Dynamic cooperative speed regulation control system and method for main shaft and feed shaft of machine tool

Abstract

The invention provides a dynamic cooperative speed regulation control system and method for a machine tool spindle and a feed shaft, and the method comprises the steps: building a spindle energy transmission model and a feed shaft load-motion model, and constructing a strong correlation coupling relation between the two through cutting power; collecting main shaft power, torque and rotating speed and feed shaft cutting force, acceleration and displacement data in real time, and fusing to generate a combined state vector; calculating spindle margin and time correction based on spindle actual power and torque, and identifying working condition abrupt change according to the joint state vector; the feeding speed and the cutting depth are dynamically adjusted with the spindle margin sum as the constraint condition, and a cooperative adjustment strategy is generated; and converting the cooperative adjustment strategy into a feed shaft motion instruction and executing the feed shaft motion instruction. By constructing a real-time coupling closed loop of'spindle power-torque-rotating speed 'and'feed shaft speed-acceleration-position', the spindle is always locked to operate in a maximum power-torque envelope efficient area, the speed of the feed shaft is dynamically adjusted according to the spindle margin, locked rotor is completely eradicated, and no-load is eliminated.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for CNC machine tools, specifically to a dynamic coordinated speed control system and method for machine tool spindle and feed axis. Background Technology

[0002] Currently, mainstream CNC systems generally adopt a control architecture of "constant spindle speed + independent interpolation of feed axes" or "constant spindle power + fixed feed axis speed." The spindle and feed axes are only indirectly coupled through a simple speed switch or a fixed cutting parameter table, lacking a real-time energy-load closed loop. Current CNC machine tools still have some shortcomings:

[0003] Poor adaptability to working conditions: When faced with sudden load changes such as free-form surfaces, varying depth of cut, and intermittent cutting, the feed axis still runs at a fixed speed or a simple multiplication factor, which can easily lead to spindle stall, tool chipping, or unloaded sliding.

[0004] Lack of synergistic constraints: Spindle margins (power margin, torque margin) cannot be quantified in real time and used as hard constraints for feed axis speed regulation, resulting in a mismatch between material removal rate (MRR) and tool load.

[0005] Therefore, there is an urgent need for a dynamic coordinated speed control method for machine tool spindles and feed axes to solve the above problems. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a dynamic coordinated speed control system and method for machine tool spindles and feed axes, thereby solving the problems mentioned in the background section.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for dynamic coordinated speed control of a machine tool spindle and feed axis, comprising the following steps:

[0008] S1: Establish the spindle energy transfer model and the feed axis load-motion model, and construct a strong correlation coupling relationship between the two through cutting power;

[0009] S2: Real-time acquisition of spindle power, torque, speed, and feed axis cutting force, acceleration, and displacement data, which are then fused to generate a joint state vector;

[0010] S3: Calculate the spindle margin and time correction based on the actual power and torque of the spindle, and identify sudden changes in working conditions based on the joint state vector;

[0011] S4: Using the spindle margin as a constraint, dynamically adjust the feed rate and depth of cut to generate a coordinated adjustment strategy;

[0012] S5: Convert the coordinated adjustment strategy into feed axis motion commands and execute them;

[0013] S6: Collect spindle and feed axis status data after execution, evaluate control performance indicators, update model parameters and collaborative strategies, and form closed-loop feedback optimization.

[0014] As a preferred embodiment, the specific steps of S1 include:

[0015] Construct a dynamic relationship model between spindle power, torque, and speed, and define the maximum power-torque envelope curve of the spindle;

[0016] Construct a dynamic model of the feed axis cutting force, acceleration, position, and motion velocity;

[0017] By associating cutting power with spindle parameters and feed axis parameters, a spindle-feed axis coupling relationship model is formed;

[0018] The formula for the spindle-feed axis coupling relationship model is: P c =F c v = η c P s ;

[0019] Among them, P c F represents cutting power. c η represents the cutting force, v represents the feed rate of the feed axis, and η represents the feed rate of the feed axis. c P represents the spindle power transmission efficiency. s This indicates the spindle power.

[0020] As a preferred embodiment, the joint state vector is specifically X = [P s T s ω s F c [a, x] T ;

[0021] Among them, P s T represents spindle power. s Represents torque, ω s F represents rotational speed. c denoted by , where 'a' represents the cutting force, 'a' represents the acceleration, and 'x' represents the feed axis displacement.

[0022] As a preferred embodiment, the conflict type determination process includes conflict type classification and type matching;

[0023] The conflict types are categorized as follows: node conflict, path segment conflict, direction conflict, and dynamic obstacle conflict.

[0024] Type matching specifically involves matching conflict types based on conflict detection results and recording conflict logs.

[0025] As a preferred embodiment, the step of calculating the spindle margin and time correction based on the actual spindle power and torque, and identifying sudden changes in operating conditions based on the joint state vector, specifically includes:

[0026] Calculate the spindle power margin based on the difference between the actual spindle power and the maximum allowable power;

[0027] Predict time correction based on spindle power variation trend;

[0028] Analyze the rate of change of the joint state vector, and identify the type of sudden change in operating conditions through threshold determination or machine learning models.

[0029] Among them, the types of sudden changes in working conditions include freeform surfaces, variable depth of cut, or intermittent cutting;

[0030] Spindle margin calculation includes:

[0031] The difference between the actual power of the spindle and the maximum power envelope at the current speed is calculated as the power margin.

[0032] Power margin: M P =P m (ω s )-P s ;

[0033] Among them, P m (ω s ) represents rotational speed ω s Maximum permissible power;

[0034] The difference between the actual spindle torque and the maximum torque envelope at the current speed is calculated as the torque margin.

[0035] Torque margin: M T =T m (ω s )-T s ;

[0036] Among them, T m (ω s ) represents rotational speed ω s Maximum permissible torque;

[0037] In this embodiment, the overall margin is taken as the minimum value M = min(M P M T ).

[0038] Based on the changing trends of power margin and torque margin, predict the time correction required for spindle margin exhaustion;

[0039]

[0040] Where α represents the safety factor and Δt represents the time correction amount.

[0041] The identification of sudden changes in operating conditions includes:

[0042] When the rate of change of cutting force in the joint state vector exceeds the first threshold, it is determined to be an intermittent cutting condition.

[0043] When the product of the feed axis displacement rate and the spindle torque rate exceeds the second threshold, it is determined to be a variable depth of cut condition.

[0044] When the spindle speed fluctuation rate is lower than the third threshold and the feed rate is consistently lower than the set value, it is determined to be a risk of feed no-load.

[0045] As a preferred embodiment, the step of dynamically adjusting the feed rate and depth of cut based on the spindle margin as a constraint to generate a coordinated adjustment strategy specifically includes:

[0046] The constraints are that the spindle margin is not lower than the safety threshold and the feed axis acceleration does not exceed the mechanical limit.

[0047] Dynamically adjust the feed rate and depth of cut according to the working condition:

[0048] If the spindle margin is sufficient, increase the feed rate to maximize the material removal rate;

[0049] If the spindle margin is insufficient, reduce the feed rate or depth of cut to avoid stalling;

[0050] If intermittent cutting is detected, the feed axis impact is suppressed through acceleration compensation;

[0051] Adjustment commands for feed axis speed, acceleration, and position are generated based on model predictive control or fuzzy logic.

[0052] As a preferred embodiment, the dynamic adjustment strategy includes:

[0053] In free-form surface conditions, the feed rate should be adjusted first to keep the spindle power within the high-efficiency envelope;

[0054] Under variable cutting depth conditions, the cutting depth is adjusted first, and the feed rate fluctuation is compensated by model prediction control.

[0055] In intermittent cutting conditions, an acceleration decay command is generated to suppress the impact response of the feed axis.

[0056] As a preferred embodiment, the dynamic adjustment strategy further includes executing a differentiated collaborative adjustment strategy based on margin partitioning, specifically:

[0057] If M > 20% * P m (ω sOnce the area is identified as a safe zone, the feed rate is increased first to maximize the material removal rate, and then the depth of cut is increased only when the feed rate reaches its upper limit.

[0058] If 10%*P m (ω s ) < M < 20% * P m (ω s This area is designated as a warning zone, and the cutting force is adjusted proportionally with the goal of maintaining a constant cutting force. Freeze depth;

[0059] Where v represents the feed speed of the feed axis, v c F represents the current feed rate of the feed axis. s F represents the target cutting force. c Indicates the current cutting force;

[0060] If M < 10% * P m (ω s If the area is identified as a danger zone, spindle protection will be prioritized, and the feed rate will be forcibly reduced to 80% of the current value. If the margin is still insufficient, the depth of cut will be reduced to 90% of the current value simultaneously.

[0061] As a preferred embodiment, a dynamic coordinated speed control system for a machine tool spindle and feed axis is provided to implement the above-mentioned dynamic coordinated speed control method for a machine tool spindle and feed axis, comprising:

[0062] Spindle sensor module: used to collect spindle power, torque, and speed in real time;

[0063] Feed axis sensor module: used to collect feed axis cutting force, acceleration, and displacement in real time;

[0064] Data processing unit: used for filtering and noise reduction and generating joint state vectors, and also used to establish the spindle energy transfer model and feed axis load-motion model, and to build a strong correlation coupling relationship between the two through cutting power;

[0065] Margin Calculation and Operating Condition Identification Module: Used to calculate spindle margin and time correction amount, and identify sudden changes in operating conditions;

[0066] Collaborative strategy generation module: Generates feed axis adjustment commands based on model predictive control;

[0067] Instruction execution module: converts adjustment instructions into servo drive signals;

[0068] Closed-loop optimization module: Evaluates control performance and updates model parameters and strategies.

[0069] This invention provides a dynamic coordinated speed control system and method for machine tool spindles and feed axes, which has the following advantages: By establishing a real-time coupling mechanism between the spindle energy transfer model and the feed axis dynamic model, a dynamic coordinated control framework for the spindle-feed axis with cutting power as the core is constructed, enabling the spindle to always operate in the high-efficiency zone of the "maximum power-torque envelope": The envelope boundary is dynamically generated based on the motor characteristic curve, and the spindle power margin and torque margin are monitored in real time. The spindle maintains efficient and stable operation through comprehensive margin hierarchical forced constraints; At the same time, the feed axis is driven to dynamically adjust its speed according to the spindle margin. When the margin is insufficient, the speed is forcibly reduced to avoid spindle stall. When "low speed fluctuation rate + feed speed continuously lower than the set value" is detected, the speed is increased to prevent no-load operation, and intermittent speed fluctuation is addressed. The system generates acceleration decay commands to suppress impact during cutting conditions, prioritizes freezing the depth of cut and uses model predictive control to compensate for speed fluctuations during variable depth of cut conditions, and maintains spindle power following the efficient envelope through speed fine-tuning during free-form surface conditions. Based on this, a margin partitioning strategy maximizes material removal rate and keeps tool load constant under complex conditions: the safety zone is dynamically adjusted according to the principle of "increase speed first, then increase depth," effectively improving material removal rate. Finally, the system uses recursive least squares to update the spindle damping coefficient, feed axis friction coefficient, and cutting force model parameters in real time, constructing a comprehensive evaluation index and dynamically adjusting strategy parameters to achieve rapid recovery to steady state after sudden changes in operating conditions. This effectively improves machining efficiency and reduces equipment failure rate, making it suitable for high-precision, high-dynamic machining scenarios such as aerospace and mold manufacturing. Attached Figure Description

[0070] Figure 1 This is a flowchart of the dynamic coordinated speed control method for machine tool spindle and feed axis according to the present invention;

[0071] Figure 2 This is a block diagram of the dynamic coordinated speed control system for the machine tool spindle and feed axis according to the present invention. Detailed Implementation

[0072] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0073] The following disclosure provides many different embodiments or examples for implementing various structures of the invention. To simplify the disclosure, specific examples of components and arrangements are described below. These are merely examples and are not intended to limit the invention. Furthermore, reference numerals and / or letters may be repeated in different examples; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed. In addition, examples of various specific processes and materials are provided in this invention, but those skilled in the art will recognize the application of other processes and / or the use of other materials.

[0074] like Figure 1 As shown, this embodiment of the invention provides a method for dynamic coordinated speed control of a machine tool spindle and feed axis, comprising the following steps:

[0075] S1: Establish the spindle energy transfer model and the feed axis load-motion model, and construct a strong correlation coupling relationship between the two through cutting power;

[0076] Specifically, a dynamic relationship model of spindle power, torque and speed is constructed, and the maximum power-torque envelope curve of the spindle is defined;

[0077] Specifically, the relationship between spindle power, torque, and speed in the dynamic relationship model is: P s =T s ω s / η s ;

[0078] Among them, P s Indicates spindle power, T s Represents torque, ω s Indicates rotational speed, η s Indicates transmission efficiency;

[0079] The maximum power-torque envelope curve of the spindle is specifically based on the motor characteristic curve, defining the maximum permissible power and maximum permissible torque at different speeds;

[0080] It should be noted that the specific characteristic curve of the motor is determined by the motor's own parameters.

[0081] Construct a dynamic model of the feed axis cutting force, acceleration, position, and motion velocity;

[0082] In this embodiment, the cutting force model formula is: F c =k*f*d*μ;

[0083] Among them, F c denoted by , k represents the cutting force, f represents the feed rate, d represents the depth of cut, and μ represents the material coefficient.

[0084] The dynamic model formula is: F c =ma + bv + F f ;

[0085] Where m is the feed axis mass, a represents the acceleration, b represents the damping coefficient, v represents the feed axis feed rate, and F... f It represents friction.

[0086] By associating the cutting power with the spindle parameters and the feed axis parameters, a spindle-feed axis coupling relationship model is formed.

[0087] The formula for the spindle-feed axis coupling relationship model is: P c =F c v = η c P s ;

[0088] Among them, P c η represents cutting power. c This indicates the efficiency of spindle power transmission.

[0089] S2: Real-time acquisition of spindle power, torque, speed, and feed axis cutting force, acceleration, and displacement data, which are then fused to generate a joint state vector;

[0090] It is understandable that these data collected in real time are specifically acquired by deploying multiple sensors on the spindle and feed axis, and by using these sensors to collect data.

[0091] Specifically, it collects data on spindle power, torque, speed, and feed axis cutting force, acceleration, and displacement.

[0092] The collected data is filtered and noise reduced.

[0093] By fusing spindle and feed axis data, a joint state vector X = [P] is generated, containing power, torque, speed, cutting force, acceleration, and displacement. s T s ω s F c [a, x] T .

[0094] Where x represents the feed axis displacement.

[0095] S3: Calculate the spindle margin and time correction based on the actual power and torque of the spindle, and identify sudden changes in working conditions based on the joint state vector;

[0096] Calculate the spindle power margin based on the difference between the actual spindle power and the maximum allowable power;

[0097] Predict time correction based on spindle power variation trend;

[0098] Analyze the rate of change of the joint state vector, and identify the type of sudden change in operating conditions through threshold determination or machine learning models.

[0099] Among them, the types of sudden changes in working conditions include freeform surfaces, variable depth of cut, or intermittent cutting.

[0100] Spindle margin calculation includes:

[0101] The difference between the actual power of the spindle and the maximum power envelope at the current speed is calculated as the power margin.

[0102] Power margin: M P =P m (ω s )-P s ;

[0103] Among them, P m (ω s ) represents rotational speed ω s Maximum permissible power;

[0104] The difference between the actual spindle torque and the maximum torque envelope at the current speed is calculated as the torque margin.

[0105] Torque margin: M T =T m (ω s )-T s ;

[0106] Among them, T m (ω s ) represents rotational speed ω s Maximum permissible torque;

[0107] In this embodiment, the overall margin is taken as the minimum value M = min(M P M T ).

[0108] Based on the changing trends of power margin and torque margin, predict the time correction required for spindle margin exhaustion;

[0109]

[0110] Where α represents the safety factor and Δt represents the time correction amount.

[0111] Identification of sudden changes in operating conditions includes:

[0112] When the rate of change of cutting force in the joint state vector exceeds the first threshold, it is determined to be an intermittent cutting condition.

[0113] When the product of the feed axis displacement rate and the spindle torque rate exceeds the second threshold, it is determined to be a variable depth of cut condition.

[0114] When the spindle speed fluctuation rate is lower than the third threshold and the feed rate is consistently lower than the set value, it is determined to be a risk of feed no-load.

[0115] It should be noted that the first threshold, the second threshold, and the third threshold are all preset thresholds, and are specifically set based on actual machine tool data and / or production data.

[0116] By constructing a real-time coupled closed loop of "spindle power-torque-speed" and "feed axis speed-acceleration-position", the spindle is always locked in the high-efficiency zone of the maximum power-torque envelope, while the feed axis dynamically adjusts its speed according to the spindle margin, thus preventing stalling and eliminating no-load operation.

[0117] S4: Using the spindle margin as a constraint, dynamically adjust the feed rate and depth of cut to generate a coordinated adjustment strategy;

[0118] Specifically, the constraints are that the spindle margin is not lower than the safety threshold and the feed axis acceleration does not exceed the mechanical limit.

[0119] Dynamically adjust the feed rate and depth of cut according to the working condition:

[0120] If the spindle margin is sufficient, increase the feed rate to maximize the material removal rate;

[0121] If the spindle margin is insufficient, reduce the feed rate or depth of cut to avoid stalling;

[0122] If intermittent cutting is detected, the feed axis impact is suppressed through acceleration compensation;

[0123] Adjustment commands for feed axis speed, acceleration, and position are generated based on model predictive control or fuzzy logic.

[0124] In this embodiment, the dynamic adjustment strategy includes:

[0125] In free-form surface conditions, the feed rate should be adjusted first to keep the spindle power within the high-efficiency envelope;

[0126] Under variable cutting depth conditions, the cutting depth is adjusted first, and the feed rate fluctuation is compensated by model prediction control.

[0127] In intermittent cutting conditions, an acceleration decay command is generated to suppress the impact response of the feed axis.

[0128] It also includes implementing differentiated collaborative adjustment strategies based on margin partitioning;

[0129] The specific classification of spindle margin and the corresponding adjustment strategies are as follows:

[0130] If M > 20% * P m (ω sOnce the area is identified as a safe zone, the feed rate is increased first to maximize the material removal rate, and then the depth of cut is increased only when the feed rate reaches its upper limit.

[0131] If 10%*P m (ω s ) < M < 20% * P m (ω s This area is designated as a warning zone, and the cutting force is adjusted proportionally with the goal of maintaining a constant cutting force. Freeze depth;

[0132] Where v represents the feed speed of the feed axis, v c F represents the current feed rate of the feed axis. s F represents the target cutting force. c Indicates the current cutting force;

[0133] If M < 10% * P m (ω s If the area is identified as a danger zone, spindle protection will be prioritized, and the feed rate will be forcibly reduced to 80% of the current value. If the margin is still insufficient, the depth of cut will be reduced to 90% of the current value simultaneously.

[0134] Understandably, by using real-time margin constraints, spindle stalling or motor overheating caused by sudden changes in cutting load can be avoided. Within the safe zone, the feed rate and depth of cut can be dynamically increased, effectively improving the material removal rate. By using a working condition identification correction and adjustment strategy, the adaptation problem of complex working conditions such as sudden changes in free-form surface trajectory and variable depth of cut load fluctuations can be solved. Through hierarchical decision-making and dynamic constraint verification, the maximum MRR and constant load can be achieved while ensuring spindle safety. This is suitable for real-time collaborative control of complex working conditions.

[0135] Furthermore, when faced with sudden changes in working conditions such as free-form surfaces, variable depth of cut, and intermittent cutting, the system automatically switches between strategies such as "speeding up first and then increasing depth of cut", "depth of cut freeze + speed compensation" or "acceleration decay" to ensure maximum material removal rate and constant tool load, achieving efficient, safe and highly adaptable intelligent machining.

[0136] In this embodiment, the formula for the incremental feed rate in the safe zone strategy is:

[0137]

[0138] Among them, v new This indicates the adjusted feed rate of the feed axis, δ represents the step size coefficient used to control the adjustment range of the feed rate, ΔF represents the cutting force deviation, and v max This indicates the maximum permissible feed rate of the feed axis.

[0139] Specifically, in the coordinated control of the machine tool spindle and feed axis, the feed speed is dynamically adjusted through real-time cutting force feedback to ensure that the machining process is within a safe range.

[0140] Furthermore, when faced with sudden changes in working conditions such as free-form surfaces, variable depth of cut, and intermittent cutting, the system automatically switches between strategies such as "speeding up first and then increasing depth of cut", "depth of cut freeze + speed compensation" or "acceleration decay" to ensure maximum material removal rate and constant tool load, achieving efficient, safe and highly adaptable intelligent machining.

[0141] S5: Convert the coordinated adjustment strategy into feed axis motion commands and execute them;

[0142] S6: Collect spindle and feed axis status data after execution, evaluate control performance indicators, update model parameters and collaborative strategies, and form closed-loop feedback optimization.

[0143] Collect spindle and feed axis status data after execution;

[0144] Evaluate spindle load stability, material removal rate, and feed axis tracking error;

[0145] Update the spindle energy model parameters, feed axis dynamics model parameters, and collaborative adjustment strategies based on the evaluation results;

[0146] Repeat steps S1 to S6 to form a closed-loop control.

[0147] The model parameter updates employ recursive least squares (RLS), including:

[0148] Spindle power, torque, and feed cutting force are the observed variables;

[0149] Real-time correction of the damping coefficient in the spindle energy model and the friction coefficient in the feed shaft dynamics model;

[0150] The constraint threshold in step S4 is updated based on the corrected parameters.

[0151] The machine tool spindle and feed axis dynamic coordinated speed control method provided in this embodiment establishes a real-time coupling mechanism between the spindle energy transfer model and the feed axis dynamic model, constructing a spindle-feed axis dynamic coordinated control framework with cutting power as the core. This ensures that the spindle always operates within the high-efficiency zone of the "maximum power-torque envelope": The envelope boundary is dynamically generated based on the motor characteristic curve, and the spindle power margin and torque margin are monitored in real time. The spindle maintains efficient and stable operation through comprehensive margin-based hierarchical forced constraints. Simultaneously, the feed axis is dynamically speed-adjusted according to the spindle margin. When the margin is insufficient, forced speed reduction and depth reduction are implemented to avoid spindle stall. When "low speed fluctuation rate + feed rate continuously lower than the set value" is detected, the speed is increased to prevent idle load. Furthermore, acceleration is generated for intermittent cutting conditions. Speed ​​decay commands suppress impact; in variable depth-of-cut conditions, the depth of cut is frozen first, and model predictive control is used to compensate for speed fluctuations; in free-form surface conditions, speed fine-tuning maintains spindle power following the efficient envelope. Based on this, a margin partitioning strategy maximizes material removal rate and keeps tool load constant under complex conditions: the safety zone is dynamically adjusted according to the principle of "speed first, then depth increase," effectively improving material removal rate. Finally, the spindle damping coefficient, feed axis friction coefficient, and cutting force model parameters are updated in real time using recursive least squares, constructing a comprehensive evaluation index and dynamically adjusting strategy parameters to achieve rapid recovery to steady state after sudden changes in operating conditions. This effectively improves machining efficiency and reduces equipment failure rate, making it suitable for high-precision, high-dynamic machining scenarios such as aerospace and mold manufacturing.

[0152] like Figure 2 As shown, this embodiment also provides a dynamic coordinated speed control system for machine tool spindle and feed axis, used to implement the above-mentioned dynamic coordinated speed control method for machine tool spindle and feed axis, including:

[0153] Spindle sensor module: used to collect spindle power, torque, and speed in real time;

[0154] Feed axis sensor module: used to collect feed axis cutting force, acceleration, and displacement in real time;

[0155] Data processing unit: used for filtering and noise reduction and generating joint state vectors, and also used to establish the spindle energy transfer model and feed axis load-motion model, and to build a strong correlation coupling relationship between the two through cutting power;

[0156] Margin Calculation and Operating Condition Identification Module: Used to calculate spindle margin and time correction amount, and identify sudden changes in operating conditions;

[0157] Collaborative strategy generation module: Generates feed axis adjustment commands based on model predictive control;

[0158] Instruction execution module: converts adjustment instructions into servo drive signals;

[0159] Closed-loop optimization module: Evaluates control performance and updates model parameters and strategies.

[0160] In this embodiment, the data processing unit constructs a dynamic relationship model between spindle power, torque, and rotational speed, and defines the maximum power-torque envelope curve of the spindle.

[0161] Specifically, the relationship between spindle power, torque, and speed in the dynamic relationship model is: P s =T s ω s / η s ;

[0162] Among them, P s Indicates spindle power, T s Represents torque, ω s Indicates rotational speed, η s Indicates transmission efficiency;

[0163] The maximum power-torque envelope curve of the spindle is specifically based on the motor characteristic curve, defining the maximum permissible power and maximum permissible torque at different speeds;

[0164] It should be noted that the specific characteristic curve of the motor is determined by the motor's own parameters.

[0165] Construct a dynamic model of the feed axis cutting force, acceleration, position, and motion velocity;

[0166] In this embodiment, the cutting force model formula is: F c =k*f*d*μ;

[0167] Among them, F c denoted by , k represents the cutting force, f represents the feed rate, d represents the depth of cut, and μ represents the material coefficient.

[0168] The dynamic model formula is: F c =ma + bv + F f ;

[0169] Where m is the feed axis mass, a represents the acceleration, b represents the damping coefficient, v represents the feed axis feed rate, and F... f It represents friction.

[0170] By associating the cutting power with the spindle parameters and the feed axis parameters, a spindle-feed axis coupling relationship model is formed.

[0171] The formula for the spindle-feed axis coupling relationship model is: P c =F c v = η c P s ;

[0172] Among them, P cRepresents cutting power, η c This indicates the efficiency of spindle power transmission.

[0173] Specifically, the data processing unit performs filtering and noise reduction processing on the collected spindle power, torque, speed, and feed axis cutting force, acceleration, and displacement data.

[0174] By fusing spindle and feed axis data, a joint state vector X = [P] is generated, containing power, torque, speed, cutting force, acceleration, and displacement. s T s ω s F c [a, x] T .

[0175] Where x represents the feed axis displacement.

[0176] Specifically, the margin calculation and operating condition identification module calculates the spindle power margin based on the difference between the actual spindle power and the maximum allowable power;

[0177] Predict time correction based on spindle power variation trend;

[0178] Analyze the rate of change of the joint state vector, and identify the type of sudden change in operating conditions through threshold determination or machine learning models.

[0179] Among them, the types of sudden changes in working conditions include freeform surfaces, variable depth of cut, or intermittent cutting.

[0180] Spindle margin calculation includes:

[0181] The difference between the actual power of the spindle and the maximum power envelope at the current speed is calculated as the power margin.

[0182] Power margin: M P =P m (ω s )-P s ;

[0183] Among them, P m (ω s ) represents rotational speed ω s Maximum permissible power;

[0184] The difference between the actual spindle torque and the maximum torque envelope at the current speed is calculated as the torque margin.

[0185] Torque margin: M T =T m (ω s )-T s ;

[0186] Among them, T m (ω s ) represents rotational speed ωs Maximum permissible torque;

[0187] In this embodiment, the overall margin is taken as the minimum value M = min(M P M T ).

[0188] Based on the changing trends of power margin and torque margin, predict the time correction required for spindle margin exhaustion;

[0189]

[0190] Where α represents the safety factor and Δt represents the time correction amount.

[0191] Identification of sudden changes in operating conditions includes:

[0192] When the rate of change of cutting force in the joint state vector exceeds the first threshold, it is determined to be an intermittent cutting condition.

[0193] When the product of the feed axis displacement rate and the spindle torque rate exceeds the second threshold, it is determined to be a variable depth of cut condition.

[0194] When the spindle speed fluctuation rate is lower than the third threshold and the feed rate is consistently lower than the set value, it is determined to be a risk of feed no-load.

[0195] It should be noted that the first threshold, the second threshold, and the third threshold are all preset thresholds, and are specifically set based on actual machine tool data and / or production data.

[0196] By constructing a real-time coupled closed loop of "spindle power-torque-speed" and "feed axis speed-acceleration-position", the spindle is always locked in the high-efficiency zone of the maximum power-torque envelope, while the feed axis dynamically adjusts its speed according to the spindle margin, thus preventing stalling and eliminating no-load operation.

[0197] The collaborative strategy generation module uses the constraints that the spindle margin is not lower than the safety threshold and the feed axis acceleration does not exceed the mechanical limit.

[0198] Dynamically adjust the feed rate and depth of cut according to the working condition:

[0199] If the spindle margin is sufficient, increase the feed rate to maximize the material removal rate;

[0200] If the spindle margin is insufficient, reduce the feed rate or depth of cut to avoid stalling;

[0201] If intermittent cutting is detected, the feed axis impact is suppressed through acceleration compensation;

[0202] Adjustment commands for feed axis speed, acceleration, and position are generated based on model predictive control or fuzzy logic.

[0203] In this embodiment, the dynamic adjustment strategy includes:

[0204] In free-form surface conditions, the feed rate should be adjusted first to keep the spindle power within the high-efficiency envelope;

[0205] Under variable cutting depth conditions, the cutting depth is adjusted first, and the feed rate fluctuation is compensated by model prediction control.

[0206] In intermittent cutting conditions, an acceleration decay command is generated to suppress the impact response of the feed axis.

[0207] It also includes implementing differentiated collaborative adjustment strategies based on margin partitioning;

[0208] The specific classification of spindle margin and the corresponding adjustment strategies are as follows:

[0209] If M > 20% * P m (ω s Once the area is identified as a safe zone, the feed rate is increased first to maximize the material removal rate, and then the depth of cut is increased only when the feed rate reaches its upper limit.

[0210] If 10%*P m (ω s ) < M < 20% * P m (ω s This area is designated as a warning zone, and the cutting force is adjusted proportionally with the goal of maintaining a constant cutting force. Freeze depth;

[0211] Where v represents the feed speed of the feed axis, v c F represents the current feed rate of the feed axis. s F represents the target cutting force. c Indicates the current cutting force;

[0212] If M < 10% * P m (ω s If the area is identified as a danger zone, spindle protection will be prioritized, and the feed rate will be forcibly reduced to 80% of the current value. If the margin is still insufficient, the depth of cut will be reduced to 90% of the current value simultaneously.

[0213] In this embodiment, the closed-loop optimization module collects the spindle and feed axis status data after execution;

[0214] Evaluate spindle load stability, material removal rate, and feed axis tracking error;

[0215] Update the spindle energy model parameters, feed axis dynamics model parameters, and collaborative adjustment strategies based on the evaluation results;

[0216] The steps of the dynamic coordinated speed control method for the spindle and feed axis of a repeating machine tool are used to form a closed-loop control.

[0217] The model parameter updates employ recursive least squares (RLS), including:

[0218] Spindle power, torque, and feed cutting force are the observed variables;

[0219] Real-time correction of the damping coefficient in the spindle energy model and the friction coefficient in the feed shaft dynamics model;

[0220] The constraint thresholds in the method steps of updating the dynamic coordinated speed control of the machine tool spindle and feed axis based on the corrected parameters.

[0221] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for dynamic coordinated speed control of machine tool spindle and feed axis, characterized in that, Includes the following steps: S1: Establish the spindle energy transfer model and the feed axis load-motion model, and construct a strong correlation coupling relationship between the two through cutting power; S2: Real-time acquisition of spindle power, torque, speed, and feed axis cutting force, acceleration, and displacement data, which are then fused to generate a joint state vector; S3: Calculate the spindle margin and time correction based on the actual power and torque of the spindle, and identify sudden changes in working conditions based on the joint state vector; S4: Using the spindle margin as a constraint, dynamically adjust the feed rate and depth of cut to generate a coordinated adjustment strategy; S5: Convert the coordinated adjustment strategy into feed axis motion commands and execute them; S6: Collect spindle and feed axis status data after execution, evaluate control performance indicators, update model parameters and collaborative strategies, and form closed-loop feedback optimization.

2. The method for dynamic coordinated speed control of machine tool spindle and feed axis according to claim 1, characterized in that, The specific steps of S1 include: Construct a dynamic relationship model between spindle power, torque, and speed, and define the maximum power-torque envelope curve of the spindle; Construct a dynamic model of the feed axis cutting force, acceleration, position, and motion velocity; By associating cutting power with spindle parameters and feed axis parameters, a spindle-feed axis coupling relationship model is formed; The formula for the spindle-feed axis coupling relationship model is: P c =F c v = η c P s ; Among them, P c F represents cutting power. c η represents the cutting force, v represents the feed rate of the feed axis, and η represents the feed rate of the feed axis. c P represents the spindle power transmission efficiency. s This indicates the spindle power.

3. The method for dynamic coordinated speed control of machine tool spindle and feed axis according to claim 1, characterized in that, The joint state vector is specifically X = [P s T s ω s F c [a, x] T ; Among them, P s T represents spindle power. s Represents torque, ω s F represents rotational speed. c denoted by , where 'a' represents the cutting force, 'a' represents the acceleration, and 'x' represents the feed axis displacement.

4. The method for dynamic coordinated speed control of machine tool spindle and feed axis according to claim 1, characterized in that, The conflict type determination process includes conflict type classification and type matching; The conflict types are categorized as follows: node conflict, path segment conflict, direction conflict, and dynamic obstacle conflict. Type matching specifically involves matching conflict types based on conflict detection results and recording conflict logs.

5. The method for dynamic coordinated speed control of machine tool spindle and feed axis according to claim 1, characterized in that, The calculation of spindle margin and time correction based on actual spindle power and torque, and the identification of sudden changes in operating conditions based on the joint state vector, specifically include: Calculate the spindle power margin based on the difference between the actual spindle power and the maximum allowable power; Predict time correction based on spindle power variation trend; Analyze the rate of change of the joint state vector, and identify the type of sudden change in operating conditions through threshold determination or machine learning models. Among them, the types of sudden changes in working conditions include freeform surfaces, variable depth of cut, or intermittent cutting; Spindle margin calculation includes: The difference between the actual power of the spindle and the maximum power envelope at the current speed is calculated as the power margin. Power margin: M P =P m (ω s )-P s ; Among them, P m (ω s ) represents rotational speed ω s Maximum permissible power; The difference between the actual spindle torque and the maximum torque envelope at the current speed is calculated as the torque margin. Torque margin: M T =T m (ω s )-T s ; Among them, T m (ω s ) represents rotational speed ω s Maximum permissible torque; In this embodiment, the overall margin is taken as the minimum value M = min(M P M T ). Based on the changing trends of power margin and torque margin, predict the time correction required for spindle margin exhaustion; Where α represents the safety factor and Δt represents the time correction amount; The identification of sudden changes in operating conditions includes: When the rate of change of cutting force in the joint state vector exceeds the first threshold, it is determined to be an intermittent cutting condition. When the product of the feed axis displacement rate and the spindle torque rate exceeds the second threshold, it is determined to be a variable depth of cut condition. When the spindle speed fluctuation rate is lower than the third threshold and the feed rate is consistently lower than the set value, it is determined to be a risk of feed no-load.

6. The method for dynamic coordinated speed control of machine tool spindle and feed axis according to claim 1, characterized in that, The aforementioned strategy of dynamically adjusting feed rate and depth of cut under the constraint of spindle margin to generate a coordinated adjustment strategy specifically includes: The constraints are that the spindle margin is not lower than the safety threshold and the feed axis acceleration does not exceed the mechanical limit. Dynamically adjust the feed rate and depth of cut according to the working condition: If the spindle margin is sufficient, increase the feed rate to maximize the material removal rate; If the spindle margin is insufficient, reduce the feed rate or depth of cut to avoid stalling; If intermittent cutting is detected, the feed axis impact is suppressed through acceleration compensation; Adjustment commands for feed axis speed, acceleration, and position are generated based on model predictive control or fuzzy logic.

7. The method for dynamic coordinated speed control of machine tool spindle and feed axis according to claim 6, characterized in that, The dynamic adjustment strategy includes: In free-form surface conditions, the feed rate should be adjusted first to keep the spindle power within the high-efficiency envelope; Under variable cutting depth conditions, the cutting depth is adjusted first, and the feed rate fluctuation is compensated by model prediction control. In intermittent cutting conditions, an acceleration decay command is generated to suppress the impact response of the feed axis.

8. The method for dynamic coordinated speed control of machine tool spindle and feed axis according to claim 7, characterized in that, The dynamic adjustment strategy also includes implementing a differentiated collaborative adjustment strategy based on margin partitioning, specifically: If M > 20% * P m (ω s Once the area is identified as a safe zone, the feed rate is increased first to maximize the material removal rate, and then the depth of cut is increased only when the feed rate reaches its upper limit. If 10%*P m (ω s ) < M < 20% * P m (ω s This area is designated as a warning zone, and the cutting force is adjusted proportionally with the goal of maintaining a constant cutting force. Freeze depth; Where v represents the feed speed of the feed axis, v c F represents the current feed rate of the feed axis. s F represents the target cutting force. c Indicates the current cutting force; If M < 10% * P m (ω s If the area is identified as a danger zone, spindle protection will be prioritized, and the feed rate will be forcibly reduced to 80% of the current value. If the margin is still insufficient, the depth of cut will be reduced to 90% of the current value simultaneously.

9. A dynamic coordinated speed control system for a machine tool spindle and feed axis, used to implement the dynamic coordinated speed control method for a machine tool spindle and feed axis as described in any one of claims 1-8, characterized in that, include: Spindle sensor module: used to collect spindle power, torque, and speed in real time; Feed axis sensor module: used to collect feed axis cutting force, acceleration, and displacement in real time; Data processing unit: used for filtering and noise reduction and generating joint state vectors, and also used to establish the spindle energy transfer model and feed axis load-motion model, and to build a strong correlation coupling relationship between the two through cutting power; Margin Calculation and Operating Condition Identification Module: Used to calculate spindle margin and time correction amount, and identify sudden changes in operating conditions; Collaborative strategy generation module: Generates feed axis adjustment commands based on model predictive control; Instruction execution module: converts adjustment instructions into servo drive signals; Closed-loop optimization module: Evaluates control performance and updates model parameters and strategies.

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