Numerical control machining control method and device, numerical control machine tool, medium and program product

Abstract

The invention provides a numerical control machining control method and device, a numerical control machine tool, a medium and a program product, and relates to the technical field of numerical control machining. The numerical control machining control method comprises the steps that in response to a received first setting instruction of a self-adaptive learning state, a main shaft of a numerical control machine tool is controlled to machine a part based on a fixed feeding rate under a preset machining condition, and a corresponding main shaft power curve is generated; performing adaptive learning based on the main shaft power curve and the configured power range to obtain a processing model; and in response to a received second setting instruction of the self-adaptive control state, in the machining process of the part, the sensed load of the main shaft is input into the machining model, so that the machining model outputs the feeding rate of the main shaft based on the self-adaptive adjustment operation, and the main shaft operates based on the changing feeding rate. According to the technical scheme, the machining model is used for adjusting the feeding rate of the machine tool spindle in real time based on the detected real-time load, and the machining efficiency is higher.

Description

Background Art

[0002] During the numerical control machining process, due to factors such as material properties, workpiece shape, and tool wear, it is necessary to adjust the machining parameters during the machining process to reduce the influence of these factors. Currently, the adjustment methods generated during machining mainly rely on manual experience. However, as the working conditions of numerical control machining become more and more complex, manual experience is difficult to cope with complex and uncertain changes, which in turn leads to a decline in the machining quality of parts and a reduction in machining efficiency.

[0003] It should be noted that the information disclosed in the above background art section is only used to enhance the understanding of the background of the present disclosure. Therefore, it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0004] The purpose of the present disclosure is to provide a numerical control machining control method, a numerical control machining control device, and a computer program product, which can at least overcome the problems of the decline in the machining quality of parts and the reduction in machining efficiency in the related art to a certain extent.

[0005] Other features and advantages of the present disclosure will become apparent through the following detailed description, or will be learned in part through the practice of the present disclosure.

[0006] According to one aspect of the present disclosure, a numerical control machining control method is provided, including: in response to a first setting instruction of an adaptive learning state, controlling the spindle of the numerical control machine tool to machine a part based on a fixed feed rate under preset machining conditions, and generating a corresponding spindle power curve; performing adaptive learning based on the spindle power curve and a configured power range to obtain a machining model; in response to a second setting instruction of an adaptive control state, during the machining process of the part, inputting the sensed load of the spindle into the machining model, so that the machining model outputs the feed rate of the spindle based on an adaptive adjustment operation, so that the spindle operates based on the changing feed rate.

[0007] In an embodiment of the present disclosure, performing adaptive learning based on the spindle power curve and a configured power range to obtain a machining model includes: determining a corresponding spindle load curve based on the spindle power curve and the fixed feed rate; determining a cutting stage for machining the part based on the spindle load curve; obtaining tool parameters of the tool mounted on the spindle and part parameters of the part based on the machining conditions; predicting a feed speed curve of the part based on the spindle load curve, the cutting stage, the tool parameters, and the part parameters; adjusting the feed speed curve based on the power range to obtain the machining model based on the adjustment result.

[0008] In one embodiment of the present disclosure, adjusting the feed speed curve based on the power range to obtain the machining model based on the adjustment result includes: obtaining a corresponding initial power curve based on the spindle load curve and the feed speed curve; detecting whether the initial power curve is within the power range; if there is a power curve segment exceeding the power range, adjusting the speed of the speed curve segment corresponding to the feed speed curve in the power curve segment, and using the adjusted feed speed curve as the curve model to obtain the machining model based on the curve model.

[0009] In one embodiment of the present disclosure, predicting the feed speed curve of the part based on the spindle load curve, the cutting stage, the tool parameters, and the part parameters includes: constructing a speed prediction model based on the spindle load curve, the cutting stage, the tool parameters, and the part parameters; training the speed prediction model based on the historical machining data with the tool parameters and the part parameters to obtain the speed prediction model; predicting the feed speed curve based on the trained speed prediction model.

[0010] In one embodiment of the present disclosure, inputting the sensed load of the spindle into the machining model so that the machining model outputs the feed rate of the spindle based on the adaptive adjustment operation includes: during the machining of the part, when it is sensed that the load of the spindle increases, the machining model controls the reduction of the feed rate based on the increased amount of the load; when it is sensed that the load of the spindle increases, the machining model controls the increase of the feed rate based on the decreased amount of the load.

[0011] In one embodiment of the present disclosure, it further includes: determining the idle cutting stage for machining the part based on the spindle load curve, and configuring an idle cutting rate for the idle cutting stage, where the idle cutting rate is less than or equal to the maximum allowable rate configured for the spindle; during the machining of the part, it further includes: when it is sensed that the spindle switches from the idle cutting stage to the cutting stage, outputting the deceleration amplitude of the idle cutting rate based on the machining model, and outputting the feed rate for the spindle to start cutting.

[0012] In one embodiment of the present disclosure, the sensed load of the spindle is input into the machining model, and the machining model outputs the feed rate of the spindle based on an adaptive adjustment operation, so that the spindle operates based on the varying feed rate. It further includes: when the sensed load of the spindle exceeds the maximum value in the spindle load curve, or the machining size of the part does not match the preset size, the feed rate of the spindle is reduced to the minimum allowable rate configured for the spindle or machining is stopped until the sensed load of the spindle drops within the range of the spindle load curve, or the machining of the allowance of the machining size is completed.

[0013] In one embodiment of the present disclosure, it further includes: in the adaptive learning state, the maximum allowable rate and the minimum allowable rate are configured based on the wear data of the tool, the set power of the numerical control machine tool, and the machining process requirements of the part.

[0014] In one embodiment of the present disclosure, before responding to the first setting instruction for the adaptive learning state, it further includes: configuring the first setting instruction and the second setting instruction based on the adaptive control identifier, so that when the first setting instruction is received, an adaptive learning state interface is entered, and when the second setting instruction is received, an adaptive control state interface is entered. The adaptive learning state interface displays the spindle power curve, and the adaptive control state interface displays the feed rate curve generated when the spindle operates based on the varying feed rate.

[0015] According to another aspect of the present disclosure, a numerical control machining control device is provided, including: a generation module, configured to, in response to receiving a first setting instruction for the adaptive learning state, control the spindle of the numerical control machine tool to machine a part based on a fixed feed rate under preset machining conditions, and generate a corresponding spindle power curve; a learning module, for adaptively learning based on the spindle power curve and the configured power range to obtain a machining model; an output module, configured to, in response to receiving a second setting instruction for the adaptive control state, during the machining of the part, input the sensed load of the spindle into the machining model, and the machining model outputs the feed rate of the spindle based on an adaptive adjustment operation, so that the spindle operates based on the varying feed rate.

[0016] According to yet another aspect of the present disclosure, a numerical control machine tool is provided, including: a processor; and a memory for storing executable instructions of the processor; the processor is configured to execute the numerical control machining control method described in the first aspect above by executing the executable instructions.

[0017] According to another aspect of the present disclosure, there is provided a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the above-mentioned computer program product is implemented.

[0018] According to still another aspect of the present disclosure, there is provided a computer program product having a computer program stored thereon, and when the computer program is executed by a processor, the above-mentioned computer program product is implemented.

[0019] In the numerical control machining control solution provided by the embodiments of the present disclosure, in the adaptive learning stage, learning is performed through the spindle power curve generated at a fixed feed rate, and the change of the spindle load can be determined based on the change of the spindle power during the machining process, providing an accurate model basis for subsequent adaptive control. In the adaptive control stage, according to the real-time sensed spindle load, the feed rate of the machine tool spindle is adjusted in real time based on the detected real-time load by using the machining model, achieving "fast when it should be fast and slow when it should be slow", realizing the cutting operation of "constant power and variable feed" in the entire machining process, improving the versatility and flexibility of the numerical control machine tool, reducing manual intervention, and making the machining process more automated.

[0020] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure. Obviously, the accompanying drawings in the following description are only some embodiments of the present disclosure, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0022] Figure 1 A schematic diagram showing a numerical control machining control system in an embodiment of the present disclosure;

[0023] Figure 2 A schematic flow chart showing a numerical control machining control method in an embodiment of the present disclosure;

[0024] Figure 3 A schematic flow chart showing another numerical control machining control method in an embodiment of the present disclosure;

[0025] Figure 4 A schematic diagram of the interface of a numerical control machining control system in an embodiment of the present disclosure;

[0026] Figure 5 A schematic diagram of the interface of another numerical control machining control system in an embodiment of the present disclosure;

[0027] Figure 6A schematic diagram of an interface of another CNC machining control system in an embodiment of the present disclosure is shown;

[0028] Figure 7 A schematic diagram of a numerical control machining control device according to an embodiment of the present disclosure is shown;

[0029] Figure 8 A structural block diagram of a CNC machine tool in an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0030] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the disclosure will be more comprehensive and complete and to fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0031] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0032] CNC machining is a common machining method in modern manufacturing. It uses computer-controlled machine tools to achieve high-precision and high-efficiency machining processes. Adaptive control technology is a technology that automatically adjusts the control strategy according to the changes in system parameters in a real-time dynamic environment, which can effectively improve the stability and performance of the system. At present, in the process of CNC machining, due to the influence of factors such as material properties, workpiece shape, and tool wear, the system parameters will change during the machining process. In most CNC machining fields in China, the changes in system parameters during the machining process are mostly controlled and adjusted through human experience. This traditional human control method is often difficult to adapt to complex and uncertain changes, which can easily lead to a decrease in machining quality and machining efficiency. The application of adaptive control technology can automatically adjust the control strategy according to real-time system parameter information, so that the system can respond to external changes in a timely and accurate manner, thereby ensuring the stability and machining accuracy of the machining process.

[0033] According to the present disclosure, a CNC machining control system for CNC machining is an adaptive control system installed on a CNC machine tool system in an embedded manner. The adaptive control system is operated and used through a control panel and a display screen provided by the CNC machine tool.

[0034] As Figure 1 shown, the numerical control processing control system includes an adaptive control module 102, a spindle drive module 104, a control panel 106, and a numerical control program module 108.

[0035] Among them, the adaptive control module 102 receives the spindle load / power value detected in real time by the spindle drive module 104, and the adaptive control module 102 can also issue instructions to the spindle drive module 104 to affect its operating state.

[0036] The adaptive control module 104 can calculate an appropriate feed rate and transmit this information to the control panel 106. The operator can view or adjust relevant parameters through the control panel 106 to achieve human-machine interaction.

[0037] The numerical control program module 108 can send start or cancel instructions to the adaptive control module 104 to control the working state of the adaptive control module 102, and the adaptive control module 102 performs comprehensive processing based on the instructions of the numerical control program module and the information of other modules.

[0038] In some embodiments, the control panel 106 includes system status display, program number and tool number display, power display curve, feed rate multiplier display curve, time display, and other contents.

[0039] In some embodiments, the adaptive control module 102 can include three working modes, namely the feed control mode, the monitoring mode, and the event recording mode. The feed control mode continuously measures the actual spindle load, automatically adjusts the feed rate in real time, and when an overload occurs, alarms and stops the machining, while saving cutting dynamic data and other event data. This mode can be used in the adaptive control state. The monitoring mode continuously measures the actual spindle load but does not adjust the feed rate. This mode can be used in the adaptive learning state. The event recording mode only saves the machining data and status data in the memory of the machine tool and does not perform adaptive control or monitoring.

[0040] In some embodiments, the numerical control processing control system has the functions of automatically collecting, automatically learning, and automatically controlling the numerical control machine tool. By real-time monitoring the spindle load, analyzing and calculating using the internal expert system, that is, the machining model, it automatically adjusts the feed rate multiplier of the machine tool, so as to control the numerical control machine tool to operate in the best state.

[0041] In some embodiments, the numerical control processing control system can effectively improve the machining efficiency of the numerical control machine tool. At the same time, it can realize the protection of the spindle, tool, and parts, real-time monitor the tool damage and wear status, and prevent accidents from occurring.

[0042] In some embodiments, the numerical control processing control system can also retain the actual working conditions of the machine tool within 48 consecutive hours, provide graphic analysis functions and production report functions, and can assist equipment operators in improving processing methods, assist process technicians in iteratively optimizing processing technologies and processing programs, and assist production managers in effectively controlling the production process.

[0043] In some embodiments, the numerical control processing control system can, by real-time monitoring the spindle load of the machine tool, calculate and analyze using the processing model of the internal expert system, and give the optimal feed rate of the machine tool in real time, achieving "fast when it should be fast, slow when it should be slow" and "constant power, variable feed" cutting, and improving efficiency while ensuring part quality, equipment safety, product safety, and tool safety.

[0044] In the present disclosure, through the adaptive numerical control processing control system and the adaptive numerical control processing control method, the processing efficiency can be optimized, the labor intensity of the operator can be reduced, and the tool usage cost and equipment maintenance cost can be saved, significantly improving the efficiency of the numerical control processing process.

[0045] Next, each step of the computer program product in the present exemplary embodiment will be described in more detail in conjunction with the accompanying drawings and embodiments.

[0046] Figure 2 The flowchart of a numerical control processing control method in an embodiment of the present disclosure is shown.

[0047] As Figure 2 shown, the numerical control processing control method according to an embodiment of the present disclosure, applied to a numerically controlled machine tool, includes:

[0048] Step S202, in response to a first setting instruction for the adaptive learning state, control the spindle of the numerically controlled machine tool to machine a part at a fixed feed rate under preset processing conditions, and generate a corresponding spindle power curve.

[0049] In some embodiments, for the machining operation of a part, after the numerically controlled machine tool receives the first setting instruction for the adaptive learning state, according to the preset processing conditions, including processing procedures, processing materials, cutting depths, tool types, and other elements, then control the spindle to machine the part at a fixed feed rate. During the machining process, the power data of the spindle can be real-time collected through the power sensor on the spindle, and a corresponding spindle power curve can be generated.

[0050] In some embodiments, the adaptive learning can learn the entire process of an operator manually controlling the numerically controlled machine tool to machine a product using any tool. During the automatic learning stage, the system can automatically learn the spindle load during the actual machining process of each tool, and collect the spindle load parameters of the entire machining process of each tool for decision-making.

[0051] Step S204: An adaptive learning is performed based on the spindle power curve and the configured power range to obtain a machining model.

[0052] In some embodiments, the configured power range is used to control the machine tool spindle to operate at a relatively constant power within a power range with less fluctuation.

[0053] In some embodiments, in the adaptive learning state, the machine tool spindle has a fixed feed rate. Therefore, based on the spindle power curve, a spindle load curve can be obtained. The adaptive learning process may include: establishing a relationship model between machining parameters and spindle power based on the spindle load curve, that is, the machining model. The machining model is used to predict the power demand of the spindle and the reasonable feed rate under different machining conditions, etc.

[0054] Step S206: In response to the second setting instruction for the adaptive control state, during the machining of the part, the sensed load of the spindle is input into the machining model, so that the machining model outputs the feed rate of the spindle based on the adaptive adjustment operation, so that the spindle operates based on the varying feed rate.

[0055] In some embodiments, when the second setting instruction for the adaptive control state is received, during the actual machining of the part, a force sensor such as a dynamometer can be used to sense the load condition of the spindle, and the sensed load data is input into the machining model. The machining model will analyze and calculate the load data according to the preset adaptive adjustment rules. For example, if the load increases, the model will calculate the value of the feed rate that needs to be reduced; if the load decreases, the model will calculate the value of the feed rate that can be increased, and then the calculated feed rate is output to the spindle control system, so that the spindle operates based on the changed feed rate, thereby realizing adaptive control.

[0056] In this embodiment, in the adaptive learning stage, by learning through the spindle power curve generated at a fixed feed rate, the change of the spindle load can be determined based on the change of the spindle power during the machining process, providing an accurate model basis for the subsequent adaptive control. In the adaptive control stage, according to the real-time sensed spindle load, the machining model adjusts the feed rate of the machine tool spindle in real time based on the detected real-time load, achieving "fast when it should be fast, slow when it should be slow", realizing the cutting operation of "constant power, variable feed" in the entire machining process, improving the versatility and flexibility of the CNC machine tool, reducing manual intervention, and making the machining process more automated.

[0057] As Figure 3 shown, in an embodiment of the present disclosure, performing an adaptive learning based on the spindle power curve and the configured power range to obtain a machining model includes:

[0058] Step S302: Determine the corresponding spindle load curve based on the spindle power curve and a fixed feed rate.

[0059] In some embodiments, according to the generated spindle power curve and the fixed feed rate, using the relationship between power, load, and feed rate, such as the power formula P = Fv, where P is the spindle power, F is the load, and v is the feed rate, the spindle load curve is obtained.

[0060] Step S304: Determine the cutting stage for machining the part based on the spindle load curve.

[0061] In some embodiments, analyze the spindle load curve. Since during the cutting stage, the tool and the workpiece interact, a relatively large load will be generated, while the load is relatively small during the non-cutting stage (such as the air-cutting stage).

[0062] Step S306: Obtain the tool parameters of the tool installed on the spindle and the part parameters of the part based on the machining conditions.

[0063] In some embodiments, based on the preset machining conditions, the tool parameters of the tool installed on the spindle can be obtained from the tool management system of the machine tool. These parameters include the type, size, material, etc. of the tool. The part parameters of the part can be obtained from the design document or database of the part, such as the material properties, shape, size, etc. of the part. By introducing the tool and the corresponding part material into the machining model, for each tool and the corresponding part material during the machining process, it is beneficial to adjust the feed rate to the value that is most suitable for the tool and the part.

[0064] Step S308: Predict the feed speed curve of the part based on the spindle load curve, cutting stage, tool parameters, and part parameters.

[0065] In some embodiments, by comprehensively considering the spindle load curve, cutting stage, tool parameters, and part parameters, a prediction model can be established using machine learning algorithms or methods based on physical models.

[0066] Step S310: Adjust the feed speed curve based on the power range to obtain the machining model based on the adjustment result.

[0067] In some embodiments, according to the configured power range, adjust the predicted feed speed curve, calculate the corresponding power curve under this feed speed curve, check whether this power curve is within the power range. If there is a part that exceeds the power range, adjust the feed speed curve corresponding to this part. For example, reduce the feed speed of the part that exceeds the power upper limit and increase the feed speed of the part that is lower than the power lower limit. Take the adjusted feed speed curve as the curve model, and combine other relevant parameters to finally obtain the machining model.

[0068] In this embodiment, by determining the spindle load curve and the cutting stage, the load change situation and cutting state during the machining process can be understood, providing a prediction basis for subsequent feed speed prediction. In addition, obtaining the tool and part parameters fully considers the influence of the characteristics of the tool and part on the machining process, making the predicted feed speed curve more in line with the actual machining requirements. Adjusting the power range of the feed speed curve ensures that the power during the machining process is in a relatively constant state, enabling the obtained machining model to accurately reflect the various parameter relationships during the machining process and providing a more reliable basis for adaptive control, thereby facilitating the improvement of machining stability, efficiency, and quality.

[0069] In an embodiment of the present disclosure, adjusting the feed speed curve based on the power range to obtain a machining model based on the adjustment result includes:

[0070] Obtaining a corresponding initial power curve based on the spindle load curve and the feed speed curve; detecting whether the initial power curve is within the power range; if there is a power curve segment exceeding the power range, adjusting the speed curve segment in the feed speed curve corresponding to the power curve segment, and using the adjusted feed speed curve as the curve model to obtain the machining model based on the curve model.

[0071] In some embodiments, if it is found that there is a power curve segment exceeding the power range in the initial power curve, it indicates that the currently predicted feed speed will cause large fluctuations in the spindle power during these time periods. For these power curve segments exceeding the power range, find the corresponding speed curve segment in the feed speed curve, and adjust the speed curve segment according to the relationship between power and feed speed. If the power exceeds the upper limit, reduce the feed speed of this speed curve segment; if the power is lower than the lower limit, increase the feed speed of this speed curve segment. After adjustment, a new feed speed curve is obtained.

[0072] In this embodiment, by calculating the initial power curve and comparing it with the power range, the problem of large power fluctuations that may be caused by the predicted feed speed can be discovered in a timely manner to achieve a "constant power, variable feed" cutting process. "Constant power, variable feed" cutting can adjust the feed speed in real time according to factors such as the material and allowance of the part. In the part where the material is softer or the machining allowance is smaller, increase the feed speed to make full use of the power of the machine tool and accelerate the material removal speed; in the part where the material is harder or the machining allowance is larger, reduce the feed speed to ensure that the cutting force is within a reasonable range, keep the spindle running at a constant power, and prevent the machining efficiency from being reduced due to overload. In this way, the overall machining efficiency can be effectively improved on the premise of ensuring the machining quality.

[0073] In an embodiment of the present disclosure, predicting the feed speed curve of a part based on the spindle load curve, cutting stage, tool parameters, and part parameters includes:

[0074] Construct a speed prediction model based on the spindle load curve, cutting stage, tool parameters, and part parameters; train the speed prediction model based on historical machining data with tool parameters and part parameters to obtain the speed prediction model; predict the feed speed curve based on the trained speed prediction model.

[0075] In some embodiments, considering factors such as the spindle load curve, cutting stage, tool parameters, and part parameters comprehensively, select a suitable modeling method to construct the speed prediction model. For example, a modeling method based on physical principles can be adopted. According to the cutting force, etc., establish a mathematical relationship model between the load, tool, part, and feed speed. Or a machine learning algorithm can be used. Take the characteristics of the spindle load curve (such as the average value, maximum value, change rate, etc. of the load), cutting stage information (cutting stage or non-cutting stage), tool parameters (tool type, size, etc.), and part parameters (part material, shape, etc.) as the nodes of the input layer, and the feed speed as the node of the output layer to construct the prediction model.

[0076] In some embodiments, collect historical machining data with tool parameters and part parameters. These data contain information such as the spindle load, cutting stage, and actual feed speed under different machining conditions. Use these historical data to train the speed prediction model. By continuously adjusting the parameters of the model, make the output of the model as close as possible to the actual feed speed.

[0077] In this embodiment, a speed prediction model is constructed using multiple factors, fully considering various actual situations in the machining process, enabling the model to more accurately reflect the relationship between machining parameters such as load and feed speed. Furthermore, the feed rate of the spindle can be reasonably adjusted according to the actual machining situation, thereby improving machining efficiency and quality.

[0078] In an embodiment of the present disclosure, input the sensed load of the spindle into the machining model, so that the machining model outputs the feed rate of the spindle based on the adaptive adjustment operation, including:

[0079] During the machining of a part, when it is sensed that the load of the spindle increases, the machining model controls the reduction of the feed rate based on the increased amount of the load; when it is sensed that the load of the spindle increases, the machining model controls the increase of the feed rate based on the reduced amount of the load.

[0080] In some embodiments, for machining the same part in the adaptive learning state and the adaptive control state, the part can be machined directly according to the adjusted feed speed curve output by the machining model.

[0081] In some embodiments, when the spindle power is constant or limited within a certain range, when it is sensed that the spindle load increases, in order to maintain power stability, the feed rate that needs to be reduced can be calculated in real time according to the power calculation formula. Similarly, when the load decreases, the increase amount of the feed rate can be calculated.

[0082] In some embodiments, when it is sensed that the spindle load changes, the load change amount is input into the trained model, and the model can output the corresponding feed rate adjustment amount. During the machining process, real-time load and feed rate data are continuously collected to update and optimize the established model in real time. As the machining data accumulates continuously, the prediction accuracy of the model will continuously improve.

[0083] In this embodiment, when the load increases, the feed rate is reduced. By reducing the feed rate in a timely manner, the cutting force can be reduced, and the spindle overload can be prevented. When the load decreases, the feed rate is increased, which can shorten the machining time and improve the production efficiency. This way of adaptively adjusting the feed rate enables the spindle to dynamically adjust its operating state according to the actual load conditions, improves the stability and reliability of the machining process, and also reduces the machining cost.

[0084] In an embodiment of the present disclosure, it further includes: determining the idle cutting stage for machining the part based on the spindle load curve, and configuring an idle cutting rate for the idle cutting stage, where the idle cutting rate is less than or equal to the maximum allowable rate configured for the spindle.

[0085] In some embodiments, the spindle load curve is analyzed, and by setting a suitable load threshold, the stage where the load is lower than the threshold is determined as the idle cutting stage. According to the performance of the machine tool, the characteristics of the tool, and the requirements of the machining process, an idle cutting rate is configured for the determined idle cutting stage. The idle cutting rate needs to be less than or equal to the maximum allowable rate configured for the spindle to ensure the safe operation of the spindle during the idle cutting stage. The maximum allowable rate can be determined by factors such as the mechanical structure of the machine tool and the motor power.

[0086] In an embodiment of the present disclosure, during the machining process of the part, it further includes: sensing that the spindle switches from the idle cutting stage to the cutting stage, outputting the deceleration amplitude of the idle cutting rate based on the machining model, and outputting the feed rate for the spindle to start cutting.

[0087] In some embodiments, during the machining of a workpiece, the state of the spindle is monitored in real time. When it is sensed that the spindle switches from the idle cutting stage to the cutting stage, the machining model calculates the deceleration amplitude of the idle cutting rate according to preset algorithms and rules. This deceleration amplitude is to enable the spindle to start cutting at an appropriate feed rate when entering the cutting stage, preventing tool damage or a decline in machining quality due to an excessively high feed rate. According to the calculated deceleration amplitude, the feed rate at which the spindle starts cutting is output, and the spindle control system adjusts the feed rate of the spindle so that the spindle starts cutting at this feed rate.

[0088] In this embodiment, configuring an appropriate idle cutting rate for the idle cutting stage allows the tool to move quickly during the non-cutting stage, improving the utilization efficiency of non-cutting time. Limiting the idle cutting rate within the maximum allowable rate range ensures the safe operation of the spindle during the idle cutting stage. When switching from the idle cutting stage to the cutting stage, calculating the deceleration amplitude through the machining model and adjusting the feed rate can automatically reduce the feed rate when the tool cuts into the material to achieve impact protection for the tool.

[0089] In an embodiment of the present disclosure, the sensed load of the spindle is input into the machining model, and the machining model outputs the feed rate of the spindle based on an adaptive adjustment operation so that the spindle operates based on the varying feed rate. It further includes:

[0090] When the sensed load of the spindle exceeds the maximum value in the spindle load curve, or the machining size of the part does not match the preset size, the feed rate of the spindle is lowered to the minimum allowable rate configured for the spindle or machining is stopped until the sensed load of the spindle drops within the range of the spindle load curve, or the machining of the remaining amount of the machining size is completed.

[0091] In some embodiments, it can be realized that the machining size of the part does not match the preset size by the actual feed rate curve not matching the predicted feed rate curve.

[0092] In some embodiments, by monitoring the power change of the spindle, if it is found that the power abnormally increases or decreases, it may mean that the machining size of the part has changed. It is also possible to monitor machining parameters such as the feed per revolution and cutting depth in real time. If these parameters show abnormal fluctuations during machining, it may cause deviations in the machining size of the part.

[0093] In some embodiments, when a tool breakage event occurs during machining, the system immediately stops machining and issues an alarm simultaneously. This ensures that the operator can replace the tool in a timely manner, thereby avoiding possible serious damage to the part, tool holder, fixture, and machine tool.

[0094] In this embodiment, when the spindle load exceeds the maximum value, reducing the feed rate or stopping the machine can prevent serious damage to the machine tool and the cutting tool caused by overload, protecting the safety and service life of the equipment. When the machining size does not match the preset size, reducing the feed rate can provide more time for the operator to check and adjust the machining process, preventing more scrap from being generated due to continued high-speed machining, improving the machining quality and the finished product rate. After the machining returns to normal, the feed rate is adjusted in a timely manner to a suitable value, ensuring the continuity and efficiency of the machining process. This rapid response and processing mechanism for abnormal situations enhances the stability and reliability of the machining process of the CNC machine tool, reducing the machining risks and costs.

[0095] In one embodiment of the present disclosure, it further includes: in the adaptive learning state, configuring the maximum allowable rate and the minimum allowable rate based on the tool wear data, the set power of the CNC machine tool, and the machining process requirements of the part.

[0096] In some embodiments, during the operation of the CNC machine tool, the cutting tool will gradually wear, and the degree of tool wear will directly affect the machining quality and efficiency. A tool wear monitoring system can be used, or the tool can be measured and inspected regularly to record information such as the tool wear amount and wear pattern to obtain the tool wear data.

[0097] In some embodiments, in the adaptive learning state, it is necessary to clarify the rated power, the maximum power of the CNC machine tool, and the power requirements under different working conditions.

[0098] In some embodiments, different parts have different machining process requirements, and the machining process requirements include machining accuracy, surface quality, machining efficiency, etc.

[0099] In some embodiments, when configuring the maximum allowable rate, it is necessary to consider the tool wear condition and the power limit of the CNC machine tool. If the tool is severely worn, an excessive feed rate may cause tool damage; if the feed rate exceeds the power tolerance range of the machine tool, it will cause the machine tool to be overloaded, affecting the machining quality and the machine tool life. Therefore, the maximum allowable rate should be within the range that the tool can withstand and should not exceed the maximum power of the machine tool.

[0100] In some embodiments, when configuring the minimum allowable rate, it is necessary to consider the balance between machining efficiency and machining quality. If the feed rate is too low, it will result in too long machining time and reduced production efficiency, but if the feed rate is too low to meet the requirements of the machining process, it will also affect the machining quality. Therefore, the minimum allowable rate should ensure the machining quality and, at the same time, improve the machining efficiency as much as possible.

[0101] In this embodiment, by automatically setting the maximum allowable rate and the minimum allowable rate of the cutting curve, the feed rate in the machining process is automatically controlled between the maximum allowable rate and the minimum allowable rate, so as to achieve "constant power, variable feed" cutting while enabling rapid feed during non-cutting tool movements.

[0102] In one embodiment of the present disclosure, before responding to the first setting instruction of the adaptive learning state, it further includes:

[0103] Configuring the first setting instruction and the second setting instruction based on the adaptive control identifier, so that when the first setting instruction is received, it enters the adaptive learning state interface, and when the second setting instruction is received, it enters the adaptive control state interface. The adaptive learning state interface displays the spindle power curve, and the adaptive control state interface displays the feed rate curve generated by the spindle running based on the changing feed rate.

[0104] In some embodiments, it is first necessary to define an adaptive control identifier in the adaptive control system of the numerical control machine tool, which can be represented by "OACM".

[0105] In some embodiments, according to the adaptive control identifier, the first setting instruction and the second setting instruction are configured. The first setting instruction is used to trigger the adaptive learning state. When the control system receives the first setting instruction, the adaptive control identifier is set to a value representing the adaptive learning state. Similarly, the second setting instruction is used to trigger the adaptive control state. When the second setting instruction is received, the adaptive control identifier is set to a value representing the adaptive control state. These two setting instructions can be sent to the control system of the numerical control machine tool through methods such as the operation panel and the host computer software.

[0106] According to the adaptive application requirements, the first setting instruction and the second setting instruction include the start and end instructions added for each tool in the program. The specific instructions are:

[0107] Start command: REF1~

[0108] Specifically: CC_IMD_WRITE(“OACM”,0,0,0,1)

[0109] End command: REF0

[0110] Specifically: CC_IMD_WRITE(“OACM”,0,0,0,999).

[0111] In some embodiments, in the adaptive learning status interface, it is necessary to display the spindle power curve. To achieve this function, a data acquisition and display module needs to be integrated into the control system of the CNC machine tool. In the adaptive learning state, the control system will collect the power data of the spindle in real time and plot these data as a curve and display it on the interface. A professional graphics display library can be used to implement the plotting and display of the curve, and at the same time, some interactive functions such as zooming in, zooming out, and panning are provided to facilitate the operator to observe and analyze the spindle power curve.

[0112] As Figure 4 shown, the adaptive learning status interface includes a data selection area: there is a "process" drop-down box to select the process, the "reference number" is displayed as "4:3 (learning)", and there are also function buttons such as "Open...", "Save Range...", "Print", "Statistics", and "Close".

[0113] The adaptive learning status interface also includes a main display area, which shows the relevant data graph with the "reference number" displayed as "4:3 (learning)" under the selected process. The two curves below are the power curve and the feed curve respectively:

[0114] The first curve is the feed curve, indicating that the equipment feed rate is constant during the time period from 10:51:30 to 10:52:25.

[0115] The second curve is the load curve, which has many fluctuations, representing that the spindle load is constantly changing.

[0116] In some embodiments, in the adaptive control status interface, it is necessary to display the feed rate curve generated by the spindle running based on the changing feed rate. Similarly, a data acquisition and display module needs to be integrated into the control system. In the adaptive control state, the control system will collect the feed rate data of the spindle in real time and plot these data as a curve and display it on the interface. Similar to the adaptive learning status interface, some interactive functions can also be provided to facilitate the operator to observe and analyze the feed rate curve.

[0117] As Figure 5 shown, the adaptive control status interface also includes a data selection area: there is a "process" drop-down box to select the process, the "reference number" is displayed as "4:3 (control)", and there are also function buttons such as "Open...", "Save Range...", "Print", "Statistics", and "Close".

[0118] The adaptive learning status interface also includes a main display area, which shows the relevant data graph with the "reference number" displayed as "4:3 (control)" under the selected process. The two curves below are the power curve and the feed curve respectively:

[0119] The first curve is the feed curve, indicating that the feed rate of the equipment changes during the period from 10:51:30 to 10:52:25.

[0120] The second curve is the load curve, which has many fluctuations, representing that the spindle load is constantly changing. Combining the changing feed rate and spindle load can keep the spindle power within a relatively stable range.

[0121] Such as Figure 6 shown, in the adaptive control state, the feed rate is adjusted in real time according to the load change, and the system automatically senses the speed increase during non-cutting.

[0122] In some embodiments, when the control system receives the first setting instruction, it sets the adaptive control flag to the adaptive learning state and switches to the adaptive learning state interface to display the spindle power curve. When it receives the second setting instruction, it sets the adaptive control flag to the adaptive control state and switches to the adaptive control state interface to display the feed rate curve. In this way, the switching between different states and the display of the corresponding interfaces are realized.

[0123] According to another embodiment of the present disclosure, a numerical control machining control method includes:

[0124] In response to the first setting instruction in the adaptive learning state received, the numerical control machining control system, i.e., the ACM adaptive system, will learn the spindle power fluctuation situation during each machining step and when each tool is cutting in the whole machining process. When the adaptive system is learning, it will record the waveform diagram during normal machining in real time. Through curve analysis, it can be found that regardless of whether it is cutting or idle running at present, and regardless of whether the load is large or small during the machining process, the feed rate is fixed, and what changes is the spindle power. The adaptive learning monitors and records the spindle power curve through implementation.

[0125] After the adaptive learning ends, in response to the second setting instruction of the adaptive control state, the system is set to adaptive control, changing the current cutting state of "constant rate, variable power" to "constant power, variable feed" for machining. After being set to the adaptive control state, the cutting conditions can be monitored in real time during machining. The adaptive control system automatically adjusts the feed rate of each tool path to the most appropriate value. The cutting process is no longer limited by the feed rate set in the program. During the adaptive control process, the feed rate is adjusted in real time according to the change of the machining power. When the load is large, the system can sense it automatically and reduce the feed rate; when the load is small, the system can also sense it automatically and increase the feed rate. During adaptive control, when the tool changes from dry cutting to cutting into the material, the adaptive system immediately reduces the speed, reducing the damage to the tool and the workpiece caused by the impact. When the tool enters the material from dry cutting, the impact protection function is immediately activated. In the system control state, if a sudden situation occurs (such as: overload impact of the tool or workpiece, increase in the allowance of the workpiece blank, etc.), the feed rate will automatically decrease to the minimum allowable rate determined by the adaptive control system. After the extreme situation passes, the feed rate can be adjusted to the maximum allowable rate.

[0126] In some embodiments, a machining model is configured based on an intelligent expert system to continuously monitor the actual load of the spindle based on the machining model. For a specified tool and part material, the most reasonable feed speed is calculated and adjusted in real time. Under the condition of a small load, the feed speed is increased; when the load is large, the feed speed is decreased. In addition, the configured machining model can ensure that key machining parameters do not exceed a reasonable range and can give an alarm for an impending danger and stop the operation of the machine tool when necessary.

[0127] In some embodiments, the machining model obtained through adaptive learning based on the spindle power curve and the configured power range can be generated based on different machining conditions. And according to different machine tool configurations, the system can provide two solutions: software embedded type and hardware external type.

[0128] In this embodiment, the application of adaptive control can effectively improve the stability and performance of the CNC machining system, enabling the system to better adapt to changes in the external environment, thereby improving the machining quality and machining efficiency.

[0129] It should be noted that the above-mentioned drawings are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present invention, rather than for limiting purposes. It is easy to understand that the processes shown in the above-mentioned drawings do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be executed synchronously or asynchronously in, for example, multiple modules.

[0130] Next, refer to Figure 7Describe the numerical control machining control device 700 according to this embodiment of the present invention. Figure 7 The shown numerical control machining control device 700 is merely an example and shall not impose any limitation on the functions and usage scope of the embodiments of the present invention.

[0131] The numerical control machining control device 700 is presented in the form of a hardware module. The components of the numerical control machining control device may include but are not limited to: a generation module 702, which is configured to, in response to a first setting instruction of an adaptive learning state, control the spindle of the numerical control machine tool to machine a part based on a fixed feed rate under preset machining conditions and generate a corresponding spindle power curve; a learning module 704, which is used for the user to perform adaptive learning based on the spindle power curve and a configured power range to obtain a machining model; an output module 706, which is configured to, in response to a second setting instruction of an adaptive control state, during the machining of a part, input the sensed load of the spindle into the machining model, so that the machining model outputs the feed rate of the spindle based on an adaptive adjustment operation, so that the spindle operates based on a varying feed rate.

[0132] Those skilled in the art can understand that various aspects of the present invention can be implemented as a system, a method, or a program product. Therefore, various aspects of the present invention can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which can be collectively referred to as "circuit", "module", or "system" here.

[0133] The following refers to Figure 8 to describe the numerical control machine tool 800 according to this embodiment of the present invention. Figure 8 The shown numerical control machine tool 800 is merely an example and shall not impose any limitation on the functions and usage scope of the embodiments of the present invention.

[0134] As Figure 8 shown, the numerical control machine tool 800 is presented in the form of a general-purpose computing device. The components of the numerical control machine tool 800 may include but are not limited to: the at least one processing unit 810 described above, the at least one storage unit 820 described above, and a bus 830 connecting different system components (including the storage unit 820 and the processing unit 810).

[0135] Among them, the storage unit stores program codes, and the program codes can be executed by the processing unit 810, so that the processing unit 810 executes the steps according to various exemplary embodiments of the present invention described in the "exemplary method" section of this specification above. For example, the processing unit 810 can execute the solution described in steps S202 to S206 as Figure 2 shown.

[0136] The storage unit 820 may include a readable medium in the form of a volatile storage unit, such as a random access memory (RAM) 8201 and / or a cache storage unit 8202, and may further include a read-only memory (ROM) 8203.

[0137] The storage unit 820 may also include a program / utility 8204 having a set (at least one) of program modules 8205. Such program modules 8205 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment.

[0138] The bus 830 may represent one or more of several types of bus structures, including a storage unit bus or storage unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of a variety of bus structures.

[0139] The numerical control machine tool 800 may also communicate with one or more external devices 870 (such as a keyboard, a pointing device, a Bluetooth device, etc.), may also communicate with one or more devices that enable a user to interact with the numerical control machine tool 800, and / or may communicate with any device that enables the numerical control machine tool 800 to communicate with one or more other computing devices (such as a router, a modem, etc.). Such communication may be carried out through an input / output (I / O) interface 850. Moreover, the numerical control machine tool 800 may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 860. As shown in the figure, the network adapter 860 communicates with other modules of the numerical control machine tool 800 through the bus 830. It should be understood that although not shown in the figure, other hardware and / or software modules may be used in conjunction with the numerical control machine tool 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0140] Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software, or can be implemented by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a numerical control machine tool, etc.) to execute the method according to the embodiments of the present disclosure.

[0141] In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of this specification. In some possible implementation manners, various aspects of the present invention may also be implemented in the form of a program product, which includes program code. When the program product runs on a numerically controlled machine tool, the program code is used to cause the numerically controlled machine tool to execute the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of this specification.

[0142] The program product for implementing the above method according to an embodiment of the present invention may be a portable compact disc read-only memory (CD-ROM) and includes program code, and may run on a numerically controlled machine tool, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, the readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0143] The program product may adopt any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0144] The computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries the readable program code. Such a propagated data signal may take various forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The readable signal medium may also be any readable medium other than the readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0145] The program code contained on the readable medium may be transmitted by any appropriate medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination of the above.

[0146] The program code for performing the operations of the present invention can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., by connecting through the Internet using an Internet service provider).

[0147] It should be noted that although several modules or units of a device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more of the above-described modules or units can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0148] In addition, although the steps of the methods in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in that specific order, or that all of the steps shown must be performed to achieve the desired result. Additionally or alternatively, some steps can be omitted, multiple steps can be combined into one step for execution, and / or one step can be decomposed into multiple steps for execution, etc.

[0149] From the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software, or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (which can be a personal computer, a server, a mobile terminal, or a numerical control machine tool, etc.) to execute the methods according to the embodiments of the present disclosure.

[0150] Other embodiments of the present disclosure will be readily apparent to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include known common general knowledge or conventional technical means in the technical field not disclosed in the present disclosure. The specification and examples are only to be considered as exemplary, and the true scope and spirit of the present disclosure are pointed out by the appended claims.

Claims

1. A numerical control machining control method, characterized in that: Applied to CNC machine tools, including: In response to the received first setting instruction of the adaptive learning state, the spindle of the CNC machine tool is controlled to process the part based on a fixed feed rate under preset processing conditions, and a corresponding spindle power curve is generated; Performing adaptive learning based on the spindle power curve and the configured power range to obtain a machining model; In response to a second setting instruction of the received adaptive control state, during the machining of the part, the sensed load of the spindle is input into the machining model, so that the machining model outputs the feed rate of the spindle based on an adaptive adjustment operation, so that the spindle operates based on the changed feed rate.

2. The CNC machining control method according to claim 1, characterized in that: Adaptive learning is performed based on the spindle power curve and the configured power range to obtain a machining model, including: determining a corresponding spindle load curve based on the spindle power curve and the fixed feed rate; determining a cutting phase for machining the part based on the spindle load curve; Acquiring tool parameters of a tool mounted on the spindle and part parameters of the part based on the machining conditions; predicting a feed rate profile of the part based on the spindle load profile, the cutting stage, the tool parameters, and the part parameters; The feed speed curve is adjusted based on the power range to obtain the machining model based on the adjustment result.

3. The CNC machining control method according to claim 2, characterized in that: Adjusting the feed speed curve based on the power range to obtain the machining model based on the adjustment result includes: Obtaining a corresponding initial power curve based on the spindle load curve and the feed speed curve; Detecting whether the initial power curve is within the power range; If there is a power curve segment that exceeds the power range, speed adjustment is performed on the speed curve segment in the feed speed curve corresponding to the power curve segment, and the adjusted feed speed curve is used as a curve model to obtain the processing model based on the curve model.

4. The CNC machining control method according to claim 2, characterized in that: Predicting a feed rate curve of the part based on the spindle load curve, the cutting stage, the tool parameters and the part parameters comprises: constructing a speed prediction model based on the spindle load curve, the cutting stage, the tool parameters and the part parameters; Performing model training on the speed prediction model based on historical processing data having the tool parameters and the part parameters to obtain the speed prediction model; The feed speed curve is predicted based on the trained speed prediction model.

5. The CNC machining control method according to claim 1, characterized in that: Inputting the sensed load of the spindle into the machining model so that the machining model outputs the feed rate of the spindle based on an adaptive adjustment operation, comprising: During the process of machining the part, it is sensed that the load of the spindle increases, and the machining model controls the feed rate to decrease based on the increase in the load; An increase in the load on the spindle is sensed, and the feed rate is increased by control based on the amount of reduction in the load by the machining model.

6. The CNC machining control method according to claim 2, characterized in that: Also includes: Determine an air cutting stage for machining the part based on the spindle load curve, and configure an air cutting rate for the air cutting stage, wherein the air cutting rate is less than or equal to a maximum allowable rate configured for the spindle; The processing of the parts also includes: The spindle is sensed to switch from the air cutting stage to the cutting stage, and the speed reduction amplitude of the air cutting rate is output based on the processing model, and the feed rate of the spindle to start cutting is output.

7. The CNC machining control method according to claim 6, characterized in that: The sensed load of the spindle is input into the machining model, so that the machining model outputs the feed rate of the spindle based on an adaptive adjustment operation, so that the spindle operates based on the changed feed rate, and further includes: If the sensed load of the spindle exceeds the maximum value in the spindle load curve, or the machining size of the part does not match the preset size, the feed rate of the spindle is reduced to the minimum allowable rate of the spindle configuration or the machining is stopped until the sensed load of the spindle is reduced to within the range of the spindle load curve, or the machining of the excess of the machining size is completed.

8. The CNC machining control method according to claim 7, characterized in that: Also includes: In the adaptive learning state, the maximum allowable rate and the minimum allowable rate are configured based on wear data of the tool, a set power of the CNC machine tool, and a machining process requirement of a part.

9. The CNC machining control method according to any one of claims 1 to 8, characterized in that: Before responding to the received first setting instruction of the adaptive learning state, the method further includes: The first setting instruction and the second setting instruction are configured based on the adaptive control identifier, so that when the first setting instruction is received, an adaptive learning state interface is entered, and when the second setting instruction is received, an adaptive control state interface is entered, the adaptive learning state interface displays the spindle power curve, and the adaptive control state interface displays the feed rate curve generated by the spindle based on the changed feed rate operation.

10. A numerically controlled machine tool, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; Wherein, the processor is configured to execute the CNC machining control method according to any one of claims 1 to 9 by executing the executable instructions.

Images