A tool acceleration control method and system based on machining resistance analysis

Abstract

The present application relates to the technical field of numerical control machining, more specifically, to a tool acceleration control method and system based on machining resistance analysis. The scheme includes detailed analysis of collected data through machine learning algorithms to identify specific patterns and abnormal states in the machining process; automatically adjust the feed rate of the tool according to the analysis results to adapt to changes in material properties; adjust the cutting speed in real time according to the tool wear degree and spindle load to maximize tool service life and machining quality; set up an emergency response mechanism to immediately adjust the operation or stop when overload or potential tool breakage risk is detected to protect the tool and workpiece from damage; after machining is completed, the system compares the final machining effect data with the preset target and iteratively optimizes the control strategy through a feedback loop, and through fast segmentation of the tool working process, risk control and efficiency improvement of the tool cutting process are realized.

Description

Technical Field

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

[0002] In the field of CNC machining, the research on the methods, significance, and importance of tool control lies in achieving precise control of the workpiece material removal process through precise monitoring and intelligent adjustment of the tool's motion trajectory, cutting speed, and feed rate, thereby improving machining accuracy and surface quality while ensuring 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, meeting the modern manufacturing industry's demand 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 emergence of the technology of the present 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 have the following problems in processing: 1) long air cutting time: for the safety of processing, a safety distance has to be set, resulting in increased processing time; 2) short tool life: when the tool contacts a high-hardness workpiece, impact occurs, tool chatter occurs, 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 go" leads to dimensional error and deviation in incoming materials. The difficulty of the technology lies in how to accurately perceive the processing status in real time and respond quickly. The key point is to improve the intelligence level of the system and realize dynamic optimization and precise control of parameters such as tool path, cutting force and feed speed 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 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] Adjust cutting speed in real time according to tool wear and spindle load to maximize tool life and processing quality;

[0011] Set up an emergency response mechanism to immediately adjust operations 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 target, and iteratively optimizes the control strategy through feedback loop.

[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 using 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 period that satisfies the first calculation formula is set as the first stage;

[0022] The fifth stage is set for those that meet the second calculation formula;

[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] Where 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, automatically adjusting the feed rate of the tool according to the analysis results to adapt to changes in material properties specifically includes:

[0039] When calculating the tool's current state in the first stage, if the resistance at the current moment satisfies the fourth calculation formula, the tool's travel speed is reduced to a protective operating state according to the preset reverse resistance, and the tool is forcibly adjusted to the second stage. Otherwise, the tool continues to move according to the acceleration preset in the first stage.

[0040] When the travel stage is in the second stage and the speed is at the preset speed of the second stage, the travel 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 will be carried out at the preset speed of the third stage;

[0041] If the time when the fifth calculation formula is not satisfied exceeds 10ms during the third stage, the vehicle exits the third stage and enters the fourth stage, and moves at the speed preset in 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] wherein, Z + is the current moment tool resistance, Z - is the current moment tool resistance, jg is an interval, and zy is a resistance change margin;

[0046] The fifth calculation formula is:

[0047] Z>Y2

[0048] In one or more embodiments, preferably, the real-time adjustment of the cutting speed according to the tool wear degree and the spindle load, maximizes the tool service life and the processing quality, specifically comprising:

[0049] obtaining the current maximum wear size of the tool in real time;

[0050] if the sixth calculation formula is met, adjusting the cutting speed by using the seventh calculation formula;

[0051] The sixth calculation formula is:

[0052] CMAX>s2x YC

[0053] wherein, CMAX is the current maximum wear size of the tool, s2 is a preset wear coefficient, and YC is a tool wear degree margin;

[0054] The seventh calculation formula is:

[0055] VD=V0÷s2

[0056] wherein, VD is the cutting speed, and V0 is the current cutting speed.

[0057] In one or more embodiments, preferably, the emergency response mechanism is set to immediately adjust the operation or stop to protect the tool and the workpiece from damage when the overload or the potential tool breakage risk is detected, specifically comprising:

[0058] defining and setting the evaluation parameters of the overload and the tool breakage risk, including the maximum allowable value of the cutting force, the critical frequency and amplitude of the tool vibration;

[0059] continuously analyzing the monitoring data to identify whether there is an overload or a tool breakage risk, and if the evaluation parameters of the overload and the tool breakage risk are exceeded, it is considered that there is an overload or a tool breakage risk;

[0060] once the monitoring data exceeds any one of the preset risk thresholds, the emergency response mechanism is immediately started, wherein the emergency response mechanism includes automatically reducing the feed rate of the machine tool, pausing the processing operation or completely stopping the machine tool from running;

[0061] After an emergency stop, the system should lock all machine tool operations. After the operator confirms and takes appropriate measures, the system should be manually reset and processing should be gradually resumed.

[0062] In one or more embodiments, preferably, after the processing is completed, the system collects final processing effect data and compares it with the preset target, and iteratively optimizes the control strategy through a feedback loop, specifically including:

[0063] After machining is completed, sensors and measurement tools deployed on the machine tool are used to collect data on the final machining results, including workpiece dimensional accuracy, surface roughness, and detailed records of any cutting marks or errors;

[0064] 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;

[0065] Based on the results of the 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 goals are achieved or exceeded.

[0066] According to a second aspect of an embodiment of the present invention, a tool acceleration control system based on machining resistance analysis is provided.

[0067] In one or more embodiments, preferably, the tool acceleration control system based on machining resistance analysis includes:

[0068] Data acquisition module, used to monitor the cutting force, vibration and position of the tool in real time using sensors and data acquisition boards;

[0069] 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;

[0070] A 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;

[0071] Depth adjustment module, used to adjust the cutting speed in real time according to the degree of tool wear and spindle load, maximizing tool life and processing quality;

[0072] Emergency warning module, used to set up emergency response mechanism, immediately adjust operation or shut down the machine when overload or potential tool breakage risk is detected to protect tool and workpiece from damage;

[0073] The iterative optimization module is used to collect the final processing effect data after the processing is completed, compare it with the preset target, and iteratively optimize the control strategy through feedback loop.

[0074] According to a third aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method according to any one of the first aspect of the embodiment of the present invention is implemented.

[0075] According to a fourth aspect of an embodiment of the present invention, an electronic device is provided, 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.

[0076] The technical solutions provided by the embodiments of the present invention may have the following beneficial effects:

[0077] In the solution of the present invention, by utilizing 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, thereby achieving efficient online control.

[0078] In the solution of the present invention, operation and shutdown protection are centralized through emergency response, and the entire tool use process can be accelerated.

[0079] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purposes 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.

[0080] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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.

[0082] 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.

[0083] Figure 2A flowchart of real-time monitoring of cutting force, vibration, and position of the tool using sensors and data acquisition boards in a machining resistance analysis-based tool acceleration control method according to an embodiment of the present invention.

[0084] Figure 3 A flowchart of detailed analysis of collected data through machine learning algorithms to identify specific patterns and abnormal states in the machining process in a machining resistance analysis-based tool acceleration control method according to an embodiment of the present invention.

[0085] Figure 4 A flowchart of automatic adjustment of the feed rate of the tool according to the analysis results to adapt to changes in material properties in a machining resistance analysis-based tool acceleration control method according to an embodiment of the present invention.

[0086] Figure 5 A flowchart of real-time adjustment of cutting speed according to the degree of tool wear and spindle load to maximize tool life and machining quality in a machining resistance analysis-based tool acceleration control method according to an embodiment of the present invention.

[0087] Figure 6 A flowchart of setting an emergency response mechanism to immediately adjust the operation or stop to protect the tool and workpiece from damage when detecting overload or potential tool breakage risk in a machining resistance analysis-based tool acceleration control method according to an embodiment of the present invention.

[0088] Figure 7 A flowchart of comparing the final machining effect data with the preset target after machining is completed, and iteratively optimizing the control strategy through a feedback loop in a machining resistance analysis-based tool acceleration control method according to an embodiment of the present invention.

[0089] Figure 8 A structural diagram of a machining resistance analysis-based tool acceleration control system according to an embodiment of the present invention.

[0090] Figure 9 A structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0091] 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 may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. 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., and do not represent the order of precedence, nor do they limit "first" and "second" to be different types.

[0092] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making any creative efforts shall fall within the scope of protection of the present invention.

[0093] In the field of CNC machining, the research on the methods, significance, and importance of tool control lies in achieving precise control of the workpiece material removal process through precise monitoring and intelligent adjustment of the tool's motion trajectory, cutting speed, and feed rate. This aims 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, meeting the modern manufacturing industry's demand 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.

[0094] Before the emergence of the technology of the present 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 have the following problems in processing: 1) long air cutting time: for the safety of processing, a safety distance has to be set, resulting in increased processing time; 2) short tool life: when the tool contacts a high-hardness workpiece, impact occurs, tool chatter occurs, 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 go" leads to dimensional error and deviation in incoming materials. The difficulty of the technology lies in how to accurately perceive the processing status in real time and respond quickly. The key point is to improve the intelligence level of the system and realize dynamic optimization and precise control of parameters such as tool path, cutting force and feed speed to adapt to changes in material properties and requirements of processing technology.

[0095] In an embodiment of the present invention, a tool acceleration control method and system based on machining resistance analysis is provided. This solution achieves risk control and efficiency improvement of the tool cutting process by quickly segmenting the tool's working process.

[0096] According to a first aspect of an embodiment of the present invention, a tool acceleration control method based on machining resistance analysis is provided.

[0097] 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.

[0098] In one or more embodiments, preferably, the tool acceleration control method based on machining resistance analysis includes:

[0099] S101, using sensors and data acquisition boards to monitor the cutting force, vibration and position of the tool in real time;

[0100] S102. Analyze the collected data in detail using machine learning algorithms to identify specific patterns and abnormal conditions in the processing process;

[0101] S103, automatically adjusting the feed rate of the tool according to the analysis results to adapt to changes in material properties;

[0102] S104, adjust cutting speed in real time according to tool wear and spindle load to maximize tool life and processing quality;

[0103] 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;

[0104] 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.

[0105] In an embodiment of the present invention, a tool acceleration control method is provided. Its key focus is on improving the system's intelligence level, enabling dynamic optimization and precise control of parameters such as tool path, cutting force, and feed rate to adapt to changes in material properties and machining process requirements. This tool acceleration control method and system, based on machining resistance analysis, offers significant advantages in the field of CNC machining. Through intelligent algorithm optimization and adjustment, it significantly improves machining efficiency and effectively extends tool life, resulting in significant economic and production benefits.

[0106] 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 the tool in real time.

[0107] 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:

[0108] S201, installing a three-axis force sensor at a preset position of the machine tool to measure the cutting force exerted on the tool;

[0109] S202, an accelerometer to monitor the vibration characteristics of the tool;

[0110] S203, using an encoder to record the real-time position of the tool;

[0111] S204: Deploy infrared sensors to monitor cutting tools.

[0112] An integrated sensor system and data acquisition board card are used to monitor the cutting force, vibration, and position of the tool in real time on a CNC machine. First, a three-axis force sensor is installed near the tool turret of the machine. This sensor can accurately measure the cutting force of the tool in three orthogonal directions (X, Y, and Z axes). The sensor usually contains piezoelectric elements that can convert mechanical signals into electrical signals, allowing the operator to understand the force the tool is subjected to during different cutting stages. Then, to monitor the vibration characteristics of the tool, an accelerometer is installed on the tool holder. This sensor can detect the small vibrations generated by the tool during machining, 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. To accurately record the real-time position of the tool, an encoder is installed on the spindle of the machine. This device can indirectly measure the position of the tool by calculating the precise angle of the spindle rotation. This is particularly important for maintaining machining accuracy and manufacturing complex parts, ensuring that each cutting path meets the preset machining program. Finally, to monitor the temperature of the tool and workpiece, an infrared sensor is deployed in the working area of the machine. This type of sensor can non-contact measure the surface temperature of objects by capturing infrared radiation emitted from the tool and workpiece surface. Temperature monitoring is a key measure to avoid overheating that can accelerate tool wear or change the properties of the workpiece material. All these sensors are connected to a high-speed data acquisition board card that has the ability to synchronize and process different sensor signals. Through professional software, the collected data is analyzed and visualized in real time, and the operator can obtain detailed information about the tool status and machining process, and adjust the machine parameters according to these information to optimize the machining 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 quick adaptation to different machine configurations and machining tasks, providing a universal and efficient solution to improve the accuracy and efficiency of CNC machining.

[0113] Figure 3 is a flowchart of a tool acceleration control method based on machining resistance analysis in an embodiment of the present application.

[0114] As Figure 3 shown, in one or more embodiments, preferably, the detailed analysis of the collected data by a machine learning algorithm to identify specific patterns and abnormal states in the machining process specifically includes:

[0115] S301, acquire historical data of the same workpiece, and perform automatic data analysis;

[0116] S302, setting the first stage as the fast entry stage, the second stage as the protection start stage, the third stage as the acceleration working stage, the fourth stage as the unprotected acceleration stage, and the fifth stage as the fast exit stage;

[0117] S303, setting the time period that satisfies the first calculation formula as the first stage;

[0118] S304, setting the items that meet the second calculation formula as the fifth stage;

[0119] S305: setting the items that satisfy the third calculation formula to the third stage;

[0120] S306: The stage before the third stage and after the first stage is set as the second stage;

[0121] S307, setting the stage after the third stage and before the fifth stage to the fourth stage;

[0122] The first calculation formula is:

[0123] t <T×k1

[0124] Z <Y1

[0125] Where 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;

[0126] The second calculation formula is:

[0127] t>T×k2

[0128] Z <Y1

[0129] Wherein, k2 is the second contrast coefficient;

[0130] The third calculation formula is:

[0131] T×k3 <t<T×k4

[0132] Z>Y2

[0133] Among them, Y2 is the preset second resistance comparison value, k3 is the third comparison coefficient, and k4 is the fourth comparison coefficient.

[0134] In the embodiment of the present application, historical machining data of the same workpiece is acquired, which covers the whole process from the previous machining to the finished product. Using data analysis software, automatic analysis is performed on the data so that the machine learning algorithm learns and identifies various machining states and modes. Based on the analysis results, the machining process is divided into five stages: fast entry stage, protection start stage, acceleration work stage, unprotected acceleration stage and fast exit stage. The setting of these stages aims to optimize the service life of the tool and the machining efficiency while guaranteeing the quality of the workpiece. Specifically, the following calculation formulas are set to define each stage: the first stage (fast entry stage) meets the condition: t < T x k1 and Z < Y1; wherein t is the current time, T is the total machining time, k1 is the first comparison coefficient, Z is the real-time resistance, and Y1 is the preset first resistance comparison value. The fifth stage (fast exit stage) meets the condition: t > T x k2 and Z < Y1; wherein k2 is the second comparison coefficient. The third stage (acceleration work stage) meets the condition: T x k3 < t < T x k4 and Z > Y2; wherein 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 start stage) is set to be before the third stage and after the first stage. The fourth stage (unprotected acceleration stage) is set to be after the third stage and before the fifth stage.

[0135] Figure 4 is a flowchart of automatically adjusting the feed rate of the tool according to the analysis results to adapt to the change of material properties in a tool acceleration control method based on machining resistance analysis according to an embodiment of the present application.

[0136] As shown in Figure 4 in one or more embodiments, preferably, the automatically adjusting the feed rate of the tool according to the analysis results to adapt to the change of material properties specifically comprises:

[0137] S401, when the tool is in the first stage at the current time, if the current time resistance meets the fourth calculation formula, the tool running speed is reduced to the protection running state according to the preset reverse resistance, and is forcibly adjusted to the second stage, otherwise the tool continuously runs according to the preset acceleration of the first stage;

[0138] S402, when the running stage is in the second stage, if the speed is at the preset speed of the second stage, the running speed is not reduced, and the current time resistance is continuously monitored; if the current time resistance meets the fifth calculation formula, the third stage is forcibly switched to, and the tool runs according to the preset speed of the third stage;

[0139] S403, when in the third stage, if the time that does not meet the fifth calculation formula exceeds 10ms, the third stage is exited, the fourth stage is entered, and the tool runs according to the preset speed of the fourth stage;

[0140] S404, when entering the fifth stage, proceeding according to the preset speed of the fifth stage;

[0141] The fourth calculation formula is:

[0142] |Z - -Z + |÷jg>zy

[0143] 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;

[0144] The fifth calculation formula is:

[0145] Z>Y2

[0146] In an embodiment of the present invention, the tool path and feed rate are automatically adjusted based on real-time data analysis results to adapt to changes in material properties. This process is achieved through integrated sensor data, machine learning algorithms, and dynamic adjustment mechanisms. First, when calculating the tool in the first stage at the current moment, the system checks whether the resistance at the current moment satisfies the fourth calculation formula: |(Z + -Z - )÷jg|>zy; where Z + and Z -where z represents the tool resistance before and after the current moment, jg represents the sampling interval, and zy represents the preset resistance variation margin. If this formula is satisfied, indicating that the resistance variation exceeds the allowable range, the system automatically reduces the tool speed to a protective operating state and forcibly switches the machining phase to the second phase. If this formula is not satisfied, the tool continues to move at the acceleration preset for the first phase. In the second phase, if the tool speed is already at the preset speed for the second phase, the system will not reduce the speed further. Simultaneously, the system continues to monitor the current resistance. 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 phase and proceed at the preset speed for the third phase. In the third phase, the system continuously monitors whether the resistance meets the fifth calculation formula. If this formula is not satisfied for more than 10ms, the system determines that the machining conditions for the third phase are no longer required and exits the third phase, entering the fourth phase at the preset speed for the fourth phase. Finally, when the machining enters the fifth phase, the system continues at the preset fifth phase speed until the machining is complete. Through this dynamic adjustment mechanism, the present invention can respond to changes in material properties in real time, optimize the machining process, and improve machining efficiency and workpiece quality. This adaptive adjustment is particularly suitable for situations where material properties may change significantly during machining, such as in the machining of composite or inhomogeneous materials.

[0147] Figure 5 This 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 life and machining quality.

[0148] 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:

[0149] S501, obtaining the current maximum loss size of the tool in real time;

[0150] S502: If the sixth calculation formula is satisfied, the cutting speed is adjusted using the seventh calculation formula;

[0151] The sixth calculation formula is:

[0152] CMAX>s2×YC

[0153] 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;

[0154] The seventh calculation formula is:

[0155] VD=V0÷s2

[0156] Among them, VD is the cutting speed and V0 is the current cutting speed.

[0157] In an embodiment of the present invention, cutting speed and depth are dynamically adjusted by real-time monitoring of tool wear and workpiece temperature, aiming to maximize tool life and machining quality. This process utilizes sensor data, real-time feedback mechanisms, and computational algorithms to optimize machining parameters. First, the system acquires the current maximum tool wear dimension (CMAX) and tool temperature (DT) in real time. This data is obtained using sensors installed on the machine tool, such as infrared sensors measuring tool temperature and optical or contact measurement methods to determine tool wear dimensions. Next, the system evaluates whether the sixth calculation formula is satisfied. This formula considers the workpiece hardness (not directly given, but inferred to be an influencing factor), tool temperature, and tool wear: workpiece hardness DT > s1 × YT; CMAX > s2 × YC; workpiece hardness DT > s1 × YT; CMAX > s2 × YC. Where DT is the tool temperature, s1 is a preset temperature coefficient, and YT is a preset temperature margin. CMAX is the current maximum tool wear dimension, s2 is a preset wear coefficient, and YC is the tool wear margin. If the above conditions are met, indicating severe tool wear or excessive temperature, 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 and 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 tool wear increases or temperature rises, thereby reducing further tool wear and overheating, extending tool life, and maintaining machining quality. Conversely, if the tool is in good condition, the system may moderately increase the cutting depth and speed to improve machining efficiency.

[0158] Figure 6 This 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.

[0159] 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 the machine when an overload or potential tool breakage risk is detected to protect the tool and workpiece from damage, specifically including:

[0160] S601, defining and setting assessment parameters for overload and tool breakage risks, including the maximum allowable value of cutting force, and the critical frequency and amplitude of tool vibration;

[0161] S602: Continuously analyze 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 risks, it is considered that there is a risk of overload or tool breakage.

[0162] S603. Once the monitoring data exceeds any preset risk threshold, an emergency response mechanism is immediately activated, wherein the emergency response mechanism includes automatically reducing the feed rate of the machine tool, suspending the machining operation, or completely stopping the machine tool operation;

[0163] S604. After an emergency stop, the system should lock all machine tool operations. After the operator confirms and takes appropriate measures, the system should be manually reset and processing should be gradually resumed.

[0164] 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. This mechanism maintains the safety and reliability of machining operations by monitoring, evaluating, and responding to key parameters in the machining process in real time. First, a series of assessment parameters are defined and set to determine the risk of overload and tool breakage. These parameters include the maximum allowable cutting force, the critical frequency and amplitude of tool vibration, and the safety threshold of tool temperature. These parameters are predetermined based on the physical properties of the tool and workpiece materials, as well as the performance characteristics of the machine tool. During the machining process, the system collects real-time data such as cutting force, tool vibration, and temperature from sensors installed on the machine tool. This data is then transmitted to an analysis module for continuous analysis to identify the risk of overload or tool breakage. The analysis module uses an algorithm to compare the monitored data with pre-set risk assessment parameters. If any parameter exceeds its threshold, the risk of overload or tool breakage is determined. Once a risk is determined, the emergency response mechanism is immediately activated. This mechanism can automatically reduce the machine tool's feed rate, suspend machining operations, or, in more critical situations, completely shut down the machine tool. For example, if the system detects a sudden rise in tool temperature approaching a safety threshold, it may first reduce the feed rate to attempt to reduce tool load and cool the tool. 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 could cause more serious damage or safety accidents. The operator must inspect and take appropriate measures, such as replacing the tool or re-adjusting the machining parameters, and then manually reset the system and gradually resume machining.

[0165] Figure 7This is a flowchart of a tool acceleration control method based on machining resistance analysis in an embodiment of the present invention, in which after machining is completed, the system collects final machining effect data and compares it with the preset target, and iteratively optimizes the control strategy through a feedback loop.

[0166] 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:

[0167] S701. After the machining is completed, use sensors and measuring tools deployed on the machine tool to collect data on the final machining results, wherein the data on the final machining results includes detailed records of the workpiece dimensional accuracy, surface roughness, and any cutting marks or errors;

[0168] S702, comparing and analyzing the collected processing effect data with the preset processing target parameters to form a geometric dimension deviation, surface quality evaluation, and processing efficiency measurement;

[0169] S703. Adjust and optimize the machining control strategy based on the results of the comparative analysis. 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.

[0170] In an embodiment of the present invention, a post-processing data analysis and control strategy optimization process is described, aiming to improve processing efficiency and quality through continuous iteration. This process utilizes sensors and measurement tools on the machine tool to collect data and continuously optimize processing parameters through a feedback loop. First, after the processing task is completed, the system uses sensors and measurement tools installed on the machine tool, such as a laser scanner, a three-dimensional coordinate measuring machine, or an optical comparator, to collect data on the final processing results. This data includes detailed records of the workpiece's dimensional accuracy, surface roughness, and any cutting marks or errors. These indicators directly reflect the combined effects of tool performance and machine tool status during the processing. The system then compares and analyzes the collected actual processing data with the preset processing target parameters. This analysis involves calculating geometric deviations, evaluating surface quality, and measuring processing efficiency. For example, if the preset target surface roughness of a workpiece is Ra 0.8 microns, and the actual measurement result is Ra 1.2 microns, the system will record this deviation and analyze the cause. Based on the results of this comparative analysis, the system will adjust and optimize the processing control strategy. This includes but is not limited to adjusting tool paths, feed rates, and speed parameters. Based on the analysis results, the system automatically suggests improvements, such as increasing the use of cutting fluid to reduce temperatures or adjusting the tool path to avoid vibration. After each iteration, the system continues to adjust the control strategy until the preset machining target is achieved or exceeded. This process may involve multiple iterations, each one analyzing and optimizing based on the data from the previous run. This feedback loop ensures continuous improvement in the machining process, gradually approaching or even exceeding quality requirements.

[0171] According to a second aspect of an embodiment of the present invention, a tool acceleration control system based on machining resistance analysis is provided.

[0172] Figure 8 This is a structural diagram of a tool acceleration control system based on machining resistance analysis according to an embodiment of the present invention.

[0173] In one or more embodiments, preferably, the tool acceleration control system based on machining resistance analysis includes:

[0174] 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;

[0175] The stage learning module 802 is used to perform detailed analysis of the collected data using machine learning algorithms to identify specific patterns and abnormal conditions in the processing process;

[0176] a stage adjustment and acceleration setting module 803 for automatically adjusting the feed rate of the tool according to the analysis results to adapt to changes in material properties;

[0177] Depth adjustment module 804, used to adjust cutting speed in real time according to tool wear and spindle load to maximize tool life and processing quality;

[0178] Emergency warning module 805 is used to set up an emergency response mechanism. 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 workpiece from damage.

[0179] The iterative optimization module 806 is used to collect the final processing effect data after the processing is completed, compare it with the preset target, and iteratively optimize the control strategy through a feedback loop.

[0180] In the embodiment of the present invention, a system applicable to 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.

[0181] According to a third aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method according to any one of the first aspect of the embodiment of the present invention is implemented.

[0182] According to a fourth aspect of the embodiments of the present invention, an electronic device is provided. Figure 9 It is a structural diagram of an electronic device in one embodiment of the present invention. Figure 9 The electronic device shown is a general tool acceleration control device based on machining resistance analysis. Figure 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 aforementioned embodiments, and the processor 902 can execute the program stored in the memory 904.

[0183] The processor 902 may include one or more processing cores. The processor 902 utilizes various interfaces and circuits to connect various components within the electronic device 900. It executes various functions and processes data within the electronic device 900 by running or executing instructions, programs, code sets, or instruction sets stored in the memory 904, and by accessing data stored in the memory 904. Optionally, the processor 902 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a 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 primarily processes the operating system, user interface, and target applications; the GPU is responsible for rendering and drawing display content; and the modem handles wireless communications. 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.

[0184] 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 documents) created by the electronic device 900 during use.

[0185] The wireless module 906 is used to receive and transmit electromagnetic waves, realize the mutual conversion between electromagnetic waves and electrical signals, and thus communicate with a communication network or other devices, for example, communicate with a base station based on a mobile communication protocol. The wireless module 906 may include various existing circuit components 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 can communicate with various networks such as the Internet, an enterprise intranet, a wireless network, or communicate with other electronic devices via 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 protocols that have not yet been developed.

[0186] The technical solutions provided by the embodiments of the present invention may have the following beneficial effects:

[0187] In the solution of the present invention, by utilizing 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, thereby achieving efficient online control.

[0188] In the solution of the present invention, operation and shutdown protection are centralized through emergency response, and the entire tool use process can be accelerated.

[0189] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, 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 magnetic disk storage and optical storage, etc.) containing computer-usable program code.

[0190] 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 flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, 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 flowcharts and / or block diagrams. 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.

[0191] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0192] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that are executed on the computer or other programmable apparatus provide steps for implementing the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0193] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the apparent to those skilled in the art that such modifications and variations are intended to be included within the scope of the application. Accordingly, the application is to be construed as limited only by the prior art to which it pertains.

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 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; Adjust cutting speed in real time according to tool wear and spindle load to maximize tool life and processing quality; Set up an emergency response mechanism to immediately adjust operations 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 target, and iteratively optimizes the control strategy through feedback loop; The detailed analysis of the collected data using machine learning algorithms to identify specific patterns and abnormal conditions in the processing process includes: 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 period that satisfies the first calculation formula is set as the first stage; The fifth stage is set for those that meet the second calculation formula; 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: Where 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: Wherein, Y2 is the preset second resistance comparison value, k3 is the third comparison coefficient, and k4 is the fourth comparison coefficient; The automatic adjustment of the feed rate of the tool according to the analysis results to adapt to changes in material properties specifically includes: When calculating the tool's current state in the first stage, if the resistance at the current moment satisfies the fourth calculation formula, the tool's travel speed is reduced to a protective operating state according to the preset reverse resistance, and the tool is forcibly adjusted to the second stage. Otherwise, the tool continues to move according to the acceleration preset in the first stage. When the travel stage is in the second stage and the speed is at the preset speed of the second stage, the travel 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 will be carried out at the preset speed of the third stage; If the time when the fifth calculation formula is not satisfied exceeds 10ms during the third stage, the vehicle will exit the third stage and enter the fourth stage, and will proceed at the speed preset in the fourth stage. When entering the fifth stage, the vehicle moves at the preset speed of the fifth stage; The fourth calculation formula is: |Z - -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.

2. The tool acceleration control method based on machining resistance analysis according to 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. The tool acceleration control method based on machining resistance analysis according to claim 1, 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 the sixth calculation formula is satisfied, the cutting speed is adjusted using the seventh calculation formula; The sixth calculation formula is: 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: Among them, VD is the cutting speed and V0 is the current cutting speed.

4. The tool acceleration control method based on machining resistance analysis according to 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, an emergency response mechanism is immediately activated, wherein the emergency response mechanism includes automatically reducing the feed rate of the machine tool, suspending the machining operation, or completely stopping the machine tool operation; After an emergency stop, the system should lock all machine tool operations. After the operator confirms and takes appropriate measures, the system should be manually reset and processing should be gradually resumed.

5. The tool acceleration control method based on machining resistance analysis according to 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 machining is completed, sensors and measurement tools deployed on the machine tool are used to collect data on the final machining results, including 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 achieved or exceeded.

6. 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 5, 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; A 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 the cutting speed in real time according to the degree of tool wear and spindle load, maximizing tool life and processing quality; Emergency warning module, used to set up emergency response mechanism, immediately adjust operation or shut down the machine when overload or potential tool breakage risk is detected to protect tool and workpiece from damage; The iterative optimization module is used to collect the final processing effect data after the processing is completed, compare it with the preset target, and iteratively optimize the control strategy through feedback loop.

7. 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 5 when executed by a processor.

8. An electronic device comprising a memory and a processor, characterized in that: The memory is configured 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 to 5.

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