Method and system for cooling power tool turret of turning and milling composite machine tool

Abstract

The invention discloses a power tool turret cooling method and system of a turning and milling composite machine tool, and relates to the technical field of machine tool cooling. The method comprises the steps that operation information is collected in the machining process; constructing a processing continuity degradation path model in the processing controller based on the operation information; evaluating evolution trends and relative competition relationships of various machining continuity degradation paths in real time by utilizing a machining controller, and identifying continuous degradation path risks; a cooling scheduling instruction is generated according to the recognition result, control optimization of a cooling intervention time sequence structure, rhythm continuity and a staged action mode is executed through the cooling scheduling instruction, and a cooling control response strategy is generated; and executing cooling intervention control management according to the cooling control response strategy. The technical problems that in the prior art, a power tool turret of a turning and milling composite machine tool is difficult to dynamically regulate and control cooling according to the machining process, so that the cooling effect is poor, and the machining continuity is affected are solved, and the technical effects of improving the cooling effect and guaranteeing the machining continuity are achieved.

Description

Technical Field

[0001] This invention relates to the field of machine tool cooling technology, specifically to a method and system for cooling the power turret of a milling and turning machine tool. Background Technology

[0002] Milling-turning composite machine tools are widely used in the efficient machining of complex parts. The power turret, as the core execution unit, is highly susceptible to factors such as accumulated heat load, fluctuating cutting rhythm, and unstable tool positioning under conditions of prolonged mixed cutting, frequent process switching, and multi-axis simultaneous machining. Existing cooling methods primarily rely on fixed cooling volumes or simple temperature threshold triggering, lacking the ability to recognize the evolution of load changes, tool position switching rhythms, and the continuity of machining behavior during the machining process. This results in cooling intervention timing failing to match the actual machining conditions. Especially in scenarios where the power turret operates continuously, insufficient cooling control or unreasonable timing can easily lead to increased thermal drift, decreased machining continuity, and fluctuations in machining accuracy, making it difficult to meet the stability and machining quality requirements of high-performance milling-turning composite machine tools. Summary of the Invention

[0003] This application provides a method and system for cooling the power turret of a milling and turning machine tool, which solves the technical problem in the prior art that the power turret of a milling and turning machine tool is difficult to dynamically adjust the cooling according to the machining process, resulting in poor cooling effect and affecting the continuity of machining.

[0004] The first aspect of this application provides a method for cooling the power turret of a milling and turning machine tool, the method comprising: During the machining process, operational information is collected to characterize the evolutionary behavior of the machine tool. This operational information includes load stability indicators, process rhythm maintenance status, tool position switching continuity, and continuous characteristics of machining behavior. Based on this operational information, a machining continuity degradation path model is constructed in the machining controller. This model characterizes multiple machining continuity degradation paths that may occur under the current machining conditions, and each machining continuity degradation path corresponds to different dominant evolutionary characteristics. During machining execution, the machining controller is used to evaluate the evolution trend and relative competition of multiple machining continuity degradation paths in real time, identifying continuity degradation path risks. Cooling scheduling instructions are generated based on the identification results. The timing structure, rhythm continuity, and staged action mode of cooling intervention are optimized using these cooling scheduling instructions to generate a cooling control response strategy. Cooling intervention control management is executed based on the cooling control response strategy.

[0005] A second aspect of this application provides a power turret cooling system for a milling and turning machine tool, the system comprising: Information Acquisition Module: Acquires operational information during machining to characterize the evolutionary behavior of the machine tool. This operational information includes load stability indicators, process rhythm maintenance status, tool position switching continuity, and continuous characteristics of machining behavior. Model Construction Module: Based on the operational information, constructs a machining continuity degradation path model in the machining controller. This model characterizes multiple machining continuity degradation paths that may occur under the current machining conditions, and each machining continuity degradation path corresponds to different dominant evolutionary characteristics. Risk Identification Module: During machining, utilizes the machining controller to evaluate the evolution trend and relative competition of multiple machining continuity degradation paths in real time, identifying the risks of continuity degradation paths. Control Optimization Module: Generates cooling scheduling instructions based on the identification results. Uses these instructions to optimize the timing structure, rhythm continuity, and phased action mode of cooling intervention, generating a cooling control response strategy. Control Management Module: Performs cooling intervention control management based on the cooling control response strategy.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: First, operational information characterizing the evolutionary behavior of the machine tool is collected during the machining process. This information includes load stability indicators, process rhythm maintenance status, tool position switching continuity, and machining behavior continuity characteristics. Next, based on this operational information, a machining continuity degradation path model is constructed in the machining controller. This model characterizes various possible machining continuity degradation paths under the current machining conditions, with each path corresponding to different dominant evolutionary characteristics. During machining execution, the machining controller is used to evaluate the evolution trends and relative competition of various machining continuity degradation paths in real time, identifying continuity degradation path risks. Then, cooling scheduling commands are generated based on the identification results. These commands are used to optimize the timing structure, rhythm continuity, and phased action mode of cooling intervention, generating a cooling control response strategy. Finally, cooling intervention control management is executed according to the cooling control response strategy. This solves the technical problem in existing milling and turning machine tools where the power turret cannot dynamically adjust cooling according to the machining process, leading to poor cooling effects and compromised machining continuity. This approach improves cooling efficiency and ensures machining continuity. Attached Figure Description

[0007] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1 A schematic diagram of a method for cooling the power turret of a milling and turning machine tool provided in this application embodiment; Figure 2 This is a schematic diagram of the power turret cooling system of a milling and turning machine tool provided in an embodiment of this application.

[0009] Explanation of reference numerals in the attached diagram: Information acquisition module 11, model building module 12, risk identification module 13, control optimization module 14, control management module 15. Detailed Implementation

[0010] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0011] Example 1, as Figure 1 As shown, this application provides a method for cooling the power turret of a milling and turning machine tool, wherein the method includes: During the machining process, operational information is collected to characterize the evolutionary behavior of the machine tool. This operational information includes load stability indicators, process rhythm maintenance status, tool position switching continuity, and continuous characteristics of machining behavior.

[0012] In this embodiment, the machine tool's built-in drive current sensor, spindle load detection module, and servo response monitoring module are used to collect real-time load curves of the power turret at each machining cycle. By statistically analyzing the fluctuation amplitude, stable range, and abnormal peak values ​​of the load curves, load stability indicators are obtained. The preset machining rhythm is read from the machining program execution controller, and combined with the action feedback signal of the turret indexing mechanism, the trend of tool cutting force changes, and the feed rate maintenance, the degree to which the actual machining behavior maintains the preset rhythm is analyzed to obtain the process rhythm maintenance state. During tool position switching, based on the turret indexing angle sensor, locking state monitoring signal, and tool contact transient load changes, the continuity, smoothness, and time deviation of the tool position switching action are quantified to form tool position switching coherence parameters. At the same time, the long-term behavior of the machine tool during machining is monitored. By accumulating and statistically analyzing the duration of continuous cutting segments, rhythm fluctuation frequency, machining interruption interval, and stability characteristics of machining actions during machining, machining behavior continuity characteristics characterizing the overall machining behavior stability are generated. The above-mentioned operational information is continuously acquired at a fixed sampling period throughout the entire processing cycle and input into the processing controller in real time to reflect the evolution status of the machine tool at different stages.

[0013] Based on the operational information, a processing continuity degradation path model is constructed in the processing controller. The processing continuity degradation path model is used to characterize a variety of processing continuity degradation paths that may occur under the current processing conditions, and each processing continuity degradation path corresponds to a different dominant evolutionary feature.

[0014] In this embodiment, the collected load stability indicators, process rhythm maintenance status, tool position switching continuity, and machining behavior continuity features are uniformly formatted and normalized to form a continuous feature sequence that can be used to characterize the evolution trend of machining state. The machining controller performs time-series analysis on this continuous feature sequence, identifying behavioral patterns such as increased load fluctuations, increased rhythm offsets, prolonged tool position switching time, and increased machining behavior interruption frequency to determine key evolutionary directions that may lead to a decrease in machining continuity. For each different evolutionary direction, the machining controller uses a built-in multidimensional state clustering algorithm to divide the machining continuity feature sequence into different state subsets, and constructs multiple machining continuity degradation paths based on the transition relationships between state subsets. Each degradation path corresponds to a specific dominant evolutionary feature, such as a degradation path dominated by load fluctuations, a degradation path dominated by rhythm offsets, or a degradation path dominated by abnormal tool position switching. The processing controller stores the aforementioned degradation paths and their corresponding dominant evolutionary features in the form of path topology, thereby forming a processing continuity degradation path model to characterize the evolutionary trends of multiple potential processing continuity degradation under the current processing conditions.

[0015] For example, the machining controller acquires a continuous feature vector containing load stability indicators, process rhythm maintenance status, tool position switching consistency, and machining behavior continuity characteristics within a fixed sampling period (e.g., 200ms). ,in At the sampling time, the feature vector is represented as: ,in: Indicates at time The load stability index (the larger the value, the more obvious the load fluctuation). This indicates a deviation in the process rhythm. This represents the tool position switching continuity index (value 0~1). This indicates the persistence of processing behavior. Taking a specific processing step as an example, in five consecutive sampling points... The values ​​can be 8%, 12%, 20%, 28%, or 35%. The values ​​can be 0.95, 0.92, 0.88, 0.80, and 0.76. The values ​​can be 0.92, 0.90, 0.85, 0.78, and 0.72. The processing controller performs differential calculations on adjacent sampling points, such as load changes. The same applies to other characteristics; they can be determined by judgment. Does it consistently exceed the set threshold? (e.g., 5%), and Is it consistently below the threshold? (For example, -0.04) to identify continuous evolution trends such as increased load fluctuations and decreased tool position consistency. After identifying the above evolution trends, the continuous feature vector is... The state clustering module of the input processing controller classifies processing states into categories such as slightly fluctuating states (state A), moderately degraded states (state B), and significantly degraded states (state C), and forms a state transition sequence similar to A→A→B→C→C based on the sampling time sequence. To identify the dominant evolutionary features of the degradation path, the processing controller calculates the contribution of the changes in each feature using the formula: Calculate the dominant contribution of load metrics throughout the degradation process, where As the load-dominant contribution factor, , , For the changes in other characteristics. If If the maximum value is found, the state transition sequence is identified as a load-dominated processing continuous degradation path. The processing controller ultimately stores the state sequence, evolution direction, and dominant characteristics as path units, forming a processing continuous degradation path model with clear data basis, reproducible calculation rules, and verifiable state transition relationships. This model is used for subsequent degradation path competition analysis and cooling scheduling strategy generation.

[0016] Furthermore, based on the aforementioned operational information, a processing continuity degradation path model is constructed in the processing controller, including: The collected load stability indicators, process rhythm maintenance status, tool position switching continuity, and machining behavior continuity characteristics are jointly normalized to form a set of machining continuity states that characterize the continuity level of the machining process. The direction of continuous evolution is identified in the set of machining continuity states. The degradation path is divided using the direction of continuous evolution. The correspondence between the set of machining continuity states, the direction of continuous evolution, and the degradation path is associated and stored to form a machining continuity degradation path model.

[0017] Specifically, the collected load stability indicators, process rhythm maintenance status, tool position switching continuity, and machining behavior continuity characteristics are formatted and jointly normalized. Specifically, by scaling, adjusting the mean and variance, and aligning the feature values ​​of different physical quantity dimensions, these features are transformed into dimensionless state values ​​that can directly participate in continuity analysis. A set of machining continuity states representing the continuity level of the machining process is then constructed according to the machining time sequence. Within this set of machining continuity states, the machining controller performs time-series difference analysis and trend identification to calculate the direction, rate, and degree of continuity shift between adjacent states, based on the increase in load fluctuations. Patterns such as trends, rhythm offset trends, decreasing trends in tool position switching continuity, and interruption trends in machining behavior are used to identify the main evolution directions of machining continuity over time. After identifying at least one continuous evolution direction, based on the machining state change patterns corresponding to different evolution directions, the machining continuity state set is clustered or divided into intervals according to the evolution direction using a machining controller to obtain multiple degradation paths with different evolution characteristics. The machining continuity state set, the continuous evolution direction, and the correspondence between each degradation path are associated and stored in a path mapping structure, thereby forming a machining continuity degradation path model that can be used to describe the multi-trend evolution of the machining continuity decline process.

[0018] Furthermore, identifying the direction of continuous evolution within the set of processing continuity states includes: The state values ​​in the set of processing continuity states are arranged in time sequence to form a state change sequence that reflects the change of processing continuity over time; the state values ​​in adjacent control cycles are compared differentially to identify the magnitude and direction of change; and the direction of continuous evolution is identified based on the identification results.

[0019] First, the state values ​​in the processing continuity state set are arranged according to the time sequence of the actual processing process to form a state change sequence that reflects the trend of processing continuity over time. Then, the processing controller performs differential comparisons on the state values ​​within adjacent control cycles. By calculating the difference in state values, the magnitude of change of each continuity feature in adjacent cycles is identified, and its direction of change is determined, including negative evolution (a downward trend in continuity) or positive evolution (a trend of improving continuity). After obtaining the above magnitude and direction of change, the main evolution direction of the current processing continuity feature is comprehensively determined based on the consistency of the direction of change, the significance of the magnitude of change, and the cumulative effect of the duration of change. When the direction of change shows negative accumulation and matches the preset continuity degradation judgment threshold, it is identified as processing continuity evolving towards degradation; if the direction of change shows positive accumulation, it is identified as processing continuity evolving towards recovery.

[0020] During the processing, the processing controller is used to evaluate the evolution trend and relative competition of multiple processing continuous degradation paths in real time, and to identify the risks of continuous degradation paths.

[0021] The processing controller invokes a pre-built processing continuity degradation path model to map real-time collected operational information to the corresponding processing continuity state. Based on the mapping relationship between the state and the degradation path, the current evolution position of each degradation path is updated. The state change rate, continuity shift degree, and corresponding dominant evolutionary characteristics of each degradation path are dynamically calculated to obtain path evolution trend parameters characterizing the development trend of the degradation path. The processing controller further compares and analyzes the evolution trend parameters of different degradation paths. By evaluating the magnitude of the tendency of each degradation path to strengthen or weaken, the evolution speed, and the theoretical impact on processing continuity, a competition relationship evaluation result reflecting the competition between different degradation paths is formed. When the evolution trend of a certain degradation path is significantly stronger than that of other paths in the competition relationship evaluation result, and its continuity decline risk exceeds a preset threshold, the path is determined to be the current dominant continuity degradation path and identified as a continuity degradation path risk that requires cooling intervention.

[0022] Furthermore, the processing controller is used to evaluate in real time the evolution trends and relative competition among multiple processing continuous degradation paths, and to identify continuous degradation path risks, including: In the processing continuity degradation path model, path advantage characterization parameters are constructed for each processing continuity degradation path. These parameters are used to quantify the degree of evolutionary dominance of the corresponding degradation path relative to other degradation paths. The processing controller is used to dynamically compare the path advantage characterization parameters of each processing continuity degradation path to form a path competition state evaluation result of the relative competition state of different degradation paths. The risk of continuity degradation path is identified based on the path competition state evaluation result.

[0023] In the processing continuity degradation path model, a path advantage characterization parameter is constructed for each processing continuity degradation path to quantify its degree of evolutionary dominance. The path advantage characterization parameter is calculated based on real-time operation information and reflects the evolution intensity, rate of change, and potential impact on processing continuity of the degradation path in the current processing stage. After obtaining the path advantage characterization parameter corresponding to each degradation path, the processing controller dynamically compares the above parameters. By evaluating the relative magnitude, growth trend, and deviation from the historical stage advantage level of each path advantage value, a path competition state evaluation result that reflects the dominant competitive relationship between different degradation paths is formed. When the path competition state evaluation result shows that the advantage characterization parameter of a certain degradation path is significantly higher than that of other paths, or its evolution trend shows a rapid amplification and reaches the preset risk judgment threshold, the processing controller identifies the degradation path as a target degradation path with potential processing continuity decline risk, that is, identifies it as a continuous degradation path risk.

[0024] For example, the machining controller targets each machining continuity degradation path P i (i = 1…n) Construct path advantage representation parameters A i This parameter is used to quantify the degree of evolutionary dominance of the degradation path within the current control period. The path dominance characterization parameter can be calculated using the following formula: Wherein, ΔS i The variation magnitude of the continuous characteristics corresponding to the degradation path within adjacent control periods is represented by ΔS. i =S i (t)-S i (t-1) is calculated; R i The rate of change of the continuous characteristic within a preset sliding window is obtained by averaging the differences over the most recent M control cycles; G i The deviation of the current continuity feature from the stable interval of this degradation path can be expressed by normalizing the distance. The calculation yields w1, w2, and w3, which are preset weighting coefficients. In typical applications, w1 = 0.4, w2 = 0.35, and w3 = 0.25 can be used.

[0025] For example, within a certain control period, if the continuity characteristic change of the degenerate path P1 is ΔS1=0.18, the rate of change is R1=0.22, and the deviation is G1=0.15, then its path advantage characterization parameters are: Within the same period, the degradation path P2 has ΔS2=0.32, R2=0.25, and G2=0.21. Therefore: The processing controller determines the dominant degradation path by comparing the Ai values ​​of each degradation path. When the Ai of a certain degradation path continuously exceeds the preset dominant threshold (such as 0.24), the path is identified as a continuous degradation path risk, and the subsequent cooling scheduling strategy is triggered accordingly.

[0026] Based on the identification results, a cooling scheduling instruction is generated. The timing structure, rhythm continuity, and phased action mode of the cooling intervention are optimized using the cooling scheduling instruction to generate a cooling control response strategy.

[0027] After the processing controller identifies the currently dominant continuous degradation path and its corresponding risk level, it maps the degradation path risk to a cooling demand level based on the impact pattern of different degradation paths on processing continuity, and generates a cooling scheduling instruction that includes cooling intervention intensity, duration, and intervention priority. The processing controller then plans the timing structure of cooling intervention on the processing time axis according to the cooling scheduling instruction, so that the timing of cooling intervention is coordinated with the processing behavior rhythm and avoids disrupting processing continuity. On this basis, the processing rhythm maintenance state and processing behavior continuity characteristics are used as continuity constraints to adjust the rhythm continuity of the interval time, cooling intensity transition amplitude, and connection mode between multiple cooling intervention nodes, ensuring that cooling intervention presents a smooth transition in time. The processing controller further combines the thermal load characteristics of the current processing stage and the turret working state, and determines the cooling action combination that can weaken the evolution trend of degradation path without affecting the stability of processing by searching and optimizing the cooling action mode (such as rapid cooling, steady cooling, slow cooling) and its action stage. Finally, based on the above-mentioned timing structure planning, rhythm continuity adjustment and action mode optimization results, a cooling control response strategy for the current processing stage is generated.

[0028] Furthermore, cooling scheduling instructions are generated based on the identification results, including: Based on the path competition state evaluation results, a path dominance representation vector is constructed for each degraded path, including the path energy accumulation rate, damage propagation sensitivity, and state amplification factor. The path dominance index of each degraded path in the current control cycle is calculated based on the path dominance representation vector. Each degraded path is classified according to its nonlinear sensitivity to cooling response, establishing highly destructive dominant degraded paths and controllable degraded paths. The nonlinear sensitivity is determined based on the gradient of the path dominance index change before and after historical cooling intervention. An inhibitory cooling effect mapping is generated based on the highly destructive dominant degraded path, and a slow-release cooling effect mapping is generated based on the controllable degraded path. Within a single scheduling cycle, the inhibitory cooling effect mapping and the slow-release cooling effect mapping are jointly input into the cooling scheduling solver. Through multi-objective competitive constraint solving, a cooling scheduling instruction that weakens the dominant degraded path while maintaining processing continuity is generated.

[0029] After obtaining the path competition state evaluation results, the processing controller extracts the corresponding evolution trend features for each processing continuity degradation path and constructs a path advantage representation vector containing the path energy accumulation rate, damage propagation sensitivity, and state amplification factor. The path energy accumulation rate reflects the rate of evolution intensity accumulation of the degradation path per unit time, the damage propagation sensitivity characterizes the sensitivity of the degradation path to processing load fluctuations and rhythm deviations, and the state amplification factor measures the risk amplification capability of small state changes in the path. The processing controller further calculates the current... The path dominance index within the initial control cycle is used to quantify the relative dominance of a path in the competitive relationship. Subsequently, by combining the gradient changes in the dominance index of each path before and after historical cooling intervention, the nonlinear sensitivity of different degenerate paths to cooling effects is determined. Based on this, degenerate paths are divided into highly destructive dominant degenerate paths and controllable degenerate paths, with highly destructive paths exhibiting a stronger tendency to disrupt processing continuity. The processing controller constructs a corresponding suppressive cooling effect mapping based on highly destructive dominant degenerate paths to rapidly reduce their dominance index, and a slow-release cooling effect mapping based on controllable degenerate paths to smoothly regulate their evolution trend. Finally, within a single scheduling cycle, the suppressive and slow-release cooling effect mappings are jointly input into the cooling scheduling solver. Through multi-objective competitive constraints, the solution weakens the dominant degenerate path while maintaining the overall continuity and rhythm of processing behavior, generating cooling scheduling instructions that satisfy the above conditions.

[0030] Furthermore, the suppression-type cooling effect mapping and the slow-release cooling effect mapping are jointly input into the cooling scheduling solver, and solved through multi-objective competitive constraints, including: With the path dominance index of weakening the dominant degradation path as the first control objective and the rate of change of the path dominance index of the controllable degradation path as the second control objective, a competitive state space of cooling targets with mutual checks and balances is established. The suppressive cooling effect mapping and the slow-release cooling effect mapping are respectively transformed into state transition operators, which are used to describe the direction, magnitude and rate of the migration of the system state position by each cooling effect in the competitive state space of cooling targets. In the competitive state space of cooling targets, the state transition operators are used to predict the state migration trajectory caused by different combinations of cooling effects, forming a trajectory prediction matrix based on the state transition operators. Based on the trajectory prediction matrix, under the constraints of processing continuity and cooling rhythm continuity, the competitive adjudication of the suppressive cooling effect mapping and the slow-release cooling effect mapping is executed, the target control strategy is output, and the cooling scheduling command is output according to the target control strategy.

[0031] First, the path dominance index, which weakens the dominant degradation path, is used as the first control objective, and the rate of change of the path dominance index of the controllable degradation path within the current control period is used as the second control objective. A competitive state space for cooling objectives with mutual checks and balances is constructed in the processing controller, allowing the two types of objectives to jointly constrain the cooling effect in a quantitative manner within this state space. Subsequently, the mappings for suppressive and slow-release cooling effects are transformed into corresponding state transition operators. These state transition operators describe the direction, magnitude, and rate of system state transitions within this competitive state space under different cooling intervention conditions, thereby converting the cooling effect into a computable state change operation. Within the competitive state space for cooling objectives… By using the aforementioned state transition operator to predict and simulate different combinations of cooling effects, a trajectory prediction matrix based on the state transition operator is formed. This matrix is ​​used to describe the possible evolution trajectory of the system state under different cooling intervention strategies. Based on the trajectory prediction matrix, the processing controller competitively decides between the suppression-type cooling effect mapping and the slow-release cooling effect mapping, under the premise of satisfying the processing continuity constraint and the cooling rhythm continuity constraint. By comparing the degree of influence of different combination strategies on the state transition trajectory, the target control strategy that best meets the control objective is determined. Finally, the corresponding cooling scheduling command is generated according to the target control strategy and issued for actual cooling intervention execution, thereby effectively suppressing the trend of processing continuity degradation and maintaining the overall processing stability.

[0032] Specifically, in the machining controller, a two-dimensional state vector is used. The system represents time The competitive state, in which The path dominance index represents the path advantage of highly destructive dominant degradation paths. The path dominance exponent change rate is defined as the rate of change of the controllable degradation path. Based on this, the suppressive cooling effect is mapped and transformed into a state transition operator. Its mathematical form is: Similarly, the slow-release cooling effect is mapped into a state transition operator. : ;in, and This is a cooling influence coefficient matrix obtained by fitting historical cooling intervention data, used to reflect the weighting of the cooling effect on the path advantage index and rate of change. and This represents the cooling intensity vector within the current cooling cycle. For example, under typical machining load conditions, the following empirical parameters can be obtained: , The former characterizes the stronger and faster weakening effect of suppressive cooling on the dominance index of highly destructive degradation paths, while the latter characterizes the stronger and more stable suppression effect of slow-release cooling on the rate of change of controllable degradation paths. Based on the above operators, the cooling scheduling solver can predict state transitions for different cooling combinations and form a set of state transition trajectories, which are used as the basis for cooling strategy selection. Under the premise of satisfying the processing continuity constraint and the cooling rhythm continuity constraint, the cooling strategy corresponding to the optimal transition trajectory is selected, and finally, a cooling scheduling instruction is generated.

[0033] Furthermore, the control optimization of the timing structure, rhythm continuity, and phased action mode of cooling intervention executed by the aforementioned cooling scheduling command generates a cooling control response strategy, including: The cooling scheduling command is decomposed into N cooling intervention units associated with different degradation paths, and a timing structure for cooling intervention is generated according to the priority order in the cooling scheduling command. Under the timing structure, the interval time and cooling intensity variation amplitude between adjacent cooling intervention units are continuously constrained and adjusted based on the processing rhythm maintenance state and the continuous characteristics of processing behavior, thus establishing a cooling intervention rhythm. Based on the timing structure and the cooling intervention rhythm, the control mode and duration are optimized to generate a cooling control response strategy.

[0034] The processing controller first analyzes the cooling intervention requirements contained in the generated cooling scheduling instructions, decomposing the cooling control requirements corresponding to different degradation paths into N cooling intervention units with independent objectives. Then, based on the risk level, cooling response priority, and path advantage index change trend of the degradation paths in the cooling scheduling instructions, the cooling intervention units are arranged in descending order of priority, constructing a temporal structure for cooling intervention on the processing time axis. This ensures that each cooling unit has a reasonable intervention sequence and execution window in time. Under this temporal structure, based on the processing rhythm maintenance state and the continuous characteristics of processing behavior, the processing controller continuously constrains and adjusts the time interval and transition amplitude of cooling intensity between adjacent cooling intervention units. This ensures that cooling intervention does not disrupt the processing rhythm and avoids introducing abrupt changes in processing load, thereby establishing a cooling intervention rhythm suitable for the current processing stage. Ultimately, based on the timing structure and cooling intervention rhythm, and combined with different degradation path response mechanisms and the current thermal load characteristics of the machine tool, the machining controller performs control optimization on the cooling action mode (including rapid cooling, steady cooling, slow cooling, etc.) and its duration. By searching for the stability of the effect and the ability to maintain machining continuity under different combinations, the optimal cooling action configuration is determined, and a cooling control response strategy for actual execution is generated to achieve the purpose of weakening the evolution trend of degradation path, maintaining machining continuity, and improving overall machining stability.

[0035] Cooling intervention control management is performed according to the cooling control response strategy.

[0036] Based on the generated cooling control response strategy, the machining controller sends the corresponding cooling command to the machine tool cooling execution module, which then adjusts the cooling pump, cooling valve, jet device, or turret localized directional cooling components in real time to ensure that cooling intervention is carried out according to the planned time sequence, rhythm continuity, and phased action. During the cooling intervention process, the machining controller uses temperature sensors, load acquisition units, and machining behavior monitoring units located in the turret and spindle areas to continuously collect machine tool operation feedback data after cooling execution, including changes in thermal load, machining rhythm deviations, and the actual impact of cooling on turret stability. The monitoring feedback data is analyzed and compared with the expected cooling control response strategy. When the monitoring results show that the cooling effect deviates from the preset control target, the cooling intensity, mode of action, or duration of action is automatically adjusted to maintain the effectiveness of the cooling response in suppressing the degradation path. At the same time, when a phased change in the processing state is detected (such as a sudden increase in load, an increase in rhythmic disturbance, or a change in tool position switching frequency), the processing controller dynamically updates the cooling control response strategy based on the new operating information, forming a real-time closed-loop cooling intervention control management mechanism to ensure that the turret thermal state remains stable and the processing continuity is maintained throughout the entire processing cycle.

[0037] Furthermore, performing cooling intervention control management according to the cooling control response strategy includes: During the cooling intervention process, monitoring sensors are used to continuously monitor the processing behavior and establish monitoring feedback results; the cooling control response strategy is dynamically updated based on the monitoring feedback results.

[0038] During the cooling intervention process, monitoring sensors deployed around the power turret, in the spindle area, and in the machining area continuously monitor the machining behavior. The monitoring includes real-time operating parameters such as local temperature rise changes in the turret, machining load fluctuation trends, machining rhythm maintenance, and tool position switching continuity. The machining controller analyzes the data collected by the monitoring sensors in real time, generating monitoring feedback results reflecting the cooling intervention effect. This feedback is used to determine whether the current cooling action effectively suppresses the evolution trend of the corresponding degradation path. Based on the monitoring feedback results, if a deviation is detected between the cooling intensity, action mode, or timing arrangement in the cooling response strategy and the current machining state, resulting in the degradation path dominance index not decreasing as expected or machining continuity being affected, the machining controller automatically adjusts the cooling control response strategy. This includes reconfiguring the cooling action mode, shortening or extending the cooling duration, optimizing the intervention rhythm, or reconstructing the priority order of cooling intervention units. Through this dynamic update mechanism, closed-loop optimization of the cooling control response strategy is achieved within the machining cycle, enabling the cooling intervention to adaptively adjust with the evolution of the machining state, thereby continuously maintaining the stability of the power turret thermal field and the reliability of machining continuity throughout the entire machining process.

[0039] Furthermore, the system identifies the trigger for a shutdown warning based on the operational information. If the trigger identification result is abnormal, the system controls the machine tool to stop based on the trigger identification result and issues a warning signal.

[0040] During machining, the machining controller continuously receives operational information such as load stability indicators, process rhythm maintenance status, tool position switching continuity, and continuous characteristics of machining behavior. It then analyzes this operational information in real time using preset anomaly triggering criteria. When any feature in the operational information is detected to exhibit an abnormal change trend exceeding a safety threshold, such as a sharp increase in load fluctuation, severe deviation in machining rhythm, or continuous failures in tool position switching, the machining controller determines this operational state as an abnormal result that triggers a shutdown warning. Upon the formation of an abnormal result, the machining controller immediately issues a shutdown command, controlling the machine tool to perform a safe shutdown operation to prevent further degradation of machining continuity that could lead to thermal damage, turret instability, or scrapping of the machined parts. Simultaneously, warning signals are output through the machine tool control interface, audible and visual warning devices, or external monitoring systems, enabling operators to promptly become aware of abnormal machine tool operation and thus achieve early intervention and handling of potential machining risks.

[0041] In summary, the embodiments of this application have at least the following technical effects: First, operational information characterizing the evolutionary behavior of the machine tool is collected during the machining process. This information includes load stability indicators, process rhythm maintenance status, tool position switching continuity, and machining behavior continuity characteristics. Next, based on this operational information, a machining continuity degradation path model is constructed in the machining controller. This model characterizes various possible machining continuity degradation paths under the current machining conditions, with each path corresponding to different dominant evolutionary characteristics. During machining execution, the machining controller is used to evaluate the evolution trends and relative competition of various machining continuity degradation paths in real time, identifying continuity degradation path risks. Then, cooling scheduling commands are generated based on the identification results. These commands are used to optimize the timing structure, rhythm continuity, and phased action mode of cooling intervention, generating a cooling control response strategy. Finally, cooling intervention control management is executed according to the cooling control response strategy. This solves the technical problem in existing milling and turning machine tools where the power turret cannot dynamically adjust cooling according to the machining process, leading to poor cooling effects and compromised machining continuity. This approach improves cooling efficiency and ensures machining continuity.

[0042] Example 2, based on the same inventive concept as the power turret cooling method for a milling and turning machine tool in the foregoing examples, such as... Figure 2 As shown, this application provides a power turret cooling system for a milling and turning machine tool, wherein the system includes: Information Acquisition Module 11: Acquires operational information during the machining process to characterize the evolutionary behavior of the machine tool. This operational information includes load stability indicators, process rhythm maintenance status, tool position switching continuity, and continuous machining behavior characteristics. Model Construction Module 12: Based on the operational information, constructs a machining continuity degradation path model in the machining controller. This model characterizes various machining continuity degradation paths that may occur under the current machining conditions, and each machining continuity degradation path corresponds to different dominant evolutionary characteristics. Risk Identification Module 13: During machining, utilizes the machining controller to evaluate the evolutionary trends and relative competition relationships of various machining continuity degradation paths in real time, identifying the risks of continuity degradation paths. Control Optimization Module 14: Generates cooling scheduling instructions based on the identification results, and uses these instructions to optimize the timing structure, rhythm continuity, and phased action mode of cooling intervention, generating a cooling control response strategy. Control Management Module 15: Performs cooling intervention control management based on the cooling control response strategy.

[0043] Furthermore, the risk identification module 13 is used to perform the following method: In the processing continuity degradation path model, path advantage characterization parameters are constructed for each processing continuity degradation path. These parameters are used to quantify the degree of evolutionary dominance of the corresponding degradation path relative to other degradation paths. The processing controller is used to dynamically compare the path advantage characterization parameters of each processing continuity degradation path to form a path competition state evaluation result of the relative competition state of different degradation paths. The risk of continuity degradation path is identified based on the path competition state evaluation result.

[0044] Furthermore, the control optimization module 14 is used to perform the following method: Based on the path competition state evaluation results, a path dominance representation vector is constructed for each degraded path, including the path energy accumulation rate, damage propagation sensitivity, and state amplification factor. The path dominance index of each degraded path in the current control cycle is calculated based on the path dominance representation vector. Each degraded path is classified according to its nonlinear sensitivity to cooling response, establishing highly destructive dominant degraded paths and controllable degraded paths. The nonlinear sensitivity is determined based on the gradient of the path dominance index change before and after historical cooling intervention. An inhibitory cooling effect mapping is generated based on the highly destructive dominant degraded path, and a slow-release cooling effect mapping is generated based on the controllable degraded path. Within a single scheduling cycle, the inhibitory cooling effect mapping and the slow-release cooling effect mapping are jointly input into the cooling scheduling solver. Through multi-objective competitive constraint solving, a cooling scheduling instruction that weakens the dominant degraded path while maintaining processing continuity is generated.

[0045] Furthermore, the control optimization module 14 is used to perform the following method: With the path dominance index of weakening the dominant degradation path as the first control objective and the rate of change of the path dominance index of the controllable degradation path as the second control objective, a competitive state space of cooling targets with mutual checks and balances is established. The suppressive cooling effect mapping and the slow-release cooling effect mapping are respectively transformed into state transition operators, which are used to describe the direction, magnitude and rate of the migration of the system state position by each cooling effect in the competitive state space of cooling targets. In the competitive state space of cooling targets, the state transition operators are used to predict the state migration trajectory caused by different combinations of cooling effects, forming a trajectory prediction matrix based on the state transition operators. Based on the trajectory prediction matrix, under the constraints of processing continuity and cooling rhythm continuity, the competitive adjudication of the suppressive cooling effect mapping and the slow-release cooling effect mapping is executed, the target control strategy is output, and the cooling scheduling command is output according to the target control strategy.

[0046] Furthermore, the control optimization module 14 is used to perform the following method: The cooling scheduling command is decomposed into N cooling intervention units associated with different degradation paths, and a timing structure for cooling intervention is generated according to the priority order in the cooling scheduling command. Under the timing structure, the interval time and cooling intensity variation amplitude between adjacent cooling intervention units are continuously constrained and adjusted based on the processing rhythm maintenance state and the continuous characteristics of processing behavior, thus establishing a cooling intervention rhythm. Based on the timing structure and the cooling intervention rhythm, the control mode and duration are optimized to generate a cooling control response strategy.

[0047] Furthermore, the model building module 12 is used to perform the following methods: The collected load stability indicators, process rhythm maintenance status, tool position switching continuity, and machining behavior continuity characteristics are jointly normalized to form a set of machining continuity states that characterize the continuity level of the machining process. The direction of continuous evolution is identified in the set of machining continuity states. The degradation path is divided using the direction of continuous evolution. The correspondence between the set of machining continuity states, the direction of continuous evolution, and the degradation path is associated and stored to form a machining continuity degradation path model.

[0048] Furthermore, the model building module 12 is used to perform the following methods: The state values ​​in the set of processing continuity states are arranged in time sequence to form a state change sequence that reflects the change of processing continuity over time; the state values ​​in adjacent control cycles are compared differentially to identify the magnitude and direction of change; and the direction of continuous evolution is identified based on the identification results.

[0049] Furthermore, the control management module 15 is used to perform the following methods: During the cooling intervention process, monitoring sensors are used to continuously monitor the processing behavior and establish monitoring feedback results; the cooling control response strategy is dynamically updated based on the monitoring feedback results.

[0050] Furthermore, the control management module 15 is used to perform the following methods: The machine tool is stopped based on the operation information. If the result of the stop detection is abnormal, the machine tool is stopped and a warning signal is issued.

[0051] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for cooling the power turret of a milling and turning machine tool, characterized in that, The method includes: During the machining process, operational information is collected to characterize the evolutionary behavior of the machine tool. This operational information includes load stability indicators, process rhythm maintenance status, tool position switching continuity, and continuous characteristics of machining behavior. Based on the operational information, a processing continuity degradation path model is constructed in the processing controller. The processing continuity degradation path model is used to characterize multiple processing continuity degradation paths that may occur under the current processing conditions, and each processing continuity degradation path corresponds to a different dominant evolutionary feature. During the processing, the processing controller is used to evaluate the evolution trend and relative competition of multiple processing continuous degradation paths in real time, and to identify the risks of continuous degradation paths; Based on the identification results, a cooling scheduling instruction is generated. The timing structure, rhythm continuity, and phased action mode of cooling intervention are optimized using the cooling scheduling instruction to generate a cooling control response strategy. Cooling intervention control management is performed according to the cooling control response strategy.

2. The power turret cooling method for a milling and turning machine tool as described in claim 1, which utilizes the machining controller to evaluate in real time the evolution trend and relative competition of multiple machining continuity degradation paths, and identifies the risk of continuity degradation paths, includes: In the processing continuity degradation path model, a path dominance characterization parameter is constructed for each processing continuity degradation path. The path dominance characterization parameter is used to quantify the degree of evolutionary dominance of the corresponding degradation path relative to other degradation paths. The processing controller is used to dynamically compare the path advantage characterization parameters of each processing continuity degradation path, and to form the path competition state evaluation results of the relative competition state of different degradation paths; The risk of continuous degradation paths is identified based on the path competition evaluation results.

3. The method for cooling the power turret of a milling and turning machine tool as described in claim 2, characterized in that, Based on the identification results, a cooling scheduling instruction is generated, including: Based on the path competition state evaluation results, a path advantage representation vector containing path energy accumulation rate, damage propagation sensitivity and state amplification factor is constructed for each degraded path. Calculate the path dominance index of each degraded path in the current control cycle based on the path dominance representation vector; Each degradation path is classified according to its nonlinear sensitivity to cooling response, and highly destructive dominant degradation paths and controllable degradation paths are established. The nonlinear sensitivity is determined based on the gradient of the path dominance index before and after historical cooling intervention. An inhibitory cooling effect mapping is generated based on the highly destructive dominant degradation path, and a slow-release cooling effect mapping is generated based on the controllable degradation path; Within a single scheduling cycle, the suppression-type cooling effect mapping and the slow-release cooling effect mapping are jointly input into the cooling scheduling solver. Through multi-objective competitive constraint solving, a cooling scheduling instruction that satisfies the weakening of the dominant degradation path while maintaining processing continuity is generated.

4. The method for cooling the power turret of a milling and turning machine tool as described in claim 3, characterized in that, The suppression-type cooling effect mapping and the slow-release cooling effect mapping are jointly input into the cooling scheduling solver, and solved through multi-objective competitive constraints, including: With the path advantage index of weakening the highly destructive dominant degradation path as the first control objective and the path advantage index of controllable degradation path as the second control objective, a cooling objective competitive state space with mutual checks and balances is established. The suppression-type cooling effect mapping and the slow-release cooling effect mapping are respectively transformed into state transition operators. The state transition operators are used to describe the direction, magnitude and rate of the system state position transition of each cooling effect in the competing state space of the cooling target. In the competitive state space of the cooling target, the state transition operator is used to predict the state transition trajectory caused by different combinations of cooling effects, forming a trajectory prediction matrix based on the state transition operator. Based on the trajectory prediction matrix, under the constraints of processing continuity and cooling rhythm continuity, a competitive decision is made between the suppression cooling effect mapping and the slow-release cooling effect mapping, and a target control strategy is output. Cooling scheduling instructions are then output according to the target control strategy.

5. The method for cooling the power turret of a milling and turning machine tool as described in claim 4, characterized in that, The control optimization of the timing structure, rhythm continuity, and phased action mode of cooling intervention is performed according to the cooling scheduling command to generate a cooling control response strategy, including: The cooling scheduling command is decomposed into N cooling intervention units associated with different degradation paths, and a timing structure for cooling intervention is generated according to the priority order in the cooling scheduling command. Under the aforementioned timing structure, with the processing rhythm maintenance state and the continuous characteristics of processing behavior as constraints, the interval time and cooling intensity variation amplitude between adjacent cooling intervention units are continuously constrained and adjusted to establish a cooling intervention rhythm. Based on the timing structure and the cooling intervention rhythm, the control mode and duration are optimized to generate a cooling control response strategy.

6. The method for cooling the power turret of a milling and turning machine tool as described in claim 1, characterized in that, Based on the aforementioned operational information, a processing continuity degradation path model is constructed in the processing controller, including: The collected load stability indicators, process rhythm maintenance status, tool position switching continuity, and machining behavior continuity characteristics are jointly normalized to form a set of machining continuity states that characterize the continuity level of the machining process. Identify the direction of continuous evolution within the set of processing continuity states; The degradation path is divided using the continuous evolution direction, and the correspondence between the processing continuous state set, the continuous evolution direction and the degradation path is associated and stored to form a processing continuous degradation path model.

7. A method for cooling the power turret of a milling and turning machine tool as described in claim 6, characterized in that, Identifying the direction of continuous evolution within the set of processing continuity states includes: The state values ​​in the set of processing continuity states are arranged in a temporal sequence to form a state change sequence that reflects the change of processing continuity over time; The state values ​​within adjacent control cycles are compared differentially to identify the magnitude and direction of change. The direction of continuous evolution is identified based on the recognition results.

8. The method for cooling the power turret of a milling and turning machine tool as described in claim 1, characterized in that, According to the cooling control response strategy, cooling intervention control management is performed, including: During the cooling intervention process, monitoring sensors are used to continuously monitor the processing behavior and establish monitoring feedback results; The cooling control response strategy is dynamically updated based on the monitoring feedback results.

9. A method for cooling the power turret of a milling and turning machine tool as described in claim 1, characterized in that, The machine tool is stopped based on the operation information. If the result of the stop detection is abnormal, the machine tool is stopped and a warning signal is issued.

10. A power turret cooling system for a milling and turning machine tool, characterized in that, A method for cooling the power turret of a milling and turning machine tool according to any one of claims 1-9, the system comprising: Information acquisition module: During the processing, it collects operational information to characterize the evolution behavior of the machine tool. The operational information includes load stability index, process rhythm maintenance status, tool position switching continuity, and continuous characteristics of processing behavior. Model building module: Based on the operation information, a processing continuity degradation path model is built in the processing controller. The processing continuity degradation path model is used to characterize multiple processing continuity degradation paths that may occur under the current processing conditions, and each processing continuity degradation path corresponds to different dominant evolutionary features. Risk identification module: During the processing, the processing controller is used to evaluate the evolution trend and relative competition of multiple continuous degradation paths in real time, and to identify the risks of continuous degradation paths; Control optimization module: Generates cooling scheduling instructions based on the identification results, and performs control optimization on the timing structure, rhythm continuity and staged action mode of cooling intervention based on the cooling scheduling instructions, and generates a cooling control response strategy; Control and Management Module: Performs cooling intervention control and management according to the cooling control response strategy.

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