 2, and the tool body supports 2 being fixedly connected to the PSC head tool holder 1, the tool body supports 2 having a PSC interface 20, and the PSC head tool holder 1 having symmetrically arranged a first groove 11 and a second groove 12.

[0041] Specifically, the PSC head tool holder 1 is provided with four positioning slots 10, which are evenly distributed on the bottom of the PSC head tool holder 1 to achieve precise positioning and stable connection with the CNC machine tool turret, providing structural support for the accuracy of high-speed tool changing. The symmetrically arranged tool body support 2 is fixedly connected to the PSC head tool holder 1, providing a symmetrical and stable support structure for the tool. At the same time, the PSC interface 20 opened on the tool body support 2 is used to connect tools with high rigidity and high precision characteristics to meet the requirements of tool connection accuracy and stability in high-speed tool changing scenarios. The PSC head tool holder 1 is symmetrically provided with a first groove 11 and a second groove 12, which optimizes the overall weight of the PSC head tool holder 1 by removing redundant materials, and reduces the load on the CNC machine tool turret, thereby improving the tool changing speed and energy recovery efficiency. It realizes the adaptability and flexibility between the CNC machine tool turret, PSC head tool holder 1 and tools, laying the foundation for the stable and efficient operation of the high-speed tool changing system.

[0042] In summary, the beneficial effects of this invention are as follows: When the CNC machine tool turret decelerates or brakes, a fuzzy PID algorithm switches the direct drive motor's operating mode to generator mode. Regenerative braking converts kinetic energy into electrical energy, which is then stored by the energy management unit. The fuzzy PID algorithm precisely controls the mode switching signal strength and braking force based on speed deviation, ensuring efficient kinetic energy conversion. Simultaneously, consuming kinetic energy through electrical energy conversion reduces wear between mechanical components. The intelligent control unit, based on model predictive control algorithms and integrating direct drive motor operating parameters, turret status parameters, and energy storage parameters, coordinates the control of each unit. On one hand, the direct drive unit achieves precise drive of the CNC machine tool turret; on the other hand, the modes of the kinetic energy recovery unit and energy release unit ensure smooth speed adjustment and mode switching. The fault diagnosis function integrated into the intelligent control unit monitors the CNC machine tool turret's status during deceleration or braking in real time, reducing the risk of downtime. The stored electrical energy is managed by the energy release unit, which analyzes historical release data to determine if there are any abnormalities in energy release, achieving intelligent energy management and improving the system's overall intelligence level.

[0043] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program goods. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0044] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0045] 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.

[0046] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0047] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A high-speed tool changing system with integrated energy recovery, characterized in that, include: A direct drive unit is configured to be electrically connected to the CNC machine tool turret using a direct drive motor, wherein the direct drive motor is used to drive the CNC machine tool turret to rotate. The kinetic energy recovery unit is configured to switch the working mode of the direct drive motor to generator mode based on a fuzzy PID algorithm when the CNC machine tool turret decelerates or brakes, and convert the kinetic energy during deceleration or braking into electrical energy through regenerative braking. The fuzzy PID algorithm takes the speed deviation as input and the mode switching signal strength and braking force as output. The energy management unit is configured to define the flow paths of energy input, storage, and consumption based on an energy flow balance model, and to store the electrical energy. The intelligent control unit is configured to coordinate and integrate fault diagnosis for the speed regulation of the direct drive unit, the mode switching of the kinetic energy recovery unit, and the energy distribution of the energy management unit, based on the speed regulation algorithm of the model predictive control and combined with the operating parameters of the direct drive motor, the state parameters of the CNC machine tool turret, and the energy storage parameters. The energy release unit is configured to acquire all historical energy release data, parse all historical energy release data, determine whether there is an energy release anomaly after storage based on the parsing results, and adjust the storage energy release based on all historical energy release data when an energy release anomaly is determined. When switching the operating mode of the direct drive motor to generator mode based on the fuzzy PID algorithm, and converting the kinetic energy during deceleration or braking into electrical energy through regenerative braking, the process includes: The kinetic energy recovery unit determines input variables and output variables. The input variable is the speed deviation between the current speed of the CNC machine tool turret and the target deceleration speed. The output variable is the trigger signal strength for mode switching of the direct drive motor and the braking force of regenerative braking. The input variables are fuzzified, and the speed deviation is divided into seven fuzzy sets: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large. The membership degree of the seven fuzzy sets is determined based on the triangular membership function. A fuzzy rule base is established according to the operating characteristics of the CNC machine tool turret. The membership degree and the fuzzy rule base are inferred based on the Mamdani inference method and the center of gravity method. The trigger signal strength for the mode switching of the direct drive motor and the braking force of the regenerative braking are output. When defining the flow paths of energy input, storage, and consumption based on the energy flow balance model, the following are included: The energy management unit is equipped with a hybrid energy storage component, which consists of a supercapacitor and an energy storage battery. The supercapacitor is used to store the electrical energy converted by regenerative braking when the CNC machine tool turret decelerates or brakes, and the energy storage battery is used to store the electrical energy in the supercapacitor that has not been consumed within a set time period. The kinetic energy recovery unit is used as the energy input end, the direct drive unit is used as the energy consumption end, and the hybrid energy storage component is used as the energy storage end. An energy flow path is established in the order of the energy input end, the energy storage end, and the energy consumption end. Obtain a sample dataset, construct an initial energy flow balance model based on the sample dataset and energy flow path, input the actual operating energy data into the initial energy flow balance model to determine the model prediction result, adjust the initial energy flow balance model based on the energy deviation between the model prediction result and the energy data, and determine the final energy flow balance model.

2. The high-speed tool changer with integrated energy recovery according to claim 1, characterized in that, The intelligent control unit includes a monitoring module and a processing module; The monitoring module collects the operating parameters of the direct drive motor, the status parameters of the CNC machine tool turret, the conversion parameters of the kinetic energy recovery unit, and the energy parameters of the energy management unit, and constructs a monitoring dataset. The processing module preprocesses the monitoring dataset, including data noise reduction and data standardization.

3. The high-speed tool changer with integrated energy recovery according to claim 2, characterized in that, The intelligent control unit also includes an output module; The output module establishes a motor speed prediction model based on the preprocessed monitoring dataset. The motor speed prediction model takes the current speed and output torque of the direct drive motor and the load of the CNC machine tool turret as inputs, and the voltage and current of the direct drive motor as outputs.

4. The high-speed tool changer with integrated energy recovery according to claim 3, characterized in that, The intelligent control unit also includes a diagnostic alarm module; The diagnostic alarm module obtains the balance prediction result of the energy flow balance model and compares it with the preprocessed monitoring dataset. If the parameters in the preprocessed monitoring dataset are not equal to the balance prediction result, fault investigation is initiated; otherwise, fault investigation is not initiated. When troubleshooting is initiated, the corresponding energy flow path is determined based on the unequal parameters and the fault location is located. The fault parameters at the fault location are collected and compared with the normal operating condition parameter range. A fault diagnosis report is generated based on the comparison results.

5. The high-speed tool changer with integrated energy recovery according to claim 4, characterized in that, When acquiring and analyzing all historical energy release data, and determining whether there are any energy release anomalies in the stored electrical energy based on the analysis results, the process includes: Acquire energy release data for each stage and determine the corresponding historical energy release data. Analyze the corresponding historical energy release data to determine energy overflow behavior, normal release behavior, and energy deficiency behavior. If the energy release data does not belong to the normal release behavior, it is determined that the stored electrical energy has an energy release anomaly; If the energy release data belongs to the normal release behavior, then it is determined that the stored electrical energy does not have any abnormal energy release.

6. The high-speed tool changer with integrated energy recovery according to claim 5, characterized in that, When an energy release anomaly is detected, adjustments are made to the stored energy release based on all historical energy release data, including: Identify all historical energy release data with energy overflow behavior and construct an energy overflow dataset; identify all historical energy release data with energy shortage behavior and construct an energy shortage dataset. The number of historical energy release data in the energy overflow dataset is counted and recorded as the overflow number; the number of historical energy release data in the energy shortage dataset is counted and recorded as the shortage number. The release of stored electrical energy is adjusted based on the amount of overflow and the amount of shortage.

7. The high-speed tool changer with integrated energy recovery according to claim 6, characterized in that, When adjusting the release of stored electrical energy based on the overflow and shortage amounts, the following are included: If the overflow amount is greater than the insufficient amount, then the energy released after storage is reduced based on the insufficient amount; If the overflow quantity is less than the insufficient quantity, then the energy released after storing the electrical energy is increased based on the overflow quantity; If the overflow quantity equals the insufficient quantity, then the energy released after storage is adjusted based on the overflow quantity and the insufficient quantity.

8. The high-speed tool changer with integrated energy recovery according to claim 7, characterized in that, Also includes: The CNC machine tool turret is used to adapt to the PSC head tool holder. The PSC head tool holder is provided with several positioning slots, and each positioning slot is used to connect to the CNC machine tool turret. The PSC head tool holder is symmetrically provided with tool body supports, and the tool body supports are fixedly connected to the PSC head tool holder. The tool body supports have PSC interfaces. The PSC headstock is symmetrically provided with a first groove and a second groove.

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