Multi-spindle cooperative control method and system for six-head full-cover tool magazine machine tool

Abstract

The invention discloses a multi-spindle cooperative control method and system of a six-head full-cover tool magazine machine tool, relates to the technical field of machine tool control, and adopts bus absolute value closed-loop control logic to formulate a four-dimensional coupling motion control strategy of heat, vibration, geometric accuracy and material cutting characteristics. Comprising main shaft rotating speed adjustment, moving speed adjustment, coordinate compensation and cutting parameter adjustment. The edge calculation module is combined with a four-dimensional coupling motion control strategy to generate a control instruction, the cloud end iteratively updates a database and an algorithm weight factor and issues global optimization parameters, and finally the control instruction is issued to an actuator of a machine tool through a bus absolute value control system. According to the method, dynamic allocation of the multi-spindle machining subtasks is achieved through the three-dimensional dynamic weighted genetic algorithm, algorithm weight factors are adjusted in combination with real-time data such as spindle health states and lathe bed shock absorption characteristics, loads of all spindles are effectively balanced, and the adaptability of a task allocation scheme and an actual machining scene is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of machine tool control, in particular to a multi-spindle collaborative control method and system for a six-spindle full-cover tool magazine machine tool. BACKGROUND

[0002] The six-spindle full-cover tool magazine machine tool has been widely used in the field of precision part machining due to the advantages of multi-spindle configuration, but its multi-spindle collaborative control technology still has many deficiencies. The traditional multi-spindle task allocation mostly adopts a static allocation mode, which does not combine the real-time health status of the spindle, the physical characteristics of the machine tool bed, and the cutting response law of the machining material, which easily leads to uneven spindle load, machining precision fluctuation, and other problems.

[0003] At the motion control level, the existing technology mostly adopts a single geometric precision compensation method, ignoring the influence of bed shock absorption characteristics, thermal distribution changes, and machining material cutting characteristics on machining precision, making it difficult to meet the demand for high-precision machining. At the same time, the traditional genetic algorithm has fixed weight factors in task allocation, which cannot be dynamically adjusted according to real-time running data of the machine tool, and the algorithm optimization effect and the adaptability to actual machining scenes are poor.

[0004] In addition, the existing control system does not deeply integrate tool life cycle management, cooling and lubrication system regulation and control, and multi-spindle collaborative control, lacks preventive prediction for tool replacement, and the adjustment of cooling and lubrication parameters does not combine spindle load and machining material characteristics, further reducing the continuity and stability of multi-spindle machining. The collaborative application of cloud and edge computing is also weak, making it difficult to achieve global iterative optimization of multi-machine tool data, limiting the overall performance improvement of the control system. SUMMARY

[0005] To solve the above technical problems, the present application provides a multi-spindle collaborative control method and system for a six-spindle full-cover tool magazine machine tool. The following technical solutions are adopted: The multi-spindle collaborative control method for a six-spindle full-cover tool magazine machine tool includes the following steps: Step 1, build a machining material, tool, and spindle performance matching database, and input machining material cutting parameters, tool types, life, adaptability, spindle performance parameters, and physical characteristic parameters of the bed material that the machine tool is adapted to; Step 2, import the three-dimensional model of the workpiece to be machined, and decompose the workpiece into several machining sub-tasks through a process analysis algorithm, label the process features for each machining sub-task, and add precision constraints and energy consumption constraints; Step 3, collect full-dimensional running data of the machine tool, and transmit the full-dimensional running data to the edge computing module; Step 4, start a three-dimensional dynamic weighted genetic algorithm, take the spindle health status, machining precision requirement, energy consumption constraint, and bed body shock absorption characteristics as the dynamic weight factors of the algorithm, and generate a six-spindle machining sub-task allocation scheme; Step 5, adopt a bus absolute value closed-loop control logic, formulate a four-dimensional coupled motion control strategy for heat, vibration, geometric precision, and material cutting characteristics, including spindle speed adjustment, moving speed adjustment, coordinate compensation, and cutting parameter adjustment; Step 6, the edge computing module generates control instructions in combination with the four-dimensional coupled motion control strategy, iteratively updates the database and algorithm weight factors in the cloud, and issues global optimization parameters, and finally issues control instructions to the actuators of the machine tool through the bus absolute value control system.

[0006] Optionally, in step 1, the physical properties of the bed body material include shock absorption coefficient, thermal conductivity coefficient, and thermal expansion coefficient, the parameters of the cooling system include spindle refrigeration system refrigeration capacity, flow rate, and pressure, and the parameters of the lubrication system include lubrication pump capacity, flow rate, and pressure.

[0007] Optionally, in step 2, the process characteristics include machining material, precision level, machining type, and cutting load range, the precision constraint index is set according to the axis motion positioning accuracy and repeat positioning accuracy of the machine tool, and the energy consumption constraint index is set according to the total power and spindle power of the machine tool.

[0008] Optionally, in step 4, the spindle health status weight of the three-dimensional dynamic weighted genetic algorithm is adjusted in real time according to the vibration, temperature, and current data of the spindle, the precision weight is set according to the precision constraint of the sub-task, the energy consumption weight is dynamically optimized according to the total power of the machine tool and the spindle load characteristics, the bed body shock absorption characteristic weight is adjusted according to the real-time vibration data of the bed body, and the machining material cutting characteristic weight is set according to the cutting response law of different materials.

[0009] Optionally, in step 4, the three-dimensional dynamic weighted genetic algorithm generates a six-spindle machining sub-task allocation scheme, which includes the following sub-steps: Step 41, construct a core fitness function: ; Where F is the fitness value of the task allocation scheme, is the spindle health status weight, is the machining precision weight, is the energy consumption weight, is the bed body shock absorption characteristic weight, is the machining material cutting characteristic weight, H is the spindle health status score, P is the machining precision compliance score, E is the energy consumption optimization score, S is the bed body shock absorption adaptation score, and M is the machining material cutting characteristic adaptation score. Step 42, genetic algorithm iteration: initialize to generate N random task allocation scheme as the initial population, through the roulette method selection, 0.7-0.9 probability single-point crossover, 0.01-0.05 probability random mutation operation iteration, when the iteration number reaches 100 generations or the fitness value converges, select the individual with the highest F value as the six-spindle machining sub-task allocation scheme.

[0010] Optionally, in step 5, the four-dimensional coupled motion control strategy includes adjusting the running speed of the spindle and the fast empty running moving speed according to the shock absorption characteristics of the natural marble bed, performing thermal compensation on the three-axis motion coordinates according to the thermal distribution data and thermal conductivity coefficient of the natural marble bed, pre-compensating the geometric accuracy according to the real-time detection data of the perpendicularity between the shafts, and dynamically adjusting the cutting parameters according to the cutting response law of different machining materials.

[0011] Optionally, in step 6, the control instructions generated by the edge computing module include spindle motion parameters, tool changing trigger threshold, lubrication and cooling basic parameters, and precision compensation coefficients. The cloud combines the operation data of multiple machine tools to iteratively update the multi-dimensional dynamic database and algorithm weight factor.

[0012] Optionally, it also includes a full life cycle tool management mechanism. The remaining life of the tool is predicted according to the cutting frequency, wear and tear, and machining material characteristics. The tool is adjusted to a temperature range matching the spindle refrigeration temperature by the machine tool cooling machine, and the tool is changed.

[0013] Optionally, it also includes a full-dimensional precision compensation mechanism. When the positioning accuracy deviation exceeds the set deviation threshold, the compensation parameters are calculated by combining vibration, thermal distribution, current and material cutting data, the spindle cutting parameters and motion coordinates are adjusted, and the three-stage filtration and two-stage sedimentation filtration of the cooling circulation system are started.

[0014] The multi-spindle collaborative control system of the six-head full-cover tool magazine machine tool, the system includes a data acquisition module, a multi-dimensional dynamic database module, a task allocation calculation module, a control strategy generation module, an edge computing module, a cloud optimization module and a bus absolute value control execution module; The data acquisition module realizes real-time sensing and acquisition of full-dimensional data of the machine tool operation; The multi-dimensional dynamic database module constructs a multi-dimensional data resource pool related to machine tool processing, and completes storage, dynamic update and rapid retrieval of multi-dimensional data; The task allocation calculation module realizes intelligent optimization and allocation of multi-spindle machining tasks through algorithms, and generates an optimal task allocation scheme in combination with machine tool hardware characteristics and processing requirements; The control strategy generation module formulates a multi-spindle collaborative motion control logic adapted to the machine tool hardware according to the task allocation scheme; The edge computing module converts the control strategy into specific executable control instructions; The cloud optimization module relies on the operation data of multiple machine tools to realize the iterative optimization of global models and algorithms, and updates the database parameters and algorithm weight factors. The bus absolute value control execution module converts the control instructions into action instructions of the machine tool actuators and completes the execution.

[0015] In summary, the present application has at least one of the following beneficial technical effects: The present application can provide a multi-spindle collaborative control method and system for a six-head full-cover tool magazine machine tool, realize dynamic distribution of multi-spindle machining sub-tasks through a three-dimensional dynamic weighted genetic algorithm, adjust algorithm weight factors in combination with real-time data such as spindle health status and bed vibration absorption characteristics, effectively balance the load of each spindle, and improve the adaptability of the task allocation scheme to the actual machining scene. The four-dimensional coupled motion control strategy of heat, vibration, geometric precision, and material cutting characteristics comprehensively considers the physical characteristics of the bed and the cutting response law of the machining material, realizes multi-dimensional precision compensation and parameter adjustment, and significantly improves the machining precision of the machine tool. The combination of tool life cycle management, full-dimensional precision compensation mechanism, and multi-spindle collaborative control realizes preventive replacement of tools and real-time compensation of machining precision, reduces the number of processing interruptions, and improves processing continuity. The dual-layer architecture of edge computing and cloud optimization is adopted, the edge computing module realizes the rapid generation and delivery of control instructions, and the cloud completes the global iterative optimization of multiple machine tool data, taking into account the real-time performance and overall optimization capability of the control system. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is a flowchart of the multi-spindle collaborative control method of the six-head full-cover tool magazine machine tool of the present application; Figure 2 is a schematic diagram of the multi-spindle collaborative control system architecture of the six-head full-cover tool magazine machine tool of the present application; Figure 3 is a schematic diagram of the multi-spindle collaborative control system field device layer architecture of the six-head full-cover tool magazine machine tool of the present application; Figure 4 is a schematic diagram of the six-head full-cover tool magazine machine tool of the present application.

[0017] Mark 1, data acquisition module; 2, multi-dimensional dynamic database module; 3, task allocation calculation module; 4, control strategy generation module; 5, edge computing module; 6, cloud optimization module; 7, bus absolute value control execution module; 81, No. 1 spindle; 86, No. 6 spindle; 9, bed. DETAILED DESCRIPTION

[0018] The present application will be further described in detail below with reference to the accompanying drawings.

[0019] The embodiment of the application discloses a multi-spindle cooperative control method and system of a six-spindle full-cover tool magazine machine tool.

[0020] Referring to Figures 1-4 , referring to Figure 1 , embodiment 1, the multi-spindle cooperative control method of the six-spindle full-cover tool magazine machine tool comprises the following steps: Step 1, a processing material, tool, spindle performance matching database is constructed, and processing material cutting parameters, tool types, service life, adaptability, spindle performance parameters and physical property parameters of the bed body material are inputted; Step 2, a three-dimensional model of a workpiece to be processed is imported, the workpiece is disassembled into a plurality of processing sub-tasks through a process analysis algorithm, process features are labeled for each processing sub-task, and precision constraints and energy consumption constraint indexes are added; Step 3, full-dimension running data of the machine tool are collected, and the full-dimension running data are transmitted to an edge computing module; Step 4, a three-dimensional dynamic weighted genetic algorithm is started, the spindle health state, processing precision requirement, energy consumption constraint and bed body shock absorption characteristic are taken as dynamic weight factors of the algorithm, and a processing sub-task distribution scheme of six spindles is generated; Step 5, a four-dimensional coupling motion control strategy of heat, vibration, geometric precision and material cutting characteristics is formulated by using a bus absolute value closed-loop control logic, including spindle speed adjustment, moving speed adjustment, coordinate compensation and cutting parameter adjustment; Step 6, the edge computing module generates control instructions in combination with the four-dimensional coupling motion control strategy, the cloud iteratively updates the database and the algorithm weight factor and issues global optimization parameters, and finally the control instructions are issued to the actuators of the machine tool through the bus absolute value control system.

[0021] In step 1 of embodiment 2, the physical properties of the bed body material include a shock absorption coefficient, a heat conduction coefficient and a thermal expansion coefficient, parameters of the cooling system include spindle refrigeration system refrigeration capacity, flow and pressure, and parameters of the lubrication system include lubrication pump capacity, flow and pressure.

[0022] In step 2 of embodiment 3, the process features include processing material, precision grade, processing type and cutting load interval, the precision constraint index is set according to the axis motion positioning accuracy and the repeat positioning accuracy of the machine tool, and the energy consumption constraint index is set according to the total power and the spindle power of the machine tool.

[0023] By adopting the above technical solutions, a database matching machining materials, cutting tools, and spindle performance is constructed. This involves digitizing and integrating the cutting characteristics of machining materials suitable for the machine tool, tool performance parameters, spindle operating parameters, and the physical properties of the machine bed material. It establishes a correlation mapping relationship between these elements, providing fundamental data support for subsequent task allocation and control strategy formulation. Simultaneously, the operating parameters of the cooling and lubrication systems are incorporated, enabling the database to cover the characteristics of the machine tool's core auxiliary systems and further improving the data dimensions. By collecting comprehensive machine tool operating data and dynamically reflecting the actual working conditions of the machine tool through real-time sensing of its operating status, accuracy indicators, and spindle health status, subsequent algorithm calculations and control strategies can be tailored to the real-time operating conditions of the machine tool, avoiding decision-making biases based on static data.

[0024] Importing the 3D model of the workpiece to be processed and breaking it down into processing sub-tasks involves using a process analysis algorithm to decompose complex processing requirements into unit tasks that can be executed by a single spindle. By labeling process characteristics such as processing material, accuracy level, processing type, and cutting load range, the processing attributes of each sub-task are clarified. Accuracy constraints are set based on the machine tool's axis motion positioning accuracy and repeatability accuracy, and energy consumption constraints are set based on the machine tool's total power and spindle power. This transforms the machine tool's hardware performance limits into boundary conditions for task execution, ensuring that the allocation of sub-tasks and the processing process do not exceed the machine tool's accuracy and energy consumption capabilities.

[0025] The core of the three-dimensional dynamic weighted genetic algorithm for generating subtask allocation schemes lies in leveraging the iterative optimization characteristics of genetic algorithms. Spindle health status, machining accuracy requirements, energy consumption constraints, and bed vibration absorption characteristics are used as dynamic weighting factors and integrated into the algorithm's fitness evaluation system. Through dynamic adjustment of these weighting factors, the algorithm prioritizes allocation schemes that best suit the spindle health status, meet machining accuracy requirements, comply with energy consumption constraints, and match the bed vibration absorption characteristics during iteration. This achieves balanced load distribution across the six spindles, maximizes the utilization of the machine tool's machining performance, and avoids problems such as single spindle overload or substandard machining accuracy.

[0026] A four-dimensional coupled motion control strategy is formulated using bus absolute value closed-loop control logic. This strategy leverages the high-precision closed-loop characteristics of bus absolute value control to comprehensively consider the impacts of four dimensions on machining: heat, vibration, geometric accuracy, and material cutting characteristics. By adjusting the spindle speed and traverse speed to adapt to the bed's vibration absorption characteristics, vibration interference is reduced. Geometric accuracy deviations are corrected through coordinate compensation. Cutting parameters are adjusted to adapt to the cutting characteristics of different machining materials. Simultaneously, thermal compensation is performed based on the bed's thermal conduction and thermal expansion characteristics. Ultimately, this approach counteracts the interference of various factors on machining accuracy, achieving coordinated synchronization of multi-spindle movements and precise control of machining accuracy.

[0027] The edge computing module, combined with a four-dimensional coupled motion control strategy, generates control commands. Leveraging the local data processing capabilities of edge computing, it quickly transforms the control strategy into specific executable commands for the machine tool, ensuring the real-time performance and field adaptability of the control commands. The cloud-based iterative updates of the database and algorithm weight factors integrate the operating data of multiple machine tools for global analysis, optimizing data matching relationships and algorithm weight settings. These global optimization parameters are then distributed to the edge computing module. Finally, the commands are sent to the actuators via a bus absolute value control system, achieving a closed loop of local real-time control and global iterative optimization. This ensures the accurate implementation of control commands and continuous optimization of the control system.

[0028] In Example 4, step 4, the spindle health status weight of the three-dimensional dynamic weighted genetic algorithm is adjusted in real time based on the vibration, temperature and current data of the spindle; the accuracy weight is set according to the accuracy constraints of the sub-task; the energy consumption weight is dynamically optimized based on the total power of the machine tool and the spindle load characteristics; the bed vibration absorption characteristic weight is adjusted based on the real-time vibration data of the bed; and the machining material cutting characteristic weight is set according to the cutting response law of different materials.

[0029] In Example 5, step 4, the three-dimensional dynamic weighted genetic algorithm for generating a six-spindle machining subtask allocation scheme includes the following sub-steps: Step 41, Construct the core fitness function: ; Where F is the fitness value of the task allocation scheme. As the weight of the main axis health status, As a weight for machining accuracy, As energy consumption weight, Weighting of the bed's shock absorption characteristics. The weights for the cutting characteristics of the machining material are: H is the spindle health status score, P is the machining accuracy compliance score, E is the energy consumption optimization score, S is the bed vibration absorption adaptation score, and M is the machining material cutting characteristics adaptation score. Step 42, Genetic Algorithm Iteration: Initialize and generate N random task allocation schemes as the initial population. Iterate through roulette wheel selection, single-point crossover with a probability of 0.7-0.9, and random mutation with a probability of 0.01-0.05. When the number of iterations reaches 100 generations or the fitness value converges, select the individual with the highest F value as the six-spindle machining sub-task allocation scheme.

[0030] By adopting the above technical solution, the spindle health status weight is adjusted in real time based on the spindle's vibration, temperature, and current data. It reflects the spindle's real-time health status by collecting core status parameters such as vibration frequency, temperature, and operating current during spindle operation. When the spindle vibration amplitude increases, the temperature exceeds a threshold, or the current becomes abnormal, the spindle's suitability for performing high-load machining sub-tasks decreases. By lowering the spindle health status weight, the algorithm reduces the workload on the spindle during task allocation, preventing spindle failure or decreased machining accuracy due to overload.

[0031] The accuracy weights are set based on the accuracy constraints of the subtasks. They are determined by combining hardware accuracy indicators such as the machine tool's axis motion positioning accuracy and repeatability accuracy, and assigning differentiated weights to different machining subtasks according to their accuracy requirements. For subtasks with high accuracy requirements, the machining accuracy weights are increased, so that the algorithm prioritizes allocating them to spindles with small positioning accuracy deviations and high operational stability during the iteration process, ensuring that the machining accuracy of the subtasks meets the standards.

[0032] The energy consumption weight is dynamically optimized based on the machine tool's total power and spindle load characteristics. It is based on the machine tool's total power limit and the spindle's power characteristics. By analyzing the ratio of the spindle's real-time load power to the total power, the energy consumption weight is adjusted. When the overall machine tool load is high, increasing the energy consumption weight will cause the algorithm to tend to allocate subtasks to spindles with lower load rates, achieving a balanced distribution of energy consumption across spindles, avoiding overload of the machine tool's total power, and improving energy utilization efficiency.

[0033] The weight of the bed's vibration absorption characteristics is adjusted based on real-time vibration data of the bed, reflecting its actual vibration absorption effect using vibration monitoring data. When the vibration amplitude in a certain area of ​​the bed is large, the weight of the bed's vibration absorption characteristics corresponding to that area is reduced. The algorithm will reduce the machining load on the spindle in that area when allocating tasks, using the bed's vibration absorption characteristics to offset the impact of machining vibration on accuracy and ensure machining stability.

[0034] The cutting characteristic weights for different machining materials are set based on their cutting response characteristics. This involves considering the differences in cutting parameter requirements and cutting response between metals like copper and aluminum, engineering plastics like PC and acrylic, and composite materials like glass fiber and carbon fiber, assigning corresponding weights to different material machining sub-tasks. For materials with high cutting difficulty and sensitive response, the cutting characteristic weights are increased, and the algorithm allocates these weights to spindle and tool combinations adapted to that material, optimizing machining results.

[0035] The fitness function is constructed by weighted summation of multi-dimensional scoring indicators to comprehensively evaluate the merits of each task allocation scheme. Among them, the spindle health status score, machining accuracy achievement score, energy consumption optimization score, bed vibration absorption adaptation score, and machining material cutting characteristic adaptation score correspond to the five optimization objectives of machine tool healthy operation, accuracy achievement, energy consumption optimization, stability assurance, and material adaptation, respectively. The weighting factor adjusts the priority of each objective according to the actual working conditions. The higher the fitness value, the more closely the allocation scheme matches the machining requirements and hardware characteristics of the machine tool.

[0036] Genetic algorithms iteratively optimize task allocation schemes by simulating the selection, crossover, and mutation processes of biological evolution. Initialize the population: Generate N random task assignment schemes as the initial population, so that the algorithm has a sufficient optimization base to cover different task assignment possibilities.

[0037] Selection Operation: The roulette wheel method is used to select individuals with high fitness values ​​to enter the next generation, following the principle of survival of the fittest, retaining the best task allocation schemes and eliminating schemes with poor fitness.

[0038] Crossover operation: Selected individuals are crossovered at a probability of 0.7-0.9, which integrates the task allocation logic of different high-quality solutions to generate a new solution that combines the advantages of both parents and expands the search space of the algorithm.

[0039] Mutation operation: Randomly mutate individuals after crossover with a probability of 0.01-0.05 to avoid the algorithm getting trapped in local optima and ensure that the globally optimal task allocation scheme can be found.

[0040] Termination condition: When the number of iterations reaches 100 generations or the fitness value converges, it means that the algorithm has found a stable optimal solution. At this time, the individual with the highest F value is selected as the final six-spindle machining subtask allocation scheme to ensure the optimality and stability of the scheme.

[0041] In Example 6, step 5, the four-dimensional coupled motion control strategy includes adjusting the spindle's operating speed and rapid idle movement speed based on the shock absorption characteristics of the natural marble bed, performing thermal compensation on the three-axis motion coordinates based on the thermal distribution data and thermal conductivity coefficient of the natural marble bed, performing pre-compensation on geometric accuracy based on real-time detection data of inter-axis perpendicularity, and dynamically adjusting cutting parameters based on the cutting response laws of different processing materials.

[0042] In Example 7, step 6, the control commands generated by the edge computing module include spindle motion parameters, tool change trigger threshold, lubrication and cooling basic parameters, and accuracy compensation coefficient. The cloud combines the operating data of multiple machine tools to iteratively update the multi-dimensional dynamic database and algorithm weight factors.

[0043] By adopting the above technical solution, the four-dimensional coupled motion control strategy takes the hardware physical characteristics of the machine tool and the cutting law of the processed material as its core basis. Through multi-dimensional parameter adjustment and compensation, it offsets various interference factors in the machining process, realizes the coordination of multi-spindle motion and ensures machining accuracy. The specific principle is as follows: Natural marble machining beds possess excellent shock absorption and high-speed stability, with their shock absorption capacity varying depending on the vibration frequency and amplitude. Adjusting the spindle speed and rapid traverse speed based on the bed's shock absorption characteristics matches the vibration frequency generated by the spindle to the bed's absorption frequency range, maximizing the bed's shock absorption to counteract vibration interference from the spindle. When excessive spindle speed can easily cause resonance, reducing the speed or adjusting the rapid traverse speed prevents vibration from accumulating and affecting machining accuracy, while simultaneously utilizing the bed's shock absorption characteristics to ensure stability during high-speed machining.

[0044] Natural marble bed frames possess physical properties such as being unaffected by temperature and having a low coefficient of thermal expansion. However, during processing, variations in ambient temperature and heat dissipation from the spindle operation can still cause localized differences in heat distribution. Thermal compensation for the three-axis motion coordinates is performed based on the bed's thermal distribution data and thermal conductivity coefficient. This involves calculating the thermal deformation in different areas of the bed, deriving the impact of thermal deformation on the three-axis motion coordinates using the thermal conductivity coefficient, and then correcting the motion command coordinates of the three axes to offset positional deviations caused by thermal deformation and ensure accurate coordinate positioning.

[0045] The perpendicularity between machine tool axes is a core indicator affecting machining geometric accuracy. There are established perpendicularity deviation standards between the X and Y axes, and between the Z and X / Y axes. Pre-compensation for geometric accuracy based on real-time perpendicularity monitoring data involves acquiring the actual perpendicularity deviation value in real time and adding a reverse compensation amount in advance to the spindle motion command. This allows the actual spindle motion trajectory to offset the geometric error caused by the perpendicularity deviation, ensuring that the geometric accuracy of the machined workpiece meets requirements.

[0046] The cutting performance of different materials varies significantly. Metals, engineering plastics, and composite materials all have different cutting resistance, tool wear rates, and surface finish requirements. By dynamically adjusting cutting parameters based on the cutting response characteristics of different materials, and combining these parameters with the cutting parameter requirements of various materials, the spindle speed, feed rate, and depth of cut are matched to ensure the cutting process adapts to the material's characteristics. This guarantees machining efficiency while avoiding tool damage or substandard workpiece quality caused by improper cutting parameters.

[0047] The edge computing module, acting as the machine tool's local computing core, directly interfaces with the machine tool's real-time operating data and four-dimensional coupled motion control strategy. The control commands it generates include spindle motion parameters, tool change trigger thresholds, basic lubrication and cooling parameters, and accuracy compensation coefficients. This transforms the abstract control strategy into specific parameter commands that the machine tool actuators can recognize. Spindle motion parameters are set based on the spindle speed and velocity adjustment requirements in the four-dimensional coupled strategy; the tool change trigger threshold is set in conjunction with tool life and machining progress; the basic lubrication and cooling parameters are set to match the spindle load and the cooling requirements of the machining material; and the accuracy compensation coefficient is set based on the calculation results of various compensation amounts. This ensures that the commands can directly guide the machine tool's actual machining actions. Simultaneously, by utilizing the local processing capabilities of edge computing, the real-time generation and issuance of commands are guaranteed, avoiding the latency issues associated with long-distance data transmission.

[0048] The cloud platform has the capability to integrate operational data from multiple machine tools. It iteratively updates a multi-dimensional dynamic database and algorithm weight factors based on this data, aggregating machining data, equipment status data, and task allocation scheme effectiveness data from different machine tools for global data analysis and pattern mining. For the multi-dimensional dynamic database, the cloud supplements it with cutting response data for different machine tools machining various materials, measured data on actual tool life, and data on changes in spindle health status, enriching the database's dimensions and accuracy. For algorithm weight factors, the cloud analyzes the optimization effects of different weight settings in various machining scenarios, and adjusts the setting rules for weight factors such as spindle health status and machining accuracy requirements based on the task allocation results of multiple machine tools, ensuring that the algorithm's optimization direction better aligns with the overall needs of actual machining. The optimized database and weight factors are then distributed to the edge computing modules of each machine tool, enabling global iterative upgrades of the control system. This allows the control strategy of a single machine tool to draw on the operational experience of multiple machine tools, improving overall control performance.

[0049] Example 8 also includes a full life-cycle tool management mechanism. The remaining tool life is predicted based on the number of cutting operations, wear, and material properties. The tool is replaced after the machine tool chiller is used to adjust the new tool to the range that matches the spindle cooling temperature.

[0050] Example 9 also includes a full-dimensional accuracy compensation mechanism. When the positioning accuracy deviation exceeds the set deviation threshold, the compensation parameters are calculated by combining vibration, heat distribution, current and material cutting data, the spindle cutting parameters and motion coordinates are adjusted, and the three-stage filtration and two-stage sedimentation filtration of the cooling circulation system are started.

[0051] By adopting the above technical solutions, the full life cycle tool management mechanism takes the actual usage status of the tool and the temperature control characteristics of the machine tool as the core basis. It achieves preventive tool replacement by predicting the remaining tool life, and at the same time uses the cooling system to match the temperature of the tool and the spindle to ensure the machining accuracy after tool replacement. The full-dimensional accuracy compensation mechanism uses the machine tool's accuracy threshold as the trigger condition, calculates compensation parameters by combining multi-dimensional operating data, and coordinates with the cooling circulation system to optimize the machining environment, thereby achieving real-time correction of machining accuracy. The specific principle is as follows: When the positioning accuracy deviation exceeds a set deviation threshold, a compensation mechanism is activated. This threshold is determined based on the machine tool's axis motion positioning accuracy and repeatability, representing the minimum standard for ensuring machining accuracy. Compensation parameters are calculated by combining vibration, heat distribution, current, and material cutting data. These data reflect the interference of different factors on accuracy: vibration data reflects the impact of bed and spindle vibration on machining position offset; heat distribution data reflects the impact of bed and spindle thermal deformation on coordinates; current data reflects the impact of spindle load changes on motion accuracy; and material cutting data reflects the impact of different material cutting resistances on tool path. By integrating this data, the compensation amount for spindle motion coordinates and cutting parameters can be accurately calculated, fundamentally offsetting accuracy deviations caused by various interference factors.

[0052] Adjusting the spindle cutting parameters and motion coordinates based on compensation parameters involves correcting the spindle's speed, feed rate, and other cutting parameters, as well as the X / Y / Z axis motion coordinates. This brings the actual spindle trajectory and cutting state back to the required accuracy range, directly correcting accuracy deviations. Activating the three-stage filtration and two-stage sedimentation filtration of the cooling circulation system is crucial because impurities in the cutting fluid can splash onto the workpiece surface, affecting surface finish. Furthermore, impurities adhering to the tool and guideways can further exacerbate accuracy deviations. The three-stage filtration and two-stage sedimentation filtration effectively remove impurities from the cutting fluid, optimizing its cleanliness. This reduces the impact of impurities on the workpiece surface and decreases wear on the tool and moving parts, thus contributing to the stability of machining accuracy from a machining environment perspective.

[0053] Example 10: Multi-spindle collaborative control system for a six-head full-coverage tool magazine machine tool. The system includes a data acquisition module 1, a multi-dimensional dynamic database module 2, a task allocation calculation module 3, a control strategy generation module 4, an edge computing module 5, a cloud optimization module 6, and a bus absolute value control execution module 7. Data acquisition module 1 enables real-time sensing and acquisition of all dimensions of machine tool operation data; The multi-dimensional dynamic database module 2 constructs a multi-dimensional data resource pool related to machine tool processing, and completes the storage, dynamic updating and fast retrieval of multi-dimensional data; The task allocation calculation module 3 uses algorithms to achieve intelligent optimization allocation of multi-spindle machining tasks, and generates the optimal task allocation scheme by combining machine tool hardware characteristics and machining requirements. Based on the task allocation scheme, the control strategy generation module 4 formulates multi-spindle collaborative motion control logic adapted to the machine tool hardware; Edge computing module 5 translates the control strategy into specific executable control instructions; The cloud-based optimization module 6 relies on the operating data of multiple machine tools to achieve iterative optimization of the global model and algorithm, and update database parameters and algorithm weight factors; The bus absolute value control execution module 7 converts the control commands into action commands for the machine tool actuator and completes the issuance and execution.

[0054] The following specific embodiments illustrate the implementation principle of the present invention: Taking the machining of a mobile phone camera assembly using a six-head full-coverage tool magazine as an example, this assembly consists of an aluminum alloy metal base and a PC plastic lens bracket. It must simultaneously meet the energy consumption constraints of a 0.005mm repeatability and a total machine power of 33.0kW. Based on the system modules marked 1 to 7 in the attached diagram, the multi-spindle collaborative control method is explained in detail: System module deployment: The multi-spindle collaborative control system for a six-head full-coverage tool magazine machine tool used in this embodiment includes a data acquisition module 1, a multi-dimensional dynamic database module 2, a task allocation calculation module 3, a control strategy generation module 4, an edge computing module 5, a cloud optimization module 6, and a bus absolute value control execution module 7. The data acquisition module 1 integrates a laser interferometer, vibration sensor, temperature sensor, current sensor, and pressure / flow sensor; the multi-dimensional dynamic database module 2 is deployed on the machine tool's local industrial control computer; and the cloud optimization module 6 is deployed on a CNC cloud server. All modules interact with each other via the TCP / IP protocol.

[0055] Multi-spindle collaborative control method: Step 1: Construct a multi-dimensional dynamic database: The multi-dimensional dynamic database module 2 inputs the cutting parameters of aluminum alloy and PC plastic that are compatible with the machine tool. The cutting speed of aluminum alloy is set to 40,000 rpm, and the cutting speed of PC plastic is set to 30,000 rpm. It also inputs the tool type, cutting life, and compatibility of Φ3.175 end mills and Φ8 drills. Φ3.175 end mills are compatible with PC plastic machining, and Φ8 drills are compatible with aluminum alloy machining. The performance parameters of spindle 8 are input, including spindle 8 power of 4.0kW and spindle 8 cooling temperature of 26 to 30 degrees Celsius. The physical property parameters of the natural marble bed are input, including shock absorption coefficient, thermal conductivity coefficient, and thermal expansion coefficient. At the same time, the cooling system parameters are input, including the cooling capacity of the spindle 8 cooling system of 20L, flow rate of 1.3L / min, and pressure of 0.5Mpa, and the lubrication system parameters, including the lubrication pump capacity of 4L, flow rate of 120 to 150cc / min, and pressure of 1.5 to 1.7MPa. The database construction is completed and the real-time update interface is enabled.

[0056] Step 2: Decompose the processing sub-tasks and set constraints: Import the 3D model of the mobile phone camera component and decompose it into two machining sub-tasks using a process analysis algorithm: drilling the aluminum alloy base and milling the PC plastic bracket. The drilling sub-task is labeled with the machining material as aluminum alloy, accuracy grade as 0.008mm, machining type as drilling, and cutting load range as high load. The milling sub-task is labeled with the machining material as PC plastic, accuracy grade as 0.005mm, machining type as milling, and cutting load range as low load. Accuracy constraints are set based on the machine tool axis motion positioning accuracy of 0.008mm / 300mm and repeatability of 0.005mm. Energy consumption constraints are set based on the total machine tool power of 33.0kW and the spindle power of 8 as 4.0kW. The energy consumption upper limit for the aluminum alloy drilling sub-task is set to 4.0kW, and the energy consumption upper limit for the PC plastic milling sub-task is set to 2.5kW.

[0057] Step 3: Collect full-dimensional operating data of the machine tool: The data acquisition module 1 acquires X / Y / Z three-axis positioning accuracy data through a laser interferometer, acquires vibration frequency and amplitude data of the natural marble bed and the six spindles 8 through a vibration sensor, acquires cooling temperature of 28 degrees Celsius and bed heat distribution data of the spindles 8 through a temperature sensor, acquires operating current data of the six spindles 8 through a current sensor, and acquires cooling system pressure of 0.5MPa and lubrication system pressure of 1.6MPa through a pressure and flow sensor. All data are transmitted to the edge computing module 5 in real time via TCP / IP protocol.

[0058] Step 4: Generate a six-spindle machining subtask allocation scheme: Task allocation calculation module 3 initiates a three-dimensional dynamic weighted genetic algorithm, using the spindle 8 health status, machining accuracy requirements, energy consumption constraints, and bed vibration absorption characteristics as dynamic weighting factors: The health status weight of spindle 8 is adjusted based on the data of spindle 8 vibration frequency of 30Hz, temperature of 28 degrees Celsius, and current of 8A. The health status weight of spindle 1 is set to 0.3 with vibration frequency of 25Hz, temperature of 27 degrees Celsius, and current of 7A; the health status weight of spindle 6 is set to 0.1 with vibration frequency of 35Hz, temperature of 29 degrees Celsius, and current of 9A. The machining accuracy weight is set based on the accuracy constraints of the sub-tasks. The accuracy requirement for the PC plastic milling sub-task is 0.005mm, and the accuracy weight is set to 0.25. The accuracy requirement for the aluminum alloy drilling sub-task is 0.008mm, and the accuracy weight is set to 0.2. The energy consumption weight is optimized based on the machine tool's total power of 33.0kW and the spindle load characteristics. The current total load power of the machine tool is 15kW, and the energy consumption weight is set to 0.2. The weight of the bed vibration absorption characteristics is adjusted based on the bed vibration amplitude data of 0.01mm, and is set to 0.15; The weight of the cutting characteristics of the processing material is set according to the cutting response law of aluminum alloy and PC plastic, and is set to 0.1.

[0059] Subsequently, the core fitness function F=w1•H+w2•P+w3•E+w4•S+w5•M was constructed, and 50 random task assignment schemes were generated as the initial population. The scheme was iterated through roulette wheel selection, single-point crossover with a probability of 0.8, and random mutation with a probability of 0.03. When the fitness value converged after 80 generations, the scheme with the highest F value was selected: the first spindle was assigned to perform the PC plastic bracket milling sub-task, the second spindle was assigned to perform the aluminum alloy base drilling sub-task, and the remaining spindles were in standby state.

[0060] Step 5: Develop a four-dimensional coupled motion control strategy: Control strategy generation module 4 employs bus absolute value closed-loop control logic to formulate a four-dimensional coupled motion control strategy: Based on the shock absorption characteristics of the natural marble bed, the milling speed of the No. 1 spindle was adjusted to 30,000 rpm and the rapid idle traverse speed was set to 2,500 mm / min. The drilling speed of the No. 2 spindle was adjusted to 40,000 rpm and the rapid idle traverse speed was set to 2,000 mm / min to avoid resonance. Based on the thermal distribution data and thermal conductivity coefficient of the natural marble bed frame, thermal compensation was performed on the motion coordinates of X-axis 220mm, Y-axis 400mm, and Z-axis 165mm, with a compensation amount of 0.002mm. Based on the real-time detection data of interaxial perpendicularity, the perpendicularity between the X-axis and Y-axis is 0.02mm / 500mm, and the geometric accuracy pre-compensation amount is set to 0.001mm; Based on the cutting response characteristics of aluminum alloy and PC plastic, the cutting feed rate of the No. 1 spindle is set to 0.1 mm / r, and the cutting feed rate of the No. 2 spindle is set to 0.08 mm / r.

[0061] Step 6: Generate and issue control commands: The edge computing module 5, combined with the four-dimensional coupled motion control strategy, generates control commands, including spindle motion parameters of 30,000 rpm for the first spindle and 40,000 rpm for the second spindle, tool change trigger threshold of 5,000 cutting cycles for the Φ3.175 milling cutter, basic lubrication and cooling parameters of 1.5 MPa pressure for the lubrication system and 1.3 L / min flow rate for the cooling system, and a precision compensation coefficient of 0.002 mm. The cloud optimization module 6 integrates the operating data of 10 DA-1320LD machine tools, iteratively updates the aluminum alloy cutting parameters and algorithm weight factors in the multi-dimensional dynamic database, adjusts the optimized aluminum alloy cutting speed to 39,000 rpm, reduces the spindle health status weight coefficient by 0.05, and sends the global optimization parameters to the edge computing module 5. The bus absolute value control execution module 7 converts the control commands into action commands for the machine tool actuators and sends them to the X / Y servo motors, Z servo drives, servo tool magazines, lubrication pumps, and chillers. This controls the No. 1 spindle to change to a Φ3.175 end mill and the No. 2 spindle to change to a Φ8 drill bit, thus starting the machining process.

[0062] Supporting mechanisms: Full lifecycle tool management mechanism: The number of cuts of the Φ3.175 milling cutter on the No. 1 spindle is monitored in real time. When the number of cuts reaches 4800, the remaining lifespan is predicted to be 200 cuts. The full life cycle tool management mechanism is activated, and the servo tool magazine is controlled to retrieve a spare Φ3.175 milling cutter. The temperature of the new tool is adjusted to 28 degrees Celsius using the machine tool chiller. After matching the spindle cooling temperature, the tool change is completed.

[0063] Full-dimensional accuracy compensation mechanism: During the machining process, data acquisition module 1 detected a positioning accuracy deviation of 0.009mm for the No. 2 spindle, exceeding the set deviation threshold of 0.008mm. It then activated a full-dimensional accuracy compensation mechanism: combining vibration amplitude of 0.015mm, bed thermal distribution deviation of 0.003mm, spindle current of 9A, and aluminum alloy cutting data, it calculated compensation parameters, adjusted the No. 2 spindle feed rate to 0.07mm / r, compensated the X-axis motion coordinate by 0.001mm, and activated the three-stage filtration and two-stage sedimentation filtration of the cooling circulation system to remove impurities from the cutting fluid, ensuring machining accuracy.

[0064] The above are all preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A multi-spindle collaborative control method for a six-head full-coverage tool magazine machine tool, characterized in that, Includes the following steps: Step 1: Construct a database for matching machining materials, cutting tools, and spindle performance, and input the cutting parameters of the machining materials, tool types, tool life, compatibility, spindle performance parameters, and physical property parameters of the bed material that are compatible with the machine tool. Step 2: Import the 3D model of the workpiece to be processed, and decompose the workpiece into several processing sub-tasks through the process analysis algorithm. Mark the process features of each processing sub-task and add accuracy constraints and energy consumption constraints. Step 3: Collect all-dimensional operating data of the machine tool and transmit the all-dimensional operating data to the edge computing module; Step 4: Start the three-dimensional dynamic weighted genetic algorithm, and use the spindle health status, machining accuracy requirements, energy consumption constraints, and bed vibration absorption characteristics as dynamic weight factors to generate a machining sub-task allocation scheme for the six spindles. Step 5: Using bus absolute value closed-loop control logic, formulate a four-dimensional coupled motion control strategy that considers thermal, vibration, geometric accuracy, and material cutting characteristics, including spindle speed adjustment, traverse speed adjustment, coordinate compensation, and cutting parameter adjustment. Step 6: The edge computing module generates control commands by combining the four-dimensional coupled motion control strategy. The cloud iteratively updates the database and algorithm weight factors and sends out global optimization parameters. Finally, the control commands are sent to the machine tool's actuators through the bus absolute value control system.

2. The multi-spindle collaborative control method for a six-head full-coverage tool magazine machine tool according to claim 1, characterized in that, In step 1, the physical properties of the bed material include the shock absorption coefficient, thermal conductivity coefficient, and thermal expansion coefficient. The parameters of the cooling system include the cooling capacity, flow rate, and pressure of the spindle cooling system. The parameters of the lubrication system include the lubrication pump capacity, flow rate, and pressure.

3. The multi-spindle collaborative control method for a six-head full-coverage tool magazine machine tool according to claim 2, characterized in that, In step 2, the process characteristics include the material to be processed, the precision grade, the type of processing, and the cutting load range. The precision constraint index is set according to the machine tool's axis motion positioning accuracy and repeatability, and the energy consumption constraint index is set according to the machine tool's total power and spindle power.

4. The multi-spindle collaborative control method for a six-head full-coverage tool magazine machine tool according to claim 3, characterized in that, In step 4, the spindle health status weight of the three-dimensional dynamic weighted genetic algorithm is adjusted in real time based on the vibration, temperature and current data of the spindle; the accuracy weight is set according to the accuracy constraints of the sub-task; the energy consumption weight is dynamically optimized based on the total power of the machine tool and the spindle load characteristics; the bed vibration absorption characteristic weight is adjusted based on the real-time vibration data of the bed; and the machining material cutting characteristic weight is set according to the cutting response law of different materials.

5. The multi-spindle collaborative control method for a six-head full-coverage tool magazine machine tool according to claim 4, characterized in that, In step 4, the three-dimensional dynamic weighted genetic algorithm generates a six-spindle machining subtask allocation scheme, which includes the following sub-steps: Step 41, Construct the core fitness function: ； Where F is the fitness value of the task allocation scheme. As the weight of the main axis health status, As a weight for machining accuracy, As energy consumption weight, Weighting of the bed's shock absorption characteristics. The weights for the cutting characteristics of the machining material are: H is the spindle health status score, P is the machining accuracy compliance score, E is the energy consumption optimization score, S is the bed vibration absorption adaptation score, and M is the machining material cutting characteristics adaptation score. Step 42, Genetic Algorithm Iteration: Initialize and generate N random task allocation schemes as the initial population. Iterate through roulette wheel selection, single-point crossover with a probability of 0.7-0.9, and random mutation with a probability of 0.01-0.

05. When the number of iterations reaches 100 generations or the fitness value converges, select the individual with the highest F value as the six-spindle machining sub-task allocation scheme.

6. The multi-spindle collaborative control method for a six-head full-coverage tool magazine machine tool according to claim 5, characterized in that, In step 5, the four-dimensional coupled motion control strategy includes adjusting the spindle's operating speed and rapid idle travel speed based on the shock absorption characteristics of the natural marble bed, performing thermal compensation on the three-axis motion coordinates based on the thermal distribution data and thermal conductivity coefficient of the natural marble bed, performing pre-compensation on geometric accuracy based on real-time detection data of inter-axis perpendicularity, and dynamically adjusting cutting parameters based on the cutting response laws of different machining materials.

7. The multi-spindle collaborative control method for a six-head full-coverage tool magazine machine tool according to claim 6, characterized in that, In step 6, the control commands generated by the edge computing module include spindle motion parameters, tool change trigger threshold, lubrication and cooling basic parameters, and accuracy compensation coefficient. The cloud combines the operating data of multiple machine tools to iteratively update the multi-dimensional dynamic database and algorithm weight factors.

8. The multi-spindle collaborative control method for a six-head full-coverage tool magazine machine tool according to claim 7, characterized in that, It also includes a full life-cycle tool management mechanism. The remaining tool life is predicted based on the number of cutting operations, wear, and material properties. The tool is replaced after the machine tool chiller is used to adjust the new tool to the range that matches the spindle cooling temperature.

9. The multi-spindle collaborative control method for a six-head full-coverage tool magazine machine tool according to claim 8, characterized in that, It also includes a full-dimensional accuracy compensation mechanism. When the positioning accuracy deviation exceeds the set deviation threshold, it calculates compensation parameters by combining vibration, heat distribution, current and material cutting data, adjusts the spindle cutting parameters and motion coordinates, and starts the three-stage filtration and two-stage sedimentation filtration of the cooling circulation system.

10. A multi-spindle collaborative control system for a six-head fully enclosed tool magazine machine tool, characterized in that: The system for implementing the multi-spindle collaborative control method of the six-head full-coverage tool magazine machine tool as described in claim 9 includes a data acquisition module, a multi-dimensional dynamic database module, a task allocation calculation module, a control strategy generation module, an edge computing module, a cloud optimization module, and a bus absolute value control execution module. The data acquisition module enables real-time sensing and acquisition of all dimensions of machine tool operation data; The multi-dimensional dynamic database module constructs a multi-dimensional data resource pool related to machine tool processing, and completes the storage, dynamic updating and fast retrieval of multi-dimensional data; The task allocation calculation module uses algorithms to intelligently optimize the allocation of multi-spindle machining tasks, and generates the optimal task allocation scheme by combining machine tool hardware characteristics and machining requirements. The control strategy generation module formulates multi-spindle collaborative motion control logic adapted to the machine tool hardware based on the task allocation scheme. The edge computing module translates control strategies into specific, executable control instructions; The cloud-based optimization module relies on the operating data of multiple machine tools to achieve iterative optimization of the global model and algorithm, and update database parameters and algorithm weight factors; The bus absolute value control execution module converts control commands into action commands for the machine tool actuator and completes the issuance and execution.

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