Cutter acceleration control method and system based on machining resistance analysis

Abstract

The invention relates to the technical field of numerical control machining, in particular to a tool acceleration control method and system based on machining resistance analysis. According to the scheme, collected data are analyzed in detail through a machine learning algorithm, and a specific mode and an abnormal state in the machining process are recognized; the feeding rate of the cutter is automatically adjusted according to the analysis result so as to adapt to the change of material characteristics; the cutting speed is adjusted in real time according to the cutter wear degree and the spindle load, and the cutter service life and the machining quality are maximized; an emergency response mechanism is arranged, and when overload or potential cutter breaking risks are detected, operation is adjusted or shutdown is conducted immediately, so that the cutter and a workpiece are protected against damage; and after machining is completed, the system collects final machining effect data and compares the final machining effect data with a preset target, iterative optimization is conducted on a control strategy through feedback circulation, and risk management and control and efficiency improvement of the cutting process of the tool are achieved by rapidly segmenting the working process of the tool.

Description

Technical Field

[0001] The present invention relates to the technical field of numerical control machining, and more specifically, to a tool acceleration control method and system based on machining resistance analysis. Background Art

[0002] In the field of CNC machining, the method, significance and importance of studying tool control are reflected in the precise monitoring and intelligent adjustment of the tool's motion trajectory, cutting speed and feed rate to achieve fine control of the workpiece material removal process, improve machining accuracy and surface quality, and ensure the stability and economy of the machining process. This control strategy not only helps to extend tool life and reduce production costs, but also adapts to complex and changing machining tasks and meets the needs of modern manufacturing for efficient, flexible and intelligent production. Therefore, in-depth research on tool control technology plays a vital role in promoting the development of CNC machining technology.

[0003] Before the invention, the existing tool control methods mainly relied on preset program instructions and mechanical adjustment mechanisms, such as G codes and mechanical blocks. These methods had the following problems in processing: 1) Long air cutting time: For the safety of processing, a safety distance had to be set, which increased the processing time; 2) Short tool life: When the tool contacts a high-hardness workpiece, it generates impact and tool chatter, resulting in tool damage and shortened life; 3) Low processing efficiency: The place with the largest cutting volume limits the processing speed, resulting in slow processing speed; 4) Large dimensional error: "Letting the tool go" leads to dimensional error and deviation in incoming materials. The technical difficulty lies in how to accurately perceive the processing status in real time and respond quickly, and the key point is to improve the intelligence level of the system, realize dynamic optimization and precise control of parameters such as tool path, cutting force and feed speed, so as to adapt to changes in material properties and requirements of processing technology. Summary of the invention

[0004] In view of the above problems, the present invention proposes a tool acceleration control method and system based on machining resistance analysis, which realizes risk control and efficiency improvement of the tool cutting process by quickly segmenting the tool's working process.

[0005] According to a first aspect of an embodiment of the present invention, a tool acceleration control method based on machining resistance analysis is provided.

[0006] In one or more embodiments, preferably, the tool acceleration control method based on machining resistance analysis includes:

[0007] Use sensors and data acquisition boards to monitor the cutting force, vibration and position of the tool in real time;

[0008] Detailed analysis of the collected data using machine learning algorithms to identify specific patterns and abnormal conditions in the processing process;

[0009] Automatically adjust the tool feed rate based on analysis results to adapt to changes in material properties;

[0010] According to the tool wear degree and spindle load, the cutting speed is adjusted in real time to maximize the tool life and processing quality;

[0011] Set up an emergency response mechanism to immediately adjust the operation or shut down the machine when overload or potential tool breakage risk is detected to protect the tool and workpiece from damage;

[0012] After processing is completed, the system collects the final processing effect data and compares it with the preset goals, and iteratively optimizes the control strategy through feedback loops.

[0013] In one or more embodiments, preferably, the use of sensors and data acquisition boards to monitor the cutting force, vibration and position of the tool in real time specifically includes:

[0014] A three-axis force sensor is installed at a preset position on the machine tool to measure the cutting force on the tool;

[0015] accelerometers to monitor the vibration characteristics of the tool;

[0016] Use encoder to record the real-time position of the tool;

[0017] Deploy infrared sensors to monitor cutting tools.

[0018] In one or more embodiments, preferably, the detailed analysis of the collected data by a machine learning algorithm to identify specific patterns and abnormal conditions in the processing process specifically includes:

[0019] Obtain historical data of the same workpiece and perform automatic data analysis;

[0020] Set the first stage as the fast entry stage, the second stage as the protection start stage, the third stage as the accelerated working stage, the fourth stage as the unprotected acceleration stage, and the fifth stage as the fast exit stage;

[0021] The time period satisfying the first calculation formula is set as the first stage;

[0022] The settings that satisfy the second calculation formula are set to the fifth stage;

[0023] The third stage is set for those that meet the third calculation formula;

[0024] The stage before the third stage and after the first stage is set as the second stage;

[0025] The stage after the third stage and before the fifth stage is set as the fourth stage;

[0026] The first calculation formula is:

[0027] t <T×k1

[0028] Z <Y1

[0029] Wherein, t is the current time, T is the total processing time, k1 is the first comparison coefficient, Z is the real-time resistance, and Y1 is the preset first resistance comparison value;

[0030] The second calculation formula is:

[0031] t>T×k2

[0032] Z <Y1

[0033] Wherein, k2 is the second contrast coefficient;

[0034] The third calculation formula is:

[0035] T×k3 <t<T×k4

[0036] Z>Y2

[0037] Among them, Y2 is the preset second resistance comparison value, k3 is the third comparison coefficient, and k4 is the fourth comparison coefficient.

[0038] In one or more embodiments, preferably, the automatic adjustment of the feed rate of the tool according to the analysis result to adapt to the change of material properties specifically includes:

[0039] When calculating the tool at the current moment in the first stage, if the resistance at the current moment satisfies the fourth calculation formula, the tool travel speed is reduced to the protection operation state according to the preset reverse resistance, and forced to adjust to the second stage, otherwise it continues to move according to the acceleration preset in the first stage;

[0040] When the traveling stage is in the second stage, and the speed is at the preset speed of the second stage, the traveling speed will not be reduced, and the resistance at the current moment will be continuously monitored. If the fifth calculation formula is satisfied, the vehicle will be forcibly switched to the third stage and travel at the preset speed of the third stage;

[0041] When in the third stage, if the time that does not satisfy the fifth calculation formula exceeds 10ms, the third stage is exited and the fourth stage is entered, and the vehicle moves at the preset speed of the fourth stage;

[0042] When entering the fifth stage, the vehicle moves at the preset speed of the fifth stage;

[0043] The fourth calculation formula is:

[0044] |Z - -Z + |÷jg>zy

[0045] Among them, Z + is the tool resistance at the current moment, Z - is the tool resistance at the current moment, jg is the adoption interval, and zy is the resistance variation margin;

[0046] The fifth calculation formula is:

[0047] Z>Y2.

[0049] In one or more embodiments, preferably, adjusting the cutting speed in real time according to the tool wear degree and the spindle load to maximize the tool life and processing quality specifically includes:

[0050] Obtain the current maximum wear size of the tool in real time;

[0051] If it is determined that the sixth calculation formula is satisfied, the cutting speed is adjusted using the seventh calculation formula;

[0052] The sixth calculation formula is:

[0053] CMAX>s2×YC

[0054] Among them, CMAX is the current maximum wear size of the tool, s2 is the preset wear coefficient, and YC is the tool wear margin;

[0055] The seventh calculation formula is:

[0056] VD=V0÷s2

[0057] Among them, VD is the cutting speed and V0 is the current cutting speed.

[0058] In one or more embodiments, preferably, the emergency response mechanism is set to immediately adjust the operation or shut down the machine when overload or potential tool breakage risk is detected to protect the tool and workpiece from damage, specifically including:

[0059] Define and set the assessment parameters for overload and tool breakage risks, including the maximum allowable value of cutting force, critical frequency and amplitude of tool vibration;

[0060] Continuously analyzing the monitoring data to identify whether there is a risk of overload or blade breakage. If the assessment parameters of the overload and blade breakage risks are exceeded, it is considered that there is a risk of overload or blade breakage;

[0061] Once the monitoring data exceeds any preset risk threshold, the emergency response mechanism is immediately activated, wherein the emergency response mechanism includes automatically reducing the feed rate of the machine tool, suspending the processing operation or completely stopping the machine tool operation;

[0062] After an emergency stop, the system should lock all machine tool operations, and after the operator confirms and takes corresponding measures, manually reset the system and gradually resume processing.

[0063] In one or more embodiments, preferably, after the processing is completed, the system collects the final processing effect data and compares it with the preset target, and iteratively optimizes the control strategy through a feedback loop, specifically including:

[0064] After the processing is completed, the sensors and measuring tools deployed on the machine tool are used to collect data on the final processing effect, which includes the workpiece dimensional accuracy, surface roughness, and detailed records of any cutting marks or errors;

[0065] Compare and analyze the collected processing effect data with the preset processing target parameters to form geometric dimension deviation, surface quality evaluation and processing efficiency measurement;

[0066] According to the results of comparative analysis, the machining control strategy is adjusted and optimized, including but not limited to tool path, feed rate and speed parameters. The control strategy is adjusted after each iteration until the preset machining goal is achieved or exceeded.

[0067] According to a second aspect of an embodiment of the present invention, a tool acceleration control system based on machining resistance analysis is provided.

[0068] In one or more embodiments, preferably, the tool acceleration control system based on machining resistance analysis includes:

[0069] Data acquisition module, used to monitor the cutting force, vibration and position of the tool in real time using sensors and data acquisition boards;

[0070] The stage learning module is used to conduct detailed analysis of the collected data through machine learning algorithms to identify specific patterns and abnormal conditions in the processing process;

[0071] The stage adjustment and acceleration setting module is used to automatically adjust the tool feed rate based on the analysis results to adapt to changes in material properties;

[0072] Depth adjustment module, used to adjust cutting speed in real time according to tool wear and spindle load to maximize tool life and processing quality;

[0073] Emergency warning module, used to set up emergency response mechanism, when overload or potential tool breakage risk is detected, immediately adjust operation or shut down to protect tool and workpiece from damage;

[0074] Iterative optimization module: After the processing is completed, the system collects the final processing effect data and compares it with the preset target, and iteratively optimizes the control strategy through feedback loop.

[0075] According to a third aspect of an embodiment of the present invention, there is provided a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method as described in any one of the first aspect of the embodiment of the present invention is implemented.

[0076] According to a fourth aspect of an embodiment of the present invention, there is provided an electronic device, comprising a memory and a processor, wherein the memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement any one of the methods described in the first aspect of the embodiment of the present invention.

[0077] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0078] In the solution of the present invention, by using sensors and data acquisition boards, the real-time monitoring system can continuously track key operating parameters such as cutting force, vibration and position of the tool to achieve efficient online control.

[0079] In the solution of the present invention, operation and shutdown protection are centrally performed through emergency response, and the entire tool use process can be accelerated.

[0080] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0081] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0083] Figure 1 The present invention is a flowchart of a tool acceleration control method based on machining resistance analysis according to an embodiment of the present invention.

[0084] Figure 2The present invention is a flowchart of a tool acceleration control method based on machining resistance analysis, which uses sensors and a data acquisition board to monitor the cutting force, vibration and position of a tool in real time.

[0085] Figure 3 It is a flowchart of a tool acceleration control method based on machining resistance analysis in one embodiment of the present invention, which uses a machine learning algorithm to perform detailed analysis on collected data and identify specific patterns and abnormal conditions in the machining process.

[0086] Figure 4 The present invention is a flowchart of a tool acceleration control method based on machining resistance analysis, which automatically adjusts the tool feed rate according to the analysis result to adapt to the change of material characteristics.

[0087] Figure 5 It is a flow chart of a tool acceleration control method based on machining resistance analysis in one embodiment of the present invention, which adjusts the cutting speed in real time according to the tool wear degree and spindle load to maximize the tool service life and machining quality.

[0088] Figure 6 It is a flowchart of setting an emergency response mechanism in a tool acceleration control method based on machining resistance analysis in one embodiment of the present invention, which immediately adjusts the operation or shuts down the machine when overload or potential tool breakage risk is detected to protect the tool and workpiece from damage.

[0089] Figure 7 It is a flowchart of a tool acceleration control method based on machining resistance analysis in which, after machining is completed, the system collects final machining effect data and compares it with a preset target, and iteratively optimizes the control strategy through a feedback loop.

[0090] Figure 8 It is a structural diagram of a tool acceleration control system based on machining resistance analysis according to an embodiment of the present invention.

[0091] Fig. 9 It is a structural diagram of an electronic device in one embodiment of the present invention. DETAILED DESCRIPTION

[0092] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0093] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0094] In the field of CNC machining, the method, significance and importance of studying tool control are reflected in the precise monitoring and intelligent adjustment of the tool's motion trajectory, cutting speed and feed rate to achieve fine control of the workpiece material removal process, aiming to improve machining accuracy, surface quality, and ensure the stability and economy of the machining process. This control strategy not only helps to extend tool life and reduce production costs, but also adapts to complex and changing machining tasks and meets the needs of modern manufacturing for efficient, flexible and intelligent production. Therefore, in-depth research on tool control technology plays a vital role in promoting the development of CNC machining technology and improving the overall competitiveness of the manufacturing industry.

[0095] Before the invention, the existing tool control methods mainly relied on preset program instructions and mechanical adjustment mechanisms, such as G codes and mechanical blocks. These methods had the following problems in processing: 1) Long air cutting time: For the safety of processing, a safety distance had to be set, which increased the processing time; 2) Short tool life: When the tool contacts a high-hardness workpiece, it generates impact and tool chatter, resulting in tool damage and shortened life; 3) Low processing efficiency: The place with the largest cutting volume limits the processing speed, resulting in slow processing speed; 4) Large dimensional error: "Letting the tool go" leads to dimensional error and deviation in incoming materials. The technical difficulty lies in how to accurately perceive the processing status in real time and respond quickly, and the key point is to improve the intelligence level of the system, realize dynamic optimization and precise control of parameters such as tool path, cutting force and feed speed, so as to adapt to changes in material properties and requirements of processing technology.

[0096] In an embodiment of the present invention, a tool acceleration control method and system based on machining resistance analysis are provided. The solution achieves risk control and efficiency improvement of the tool cutting process by quickly segmenting the tool's working process.

[0097] According to a first aspect of an embodiment of the present invention, a tool acceleration control method based on machining resistance analysis is provided.

[0098] Figure 1 The present invention is a flowchart of a tool acceleration control method based on machining resistance analysis according to an embodiment of the present invention.

[0099] In one or more embodiments, preferably, the tool acceleration control method based on machining resistance analysis includes:

[0100] S101, using sensors and data acquisition boards to monitor the cutting force, vibration and position of the tool in real time;

[0101] S102, performing detailed analysis of the collected data through machine learning algorithms to identify specific patterns and abnormal conditions in the processing process;

[0102] S103, automatically adjusting the feed rate of the tool according to the analysis result to adapt to the change of material properties;

[0103] S104, adjust the cutting speed in real time according to the tool wear degree and spindle load to maximize the tool life and processing quality;

[0104] S105. Set up an emergency response mechanism to immediately adjust the operation or shut down the machine when overload or potential tool breakage risk is detected to protect the tool and workpiece from damage;

[0105] S106. After the processing is completed, the system collects the final processing effect data and compares it with the preset target, and iteratively optimizes the control strategy through a feedback loop.

[0106] In the embodiment of the present invention, a tool acceleration control method is provided, the key point of which is to improve the intelligence level of the system, realize dynamic optimization and precise control of parameters such as tool path, cutting force and feed speed, so as to adapt to the changes in material properties and the requirements of processing technology. The tool acceleration control method and system based on processing resistance analysis have great advantages in the field of CNC processing. Through the optimization and adjustment of intelligent algorithms, not only can the processing efficiency be significantly improved, but also the service life of the tool can be effectively extended, bringing significant economic and production benefits.

[0107] Figure 2 The present invention is a flowchart of a tool acceleration control method based on machining resistance analysis, which uses sensors and a data acquisition board to monitor the cutting force, vibration and position of a tool in real time.

[0108] like Figure 2 As shown, in one or more embodiments, preferably, the use of sensors and data acquisition boards to monitor the cutting force, vibration and position of the tool in real time specifically includes:

[0109] S201, installing a three-axis force sensor at a preset position of the machine tool to measure the cutting force exerted on the tool;

[0110] S202, an accelerometer to monitor the vibration characteristics of the tool;

[0111] S203, using an encoder to record the real-time position of the tool;

[0112] S204, deploy infrared sensors to monitor cutting tools.

[0113] An integrated sensor system and data acquisition board are used to monitor the cutting force, vibration, and position of the tool on the CNC machine tool in real time. First, a three-axis force sensor is installed near the turret of the machine tool. The sensor can accurately measure the cutting force of the tool in three orthogonal directions (X, Y, and Z axes). This sensor usually contains piezoelectric elements, which can convert mechanical signals into electrical signals, allowing the operator to understand the forces exerted on the tool at different cutting stages. Next, to monitor the vibration characteristics of the tool, an accelerometer is installed on the tool holder. This sensor can detect the tiny vibrations generated by the tool during the machining process, and its output data is crucial for identifying unstable phenomena in the cutting process, such as cutting chatter or unexpected contact between the tool and the material. In order to accurately record the real-time position of the tool, an encoder is installed on the spindle of the machine tool. This device can indirectly measure the position of the tool by calculating the precise angle of spindle rotation. This is particularly important for maintaining machining accuracy and manufacturing complex parts, ensuring that each cutting path complies with the preset machining program. Finally, to monitor the temperature of the tool and workpiece, infrared sensors are deployed in the working area of ​​the machine tool. This type of sensor is able to measure the surface temperature of an object without contact by capturing infrared radiation emitted from the surface of the tool and the workpiece. Temperature monitoring is a key measure to avoid overheating that causes accelerated tool wear or changes in the material properties of the workpiece. All these sensors are connected to a high-speed data acquisition board that has the ability to synchronously acquire and process different sensor signals. Through real-time analysis and visualization of the collected data by professional software, the operator can instantly obtain detailed information about the tool status and processing process, and adjust the machine tool parameters based on this information to optimize processing efficiency and quality. In this embodiment, the selection and deployment of all sensors and data acquisition devices take into account factors such as electromagnetic interference, mechanical vibration, and temperature and humidity changes in the industrial environment to ensure the stability and reliability of the monitoring system. In addition, the design of the system allows for rapid adaptation to different machine tool configurations and processing tasks, providing a universal and efficient solution to improve the accuracy and efficiency of CNC machining.

[0114] Figure 3 It is a flowchart of a tool acceleration control method based on machining resistance analysis in one embodiment of the present invention, which uses a machine learning algorithm to perform detailed analysis on collected data and identify specific patterns and abnormal conditions in the machining process.

[0115] like Figure 3 As shown, in one or more embodiments, preferably, the collected data is analyzed in detail by a machine learning algorithm to identify specific patterns and abnormal conditions in the processing process, specifically including:

[0116] S301, obtaining historical data of the same workpiece and performing automatic data analysis;

[0117] S302, setting the first stage as the fast entry stage, the second stage as the protection start stage, the third stage as the acceleration work stage, the fourth stage as the unprotected acceleration stage, and the fifth stage as the fast exit stage;

[0118] S303, setting the time period satisfying the first calculation formula as the first stage;

[0119] S304, setting the items satisfying the second calculation formula as the fifth stage;

[0120] S305, setting the items satisfying the third calculation formula as the third stage;

[0121] S306, the stage before the third stage and after the first stage is set as the second stage;

[0122] S307, setting the stage after the third stage and before the fifth stage to the fourth stage;

[0123] The first calculation formula is:

[0124] t <T×k1

[0125] Z <Y1

[0126] Wherein, t is the current time, T is the total processing time, k1 is the first comparison coefficient, Z is the real-time resistance, and Y1 is the preset first resistance comparison value;

[0127] The second calculation formula is:

[0128] t>T×k2

[0129] Z <Y1

[0130] Wherein, k2 is the second contrast coefficient;

[0131] The third calculation formula is:

[0132] T×k3 <t<T×k4

[0133] Z>Y2

[0134] Among them, Y2 is the preset second resistance comparison value, k3 is the third comparison coefficient, and k4 is the fourth comparison coefficient.

[0135] In an embodiment of the present invention, historical processing data of the same workpiece is obtained, and these data cover the entire process from pre-processing to finished product. Using data analysis software, these data are automatically analyzed to facilitate the learning and recognition of various processing states and patterns by machine learning algorithms. Based on the analysis results, the processing process is divided into five stages: a rapid entry stage, a protection activation stage, an accelerated working stage, an unprotected acceleration stage, and a rapid exit stage. The settings of these stages are designed to optimize the service life of the tool and the processing efficiency, while ensuring the workpiece quality. Specifically, the following calculation formulas are set to define each stage: In the first stage (rapid entry stage), the condition is satisfied: t < T × k1 and Z < Y1; where t is the current time, T is the total processing duration, k1 is the first comparison coefficient, Z is the real-time resistance, and Y1 is the preset first resistance comparison value. In the fifth stage (rapid exit stage), the condition is satisfied: t > T × k2 and Z < Y1; where k2 is the second comparison coefficient. In the third stage (accelerated working stage), the condition is satisfied: T × k3 < t < T × k4 and Z > Y2; where Y2 is the preset second resistance comparison value, k3 is the third comparison coefficient, and k4 is the fourth comparison coefficient. The second stage (protection activation stage) is set to be after the first stage and before the third stage. The fourth stage (unprotected acceleration stage) is set to be after the third stage and before the fifth stage.

[0136] Figure 4 It is a flowchart of automatically adjusting the feed rate of the tool according to the analysis result to adapt to the change of material properties in a tool acceleration control method based on processing resistance analysis according to an embodiment of the present invention.

[0137] As Figure 4 shown, in one or more embodiments, preferably, the automatically adjusting the feed rate of the tool according to the analysis result to adapt to the change of material properties specifically includes:

[0138] S401. When calculating the tool at the current moment in the first stage, if the resistance at the current moment satisfies the fourth calculation formula, the tool traveling speed is reduced to the protection operation state according to the preset reverse resistance, and it is forcibly adjusted to the second stage. Otherwise, it continues to travel at the acceleration preset in the first stage;

[0139] S402. When the traveling stage is in the second stage and the speed is at the preset speed in the second stage, the traveling speed is not reduced, and the current moment resistance is continuously monitored. If it satisfies the fifth calculation formula, it is forcibly switched to the third stage and travels at the preset speed in the third stage;

[0140] S403. When in the third stage, if the time when the fifth calculation formula is not satisfied exceeds 10 ms, it exits the third stage, enters the fourth stage, and travels at the preset speed in the fourth stage;

[0141] S404, when entering the fifth stage, proceeding at the preset speed of the fifth stage;

[0142] The fourth calculation formula is:

[0143] |Z - -Z + |÷jg>zy

[0144] Among them, Z + is the tool resistance at the current moment, Z - is the tool resistance at the current moment, jg is the adoption interval, and zy is the resistance variation margin;

[0145] The fifth calculation formula is:

[0146] Z>Y2.

[0148] In the embodiment of the present invention, the tool path and feed rate are automatically adjusted according to the real-time data analysis results to adapt to the changes in material properties. This process is achieved through integrated sensor data, machine learning algorithms and dynamic adjustment mechanisms. First, when calculating the tool at the current moment in the first stage, the system checks whether the resistance at the current moment satisfies the fourth calculation formula: |(Z + -Z - )÷jg|>zy; where Z + and Z -Respectively represent the tool resistance before and after the current moment, jg is the sampling interval, and zy is the preset resistance change margin. If this formula is satisfied, it means that the resistance change exceeds the allowable range, and the system will automatically reduce the tool's travel speed to the protection operation state, and forcibly adjust the processing stage to the second stage. If this formula is not satisfied, the tool continues to move at the acceleration preset in the first stage. In the second stage, if the tool's travel speed is already at the preset speed of the second stage, the system will not further reduce the travel speed. At the same time, the system will continue to monitor the resistance at the current moment. If the fifth calculation formula is satisfied: Z>Y2; where Y2 is the preset second resistance comparison value, the system will forcibly switch to the third stage and move at the preset speed of the third stage. In the third stage, the system will continue to monitor whether the resistance satisfies the fifth calculation formula. If the time that the formula is not satisfied exceeds 10ms, the system determines that the processing conditions of the third stage are no longer needed, and thus exits the third stage, enters the fourth stage, and moves at the preset speed of the fourth stage. Finally, when the processing enters the fifth stage, the system will move at the preset fifth stage speed until the processing is completed. Through this dynamic adjustment mechanism, the present invention can respond to changes in material properties in real time, optimize the processing process, and improve processing efficiency and workpiece quality. This adaptive adjustment is particularly suitable for occasions where material properties may change significantly during processing, such as processing of composite materials or inhomogeneous materials.

[0149] Figure 5 It is a flow chart of a tool acceleration control method based on machining resistance analysis in one embodiment of the present invention, which adjusts the cutting speed in real time according to the tool wear degree and spindle load to maximize the tool service life and machining quality.

[0150] like Figure 5 As shown, in one or more embodiments, preferably, the cutting speed is adjusted in real time according to the tool wear degree and the spindle load to maximize the tool life and processing quality, specifically including:

[0151] S501, obtaining the current maximum loss size of the tool in real time;

[0152] S502, if it is determined that the sixth calculation formula is satisfied, the cutting speed is adjusted using the seventh calculation formula;

[0153] The sixth calculation formula is:

[0154] CMAX>s2×YC

[0155] Among them, CMAX is the current maximum wear size of the tool, s2 is the preset wear coefficient, and YC is the tool wear margin;

[0156] The seventh calculation formula is:

[0157] VD=V0÷s2

[0158] Among them, VD is the cutting speed and V0 is the current cutting speed.

[0159] In an embodiment of the present invention, the cutting speed and depth are dynamically adjusted by real-time monitoring of the wear degree of the tool and the temperature of the workpiece material, aiming to maximize the tool life and processing quality. This process uses sensor data, real-time feedback mechanism and calculation algorithm to achieve the goal of optimizing processing parameters. First, the system obtains the current maximum loss size (CMAX) of the tool and the temperature (DT) of the tool in real time. These data are obtained by sensors installed on the machine tool, such as using infrared sensors to measure the tool temperature, and obtaining the tool wear size by optical or contact measurement methods. Then, the system evaluates whether the sixth calculation formula is satisfied, which takes into account the hardness of the workpiece material (not directly given, but it can be inferred that it is one of the influencing factors), the temperature of the tool and the wear degree of the tool: workpiece material hardness DT>s1×YT; CMAX>s2×YC; workpiece material hardness DT>s1×YT; CMAX>s2×YC; where DT is the temperature of the tool, s1 is the preset temperature coefficient, and YT is the preset temperature margin; CMAX is the current maximum loss size of the tool, s2 is the preset wear coefficient, and YC is the tool wear margin. If the above conditions are met, indicating that the tool is severely worn or the temperature is too high, the system will automatically adjust the cutting speed and depth according to the seventh calculation formula: SD = S0 ÷ s1; VD = V0 ÷ s2; where SD is the adjusted cutting depth, VD is the adjusted cutting speed; S0 is the cutting depth before adjustment, and V0 is the cutting speed before adjustment. This adjustment mechanism reduces the cutting depth and speed accordingly when the tool wear increases or the temperature rises, thereby reducing further tool wear and avoiding overheating, extending the tool life and maintaining the processing quality. On the contrary, if the tool is in good condition, the system may moderately increase the cutting depth and speed to improve processing efficiency.

[0160] Figure 6 It is a flowchart of setting an emergency response mechanism in a tool acceleration control method based on machining resistance analysis in one embodiment of the present invention, which immediately adjusts the operation or shuts down the machine when overload or potential tool breakage risk is detected to protect the tool and workpiece from damage.

[0161] like Figure 6 As shown, in one or more embodiments, preferably, the emergency response mechanism is set to immediately adjust the operation or shut down when overload or potential tool breakage risk is detected to protect the tool and workpiece from damage, specifically including:

[0162] S601, defining and setting the assessment parameters of overload and tool breakage risk, including the maximum allowable value of cutting force, critical frequency and amplitude of tool vibration;

[0163] S602, continuously analyzing the monitoring data to identify whether there is a risk of overload or tool breakage. If the risk exceeds the assessment parameters of the overload and tool breakage risk, it is considered that there is a risk of overload or tool breakage;

[0164] S603: Once the monitoring data exceeds any preset risk threshold, the emergency response mechanism is immediately activated, wherein the emergency response mechanism includes automatically reducing the feed rate of the machine tool, suspending the processing operation, or completely stopping the operation of the machine tool;

[0165] S604. After an emergency stop, the system should lock all machine tool operations, and after the operator confirms and takes corresponding measures, manually reset the system and gradually resume processing.

[0166] In an embodiment of the present invention, an emergency response mechanism is configured to take immediate action to protect the tool and workpiece from damage when an overload or potential tool breakage risk is detected. The mechanism maintains the safety and reliability of the machining operation by real-time monitoring, evaluation and response to key parameters in the machining process. First, a series of evaluation parameters are defined and set to determine the risk of overload and tool breakage. These parameters include the maximum allowable value of the cutting force, the critical frequency and amplitude of the tool vibration, and the safety threshold of the tool temperature. These parameters are predetermined based on the physical properties of the tool and workpiece materials and the performance characteristics of the machine tool. During the machining process, the system collects data in real time, such as cutting force, tool vibration and temperature, which are obtained by sensors installed on the machine tool. Subsequently, these data are transmitted to the analysis module for continuous analysis to identify whether there is a risk of overload or tool breakage. The analysis module uses an algorithm to compare the monitoring data with the preset risk assessment parameters. If any parameter is monitored to exceed its threshold, it is considered that there is a risk of overload or tool breakage. Once the risk is determined, the emergency response mechanism is immediately activated. The mechanism can be to automatically reduce the feed rate of the machine tool, suspend the machining operation, or completely stop the machine tool in more dangerous situations. For example, if it is detected that the tool temperature suddenly rises to a level close to the safety threshold, the system may first reduce the feed rate to try to reduce the tool load and cool it down; if the temperature continues to rise above the safety threshold, the machine tool may be stopped directly to prevent tool damage or workpiece scrapping. After an emergency stop, the system locks all machine tool operations to prevent restarting the machine tool without inspection and confirmation, which may cause more serious damage or safety accidents. The operator must check and take appropriate measures, such as changing the tool or readjusting the processing parameters, and then manually reset the system and gradually resume processing.

[0167] Figure 7It is a flowchart of a tool acceleration control method based on machining resistance analysis in which, after machining is completed, the system collects final machining effect data and compares it with a preset target, and iteratively optimizes the control strategy through a feedback loop.

[0168] like Figure 7 As shown, in one or more embodiments, preferably, after the processing is completed, the system collects the final processing effect data and compares it with the preset target, and iteratively optimizes the control strategy through a feedback loop, specifically including:

[0169] S701. After the processing is completed, use sensors and measuring tools deployed on the machine tool to collect data on the final processing effect, wherein the data on the final processing effect includes detailed records of workpiece dimensional accuracy, surface roughness, and any cutting marks or errors;

[0170] S702, comparing and analyzing the collected processing effect data with the preset processing target parameters to form geometric dimension deviation, surface quality evaluation and processing efficiency measurement;

[0171] S703. Adjust and optimize the machining control strategy according to the results of the comparative analysis, and continue to adjust the feed rate and speed parameters in the control strategy after each iteration until the preset machining target is reached or exceeded.

[0172] In an embodiment of the present invention, a data analysis and control strategy optimization process after processing is described, aiming to improve processing efficiency and quality through continuous iteration. The process uses sensors and measuring tools on the machine tool to collect data and continuously optimize processing parameters through feedback loops. First, when the processing task is completed, the system uses sensors and measuring tools installed on the machine tool, such as laser scanners, three-coordinate measuring machines or optical comparators, to collect data on the final processing effect. These data include detailed records of the dimensional accuracy, surface roughness and any cutting marks or errors of the workpiece. These indicators directly reflect the comprehensive effect of tool performance and machine tool status during the processing process. Subsequently, the system will compare and analyze the collected actual processing effect data with the preset processing target parameters. This analysis involves calculating the deviation of geometric dimensions, evaluating surface quality, and measuring processing efficiency. For example, if the preset target is that the surface roughness of a workpiece is Ra0.8 microns, and the actual measurement result is Ra 1.2 microns, the system will record this deviation and analyze its cause. Based on the results of the comparative analysis, the system will adjust and optimize the processing control strategy. This includes but is not limited to the adjustment of tool paths, feed rates and speed parameters. Based on the analysis results, the system automatically makes suggestions for improvement, such as increasing the use of cutting fluid to reduce temperature, adjusting the tool path to avoid vibration, etc. After each iteration, the system will continue to adjust the control strategy until the preset processing goals are met or exceeded. This process may go through multiple iterations, each of which is analyzed and optimized based on the data of the previous processing. This feedback loop ensures that the processing process can be continuously improved, gradually approaching or even exceeding the quality requirements.

[0173] According to a second aspect of an embodiment of the present invention, a tool acceleration control system based on machining resistance analysis is provided.

[0174] Figure 8 It is a structural diagram of a tool acceleration control system based on machining resistance analysis according to an embodiment of the present invention.

[0175] In one or more embodiments, preferably, the tool acceleration control system based on machining resistance analysis includes:

[0176] The data acquisition module 801 is used to monitor the cutting force, vibration and position of the tool in real time using sensors and data acquisition boards;

[0177] The stage learning module 802 is used to perform detailed analysis of the collected data through machine learning algorithms to identify specific patterns and abnormal conditions in the processing process;

[0178] The stage adjustment and acceleration setting module 803 is used to automatically adjust the feed rate of the tool according to the analysis results to adapt to the changes in material properties;

[0179] A depth adjustment module 804 is used to adjust the cutting speed in real time according to the tool wear degree and spindle load to maximize the tool life and processing quality;

[0180] The emergency warning module 805 is used to set up an emergency response mechanism, and when an overload or potential tool breakage risk is detected, the operation is immediately adjusted or the machine is shut down to protect the tool and the workpiece from damage;

[0181] Iterative optimization module 806 is used for the system to collect the final processing effect data and compare it with the preset target after the processing is completed, and iteratively optimize the control strategy through a feedback loop.

[0182] In the embodiment of the present invention, a system suitable for different structures is realized through a series of modular designs. The system can achieve closed-loop, reliable and efficient execution through collection, analysis and control.

[0183] According to a third aspect of an embodiment of the present invention, there is provided a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method as described in any one of the first aspect of the embodiment of the present invention is implemented.

[0184] According to a fourth aspect of an embodiment of the present invention, an electronic device is provided. Fig. 9 It is a structural diagram of an electronic device in one embodiment of the present invention. Fig. 9 The electronic device shown is a general tool acceleration control device based on machining resistance analysis. Fig. 9 The electronic device 900 includes one or more (only one is shown in the figure) processors 902, a memory 904 and a wireless module 906 coupled to each other. The memory 904 stores a program that can execute the content of the above-mentioned embodiment, and the processor 902 can execute the program stored in the memory 904.

[0185] The processor 902 may include one or more processing cores. The processor 902 uses various interfaces and lines to connect various parts of the entire electronic device 900, and executes various functions and processes data of the electronic device 900 by running or executing instructions, programs, code sets or instruction sets stored in the memory 904, and calling data stored in the memory 904. Optionally, the processor 902 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), and programmable logic array (PLA). The processor 902 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU mainly processes the operating system, the user interface, and the target application; the GPU is responsible for rendering and drawing the display content; and the modem is used to process wireless communication. It is understandable that the above-mentioned modem may not be integrated into the processor 902, but may be implemented separately through a communication chip.

[0186] The memory 904 may include a random access memory (RAM) or a read-only memory (ROM). The memory 904 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 904 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the following various method embodiments, etc. The data storage area may also store data (such as the aforementioned text document) created by the electronic device 900 during use, etc.

[0187] The wireless module 906 is used to receive and send electromagnetic waves, realize the mutual conversion between electromagnetic waves and electrical signals, and thus communicate with a communication network or other equipment, for example, communicate with a base station based on a mobile communication protocol. The wireless module 906 may include various existing circuit elements for performing these functions, for example, an antenna, a radio frequency transceiver, a digital signal processor, an encryption / decryption chip, a user identity module (SIM) card, a memory, etc. The wireless module 906 may communicate with various networks such as the Internet, an intranet, a wireless network, or communicate with other electronic devices through a wireless network. The above-mentioned wireless network may include a cellular telephone network, a wireless local area network, or a metropolitan area network. The above-mentioned wireless network may use various communication standards, protocols, and technologies, including but not limited to WLAN protocols and Bluetooth protocols, and may even include those protocols that have not yet been developed.

[0188] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0189] In the solution of the present invention, by using sensors and data acquisition boards, the real-time monitoring system can continuously track key operating parameters such as cutting force, vibration and position of the tool to achieve efficient online control.

[0190] In the solution of the present invention, operation and shutdown protection are centrally performed through emergency response, and the entire tool use process can be accelerated.

[0191] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0192] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0193] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0194] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0195] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A tool acceleration control method based on machining resistance analysis, characterized in that: The method includes: Use sensors and data acquisition boards to monitor the cutting force, vibration and position of the tool in real time; Detailed analysis of the collected data using machine learning algorithms to identify specific patterns and abnormal conditions in the processing process; Automatically adjust the tool feed rate based on analysis results to adapt to changes in material properties; According to the tool wear degree and spindle load, the cutting speed is adjusted in real time to maximize the tool life and processing quality; Set up an emergency response mechanism to immediately adjust the operation or shut down the machine when overload or potential tool breakage risk is detected to protect the tool and workpiece from damage; After processing is completed, the system collects the final processing effect data and compares it with the preset goals, and iteratively optimizes the control strategy through feedback loops.

2. A tool acceleration control method based on machining resistance analysis as claimed in claim 1, characterized in that: The use of sensors and data acquisition boards to monitor the cutting force, vibration and position of the tool in real time specifically includes: A three-axis force sensor is installed at a preset position on the machine tool to measure the cutting force on the tool; Monitoring the vibration characteristics of the tool through accelerometers; Use encoder to record the real-time position of the tool; Deploy infrared sensors to monitor cutting tools.

3. A tool acceleration control method based on machining resistance analysis as claimed in claim 1, characterized in that: The collected data is analyzed in detail through machine learning algorithms to identify specific patterns and abnormal conditions in the processing process, including: Obtain historical data of the same workpiece and perform automatic data analysis; Set the first stage as the fast entry stage, the second stage as the protection start stage, the third stage as the accelerated working stage, the fourth stage as the unprotected acceleration stage, and the fifth stage as the fast exit stage; The time period satisfying the first calculation formula is set as the first stage; The settings that satisfy the second calculation formula are set to the fifth stage; The third stage is set for those that meet the third calculation formula; The stage before the third stage and after the first stage is set as the second stage; The stage after the third stage and before the fifth stage is set as the fourth stage; The first calculation formula is: Wherein, t is the current time, T is the total processing time, k1 is the first comparison coefficient, Z is the real-time resistance, and Y1 is the preset first resistance comparison value; The second calculation formula is: Wherein, k2 is the second contrast coefficient; The third calculation formula is: Among them, Y2 is the preset second resistance comparison value, k3 is the third comparison coefficient, and k4 is the fourth comparison coefficient.

4. A tool acceleration control method based on machining resistance analysis as claimed in claim 3, characterized in that: The automatic adjustment of the feed rate of the tool according to the analysis results to adapt to the change of material properties specifically includes: When calculating the tool at the current moment in the first stage, if the resistance at the current moment satisfies the fourth calculation formula, the tool travel speed is reduced to the protection operation state according to the preset reverse resistance, and forced to adjust to the second stage, otherwise it continues to move according to the acceleration preset in the first stage; When the traveling stage is in the second stage, and the speed is at the preset speed of the second stage, the traveling speed will not be reduced, and the resistance at the current moment will be continuously monitored. If the fifth calculation formula is satisfied, the vehicle will be forcibly switched to the third stage and travel at the preset speed of the third stage; When in the third stage, if the time that does not satisfy the fifth calculation formula exceeds 10ms, the third stage is exited and the fourth stage is entered, and the vehicle moves at the preset speed of the fourth stage; When entering the fifth stage, the vehicle moves at the preset speed of the fifth stage; The fourth calculation formula is: |From - -WITH + |÷jg>zy Among them, Z + is the tool resistance at the current moment, Z - is the tool resistance at the current moment, jg is the adoption interval, and zy is the resistance variation margin; The fifth calculation formula is: Z>Y2.

5. A tool acceleration control method based on machining resistance analysis as claimed in claim 4, characterized in that: The cutting speed is adjusted in real time according to the tool wear degree and spindle load to maximize the tool life and processing quality, specifically including: Obtain the current maximum wear size of the tool in real time; If it is determined that the sixth calculation formula is satisfied, the cutting speed is adjusted using the seventh calculation formula; The sixth calculation formula is: CMAX>s2×YC Among them, CMAX is the current maximum wear size of the tool, s2 is the preset wear coefficient, and YC is the tool wear margin; The seventh calculation formula is: VD=V0÷s2 Among them, VD is the cutting speed and V0 is the current cutting speed.

6. The tool acceleration control method based on machining resistance analysis as claimed in claim 1, characterized in that: The emergency response mechanism is set up to immediately adjust the operation or shut down the machine when overload or potential tool breakage risk is detected to protect the tool and workpiece from damage, including: Define and set the assessment parameters for overload and tool breakage risks, including the maximum allowable value of cutting force, critical frequency and amplitude of tool vibration; Continuously analyzing the monitoring data to identify whether there is a risk of overload or blade breakage. If the assessment parameters of the overload and blade breakage risks are exceeded, it is considered that there is a risk of overload or blade breakage; Once the monitoring data exceeds any preset risk threshold, the emergency response mechanism is immediately activated, wherein the emergency response mechanism includes automatically reducing the feed rate of the machine tool, suspending the processing operation or completely stopping the machine tool operation; After an emergency stop, the system should lock all machine tool operations, and after the operator confirms and takes corresponding measures, manually reset the system and gradually resume processing.

7. The tool acceleration control method based on machining resistance analysis as claimed in claim 1, characterized in that: After the processing is completed, the system collects the final processing effect data and compares it with the preset target, and iteratively optimizes the control strategy through a feedback loop, specifically including: After the processing is completed, the sensors and measuring tools deployed on the machine tool are used to collect data on the final processing effect, which includes the workpiece dimensional accuracy, surface roughness, and detailed records of any cutting marks or errors; Compare and analyze the collected processing effect data with the preset processing target parameters to form geometric dimension deviation, surface quality evaluation and processing efficiency measurement; The feed rate and speed parameters in the control strategy continue to be adjusted after each iteration until the preset machining target is reached or exceeded.

8. A tool acceleration control system based on machining resistance analysis, characterized in that: The system is used to implement the method according to any one of claims 1 to 7, and the system comprises: Data acquisition module, used to monitor the cutting force, vibration and position of the tool in real time using sensors and data acquisition boards; The stage learning module is used to conduct detailed analysis of the collected data through machine learning algorithms to identify specific patterns and abnormal conditions in the processing process; The stage adjustment and acceleration setting module is used to automatically adjust the tool feed rate based on the analysis results to adapt to changes in material properties; Depth adjustment module, used to adjust cutting speed in real time according to tool wear and spindle load to maximize tool life and processing quality; Emergency warning module, used to set up emergency response mechanism, when overload or potential tool breakage risk is detected, immediately adjust operation or shut down to protect tool and workpiece from damage; Iterative optimization module: After the processing is completed, the system collects the final processing effect data and compares it with the preset target, and iteratively optimizes the control strategy through feedback loop.

9. A computer-readable storage medium storing computer program instructions, characterized in that: The computer program instructions implement the method according to any one of claims 1 to 7 when executed by a processor.

10. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method according to any one of claims 1-7.

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