A control method and system of a double-channel high-speed composite engraving and milling machine

Abstract

The application discloses a kind of double-channel high-speed composite engraving and milling machine control method and system, it is related to engraving and milling machine control technical field, start CCD and probe scanning workstation, obtain machine tool shafting basic precision data by laser interferometer, collect environmental temperature, vacuum pressure, compressed air pressure data, generate machine tool initial precision benchmark library and environmental parameter set.The application adopts double-channel collaborative control mode, realizes intelligent decomposition and scheduling of processing task by load vacuum adaptive algorithm, fully develops the processing potential of double-channel equipment, effectively improves overall processing efficiency.Combining with the dynamic adjustment of processing condition workpiece material and bed vibration state, the stability of workpiece clamping is optimized, the processing deviation caused by clamping displacement is reduced, and the machining accuracy is guaranteed.Tool life prediction combines actual wear data and use history, cooperates double-channel cross tool changing scheduling strategy, reduces the time loss of tool changing downtime, reduces the probability of abnormal tool wear, prolongs tool life.

Description

Technical Field

[0001] This invention relates to the field of engraving and milling machine control technology, and in particular to a dual-channel high-speed composite engraving and milling machine control method and system. Background Technology

[0002] As 3C electronic products become increasingly thinner and more precise, the processing of their structural components places higher demands on the precision, efficiency, and collaborative control capabilities of CNC engraving and milling machines. Existing CNC engraving and milling machine control methods mostly employ single-channel control modes, lacking flexibility in task decomposition and failing to fully utilize the processing potential of dual-channel equipment. Regarding vacuum adsorption control, traditional methods simply set the negative pressure value based on workpiece dimensions without dynamically adjusting it in conjunction with the processing load and bed vibration, easily leading to workpiece misalignment and affecting processing accuracy.

[0003] In tool management, existing technologies predict tool life solely based on usage time, without considering actual tool wear. Furthermore, dual-channel tool changing lacks coordinated scheduling, leading to frequent downtime and reduced machining efficiency. Regarding error compensation, traditional compensation models fail to adequately consider the bed's vibration absorption characteristics and ambient temperature variations, offering only simple compensation for axis geometric errors. This is insufficient to offset vibration and thermal errors during high-speed machining, resulting in significant fluctuations in machining accuracy.

[0004] In addition, the parameter adjustments of existing CNC engraving and milling machine control systems are mostly static settings, which cannot optimize parameters such as spindle speed and feed rate in real time according to the processing conditions. At the same time, the application of IoT monitoring is only limited to data acquisition and lacks the ability to dynamically fine-tune processing parameters, making it difficult to achieve intelligent control of the processing process. This restricts the application effect of CNC engraving and milling machines in the field of 3C precision parts processing. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a dual-channel high-speed composite engraving and milling machine control method and system. The technical solution adopted is as follows:

[0006] A control method for a dual-channel high-speed composite engraving and milling machine includes the following steps:

[0007] Step 1: Receive the machining control requirements input by the user, analyze and extract the machining geometry features, tool requirements, vacuum adsorption conditions and dual-channel collaborative constraint parameters to form a set of machining requirement parameters;

[0008] Step 2: Start the CCD and probe scanning stage, acquire basic accuracy data of the machine tool axis system through the laser interferometer, collect ambient temperature, vacuum pressure, and compressed air pressure data, and generate the machine tool initial accuracy benchmark library and environmental parameter set;

[0009] Step 3: Decompose the machining sub-tasks using the load-vacuum adaptation algorithm, match the channel load and tool magazine resources, adjust the vacuum adsorption area and pressure, and generate a dual-channel task scheduling table and vacuum control parameters.

[0010] Step 4: Combine the temperature accuracy correlation model to dynamically optimize the dual-channel spindle speed and feed rate, and link the chiller to control the spindle cooling temperature within the set healthy temperature range;

[0011] Step 5: Based on tool usage data and CCD detection results, predict tool life, generate tool change plan and execute cross tool change, and detect spindle taper hole runout through probe and compensate tool compensation parameters;

[0012] Step 6: Combine the laser interferometer detection data with the bed vibration absorption parameters to construct a vibration error compensation model, calculate and write the shaft system error compensation value;

[0013] Step 7: Integrate processing parameters, compensation values ​​and auxiliary system control instructions to generate a dual-channel independent control program and send it to the driver. The driver executes the control program to complete the control.

[0014] Optionally, in step 1, the machining control requirements include CAD models, toolpath files generated by CAMCAM programming, machining material properties, product machining type, accuracy requirements, and production efficiency indicators;

[0015] The dual-channel collaborative constraint parameters include spindle speed range, maximum feed rate, tool clamping range, number of tool magazines, and total machine power.

[0016] Optionally, in step 2, the basic accuracy data of the machine tool axis system includes XYZ axis positioning accuracy, repeatability accuracy, and XYZ axis perpendicularity.

[0017] The environmental parameters collected are: ambient temperature (room temperature range), vacuum pressure, and compressed air pressure; CCD and probe are used to scan the flatness of the worktable clamping reference surface, and laser interferometer, in conjunction with ball bar and level, completes the shaft system accuracy detection.

[0018] Optionally, in step 3, the load vacuum adaptation algorithm integrates machining condition classification, workpiece material adaptation, and bed vibration feedback, specifically as follows:

[0019] Machining Condition Classification: Machining is divided into roughing and finishing. A basic vacuum pressure reference value is set for roughing conditions, and a vacuum pressure finishing value is set for finishing conditions.

[0020] Workpiece material compatibility: Based on the cutting resistance of different materials, a material compatibility coefficient of 0.8-1.2 is set to correct the load calculation value. The coefficient is positively correlated with the vacuum pressure.

[0021] Bed vibration feedback: Bed vibration data is collected by vibration sensors. When the vibration amplitude is greater than the vibration threshold, the corresponding channel processing load is simultaneously transferred to another channel by a set ratio based on the vacuum pressure adjustment, and the vacuum pressure is additionally increased by 0.005MPa-0.01MPa.

[0022] The tool magazine resource matching is based on the configuration of 12-20 tools per tool magazine. The vacuum adsorption area is divided and adjusted according to the workpiece size. The vacuum control parameters include negative pressure value, adsorption zone range, pressure adjustment sequence and load vibration linkage correction parameters.

[0023] Optionally, the core formula for the vacuum pressure calculation model of the load vacuum adaptation algorithm is:

[0024] ;

[0025] in It is a vacuum adaptation pressure. It is the negative pressure value of the working condition foundation. It is the material compatibility coefficient. It is a vibration-corrected negative pressure value.

[0026] Optionally, in step 5, the tool life prediction adopts the Weibull distribution model, combined with historical data on tool edge wear, tool usage time, and material type detected by CCD; cross tool changing is performed when the two channels need to share the same tool, the tool magazine is scheduled to perform the tool changing operation based on the processing priority. After the tool changing, the probe detects the spindle taper hole runout. If the runout value exceeds the threshold, the system automatically calculates and writes the tool compensation parameters.

[0027] Optionally, in step 6, the vibration error compensation model is constructed using multivariate linear regression combined with a PID compensation algorithm. The input data includes the shaft geometric error detected by the laser interferometer, vibration data within a set threshold for the spindle dynamic runout, the bed's vibration absorption coefficient, and ambient temperature data. The shaft error compensation value is the sum of the geometric error, vibration error, and thermal error. The vibration error calculation formula is:

[0028] Where k is the vibration absorption coefficient of marble, A is the vibration amplitude, and f is the vibration frequency;

[0029] The compensation value is written to the driver via absolute value control on the bus to achieve closed-loop compensation.

[0030] Optionally, in step 7, the auxiliary system control commands include oil injection commands for the automatic lubrication system, three-stage filtration control commands for the cooling circulation system, negative pressure regulation commands for the vacuum system, and pressure control commands for compressed air; the dual-channel independent control program is compatible with various CAM software.

[0031] Optionally, in step 7, during the execution of the control program by the driver, the machine status, number of processed parts, spindle temperature, and vacuum pressure data are collected in real time through the Internet of Things system. If the parameters are detected to deviate from the preset threshold, the system automatically generates a fine-tuning instruction and sends it to the driver. After processing is completed, the workpiece processing accuracy is verified by a laser interferometer. The verification indicators include XYZ axis positioning accuracy and repeatability. If the accuracy is not met, the process returns to step 6 to recalculate the axis error compensation value.

[0032] A dual-channel high-speed composite engraving and milling machine control system is disclosed to implement a dual-channel high-speed composite engraving and milling machine control method. The system includes a demand input module, a sensing and detection module, an algorithm calculation module, a tool management module, an error compensation module, an instruction generation and driving module, an Internet of Things monitoring module, and a human-machine interaction module.

[0033] The system comprises the following modules: a demand input module for receiving user-input CAD models, CAM toolpath files, and machining parameter commands; a perception and detection module for scanning the worktable reference surface, detecting machine tool axis accuracy, and collecting environmental and equipment operating status data; an algorithm calculation module for preloaded vacuum adaptation algorithms, temperature accuracy correlation models, Weibull distribution tool life prediction models, and vibration error compensation models, used for task decomposition, parameter optimization, and error compensation value calculation; a tool management module connecting a dual-channel servo tool magazine and a tool detection component for tool life recording, cross-tool changing scheduling, and tool parameter compensation; an error compensation module for storing vibration error compensation model parameters and performing real-time writing and closed-loop adjustment of axis error compensation values; an instruction generation and drive module for integrating machining parameters and auxiliary system instructions to generate a dual-channel independent control program and drive the XYZ axis servo motors and spindle to perform machining actions; an IoT monitoring module for real-time acquisition of machine tool status and machining data and issuing parameter fine-tuning commands; and a human-machine interaction module for displaying machining process data, receiving user operation commands, and outputting machining reports, while also supporting the visualization of fault warning information.

[0034] In summary, the present invention has at least one of the following beneficial technical effects:

[0035] This invention provides a control method and system for a dual-channel high-speed composite engraving and milling machine. It adopts a dual-channel collaborative control mode and realizes intelligent decomposition and scheduling of processing tasks through a load-vacuum adaptation algorithm, which fully utilizes the processing potential of the dual-channel equipment and effectively improves the overall processing efficiency.

[0036] The vacuum adsorption pressure is dynamically adjusted in combination with the workpiece material and the vibration state of the machine bed, which optimizes the stability of workpiece clamping, reduces machining deviations caused by clamping displacement, and ensures machining accuracy.

[0037] Tool life prediction combines actual wear data and usage history with a dual-channel cross-tool changing scheduling strategy, reducing downtime for tool changes and lowering the probability of abnormal tool wear, thus extending tool life.

[0038] The vibration error compensation model integrates shaft geometric error, vibration error, and thermal error, and performs compensation calculations based on the bed's vibration absorption characteristics, thereby improving the accuracy of error compensation and further enhancing the precision and stability of engraving and milling processes.

[0039] During the machining process, the Internet of Things system enables real-time data acquisition and parameter fine-tuning. Combined with the temperature accuracy correlation model, the spindle speed and feed rate are dynamically optimized, realizing intelligent adaptive control of the machining process and reducing the need for manual intervention. Attached Figure Description

[0040] Figure 1 This is a flowchart illustrating a dual-channel high-speed composite engraving and milling machine control method according to the present invention.

[0041] Figure 2 This is a schematic diagram of the control system architecture of a dual-channel high-speed composite engraving and milling machine according to the present invention;

[0042] Figure 3 This is a three-dimensional structural diagram of a dual-channel high-speed composite engraving and milling machine according to the present invention.

[0043] Explanation of reference numerals in the attached diagram: 1. Demand input module; 2. Sensing and detection module; 3. Algorithm calculation module; 4. Tool management module; 5. Error compensation module; 6. Instruction generation and driving module; 7. Internet of Things monitoring module; 8. Human-machine interaction module; 9. Bed; 10. Spindle; 11. XYZ axis; 12. Driver. Detailed Implementation

[0044] The present invention will be further described in detail below with reference to the accompanying drawings.

[0045] This invention discloses a control method and system for a dual-channel high-speed composite engraving and milling machine.

[0046] Reference Figures 1-3 , refer to Figure 1 Example 1: A control method for a dual-channel high-speed composite engraving and milling machine, comprising the following steps:

[0047] Step 1: Receive the machining control requirements input by the user, analyze and extract the machining geometry features, tool requirements, vacuum adsorption conditions and dual-channel collaborative constraint parameters to form a set of machining requirement parameters;

[0048] Step 2: Start the CCD and probe scanning stage, acquire basic accuracy data of the machine tool axis system through the laser interferometer, collect ambient temperature, vacuum pressure, and compressed air pressure data, and generate the machine tool initial accuracy benchmark library and environmental parameter set;

[0049] Step 3: Decompose the machining sub-tasks using the load-vacuum adaptation algorithm, match the channel load and tool magazine resources, adjust the vacuum adsorption area and pressure, and generate a dual-channel task scheduling table and vacuum control parameters.

[0050] Step 4: Combine the temperature accuracy correlation model to dynamically optimize the rotational speed and feed rate of the dual-channel spindle 10, and link the chiller to control the cooling temperature of the spindle 10 within the set healthy temperature range.

[0051] Step 5: Based on tool usage data and CCD detection results, predict tool life, generate tool change plan and execute cross tool change, and detect spindle taper hole runout through probe and compensate tool compensation parameters;

[0052] Step 6: Combine the laser interferometer detection data with the vibration absorption parameters of the bed 9 to construct a vibration error compensation model, calculate and write the shaft system error compensation value;

[0053] Step 7: Integrate processing parameters, compensation values ​​and auxiliary system control instructions to generate a dual-channel independent control program and send it to driver 12. Driver 12 executes the control program to complete the control.

[0054] Example 2, in step 1, the machining control requirements include CAD model, toolpath file generated by CAMCAM programming, machining material properties, product machining type, accuracy requirements and production efficiency indicators;

[0055] The dual-channel collaborative constraint parameters include the spindle speed range, maximum feed rate, tool clamping range, number of tool magazines, and total machine power.

[0056] In Example 3, step 2, the basic accuracy data of the machine tool axis system includes the positioning accuracy of XYZ axis 11, repeatability of positioning accuracy, and perpendicularity of XYZ axis 11;

[0057] The environmental parameters collected are: ambient temperature (room temperature range), vacuum pressure, and compressed air pressure; CCD and probe are used to scan the flatness of the worktable clamping reference surface, and laser interferometer, in conjunction with ball bar and level, completes the shaft system accuracy detection.

[0058] By adopting the above technical solutions, a closed-loop control system is constructed around the processing needs of precision components for 3C electronic products, from processing needs analysis to instruction execution. Through multi-dimensional data perception, intelligent algorithm scheduling, dynamic parameter optimization and precise error compensation, efficient collaboration and high-precision processing of dual-channel engraving and milling machines are achieved.

[0059] Starting with the user's machining control requirements, core parameters such as machining geometry and tool requirements are extracted through analysis to clarify the machining objectives and constraints. Simultaneously, spatial feature acquisition of the worktable reference surface is completed using CCD and probes, and basic accuracy data of the machine tool axis system is obtained using a laser interferometer. Combined with the acquisition of environmental parameters such as ambient temperature and vacuum pressure, an initial accuracy benchmark and environmental state database for the machine tool is established. This provides data benchmarks for all subsequent control stages, ensuring that the control strategy matches the actual machining conditions.

[0060] Based on a load-vacuum adaptation algorithm, the overall machining task is decomposed according to the resource characteristics of the two channels, matching the machining load of the two channels with the tool magazine resources to achieve balanced operation of the two channels. Simultaneously, roughing and finishing levels are divided according to the differences in machining conditions. The basic parameters of vacuum adsorption are adjusted based on the cutting resistance characteristics of the workpiece material. Furthermore, the vacuum pressure and load are linked and corrected based on the vibration data of the machine bed. By dynamically adjusting the vacuum adsorption area and pressure, the stability of workpiece clamping is ensured, preventing clamping displacement due to load changes or vibration, thus providing a fundamental guarantee for machining accuracy.

[0061] Based on a temperature-accuracy correlation model, the influence of ambient temperature changes on machining accuracy is analyzed by combining the characteristics of the machine bed 9. This analysis dynamically optimizes the spindle speed and feed rate. Simultaneously, a chiller is linked to precisely control the cooling temperature of the spindle 10, ensuring it operates within a stable temperature range. This reduces machining errors caused by temperature rise or fluctuations in the spindle 10, allowing machining parameters to adapt to different machining materials and working conditions, maintaining accuracy stability while ensuring machining efficiency.

[0062] By integrating historical tool usage data with CCD-detected tool edge wear, the remaining tool life is quantitatively predicted using a Weibull distribution model, allowing for advance tool change planning. When shared tool requirements exist between dual channels, cross-tool changing is performed based on machining priority to avoid overall machine downtime. After tool changing, the spindle's 10-taper hole runout is detected by a probe. For deviations exceeding thresholds, tool compensation parameters are automatically calculated and compensated to ensure tool installation accuracy and reduce the impact of tool-related factors on machining quality.

[0063] By integrating the shaft geometric error data detected by the laser interferometer with the vibration absorption parameters of the machine bed 9, a vibration error compensation model is constructed. This model integrates and calculates the shaft geometric error, vibration error, and thermal error during machining to obtain a comprehensive shaft error compensation value. The compensation value is written to the driver 12 via bus absolute value control, achieving closed-loop compensation for shaft movement. This counteracts the cumulative effects of various errors during machining, improving the machining accuracy and consistency of the machine tool.

[0064] The machining parameter error compensation values ​​and auxiliary system control requirements are summarized to generate an independent control program adapted to the dual channels, ensuring program compatibility with CAM software. The program is then sent to the driver 12. The driver 12 drives the machine tool's axis spindle 10 and other actuators to complete machining actions according to the received control program, forming a complete control link from data acquisition and analysis to instruction generation and execution, realizing automated and intelligent control of the machining process.

[0065] In Example 4, step 3, the load vacuum adaptation algorithm integrates machining condition classification, workpiece material adaptation, and bed 9 vibration feedback, specifically as follows:

[0066] Machining Condition Classification: Machining is divided into roughing and finishing. A basic vacuum pressure reference value is set for roughing conditions, and a vacuum pressure finishing value is set for finishing conditions.

[0067] Workpiece material compatibility: Based on the cutting resistance of different materials, a material compatibility coefficient of 0.8-1.2 is set to correct the load calculation value. The coefficient is positively correlated with the vacuum pressure.

[0068] Bed 9 vibration feedback: Vibration data of bed 9 is collected by vibration sensor. When the vibration amplitude is greater than the vibration threshold, the corresponding channel processing load is transferred to another channel by a set ratio value on the basis of vacuum pressure adjustment, and the vacuum pressure is increased by an additional 0.005MPa-0.01MPa.

[0069] The tool magazine resource matching is based on the configuration of 12-20 tools per tool magazine. The vacuum adsorption area is divided and adjusted according to the workpiece size. The vacuum control parameters include negative pressure value, adsorption zone range, pressure adjustment sequence and load vibration linkage correction parameters.

[0070] Example 5: The core formula of the vacuum pressure calculation model for the load vacuum adaptation algorithm is:

[0071] ;

[0072] in It is a vacuum adaptation pressure. It is the negative pressure value of the working condition foundation. It is the material compatibility coefficient. It is a vibration-corrected negative pressure value.

[0073] By adopting the above technical solution, focusing on the load characteristics of dual-channel machining, and combining the three key dimensions of machining conditions, workpiece material and bed vibration state, intelligent decomposition of machining tasks and dynamic optimization of vacuum adsorption parameters are achieved. At the same time, the characteristics of tool magazine resources are matched so that the vacuum adsorption effect is adapted to the machining load and equipment operating status, ensuring workpiece clamping stability and the balance of dual-channel machining.

[0074] Differentiated vacuum pressure benchmarks are established based on the differences in machining characteristics between roughing and finishing. Roughing is characterized by large cutting volume and high machining load, and the workpiece is subjected to stronger cutting forces, so a higher basic vacuum pressure benchmark value needs to be set to ensure secure clamping. Finishing focuses on machining accuracy, and the cutting volume and load are relatively small. Therefore, a lower vacuum pressure finishing value is set to meet clamping requirements, reduce the impact of excessive vacuum adsorption on the workpiece, and also reduce the energy consumption of the vacuum system.

[0075] Different materials exhibit significant differences in cutting resistance. Materials with higher cutting resistance experience greater cutting reaction forces during machining, making them more prone to clamping looseness. By setting material compatibility coefficients ranging from 0.8 to 1.2 for different materials—coefficients positively correlated with material cutting resistance—the calculated machining load is corrected using these coefficients. Furthermore, the coefficients are positively correlated with vacuum pressure, ensuring that the vacuum suction force increases with the material's cutting resistance, thus matching the vacuum suction force with the clamping force required by the material.

[0076] When the vibration amplitude of bed 9 exceeds the threshold, it will not only affect the machining accuracy, but may also cause displacement between the workpiece and the worktable due to vibration. By collecting vibration data of bed 9 in real time through vibration sensors, when the vibration amplitude exceeds the threshold, on the one hand, part of the machining load of the corresponding channel is transferred to another channel by a set proportion to reduce the vibration source generated during the machining process in that channel. On the other hand, the vacuum pressure is increased by an additional 0.005MPa to 0.01MPa to further enhance the stability of workpiece clamping. Through the dual measures of load transfer and vacuum pressure increase, the adverse effects of vibration are offset.

[0077] Based on the configuration of 12 to 20 tools in a single tool magazine, tool magazine resources for each channel are matched when decomposing machining sub-tasks to avoid tool resource conflicts and ensure the continuity of dual-channel machining. Simultaneously, the vacuum adsorption area is partitioned and adjusted according to the workpiece size to ensure the distribution of vacuum adsorption force matches the workpiece shape. Combined with negative pressure value, adsorption zone range, pressure adjustment sequence, and load vibration linkage correction parameters, a complete vacuum control parameter system is formed, achieving precise and time-sequential control of vacuum adsorption.

[0078] The vacuum pressure calculation model quantifies the principle by using the base negative pressure value of the working condition as a benchmark, combining it with the material compatibility coefficient to calculate the vacuum pressure correction value corresponding to the material, and then subtracting the vibration correction negative pressure value. The core formula quantifies the influence of three dimensions—processing conditions, workpiece material, and bed vibration—into specific vacuum adaptation pressure values. This allows the algorithm's logic to be implemented through a mathematical model, enabling precise calculation and dynamic adjustment of vacuum pressure and ensuring the scientific and operable nature of vacuum pressure adjustment.

[0079] In Example 6, step 5, the tool life prediction adopts the Weibull distribution model, combined with historical data on tool edge wear, tool usage time, and material type detected by CCD; cross tool changing is performed when the two channels need to share the same tool, the tool magazine is scheduled to perform the tool changing operation based on the processing priority. After the tool changing, the probe detects the runout of the spindle 10 taper hole. If the runout value exceeds the threshold, the system automatically calculates and writes the tool compensation parameters.

[0080] By adopting the above technical solution, the Weibull distribution model is suitable for describing the life distribution law of fatigue failure in mechanical parts, and the wear and failure of cutting tools during the machining process conforms to this characteristic. This model is based on historical data of tool usage time and the type of material being machined. This data reflects the wear and tear patterns of cutting tools caused by different materials. Simultaneously, by combining real-time wear data of the tool cutting edge detected by CCD, the life parameters in the model are dynamically corrected, and the remaining service life of the tool is quantitatively calculated. This allows for early prediction of tool failure, avoiding machining interruptions or workpiece scrap due to sudden tool damage.

[0081] When a dual-channel machining task requires the sharing of the same tool, the system schedules tool changes based on machining priorities. The determination of machining priorities comprehensively considers factors such as the machining progress of each channel, the urgency of the process, and the workpiece delivery requirements. This scheduling method allows one channel to complete the corresponding process using the target tool first, while the other channel performs other processes during idle periods. Tool changes are only performed when the target tool becomes available, preventing a complete shutdown of both channels due to tool sharing, ensuring the continuity of the machining process, and improving the equipment's machining efficiency.

[0082] After a tool change, the fit accuracy between the tool and the 10-tapered hole of the spindle directly affects the machining accuracy. If the runout of the 10-tapered hole exceeds a threshold, it indicates a coaxiality deviation in the tool installation. The system monitors the runout data of the 10-tapered hole in real time using a probe. The detected value is compared with a preset threshold. If it exceeds the threshold, the system automatically calculates the corresponding tool compensation parameters based on the direction and value of the runout deviation, and then writes these compensation parameters into the control system. The control system corrects the tool's motion trajectory based on the tool compensation parameters, offsetting machining errors caused by tool installation deviations, ensuring that the tool's cutting position matches the theoretical trajectory, and maintaining the stability of machining accuracy.

[0083] In Example 7, step 6, the vibration error compensation model is constructed using multivariate linear regression combined with a PID compensation algorithm. The input data includes the shaft geometric error detected by the laser interferometer, vibration data within a set threshold for the dynamic runout of the spindle 10, the vibration absorption coefficient of the bed 9, and ambient temperature data. The shaft error compensation value is the sum of the geometric error, vibration error, and thermal error. The vibration error calculation formula is:

[0084] Where k is the vibration absorption coefficient of marble, A is the vibration amplitude, and f is the vibration frequency;

[0085] The compensation value is written to driver 12 via bus absolute value control to achieve closed-loop compensation.

[0086] By employing the above technical solution, multivariate linear regression is used to analyze the linear correlation between multiple independent variables, such as shaft geometric error, spindle dynamic runout data, bed vibration absorption coefficient, and ambient temperature data, and the total shaft error. Through statistical analysis of these input data, the influence weight of each variable on the error is determined, and a quantitative relationship model between error and influencing factors is established. This provides a foundation for subsequent comprehensive error calculation, ensuring that error compensation is no longer limited to a single factor but covers the main sources of error in the machining process.

[0087] The PID compensation algorithm is combined with a multivariate linear regression model to perform proportional-integral-derivative (PID) adjustment on the deviation between the error prediction value calculated by the model and the actual detection value. The proportional stage adjusts the compensation level in real time according to the magnitude of the deviation, the integral stage eliminates long-term accumulated static deviations, and the derivative stage predicts the trend of deviation changes and adjusts in advance. This allows the compensation value to dynamically adapt to the real-time changes in error during the processing, improving the timeliness and accuracy of compensation.

[0088] The total shaft system error is decomposed into three independent components: geometric error, vibration error, and thermal error. The geometric error is directly obtained from the shaft system motion deviation detected by a laser interferometer; the thermal error is derived through correlation analysis between ambient temperature data and the thermal characteristics of the bed; and the vibration error is based on… The formula V=k•A•f is used to calculate the vibration absorption coefficient of the marble bed 9, combined with the amplitude and frequency of the spindle 10 vibration, to quantify the degree of impact of vibration on the shaft system accuracy. The three error components are superimposed to obtain the total shaft system error compensation value reflecting the actual machining state, thus achieving comprehensive cancellation of various errors.

[0089] The system transmits the calculated shaft error compensation value to the driver 12 via bus absolute value control. The driver 12 corrects the shaft motion command in real time based on the compensation value, adjusting the shaft motion trajectory to offset the error. Simultaneously, detection devices such as laser interferometers and vibration sensors continuously collect actual motion data and error data of the shaft, feeding them back to the vibration error compensation model. The model recalculates the compensation value based on the feedback data and writes it back to the driver 12, forming a closed-loop control from error detection to compensation execution and data feedback. This allows error compensation to continuously adapt to changes in the machining process, ensuring the stability of machining accuracy.

[0090] In Example 8, step 7, the auxiliary system control commands include the oil injection command for the automatic lubrication system, the three-stage filtration control command for the cooling circulation system, the negative pressure adjustment command for the vacuum system, and the pressure control command for compressed air; the dual-channel independent control program is compatible with various CAM software.

[0091] In Example 9, during step 7, when the driver 12 executes the control program, it collects data on machine status, number of processed parts, spindle 10 temperature, and vacuum pressure in real time through the Internet of Things system. If the parameters are detected to deviate from the preset threshold, the system automatically generates a fine-tuning command and sends it to the driver 12. After processing, the workpiece processing accuracy is verified by a laser interferometer. The verification indicators include the positioning accuracy and repeatability of the XYZ axis 11. If the accuracy is not met, the system returns to step 6 to recalculate the axis error compensation value.

[0092] By adopting the above technical solutions, targeted control commands are issued to the auxiliary systems of the CNC engraving and milling machine, such as automatic lubrication, cooling circulation, vacuum, and compressed air. The lubrication system's oil supply rhythm is controlled by the oil injection command to ensure effective lubrication of moving parts. The filtration process of the cooling circulation system is optimized using a three-stage filtration control command, improving the cooling and cleanliness of the cutting fluid. The pressure states of the vacuum and compressed air systems are adjusted using negative pressure regulation and pressure control commands, ensuring that the operating status of each auxiliary system is adapted to the machining requirements. Simultaneously, the dual-channel independent control program is made compatible with various CAM software, ensuring that machining data generated by different software can be recognized and executed by the system, achieving universal adaptation of the machining program.

[0093] When the driver 12 executes the control program, the IoT system collects key operating data in real time, such as machine status, number of workpieces processed, spindle 10 temperature, and vacuum pressure. The system compares the collected data with preset thresholds. If the parameters deviate, it automatically generates fine-tuning instructions and sends them to the driver 12, realizing dynamic correction of parameters during processing and ensuring the stability of the processing status. After processing, the positioning accuracy and repeatability of the XYZ axes 11 are detected by a laser interferometer. If the accuracy does not meet the standard, the axis error compensation value is recalculated, forming a closed-loop control from processing execution to accuracy verification to error correction, ensuring that the workpiece processing accuracy meets the requirements.

[0094] Example 10: A dual-channel high-speed composite engraving and milling machine control system, used to implement a dual-channel high-speed composite engraving and milling machine control method. The system includes a demand input module 1, a perception and detection module 2, an algorithm calculation module 3, a tool management module 4, an error compensation module 5, an instruction generation and driving module 6, an Internet of Things monitoring module 7, and a human-machine interaction module 8.

[0095] The system comprises the following modules: Module 1 receives user-input CAD models, CAM toolpath files, and machining parameter commands; Module 2 scans the worktable reference surface, detects machine tool axis accuracy, and collects environmental and equipment operating status data; Module 3 uses a preload vacuum adaptation algorithm, a temperature accuracy correlation model, a Weibull distribution tool life prediction model, and a vibration error compensation model to perform task decomposition, parameter optimization, and error compensation value calculation; Module 4 connects a dual-channel servo tool magazine and a tool detection component to record tool life, schedule cross-tool changes, and compensate tool parameters; Module 5 stores vibration error compensation model parameters and performs real-time writing and closed-loop adjustment of axis error compensation values; Module 6 integrates machining parameters and auxiliary system commands to generate a dual-channel independent control program and drives the XYZ axis 11 servo motor and spindle 10 to perform machining actions; Module 7 collects machine tool status and machining data in real time and issues parameter fine-tuning commands; and Module 8 displays machining process data, receives user operation commands, outputs machining reports, and supports visualization of fault warning information.

[0096] The following specific embodiments illustrate the implementation principle of the present invention:

[0097] Based on a certain model of dual-channel high-speed composite engraving and milling machine, for the precision machining of aluminum alloy parts for mobile phone mid-frames, the control system of the dual-channel high-speed composite engraving and milling machine is used to implement the control method. The system includes a demand input module 1, a perception and detection module 2, an algorithm calculation module 3, a tool management module 4, an error compensation module 5, an instruction generation and driving module 6, an IoT monitoring module 7, and a human-machine interaction module 8. The specific implementation process is as follows:

[0098] Step 1: Input and parsing of processing control requirements;

[0099] Operators input machining control requirements through the requirement input module 1, including the CAD model of the phone's mid-frame, the toolpath file generated by CAM programming, aluminum alloy material properties, machining type of 3C product structural parts, positioning accuracy requirement of 0.008mm / 300mm, and production efficiency target of 50 pieces per hour. The requirement input module 1 analyzes and extracts the machining geometric features as the curved surface and hole structure of the phone's mid-frame, the tool requirement as a Φ6 carbide end mill, the vacuum adsorption condition as full coverage of the workpiece clamping area, and the dual-channel collaborative constraint parameters as the spindle speed of 10 at 60,000 rpm, the maximum feed rate of 12 m / min, the tool clamping range of Φ3.175-Φ6, the single tool magazine of 12 tools, and the total machine power of 12.0 kW, forming a set of machining requirement parameters and transmitting them to the algorithm calculation module 3.

[0100] Step 2: Processing reference calibration and environmental parameter acquisition;

[0101] The sensing and detection module 2 activates the CCD and probe to scan the worktable clamping reference surface and acquire flatness data. It then uses a laser interferometer paired with a ballbar and level to detect the basic accuracy data of the machine tool's axis system, including the positioning accuracy of XYZ axis 11 (0.008mm / 300mm), repeatability (0.005mm), and perpendicularity (0.013mm), generating an initial accuracy reference library for the machine tool. Simultaneously, the sensing and detection module 2's temperature sensor, vacuum gauge, and compressed air pressure gauge collect ambient temperature (within the room temperature range), vacuum pressure (-0.06MPa), and compressed air pressure (0.6MPa), respectively, generating an environmental parameter set. These two types of data are then synchronized to the algorithm processing module 3.

[0102] Step 3: Decompose the processing task and adapt the vacuum parameters;

[0103] Algorithm module 3 calls the pre-loaded load vacuum adaptation algorithm to decompose the mobile phone mid-frame machining task into a roughing sub-task in channel A and a finishing sub-task in channel B, matching the resources of 12 roughing tools in the tool magazine of channel A and 12 finishing tools in the tool magazine of channel B. Algorithm module 3 sets the basic vacuum pressure benchmark value for roughing to -0.07MPa and the vacuum pressure value for finishing to -0.06MPa according to the machining conditions; it sets a material adaptation coefficient of 1.0 according to the aluminum alloy material and corrects the load calculation value; it collects vibration data of bed 9 through vibration sensor. The initial vibration amplitude does not exceed the threshold, so there is no need to adjust the load or increase the vacuum pressure. Algorithm module 3 divides the vacuum adsorption area into 4 partitions according to the workpiece size, generates a dual-channel task scheduling table and vacuum control parameters including negative pressure value, adsorption partition range, and pressure adjustment sequence, and transmits them to instruction generation and drive module 6.

[0104] Step 4: Dynamic optimization of processing parameters;

[0105] Algorithm module 3, combined with temperature accuracy correlation model, analyzes the impact of ambient temperature on marble bed 9, dynamically optimizes the spindle speed of channel A 10 to 40,000 rpm and feed rate to 8 m / min, and the spindle speed of channel B 10 to 60,000 rpm and feed rate to 4 m / min; at the same time, it sends control commands to the instruction generation and drive module 6 to link the chiller, control the cooling temperature of spindle 10 within the healthy temperature range of 26-30℃, and reduce machining errors caused by temperature rise of spindle 10.

[0106] Step 5: Tool life prediction and cross-tool change control;

[0107] The tool management module 4 retrieves the usage time of the Φ6 carbide end mill and historical data on aluminum alloy machining. Combined with tool edge wear data detected by CCD, it predicts the remaining tool life to be 2 hours using a Weibull distribution model and generates a tool change plan. During machining, both channels need to share the same Φ3.175 drill bit. The tool management module 4 schedules the tool magazine to perform a cross-tool change operation based on the priority of roughing in channel A, while milling operations are performed first in channel B. After the tool change, the probe detects a spindle taper hole runout value of 3µm, exceeding the 2µm threshold. The tool management module 4 automatically calculates the tool compensation parameters and writes them into the control system to correct tool installation deviations.

[0108] Step 6: Vibration error compensation model construction and compensation value writing;

[0109] Algorithm module 3 integrates the shaft geometric error detected by the laser interferometer, the vibration data of the main spindle 10 dynamic runout within 5µm, the vibration absorption coefficient of the marble bed 9, and the ambient temperature data. It constructs a vibration error compensation model using multivariate linear regression combined with a PID compensation algorithm, calculates the vibration error according to ∆V=k∙A∙f, and superimposes the geometric error, vibration error, and thermal error to obtain the shaft error compensation value. Error compensation module 5 stores the vibration error compensation model parameters and writes the compensation value to driver 12 via bus absolute value control, realizing closed-loop compensation of the shaft motion.

[0110] Step 7: Control command generation and execution, and process management;

[0111] The instruction generation and drive module 6 integrates machining parameters, error compensation values, and auxiliary system control instructions. These include setting the oil injection flow rate for the automatic lubrication system to 120 ml / min, the three-stage filtration control instruction for the cooling circulation system to a filtration flow rate of 40 L / min, the negative pressure adjustment instruction for the vacuum system to set the vacuum adaptation pressure for aluminum alloy machining, and the compressed air pressure control instruction to a pressure of 0.6 MPa. This generates a dual-channel independent control program compatible with CAM software and sends it to the driver 12. While the driver 12 executes the control program, the IoT monitoring module 7 collects real-time data on machine operation status, number of processed parts, spindle 10 temperature (28℃), and vacuum pressure (-0.065 MPa). If it detects that the vacuum pressure in channel A has dropped to -0.05 MPa, deviating from the preset threshold, it automatically generates a fine-tuning instruction to raise the vacuum pressure to -0.065 MPa and sends it to the driver 12. After processing, the laser interferometer of the sensing and detection module 2 verifies the positioning accuracy and repeatability of the workpiece XYZ axis 11. The initial detection shows that the positioning accuracy deviation of the workpiece in channel B is 0.009mm. If it does not meet the standard, it returns to step 6, where the algorithm calculation module 3 recalculates the axis error compensation value, and the error compensation module 5 writes it to the driver 12 again. After reprocessing, the accuracy meets the standard.

[0112] System modules work together:

[0113] The demand input module 1 completes the digital input of processing requirements; the perception and detection module 2 provides data benchmarks for the entire control process; the algorithm calculation module 3 realizes the core parameter calculation and strategy optimization; the tool management module 4 ensures the stability of tool use and the continuity of processing; the error compensation module 5 realizes closed-loop correction of accuracy; the instruction generation and drive module 6 completes the conversion and execution of control programs; the Internet of Things monitoring module 7 realizes the dynamic control of the processing process; the human-machine interaction module 8 displays processing data, receives operation instructions and outputs processing reports, and visualizes fault warning information such as excessive vibration of the machine bed 9 and insufficient tool life. The collaborative operation of each module realizes the intelligent control of the dual-channel high-speed composite engraving and milling machine.

[0114] 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 control method of a double-channel high-speed complex engraving and milling machine, characterized in that, The method comprises the following steps: Step 1, receiving user input machining control requirements, parsing and extracting machining geometric features, tool requirements, vacuum adsorption conditions and double-channel collaborative constraint parameters to form a machining requirement parameter set; Step 2, starting CCD and probe scanning workbench, acquiring machine tool axis system basic precision data through laser interferometer, collecting environmental temperature, vacuum pressure and compressed air pressure data, generating machine tool initial precision reference library and environmental parameter set; Step 3, using load vacuum adaptive algorithm to decompose machining subtasks, matching channel load and tool magazine resources, adjusting vacuum adsorption area and pressure, generating double-channel task scheduling table and vacuum control parameters; Step 4, combining temperature precision correlation model, dynamically optimizing double-channel spindle speed and feed speed, and linking cold water machine to control spindle refrigeration temperature within the set healthy temperature range; Step 5, predicting tool life based on tool usage data and CCD detection results, generating tool changing plan and executing cross tool changing, and detecting spindle taper hole runout through the probe and compensating tool compensation parameters; Step 6, combining laser interferometer detection data and bed vibration absorption parameters to build a vibration error compensation model, calculating and writing axis system error compensation values; Step 7, integrating machining parameters, compensation values and auxiliary system control instructions, generating double-channel independent control program and issuing it to the driver, and the driver executes the control program to complete the control.

2. The control method of a double-channel high-speed composite engraving and milling machine according to claim 1, characterized in that, In step 1, the machining control requirements include CAD model, tool path file generated by CAMCAM programming, machining material attributes, product machining type, precision requirements and production efficiency indicators; The double-channel collaborative constraint parameters include spindle speed range, maximum feed speed, tool clamping range, tool magazine quantity and total machine power.

3. The control method of a double-channel high-speed composite engraving and milling machine according to claim 2, characterized in that, In step 2, the machine tool axis system basic precision data includes XYZ axis positioning accuracy, repeat positioning accuracy and XYZ axis perpendicularity; The collection range of environmental parameters is: environmental temperature in room temperature range, vacuum pressure and compressed air pressure; CCD and probe are used to scan the flatness of the clamped reference surface, and laser interferometer is used to complete axis system precision detection together with ball bar and level.

4. The control method of a double-channel high-speed composite engraving and milling machine according to claim 3, characterized in that, In step 3, the load vacuum adaptive algorithm integrates machining condition classification, workpiece material adaptation and bed vibration feedback, specifically: Machining condition classification: dividing machining into rough machining and finish machining, setting a basic vacuum pressure reference value under rough machining condition, and setting a vacuum pressure finish machining value under finish machining; Workpiece material adaptation: setting a material adaptation coefficient of 0.8-1.2 according to the cutting resistance of different materials to correct the load calculation value, and the coefficient is positively correlated with vacuum pressure; Bed vibration feedback: collecting bed vibration data through vibration sensors, when the vibration amplitude is greater than the vibration threshold, the corresponding channel machining load is transferred to the other channel by a set proportion value based on vacuum pressure adjustment, and the vacuum pressure is additionally increased by 0.005MPa-0.01MPa; The basis for tool magazine resource matching is the configuration of single tool magazine 12-20 tools, the vacuum adsorption area is adjusted according to the size of the workpiece, and the vacuum control parameters include negative pressure value, adsorption partition range, pressure adjustment time sequence and load vibration linkage correction parameters.

5. The control method of a double-channel high-speed composite engraving and milling machine according to claim 4, characterized in that, The core formula of the vacuum pressure calculation model loaded with the vacuum adaptation algorithm is as follows: ； wherein is a vacuum adaptation pressure, is a working condition base negative pressure value, is a material adaptation coefficient, is a vibration correction negative pressure value.

6. The control method of a double-channel high-speed composite engraving and milling machine according to claim 5, characterized in that, In step 5, the tool life prediction adopts a Weibull distribution model, and is completed by combining the tool edge wear data detected by the CCD, the tool use time, and the historical data of the machining material type; the cross-tool changing is a tool changing operation based on the machining priority scheduling tool magazine when the double channels need to share the same tool, and after the tool changing, the probe detects the spindle taper hole runout, and if the runout value exceeds the threshold value, the system automatically calculates and writes the tool compensation parameters.

7. The control method of a double-channel high-speed composite engraving and milling machine according to claim 6, characterized in that, In step 6, the vibration error compensation model is constructed by using a multivariate linear regression combined with a PID compensation algorithm, and the input data includes the shaft system geometric error detected by the laser interferometer, the vibration data within the set threshold value of the spindle dynamic deflection, the shock absorption coefficient of the bed body, and the environmental temperature data; the shaft system error compensation value is the superposition value of the geometric error, the vibration error, and the thermal error, and the vibration error calculation formula is as follows: ; where k is the marble shock absorption coefficient, A is the vibration amplitude, and f is the vibration frequency. The compensation value is written into the driver by the bus absolute value control mode to realize closed-loop compensation.

8. The control method of a double-channel high-speed composite engraving and milling machine according to claim 7, characterized in that, In step 7, the auxiliary system control instructions include the oil injection instructions of the automatic lubrication system, the three-stage filtration control instructions of the cooling circulation system, the negative pressure adjustment instructions of the vacuum system, and the pressure control instructions of the compressed air; the double-channel independent control program is compatible with various CAM software.

9. The control method of a double-channel high-speed composite engraving and milling machine according to claim 8, characterized in that, In step 7, during the execution of the control program by the driver, the machine state, the workpiece number, the spindle temperature, and the vacuum pressure data are collected in real time through the Internet of Things system, and if it is detected that the parameters deviate from the preset threshold value, the system automatically generates a fine-tuning instruction and sends it to the driver; after the machining is completed, the workpiece machining precision is verified by the laser interferometer, and the verification indexes include the XYZ axis positioning accuracy and the repeat positioning accuracy, and if the indexes do not meet the standards, the shaft system error compensation value is recalculated in step 6.

10. A dual pass high speed hybrid mill control system, characterized by: The system for realizing the double-channel high-speed composite engraving and milling machine control method of claim 9 comprises a demand input module, a perception detection module, an algorithm operation module, a tool management module, an error compensation module, an instruction generation and driving module, an Internet of Things monitoring module, and a human-computer interaction module: The demand input module is used for receiving the CAD model, CAM tool path file and machining parameter instruction input by the user; the perception detection module is used for scanning the workbench datum plane, detecting the machine tool shafting precision, collecting the environmental and equipment operation state data; the algorithm operation module preloads the vacuum adaptive algorithm, temperature precision correlation model, Weibull distribution tool life prediction model and vibration error compensation model, and is used for completing task decomposition, parameter optimization and error compensation value calculation; the tool management module connects the double-channel servo tool magazine and tool detection assembly, and is used for realizing tool life record, cross tool changing scheduling and tool parameter compensation; the error compensation module is used for storing the vibration error compensation model parameters, and completing real-time writing and closed-loop adjustment of the shafting error compensation value; the instruction generation and driving module is used for integrating the machining parameter and auxiliary system instruction generation double-channel independent control program, and driving the XYZ axis servo motor and main shaft to execute the machining action; the Internet of Things monitoring module is used for collecting the machine state and machining data in real time, and issuing parameter fine tuning instruction; the man-machine interaction module is used for displaying the machining process data, receiving the user operation instruction and outputting the machining report, and simultaneously supports fault early warning information visualization.

Images