A boring-milling-drilling machine automation control method

Abstract

This invention discloses an automated control method for boring, milling, and drilling machines, comprising the following steps: collecting multiple sets of data during the machining process using vibration sensors, acoustic emission sensors, infrared temperature sensors, and power sensors, and constructing a digital twin model of the machine tool in the CNC system to map the physical entity state in real time; storing optimized process parameter combinations corresponding to different materials in a database; matching the historically optimal target process scheme from the database based on a similarity retrieval algorithm; generating adaptive control commands based on the real-time state and the target process scheme; displaying the machining status in real time via a touchscreen; determining whether the system has encountered an anomaly based on the machining status, and if so, pushing a graded warning and providing handling suggestions according to the anomaly. This invention allows for real-time viewing of the physical entity operating status of the boring, milling, and drilling machine, realizing the visualized real-time replication of the equipment's machining conditions and component operating postures, overcoming the drawbacks of traditional control methods that can only passively execute preset programs.

Description

Technical Field

[0001] This invention relates to the field of boring, milling and drilling machine technology, and more specifically, to an automated control method for boring, milling and drilling machines. Background Technology

[0002] A boring, milling, and drilling machine is a machine tool that integrates multiple machining functions such as boring, milling, and drilling. It is widely used in the field of mechanical processing and consists of key components such as the bed, column, spindle box, worktable, and feed system. The bed, as the basic support component, provides a stable mounting platform for the entire machine tool, ensuring the relative positional accuracy of each component during machining. The column is used to mount the spindle box, allowing it to be adjusted vertically to meet different machining height requirements. The spindle box is equipped with a precision spindle and speed change mechanism to realize the rotational movement of the cutting tool, and the speed can be adjusted according to the machining process. The worktable is used to fix the workpiece and can move precisely in the horizontal (X-axis), vertical (Y-axis), and vertical (Z-axis) directions, facilitating machining at different positions on the workpiece. The feed system is responsible for precisely driving the movement of the worktable and spindle box, ensuring the accuracy of the relative movement between the cutting tool and the workpiece.

[0003] Currently, devices on the market often use a fixed control mode of "PLC, touch screen, frequency converter, and servo drive" to achieve stepless speed regulation of the spindle and control of tool feed. However, during use, they usually lack intelligent early warning, making the detection of faults such as tool breakage and abnormal vibration rely on preset thresholds, resulting in a high false alarm rate.

[0004] Therefore, how to provide an automated control method for boring, milling, and drilling machines has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] The purpose of this invention is to provide a new technical solution for an automated control method for boring, milling, and drilling machines.

[0006] According to a first aspect of the present invention, an automated control method for a boring, milling and drilling machine is provided, comprising installing a vibration sensor at the spindle box, an acoustic emission sensor near the tool holder, an infrared temperature sensor pointing towards the tool tip, and a power sensor at the spindle motor;

[0007] The automated control method for boring and milling drilling machines includes the following steps:

[0008] S1. Collect multiple sets of data during the processing using vibration sensors, acoustic emission sensors, infrared temperature sensors, and power sensors;

[0009] S2. Based on multiple sets of data collected during the machining process, a digital twin model of the machine tool is constructed in the CNC system to map the physical entity state in real time;

[0010] S3. Store the optimized process parameter combinations for different materials in the database;

[0011] S4. Based on the similarity retrieval algorithm, match the historically optimal target process scheme from the database;

[0012] S5. Generate adaptive control commands based on real-time status and target process scheme;

[0013] S6. The processing status is displayed in real time via a touch screen, including power curve, vibration spectrum, and temperature change.

[0014] S7. Determine if the system is abnormal based on the processing status. If so, push a tiered warning and provide handling suggestions based on the abnormality.

[0015] Optionally, in step S1, processing data is collected by vibration sensors, acoustic emission sensors, infrared temperature sensors, and power sensors, and a time series alignment algorithm is used to unify multiple data into the same timeline. The central clock of the CNC system is used as the master clock source for all data acquisition hardware.

[0016] Optionally, in S2, a three-dimensional model of the machine tool is constructed using three-dimensional CAD software, including the bed, spindle, each feed axis, tool magazine, and worktable.

[0017] Optionally, S2 includes the following steps:

[0018] S21. Based on multiple sets of data collected during the processing, the multiple sets of data are fused to obtain fused data;

[0019] S22. The fused data is continuously injected into the digital twin through a real-time communication interface to control the clock synchronization and data update frequency matching between the virtual model and the physical machine tool.

[0020] S23. Drive the movement of components in the 3D model in real time according to the coordinates of each axis of the CNC system.

[0021] Optionally, after S2, the following steps are also included:

[0022] S24. Obtain tool wear information and tool wear prediction model;

[0023] S25. Generate tool wear characteristics based on tool wear information;

[0024] S26. Input the tool wear characteristics into the tool wear prediction model to generate the wear curve;

[0025] S27. Obtain real-time tool wear information;

[0026] S28. Based on the real-time wear information and wear curve of the tool, determine whether the tool has reached the tool replacement threshold. If so, issue a tool replacement alarm.

[0027] Optionally, after S28, the following steps are further included:

[0028] S29. Obtain the spindle vibration frequency;

[0029] S291. Long-term trend of generating spindle vibration spectrum based on spindle vibration frequency;

[0030] S292. Based on the long-term trend of the spindle vibration spectrum, predict the future working condition of the spindle and arrange maintenance plans in advance according to the future working condition.

[0031] Optionally, in S7, the graded warning includes prompt warning, alert warning, and emergency stop warning.

[0032] Optionally, the following steps are included after S7:

[0033] S8. Link and store actual parameters with processing quality data to form a feedback optimization closed loop.

[0034] The beneficial effects of this invention are as follows:

[0035] This invention builds a digital twin model of the machine tool in the CNC system using multi-source data, enabling real-time monitoring of the physical operating status of the boring, milling, and drilling machine. It achieves visualized real-time replication of the equipment's processing conditions and component operating postures, overcoming the limitations of traditional control systems that passively execute preset programs. This provides precise data model support for subsequent intelligent judgment and adaptive control. Furthermore, it presets and stores optimized process parameter combinations for different workpiece materials in a database. Combined with a similarity retrieval algorithm, it quickly matches historically optimal target process schemes, eliminating the need for repeated manual parameter adjustments. This avoids the problems of traditional manual process parameter setting relying on experience and having poor adaptability, significantly improving process selection efficiency and processing accuracy.

[0036] This invention displays processing status data such as power curves, vibration spectra, and temperature changes in real time via a touchscreen, transforming implicit equipment operation and cutting conditions into intuitive and visual charts. Operators can monitor core machine tool operating parameters in real time, facilitating real-time monitoring of the processing process and reducing the difficulty of manual inspection and condition judgment. It abandons the outdated method of relying solely on a single preset threshold to detect faults such as tool breakage and abnormal vibration. Based on multi-sensor fusion of comprehensive operating data, it comprehensively judges equipment anomalies, achieving accurate fault identification, tiered early warning, and simultaneous push of corresponding handling suggestions. This effectively solves the technical pain points of traditional equipment fault detection, such as high false alarm rates, lack of intelligent early warning, and lack of guidance for fault handling, reducing the probability of tool damage and machine tool downtime, and extending the service life of equipment and tools.

[0037] Other features and advantages of the invention will become clear from the following detailed description of exemplary embodiments of the invention with reference to the accompanying drawings. Attached Figure Description

[0038] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments of the invention and, together with their description, serve to explain the principles of the invention.

[0039] Figure 1 This is a structural diagram of the automated control method for boring, milling, and drilling machines according to the present invention;

[0040] Figure 2 This is a structural diagram of an embodiment of the automated control method for boring, milling, and drilling machines of the present invention;

[0041] Figure 3 This is a structural diagram of a second embodiment of the automated control method for boring, milling, and drilling machines of the present invention. Detailed Implementation

[0042] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention.

[0043] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use.

[0044] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0045] In all the examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0046] like Figures 1 to 3 As shown, this embodiment of the invention provides an automated control method for a boring, milling, and drilling machine, including installing a vibration sensor at the spindle box, an acoustic emission sensor near the tool holder, an infrared temperature sensor pointing towards the tool tip, and a power sensor at the spindle motor.

[0047] Specifically, the vibration sensor collects spindle vibration signals at a sampling frequency of 13kHz, focusing on the 0-6kHz frequency band to identify chatter and imbalance. The acoustic emission sensor collects high-frequency stress wave signals (frequency range 110kHz-1.1MHz) at a sampling frequency of 2.2MHz to monitor tool microcracks and fractures.

[0048] An infrared temperature sensor is pointed at the working area of ​​the tool tip to collect temperature signals, with a sampling frequency of 110Hz, to monitor changes in cutting heat. A power sensor (integrated into the spindle motor driver) is used to collect the instantaneous power of the spindle motor, with a sampling frequency of 1-2kHz, reflecting changes in cutting load.

[0049] The automated control method for boring and milling drilling machines includes the following steps:

[0050] S1. Collect multiple sets of data during the processing using vibration sensors, acoustic emission sensors, infrared temperature sensors, and power sensors;

[0051] S2. Based on multiple sets of data collected during the machining process, a digital twin model of the machine tool is constructed in the CNC system to map the physical entity state in real time;

[0052] S3. Store the optimized process parameter combinations for different materials in the database;

[0053] S4. Based on the similarity retrieval algorithm, match the historically optimal target process scheme from the database;

[0054] S5. Generate adaptive control commands based on real-time status and target process plan; at the same time, ensure smooth switching of composite machining such as boring, milling, and drilling through multi-axis collaborative control algorithm;

[0055] Specifically, the spindle speed is dynamically adjusted by judging power fluctuations. When a change in material hardness is detected, the speed is automatically adjusted. The feed rate is optimized by using a fuzzy PID controller based on vibration signals to suppress chatter. The toolpath compensation is driven by temperature sensor data through a thermal deformation model to fine-tune the coordinates of each axis.

[0056] S6. The processing status is displayed in real time via a touch screen, including power curve, vibration spectrum, and temperature change.

[0057] S7. Determine if the system is experiencing an anomaly based on the processing status. If so, issue a tiered warning and provide handling suggestions based on the anomaly. If the system is not experiencing anomalies, maintain the current state and continue operating.

[0058] By adjusting parameters in real time, the machining accuracy of holes is improved. At the same time, predictive maintenance avoids excessive tool wear and extends the tool life, making it easier for workers to process workpieces.

[0059] Meanwhile, the optimized parameters accumulated through the dynamic process library can be quickly applied to similar new workpieces, shortening programming and debugging time. After the operator selects the workpiece material and inputs the processing features on the touch screen, the system automatically calls the initial process parameters from the dynamic process database.

[0060] This invention builds a digital twin model of the machine tool in the CNC system using multi-source data, enabling real-time monitoring of the physical operating status of the boring, milling, and drilling machine. It achieves visualized real-time replication of the equipment's processing conditions and component operating postures, overcoming the limitations of traditional control systems that passively execute preset programs. This provides precise data model support for subsequent intelligent judgment and adaptive control. Furthermore, it presets and stores optimized process parameter combinations for different workpiece materials in a database. Combined with a similarity retrieval algorithm, it quickly matches historically optimal target process schemes, eliminating the need for repeated manual parameter adjustments. This avoids the problems of traditional manual process parameter setting relying on experience and having poor adaptability, significantly improving process selection efficiency and processing accuracy.

[0061] This invention displays processing status data such as power curves, vibration spectra, and temperature changes in real time via a touchscreen, transforming implicit equipment operation and cutting conditions into intuitive and visual charts. Operators can monitor core machine tool operating parameters in real time, facilitating real-time monitoring of the processing process and reducing the difficulty of manual inspection and condition judgment. It abandons the outdated method of relying solely on a single preset threshold to detect faults such as tool breakage and abnormal vibration. Based on multi-sensor fusion of comprehensive operating data, it comprehensively judges equipment anomalies, achieving accurate fault identification, tiered early warning, and simultaneous push of corresponding handling suggestions. This effectively solves the technical pain points of traditional equipment fault detection, such as high false alarm rates, lack of intelligent early warning, and lack of guidance for fault handling, reducing the probability of tool damage and machine tool downtime, and extending the service life of equipment and tools.

[0062] In one embodiment of the automatic control method for boring, milling and drilling machines of the present invention, in step S1, machining process data is collected by vibration sensors, acoustic emission sensors, infrared temperature sensors and power sensors, and a time series alignment algorithm is used to unify multiple data into the same time line, and the central clock of the CNC system is used as the master clock source for all data acquisition hardware.

[0063] It should be noted that all data acquisition is started simultaneously through an external control device to ensure that the data from all channels are aligned in time from the first data point.

[0064] In one embodiment of the automated control method for boring, milling and drilling machines of the present invention, in step S2, a three-dimensional model of the machine tool is constructed using three-dimensional CAD software, including the bed, spindle, each feed axis, tool magazine and worktable.

[0065] Specifically, a precise 3D model of the machine tool is constructed using 3D CAD software, thereby visually displaying the machine tool's real-time posture, motion trajectory, and interference checks. At the same time, a mathematical model describing the physical characteristics of the machine tool is embedded to simulate the machine tool's behavior from the perspective of physical principles.

[0066] The three-dimensional model includes a kinematic model, a dynamic model, a thermodynamic model, and a tool wear model.

[0067] The kinematic model describes the relationship between the motion of each axis and the position of the end-effector; the dynamic model describes the mass, stiffness, and damping characteristics, and is used to calculate vibration and chatter; the thermodynamic model describes the thermal deformation of the machine tool caused by heat sources such as motors, lead screws, and spindle bearings; the tool wear model applies a physical model of the wear formula, which correlates cutting speed, feed rate, material hardness, and wear rate.

[0068] In one embodiment of the automated control method for boring, milling, and drilling machines of the present invention, such as Figure 1 and Figure 2 As shown, step S2 includes the following steps:

[0069] S21. Based on multiple sets of data collected during the processing, the multiple sets of data are fused to obtain fused data;

[0070] S22. The fused data is continuously injected into the digital twin through a real-time communication interface to control the clock synchronization and data update frequency matching between the virtual model and the physical machine tool.

[0071] S23. Drive the movement of components in the 3D model in real time according to the coordinates of each axis of the CNC system.

[0072] Specifically, the multiple sets of data collected by the aforementioned multi-sensor are fused to obtain fused data, which is continuously injected into the digital twin through a real-time communication interface. This ensures that the virtual model and the physical machine tool are synchronized in time and that the data update frequency is matched. Based on the coordinates of each axis of the CNC system, the movement of the components in the three-dimensional model is driven in real time, making the virtual machine tool "move".

[0073] By inputting vibration data into the dynamic model, the current chatter stability boundary is calculated. By inputting temperature data into the thermodynamic model, the thermal deformation of the machine tool is simulated in real time and visualized in the 3D model. The model can also display the currently executing program segment, spindle load, tool number, etc. in real time. The calculation results of the physical model are cross-validated with the sensor data to confirm the accuracy of the model.

[0074] This invention fuses multiple sets of sensor data, including vibration, acoustic emission, temperature, and power, integrating different dimensions of operating conditions. This overcomes the shortcomings of single sensors, such as incomplete information coverage and weak feature representation, resulting in more comprehensive and representative fused data. This provides a highly complete and reliable data source for digital twin modeling. Furthermore, the fused data is continuously injected into the digital twin through a real-time communication interface. Simultaneously, it synchronizes the virtual model with the physical machine tool's clock and matches the data update frequency, eliminating issues such as delayed virtual model updates, inconsistent refresh rates, and disconnect between virtual and real-time sequences. This ensures complete synchronization between the digital twin and the physical machine tool in the time dimension.

[0075] In one embodiment of the automated control method for boring, milling, and drilling machines of the present invention, such as Figure 1 and Figure 2 As shown, after step S2, the following steps are also included:

[0076] S24. Obtain tool wear information and tool wear prediction model;

[0077] S25. Generate tool wear characteristics based on tool wear information;

[0078] S26. Input the tool wear characteristics into the tool wear prediction model to generate the wear curve;

[0079] S27. Obtain real-time tool wear information;

[0080] S28. Based on the real-time wear information and wear curve of the tool, determine whether the tool has reached the tool replacement threshold. If so, issue a tool replacement alarm.

[0081] By using a tool wear prediction model, the vibration spectrum characteristics and cutting force variation trends are analyzed to predict the remaining service life; when the predicted wear exceeds the threshold, a tool change command is automatically triggered or the cutting parameters are adjusted.

[0082] The predictive model, trained using historical wear data, is used to predict future trends and build a tool wear prediction model. An autoencoder is used to discover unknown abnormal patterns and build an anomaly detection model, thereby compensating for the insufficient accuracy of pure physical models in complex and nonlinear scenarios and achieving data-driven insights.

[0083] Furthermore, by simulating the upcoming machining program in virtual space, the parameters of the equipment in the next process can be predicted in advance. Based on the prediction results, the digital twin can recommend a set of optimized cutting parameters to ensure machining quality. The wear characteristics of the current tool are input into the prediction model, and the model will predict the wear curve of the tool at future time points based on the learned sequence rules. When the predicted curve reaches the tool change threshold, the system will issue an early warning and suggest to perform a tool change at an appropriate time.

[0084] This invention constructs and invokes a tool wear prediction model, extracts wear characteristics by combining real-time collected working condition data, and generates a continuous wear curve. It breaks away from the traditional mode of relying solely on fixed thresholds and post-fault detection, transforming post-event detection into early prediction, and realizing dynamic prediction of tool wear trends.

[0085] In one embodiment of the automated control method for boring, milling, and drilling machines of the present invention, such as Figure 3 As shown, after S28, the following steps are also included:

[0086] S29. Obtain the spindle vibration frequency;

[0087] S291. Long-term trend of generating spindle vibration spectrum based on spindle vibration frequency;

[0088] S292. Based on the long-term trend of the spindle vibration spectrum, predict the future working condition of the spindle and arrange maintenance plans in advance according to the future working condition.

[0089] This invention analyzes the long-term trend of the spindle vibration spectrum. When the amplitude of a specific frequency increases slowly, the digital twin can predict that the spindle bearing may fail after several working hours in the future, thereby arranging maintenance plans in advance, avoiding unplanned downtime, and achieving predictive maintenance.

[0090] In one embodiment of the automated control method for boring, milling and drilling machines of the present invention, in step S7, the graded early warning includes prompt early warning, warning early warning and emergency stop early warning.

[0091] Specifically, the alert system addresses minor operational deviations and initial tool wear—non-emergency anomalies—providing only informational reminders without requiring machine shutdown. These can be monitored and adjusted as needed during normal processing, minimizing unnecessary downtime and effectively ensuring production cycle time and processing efficiency. The warning system targets moderate anomalies such as significantly excessive vibration, temperature, or power, and accelerated tool wear, promptly reminding operators to perform interventions such as fine-tuning process parameters and checking tool holder clamping, preventing minor issues from escalating into major malfunctions. The emergency stop warning system addresses serious malfunctions such as impending tool breakage, severe abnormal vibration, and spindle overheating / overload, triggering emergency stop protection to prevent serious consequences such as spindle damage, machine tool deformation, workpiece scrapping, and safety accidents, providing a safety net for equipment and production.

[0092] This invention divides early warnings into three levels: prompt, warning, and emergency stop. It can accurately classify the abnormality of the machine tool, the wear condition of the cutting tool, and the severity of vibration, temperature and power exceeding the standard. This is different from the traditional uniform alarm mode and avoids confusion caused by the same reminder for the same fault, whether it is a big or small fault.

[0093] In one embodiment of the automated control method for boring, milling, and drilling machines of the present invention, the following step is further included after step S7:

[0094] S8. Link and store actual parameters with processing quality data to form a feedback optimization closed loop.

[0095] This invention binds and stores the actual operating process parameters of the machine tool, the sensor condition data, and the corresponding workpiece machining quality data, realizing a one-to-one correspondence between parameters, conditions, and machining quality. This facilitates subsequent tracing of the root cause of quality problems and quickly pinpoints whether the quality deviation is caused by process parameters, tool status, or equipment conditions.

[0096] While specific embodiments of the invention have been described in detail by way of examples, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of the invention. Those skilled in the art should understand that modifications can be made to the above embodiments without departing from the scope and spirit of the invention. The scope of the invention is defined by the appended claims.

Claims

1. An automated control method for a boring, milling, and drilling machine, characterized in that, This includes installing a vibration sensor at the spindle box, an acoustic emission sensor near the tool holder, an infrared temperature sensor pointing towards the tool tip, and a power sensor at the spindle motor. The automated control method for boring and milling drilling machines includes the following steps: S1. Collect multiple sets of data during the processing using vibration sensors, acoustic emission sensors, infrared temperature sensors, and power sensors; S2. Based on multiple sets of data collected during the machining process, a digital twin model of the machine tool is constructed in the CNC system to map the physical entity state in real time; S3. Store the optimized process parameter combinations for different materials in the database; S4. Based on the similarity retrieval algorithm, match the historically optimal target process scheme from the database; S5. Generate adaptive control commands based on real-time status and target process scheme; S6. The processing status is displayed in real time via a touch screen, including power curve, vibration spectrum, and temperature change. S7. Determine if the system is abnormal based on the processing status. If so, push a tiered warning and provide handling suggestions based on the abnormality.

2. The automated control method for boring, milling, and drilling machines according to claim 1, characterized in that, In S1, processing data is collected through vibration sensors, acoustic emission sensors, infrared temperature sensors, and power sensors. A time series alignment algorithm is used to unify multiple data points to the same timeline, and the central clock of the CNC system is used as the master clock source for all data acquisition hardware.

3. The automated control method for boring, milling, and drilling machines according to claim 1, characterized in that, In S2, a three-dimensional model of the machine tool is constructed using 3D CAD software, including the bed, spindle, each feed axis, tool magazine, and worktable.

4. The automated control method for boring, milling, and drilling machines according to claim 1, characterized in that, S2 includes the following steps: S21. Based on multiple sets of data collected during the processing, the multiple sets of data are fused to obtain fused data; S22. The fused data is continuously injected into the digital twin through a real-time communication interface to control the clock synchronization and data update frequency matching between the virtual model and the physical machine tool. S23. Drive the movement of components in the 3D model in real time according to the coordinates of each axis of the CNC system.

5. The automated control method for boring, milling, and drilling machines according to claim 4, characterized in that, Following S23, the following steps are also included: S24. Obtain tool wear information and tool wear prediction model; S25. Generate tool wear characteristics based on tool wear information; S26. Input the tool wear characteristics into the tool wear prediction model to generate the wear curve and the long-term trend of the spindle vibration spectrum based on the spindle vibration frequency. S27. Obtain real-time tool wear information; S28. Based on the real-time wear information and wear curve of the tool, determine whether the tool has reached the tool replacement threshold. If so, issue a tool replacement alarm.

6. The automated control method for boring, milling, and drilling machines according to claim 5, characterized in that, Following S28, the following steps are also included: S29. Obtain the spindle vibration frequency; S291. Long-term trend of generating spindle vibration spectrum based on spindle vibration frequency; S292. Based on the long-term trend of the spindle vibration spectrum, predict the future working condition of the spindle and arrange maintenance plans in advance according to the future working condition.

7. The automated control method for boring, milling, and drilling machines according to claim 1, characterized in that, In S7, the tiered warning system includes alert warning, warning warning, and emergency stop warning.

8. The automated control method for boring, milling, and drilling machines according to claim 1, characterized in that, The following steps are included after S7: S8. Link and store actual parameters with processing quality data to form a feedback optimization closed loop.

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