A machining control system for a numerical control tool

Abstract

The present application relates to the technical field of numerical control tool machining control, in particular to a machining control system of numerical control tool. The present application firstly obtains machining data of continuous machining of the tool through a machining monitoring module; further obtains tool grinding state abnormal parameters and spindle state abnormal parameters of each time window through a grinding degree analysis module; according to the fluctuation similarity characteristics of the tool grinding state abnormal parameters and the spindle state abnormal parameters, obtains an excessive grinding coefficient of the current time point; further obtains an excessive grinding credibility of the current time point through a credibility analysis module; finally, according to the excessive grinding coefficient and the excessive grinding credibility of the current time point, the tool machining is feedback controlled through a grinding parameter feedback control module, avoiding the traditional tool production control depending on manual or fixed parameters, and improving the automation degree, accuracy and adaptability of the tool machining control system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of numerical control tool machining control, and particularly relates to a machining control system of a numerical control tool. BACKGROUND

[0002] The numerical control tool refers to a cutting tool for drilling, milling, reaming, boring and other cutting processes, which is usually installed on a milling machine or other machining equipment for rotation to remove material by cutting surface. The numerical control tool is processed into a required shape or surface by precise cutting of a numerical control grinding machine. The specific function and quality of the numerical control tool are closely related to the requirements of the machining process. Generally, the cutting edge of the numerical control tool is ground to achieve high-precision requirements in terms of opening and sharpness, and to maintain high-efficiency application capability of the numerical control tool.

[0003] In the process of numerical control tool machining, excessive grinding of the tool can lead to a decrease in machining quality. In order to timely capture the excessive grinding and the degree of wear of the tool, the gloss and continuity of the cutting edge are generally observed by naked eye to determine whether there is excessive grinding. However, this method is easily affected by subjectivity and has low accuracy. The use of a fixed parameter mode for machining is easily affected by changes in material and equipment state, has poor flexibility, and leads to unstable tool quality. SUMMARY

[0004] In order to solve the technical problems of strong subjectivity, low accuracy and poor flexibility in the machining control of the existing numerical control tool, the purpose of the present application is to provide a machining control system of a numerical control tool, and the technical solution adopted is as follows:

[0005] The machining monitoring module acquires machining data of continuous machining of the tool in a preset time window. The machining data at least includes surface temperature data of the tool, noise decibel data of a machining area, load data of a main shaft motor and main shaft temperature data.

[0006] The grinding degree analysis module acquires tool grinding state abnormal parameters of each time window according to fluctuation characteristics of the surface temperature data in each time window, in combination with fluctuation characteristics of the noise decibel data. The main shaft state abnormal parameters of each time window are acquired according to fluctuation characteristics of the main shaft temperature data and the load data of each time window. The excessive grinding coefficient of a current time point is acquired according to fluctuation similarity characteristics of the tool grinding state abnormal parameters and the main shaft state abnormal parameters of a preset number of latest time windows.

[0007] The credibility analysis module: when the over-grinding coefficient of the current time point is less than or equal to the preset grinding threshold, the over-grinding credibility of the current time period is set to a preset constant; when the over-grinding coefficient of the current time point is greater than the preset grinding threshold, the output power of the main shaft motor is iteratively adjusted, and the over-grinding coefficient after each iteration is obtained; according to the similarity characteristics of the over-grinding coefficient of the current time point and the over-grinding coefficients obtained after all iterations, the over-grinding credibility of the current time point is obtained.

[0008] The grinding parameter feedback control module: according to the over-grinding coefficient and the over-grinding credibility of the current time point, the tool machining is feedback controlled.

[0009] Further, the method for obtaining the tool grinding state abnormality parameter comprises:

[0010] The product of the range and the mean value of the surface temperature data in the time window is taken as the heat accumulation coefficient corresponding to the time window;

[0011] The absolute average deviation of the noise decibel data in the time window is taken as the noise abnormality coefficient corresponding to the time window;

[0012] According to the heat accumulation coefficient and the noise abnormality coefficient of each time window, the tool grinding state abnormality parameter of each time window is obtained; the heat accumulation coefficient and the noise abnormality coefficient are positively correlated with the tool grinding state abnormality parameter.

[0013] Further, the method for obtaining the tool grinding state abnormality parameter according to the heat accumulation coefficient and the noise abnormality coefficient of each time window comprises:

[0014] After the weighted sum of the heat accumulation coefficient and the noise abnormality coefficient of each time window is normalized, it is taken as the tool grinding state abnormality parameter of each time window.

[0015] Further, the method for obtaining the main shaft state abnormality parameter comprises:

[0016] In each time window, the product of the absolute value of the difference between the load data at the end point of the time domain and the load data at the middle point of the time domain and the average value of the main shaft temperature data is taken as the first numerator; the sum of the absolute value of the difference between the load data at the starting point of the time domain and the load data at the middle point of the time domain and the preset positive parameter is taken as the first denominator; the ratio of the first numerator and the first denominator is taken as the main shaft state abnormality parameter corresponding to the time window.

[0017] Further, the method for obtaining the over-grinding coefficient comprises:

[0018] The normalized result of the Pearson correlation coefficient between the preset number of latest tool grinding state abnormal parameters of the time window and the spindle state abnormal parameters is taken as an excessive grinding coefficient of the current time point.

[0019] Further, the method of iteratively adjusting the output power of the spindle motor and obtaining the excessive grinding coefficient after each iteration comprises:

[0020] The output power of the spindle motor in the last time window at the current time point is taken as an iterative base power, and the product of the iterative base power and each element in a preset adjustment ratio sequence is taken as an iterative power of each iteration.

[0021] The tool machining is controlled based on the iterative power, the machining data of the preset number of time windows is obtained, and the excessive grinding coefficient corresponding to the iterative power is obtained.

[0022] Further, the method of obtaining the excessive grinding credibility of the current time point according to the excessive grinding coefficient of the current time point and the similar features of the excessive grinding coefficients obtained after all iterations comprises:

[0023] The absolute value of the difference between the mean value of the excessive grinding coefficients obtained after all iterations and the excessive grinding coefficient of the current time point is negatively correlated and normalized, and taken as the excessive grinding credibility of the current time point.

[0024] Further, the method of feedback controlling tool machining according to the excessive grinding coefficient of the current time point and the excessive grinding credibility comprises:

[0025] When the excessive grinding credibility is less than a preset credibility threshold, it is determined that the tool machining does not need to be adjusted.

[0026] When the excessive grinding credibility is greater than or equal to the preset credibility threshold, it is determined that the tool machining needs to be adjusted.

[0027] Further, when it is determined that the tool needs to be adjusted, the product of the excessive grinding credibility and the excessive grinding coefficient is taken as a grinding degree value of the tool, and input to a tool grinding machine for adjustment.

[0028] Further, the method of determining that the tool needs to be adjusted further comprises:

[0029] The adjustment frequency of the tool machining is counted, and when the adjustment frequency is greater than a preset frequency threshold, an abnormal warning signal is sent. The present application has the following beneficial effects:

[0030] The application firstly acquires the machining data of the continuous machining of the tool through the processing monitoring module in a preset time window, guarantees the continuity of the machining data, and is beneficial to analyzing the change trend of the tool machining data; meanwhile, the time window focuses on the local time domain machining characteristics of the tool, so as to improve the sensitivity of the system to the tool machining; further, in the grinding degree analysis module, the tool grinding state abnormal parameters and the spindle state abnormal parameters of each time window are acquired, the machining state of the tool is directly reflected from the surface temperature data and the noise decibel data of the tool, and the machining state of the tool is indirectly reflected from the load data and the spindle temperature data of the spindle motor from the working angle of the spindle motor, the consistency of the tool state under two angles is combined, the excessive grinding coefficient of the current time point is acquired according to the fluctuation similarity characteristics, and the excessive grinding characteristics of the tool at the current time point are represented; further, through the credibility analysis module, the excessive grinding credibility of the current time point is acquired, the credibility of the tool excessive grinding is evaluated, and the accuracy of the control system is improved; finally, in the grinding parameter feedback control module, the tool machining is feedback controlled according to the excessive grinding coefficient and the excessive grinding credibility of the current time point. The application realizes real-time monitoring of the tool machining and evaluation of the excessive grinding coefficient and the excessive grinding credibility of the tool through various sensors, controls the tool machining, avoids the traditional tool machining depending on manual operation or fixed parameters, improves the automation degree of the tool machining control, and improves the adaptability and machining accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, and the advantages thereof, below, a brief introduction will be given to the drawings needed in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0032] Figure 1 A system block diagram of a machining control system of a numerical control tool provided by an embodiment of the present application;

[0033] Figure 2 A flowchart of a method for acquiring tool grinding state abnormal parameters provided by an embodiment of the present application. DETAILED DESCRIPTION

[0034] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined application purposes, below, the specific embodiments, structure, features and effects of a machining control system of a numerical control tool according to the present application are described in detail in combination with the drawings and the preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0035] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0036] The specific scheme of a machining control system for a numerically controlled tool provided by the present invention will be described in detail below with reference to the accompanying drawings.

[0037] In an embodiment of the present invention, by installing a variety of Internet of Things sensors in the machine tool or equipment for tool processing, the processing data is monitored in real time, and the processing status of the tool is evaluated in real time, so as to feedback control the tool processing, which has the characteristics of high precision, high degree of automation, and strong adaptability, optimizes the processing process, and ensures the tool processing quality.

[0038] See also Figure 1 , which shows a system block diagram of a CNC tool processing control system provided by an embodiment of the present invention. The system includes: a processing monitoring module 101, a grinding degree analysis module 102, a credibility analysis module 103 and a grinding parameter feedback control module 104.

[0039] Processing monitoring module 101: acquires processing data of continuous tool processing in a preset time window; the processing data at least includes surface temperature data of the tool, noise decibel data of the processing area, load data of the spindle motor and spindle temperature data.

[0040] Machining CNC tools typically requires specialized equipment, which varies depending on the precision, efficiency, and workpiece shape required. These primarily include milling machines, gear shaping machines, grinders, tool grinders, and laser processing. Depending on the application and purpose of the CNC tool, the corresponding shape and specifications are processed. This also includes precise grinding control during the tool processing process. By grinding the cutting edge of the CNC tool, the tool's edge is sharpened to high precision, maintaining the tool's efficient application capabilities. The combination of these equipment enables high-precision manufacturing of CNC tools, ensuring optimal tool quality after subsequent machining.

[0041] In the process of CNC tool processing, taking the processing of milling cutters as an example, the problem of over-grinding during the milling cutter processing will lead to poor processing quality of CNC tools. It is necessary to capture the over-grinding of the milling cutter and its wear degree in time so as to provide timely feedback control for the milling cutter processing.

[0042] In the embodiment of the present application, the continuity of the machining data is ensured by setting the time window, which is beneficial to analyze the trend of the machining data of the tool; meanwhile, the time window focuses on the machining characteristics of the local time domain of the tool, so as to improve the sensitivity of the system to the machining of the tool; first, the machining data of the continuous machining of the tool is obtained by using the preset time window, so as to provide the basis for analyzing the data.

[0043] As an example, the machining monitoring sensor is configured to obtain the machining data in real time, the machining data analysis feedback system is increased, the key machining data is collected in real time by configuring the machining monitoring sensor, and the dynamic monitoring of the machining process and the accurate judgment of the opening and grinding process of the numerical control tool can be realized by combining the data analysis feedback system.

[0044] The machining monitoring sensor is configured as follows: a load sensor is installed to monitor the load data of the spindle motor, the setting range is 0-500Nm, and the accuracy is ±0.5Nm; a temperature sensor is installed to monitor the spindle temperature data of the spindle motor, the setting range is 0-120℃, and the accuracy is ±0.5℃; a temperature sensor is installed to monitor the grinding process temperature of the numerical control grinding machine, the setting range is 200-800℃, and the accuracy is ±1℃; a tool surface temperature sensor is installed to monitor the temperature data of the tool machining surface, the setting range is 200-400℃, and the accuracy is ±0.5℃; a noise sensor is installed to monitor the noise decibel data of the machining area, the setting range is 70-120dB, and the response time is <0.1s. The machining data of all the Internet of Things sensors is collected and transmitted to the control system for real-time monitoring and feedback control of the milling cutter machining.

[0045] It should be noted that in an embodiment of the present application, the collection frequency of the surface temperature data and the noise decibel data is set to 3 times per second; the collection frequency of the load data and the spindle temperature data is set to 1 time per second, and the length of the preset time window is 10 seconds, which can be adjusted by the implementer; in order to ensure the continuity of the data, the machining data in the time window does not include the data when the tool is idling, and the working principles of the sensors have been known in the prior art, which will not be described here.

[0046] The grinding degree analysis module 102: according to the fluctuation characteristics of the surface temperature data in each time window, combined with the fluctuation characteristics of the noise decibel data, the tool grinding state abnormal parameters of each time window are obtained; according to the fluctuation characteristics of the spindle temperature data and the load data of each time window, the spindle state abnormal parameters of each time window are obtained; according to the fluctuation similarity characteristics of the tool grinding state abnormal parameters and the spindle state abnormal parameters of the preset number of latest time windows, the excessive grinding coefficient of the current time point is obtained.

[0047] The surface temperature data and the noise decibel data directly reflect the machining state of the tool, and the load data and the spindle temperature data of the spindle motor indirectly reflect the machining state of the tool from the working angle of the spindle motor; therefore, the machining state consistency shown from the two angles can be used to analyze the excessive grinding characteristics of the tool, so that the excessive grinding coefficient of the current time point is obtained according to the fluctuation similarity characteristics of the tool grinding state abnormal parameters and the spindle state abnormal parameters in the preset number of latest time windows, and the excessive grinding characteristics of the tool at the current time point are analyzed, which represents the excessive grinding characteristics of the tool at the current time point.

[0048] The excessive grinding of the tool will cause the friction force to increase during the machining process, so that the surface temperature and the noise decibel show different fluctuation characteristics from the normal state, so the tool grinding state abnormal parameters of each time window are obtained according to the fluctuation characteristics of the surface temperature data in each time window, combined with the fluctuation characteristics of the noise decibel data, and the machining state of the tool is preliminarily evaluated from the angles of the surface temperature and the machining noise of the tool, so as to prepare for obtaining the excessive grinding coefficient subsequently.

[0049] Preferably, in an embodiment of the present application, the tool grinding state abnormal parameter acquisition method comprises:

[0050] Please refer to Figure 2 , which shows a flowchart of a tool grinding state abnormal parameter acquisition method provided by an embodiment of the present application; specifically comprising:

[0051] Step S201: The product of the range and the mean value of the surface temperature data in the time window is taken as the heat accumulation coefficient of the corresponding time window.

[0052] The excessive grinding of the tool will cause the friction force to increase during the machining process, so that the surface temperature and the noise decibel show different fluctuation characteristics from the normal state, so the tool grinding state abnormal parameters of each time window are obtained according to the fluctuation characteristics of the surface temperature data in each time window, combined with the fluctuation characteristics of the noise decibel data, and the machining state of the tool is preliminarily evaluated from the angles of the surface temperature and the machining noise of the tool, so as to prepare for obtaining the excessive grinding coefficient subsequently.

[0053] Step S202: The absolute average deviation of the noise decibel data in the time window is taken as the noise abnormality coefficient of the corresponding time window.

[0054] In the machining process of the numerical control tool, excessive grinding of the tool and local grinding contact point part have high temperature, which leads to increased friction in the machining process, workpiece surface burn and other conditions, causing abnormal fluctuations in noise decibels. The absolute average deviation of the noise decibel data in the time window reflects the fluctuation characteristics of the data, so the absolute average deviation of the noise decibel data in the time window is taken as the noise anomaly coefficient of the corresponding time window, which represents the fluctuation characteristics of the noise decibel data.

[0055] Step S203: obtaining the tool grinding state abnormality parameter of each time window according to the thermal accumulation coefficient and the noise anomaly coefficient of each time window.

[0056] After evaluating the abnormal machining state of the tool from the angles of surface temperature and noise decibels, the thermal accumulation coefficient and the noise anomaly coefficient are fused to obtain the tool grinding state abnormality parameter. Because the greater the thermal accumulation coefficient and the noise anomaly coefficient, the greater the degree of excessive grinding of the tool, the thermal accumulation coefficient and the noise anomaly coefficient are positively correlated with the tool grinding state abnormality parameter.

[0057] Preferably, in an embodiment of the present application, the weighted sum of the thermal accumulation coefficient and the noise anomaly coefficient of each time window is normalized as the tool grinding state abnormality parameter of each time window. The higher the value of the tool grinding state abnormality parameter, the more abnormal the grinding state of the tool, which leads to increased friction of the tool, accompanied by high decibel noise, further leading to increased grinding surface temperature.

[0058] As an example, the weighted weight of the thermal accumulation coefficient is 0.6, and the weighted weight of the noise anomaly coefficient is 0.4.

[0059] Considering that when the tool appears excessive grinding, the sharpness of the tool decreases, leading to fluctuations in the load of the motor spindle, and further leading to fluctuations in the spindle temperature, the spindle state abnormality parameter of each time window is obtained according to the fluctuation characteristics of the spindle temperature data and the load data of each time window, and the machining state of the tool is preliminarily evaluated from the spindle angle, preparing for the subsequent acquisition of the excessive grinding coefficient.

[0060] Preferably, in an embodiment of the present application, considering that excessive grinding of the tool leads to increased friction and greater load demand on the motor spindle, the load data change in the latter half of the time window is greater than that in the former half. At the same time, when the spindle load is large, the heat generated by operation is more, and the spindle temperature data is higher as a whole.

[0061] Based on this, in each time window, the absolute value of the difference between the load data of the end point of the time domain and the midpoint of the time domain is multiplied by the average value of the spindle temperature data as the first numerator; the absolute value of the difference between the load data of the starting point of the time domain and the midpoint of the time domain is summed with the preset positive zero exclusion parameter as the first denominator; and the ratio of the first numerator and the first denominator is taken as the spindle state abnormality parameter corresponding to the time window.

[0062] The calculation formula of the spindle state abnormality parameter comprises:

[0063]

[0064] Wherein, u represents the serial number of the time window; C(u) represents the spindle state abnormality parameter of the u-th time window; represents the average value of the spindle temperature data of the u-th time window; w u,end represents the load data value of the end point of the time domain of the u-th time window; w u,m represents the load data value of the midpoint of the time domain of the u-th time window; w u,s represents the load data value of the starting point of the time domain of the u-th time window; || represents taking the absolute value; c represents the preset positive zero exclusion parameter; |w u,m -w u,s |+c represents the first denominator of the u-th time window; represents the first numerator of the u-th time window.

[0065] In the calculation formula of the spindle state abnormality parameter, the difference between the load data is represented by the absolute value of the difference, so as to represent the change characteristics of the load data in the half window, and the fluctuation characteristics of the load data are represented by the ratio, The greater the value is, the greater the change of the load data in the latter half of the time window compared with the change of the load data in the former half, the load of the spindle increases with time, and the tool is more likely to be excessively ground, and the spindle state abnormality parameter is greater; the overall characteristics of the spindle temperature data are represented by the average value, The greater the value is, the higher the overall spindle temperature data is, the tool is more likely to be excessively ground, and the spindle state abnormality parameter is greater; the preset positive zero exclusion parameter is 0.001.

[0066] Preferably, in one embodiment of the present application, the preset number is 5, the time domain latest sequence of the length of 5 tool grinding state abnormal parameters and the sequence of the spindle state abnormal parameters are obtained; considering that the Pearson correlation coefficient is commonly used to evaluate the correlation between data, the closer the Pearson correlation coefficient is to 1, the stronger the positive correlation is, and the more obvious the fluctuation similarity feature is, so the normalized result of the Pearson correlation coefficient of the preset number of the latest time window tool grinding state abnormal parameters and the spindle state abnormal parameters is used as the current time point excessive grinding coefficient, the fluctuation similarity feature of the tool grinding state abnormal parameters and the spindle state abnormal parameters is represented by means of the Pearson correlation coefficient, the machining data of the tool itself angle and the spindle motor angle are combined, the excessive grinding feature of the tool is comprehensively analyzed, and the reliability of the excessive grinding coefficient is improved; wherein the linear normalization method is used to normalize the Pearson correlation coefficient.

[0067] It should be noted that in other embodiments of the present application, the implementer can also obtain the cosine similarity of the sequence of tool grinding state abnormal parameters and the sequence of spindle state abnormal parameters as the excessive grinding coefficient, and the cosine similarity and the Pearson correlation coefficient are both known technical means for those skilled in the art, and will not be described here.

[0068] The credibility analysis module 103: when the excessive grinding coefficient of the current time point is less than or equal to the preset grinding threshold, the excessive grinding credibility of the current time period is set to a preset constant; when the excessive grinding coefficient of the current time point is greater than the preset grinding threshold, the output power of the spindle motor is iteratively adjusted, and the excessive grinding coefficient after each iteration is obtained; according to the similarity feature of the excessive grinding coefficient of the current time point and all the excessive grinding coefficients obtained after iteration, the excessive grinding credibility of the current time point is obtained.

[0069] When the excessive grinding coefficient is small, it is indicated that the current tool excessive grinding is small from the perspective of machining data analysis, and normal production can still be carried out, so when the excessive grinding coefficient of the current time point is less than or equal to the preset grinding threshold, the excessive grinding credibility of the current time period is set to a preset constant.

[0070] Preferably, in one embodiment of the present application, the preset grinding threshold is 0.8, and the preset constant is 0, that is, it can be judged from the machining data that the tool excessive grinding is small, so the excessive grinding credibility of the tool is 0.

[0071] Considering the machining process of the numerical control tool under the requirement of some special structure, when machining some key areas of the tool, the machining power may be increased, that is, the output power of the spindle is increased to meet the machining requirement, at this time, the machining data of the tool will also appear the fluctuation similar to that when the tool is excessively ground, therefore, when the excessive grinding coefficient at the current time point is greater than the preset grinding threshold, the output power of the spindle motor is iteratively adjusted to obtain more excessive grinding coefficients for analysis, so when the excessive grinding coefficient at the current time point is greater than the preset grinding threshold, the output power of the spindle motor is iteratively adjusted to obtain the excessive grinding coefficient after each iteration.

[0072] Preferably, in an embodiment of the present application, the output power of the spindle motor in the last time window at the current time point is taken as the iterative basic power; the product of the iterative basic power and each element in the preset adjustment proportion sequence is taken as the iterative power of each iteration.

[0073] Based on the tool machining controlled by the iterative power, the machining data of the preset number of time windows is obtained, and the excessive grinding coefficient corresponding to the iterative power is obtained.

[0074] It should be noted that when the output power of the spindle motor in the last time window at the current time point fluctuates, the maximum value is taken as the iterative basic power; in an embodiment of the present application, the preset adjustment proportion sequence is [0.90 1 , 0.90 2 , 0.90 3 , 0.90 4 ], that is, the output power is reduced to 90% of the existing output power each time, as the iterative power, and the iteration is performed for 4 times, and the tool machining data of 5 time windows is obtained each time, and then the excessive grinding coefficient is obtained.

[0075] In other embodiments of the present application, the implementer can set other preset adjustment proportion sequences, adjust the number of iterations and the preset number to flexibly adjust the time of obtaining the machining data by iteratively adjusting the output power.

[0076] After the output power is iteratively adjusted and a plurality of excessive grinding reliabilities are obtained, the excessive grinding coefficient at the current time point can be compared with the excessive grinding coefficients obtained after all iterations, considering that the excessive grinding coefficient at the current time point and all the excessive grinding coefficients obtained after the iterations have similar characteristics, which reflects whether the motor output power affects the excessive grinding coefficient, thereby reflecting the reliability of the excessive grinding, therefore, according to the similar characteristics of the excessive grinding coefficient at the current time point and all the excessive grinding coefficients obtained after the iterations, the excessive grinding reliability at the current time point is obtained.

[0077] Preferably, in one embodiment of the present application, the smaller the difference between the excessive grinding coefficient obtained after iteration and the excessive grinding coefficient at the current time point, the greater the similarity, indicating that the output power has less impact on the excessive grinding coefficient, and the higher the reliability of the tool to occur excessive grinding. Therefore, the absolute value of the difference between the mean of all excessive grinding coefficients obtained after iteration and the excessive grinding coefficient at the current time point is negatively correlated and normalized, and used as the excessive grinding reliability at the current time point.

[0078] As an example, the calculation formula of the excessive grinding reliability includes:

[0079]

[0080] wherein Tru represents the excessive grinding reliability at the current time point; exp{} represents the exponential function with the natural constant e as the base; || represents the absolute value; D represents the excessive grinding coefficient at the current time point; K represents the number of iterations; k represents the iteration number; D k represents the excessive grinding coefficient corresponding to the kth iteration.

[0081] In the calculation formula of the excessive grinding reliability, the mean value represents the overall characteristics of the excessive grinding coefficient obtained after iteration, the absolute value of the difference represents the difference characteristics between the current and the iteration excessive grinding coefficient, and the exp{-x} function is negatively correlated and normalized to represent the similarity characteristics between the excessive grinding coefficient at the current time point and all the excessive grinding coefficients obtained after iteration. The smaller the value, the smaller the impact of the output power on the excessive grinding coefficient, the higher the reliability of the tool to occur excessive grinding, and the greater the excessive grinding reliability; wherein x represents the independent variable.

[0082] It should be noted that the current time point is the time point before iteration, for example, the normal machining tool, and the current time point is monitored at 13:15:00 to obtain an excessive grinding coefficient of 0.9. Then the output power is adjusted by iteration, and it is assumed that the iteration ends at 13:20:00. Since the non-normal machining is performed after 13:15:00, the normal machining is temporarily stopped at 13:15:00, and the current time point still refers to 13:15:00.

[0083] The grinding parameter feedback control module 104: feedback controls the tool machining according to the excessive grinding coefficient at the current time point and the excessive grinding reliability.

[0084] The excessive grinding situation and the excessive grinding credibility at the current time point are analyzed, the processing state of the tool is evaluated in real time, and then the tool processing is feedback controlled according to the excessive grinding coefficient and the excessive grinding credibility at the current time point, so that the traditional tool processing depending on manual operation or fixed parameters is avoided, the automation degree, the accuracy and the adaptability of the control system of the tool processing are improved, the tool waste and the energy loss caused by excessive grinding are reduced, and the cost is reduced.

[0085] Preferably, in an embodiment of the present application, when the excessive grinding credibility is less than the preset credibility threshold, it is determined that the tool processing does not need to be adjusted, because the lower the excessive grinding credibility is, the lower the possibility of excessive grinding of the tool is.

[0086] When the excessive grinding credibility is greater than or equal to the preset credibility threshold, it is determined that the tool fine processing state has reached a critical state, and the tool processing needs to be adjusted.

[0087] As an example, the preset credibility threshold is 0.9. It should be noted that when the excessive grinding coefficient is small, the excessive grinding credibility of the current time period is set to a preset constant, it is determined that the tool has little or no excessive grinding, and feedback adjustment is not needed, so the preset constant is less than the preset credibility threshold.

[0088] Through real-time monitoring and feedback adjustment control of the tool, the excessive grinding caused by manual subjective errors and fixed parameters is avoided, the production cost is reduced, and the production quality is ensured.

[0089] It should be noted that in an embodiment of the present application, when it is determined that the tool processing needs to be adjusted, the product of the excessive grinding credibility and the excessive grinding coefficient is taken as a grinding degree value of the tool, which is input to the tool grinding machine for adjustment, because the greater the excessive grinding credibility and the excessive grinding coefficient are, the greater the degree of excessive grinding of the tool is. Through precise grinding of the cutting edge of the milling cutter, the milling cutter quality meeting the requirements is achieved, the high-efficiency cutting ability is maintained, and the purpose of precise control of tool grinding in the automatic processing of numerical control tools is achieved.

[0090] As an example: when the grinding degree value G is greater than 0 and less than 0.3, it is determined that it is in a low excessive grinding stage, the spindle speed is increased by 2%, the feed rate is reduced by 5%, the cooling liquid flow is increased by 10%, and the grinding depth is reduced by 0.01 mm;

[0091] When G is greater than or equal to 0.3 and less than 0.6, it is determined that it is in a moderate excessive grinding stage, the spindle speed is reduced by 5%, the feed rate is reduced by 8%, the cooling liquid flow is increased by 20%, and the grinding depth is reduced by 0.03 mm;

[0092] When G is greater than or equal to 0.6 and less than or equal to 1, it is determined that it is in a moderate overgrinding stage, the spindle speed is reduced by 10%, the feed rate is reduced by 12%, the cooling liquid flow is increased by 30%, and the grinding depth is reduced by 0.05 mm.

[0093] As another example, an LSTM (Long Short-Term Memory) neural network is trained by historical data of grinding degree values and control parameters, and the LSTM neural network is used to predict optimal machining control parameters during machining; the training and use of the LSTM neural network are prior art and will not be described again.

[0094] It should be noted that in another embodiment of the present application, the frequency of feedback adjustment of tool machining is also counted, and if the frequency of feedback adjustment of tool machining is greater than a preset frequency threshold, such as 10 times per hour, it is determined that the equipment or material is abnormal, and a corresponding abnormal warning signal, such as a flashing yellow light, is issued to remind relevant personnel; the tool grinding machine is a five-axis automatic grinding equipment.

[0095] To sum up, in view of the technical problems of strong subjectivity, low precision and poor flexibility of existing numerical control tool machining control, the present application provides a numerical control tool machining control system. The present application first acquires machining data of continuous tool machining through a machining monitoring module; further through a grinding degree analysis module: acquires tool grinding state abnormal parameters and spindle state abnormal parameters of each time window; according to the fluctuation similarity characteristics of the tool grinding state abnormal parameters and the spindle state abnormal parameters, acquires an overgrinding coefficient of the current time point; further acquires an overgrinding credibility of the current time point through a credibility analysis module; finally, through a grinding parameter feedback control module, according to the overgrinding coefficient and the overgrinding credibility of the current time point, feedback controls tool machining, avoids traditional tool machining relying on manual or fixed parameters, improves the automation degree, accuracy and adaptability of the tool machining control system, and reduces the cost.

[0096] It should be noted that the above-mentioned embodiments of the present application are in the order of description only, and do not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or can be advantageous.

[0097] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other, and each embodiment mainly describes the difference from other embodiments.

Claims

1. A machining control system for a CNC tool, characterized in that: The system comprises: Processing monitoring module: acquires processing data of continuous tool processing in a preset time window; the processing data at least includes surface temperature data of the tool, noise decibel data of the processing area, load data of the spindle motor and spindle temperature data; Grinding degree analysis module: based on the fluctuation characteristics of the surface temperature data in each time window, combined with the fluctuation characteristics of the noise decibel data, obtain the tool grinding state abnormality parameter of each time window; based on the fluctuation characteristics of the spindle temperature data and the load data in each time window, obtain the spindle state abnormality parameter of each time window; based on the fluctuation similarity characteristics of the tool grinding state abnormality parameters and the spindle state abnormality parameters in a preset number of the latest time windows, obtain the over-grinding coefficient at the current time point; Credibility analysis module: when the over-grinding coefficient at the current time point is less than or equal to a preset grinding threshold, setting the over-grinding credibility of the current time period to a preset constant; when the over-grinding coefficient at the current time point is greater than the preset grinding threshold, iteratively adjusting the output power of the spindle motor to obtain the over-grinding coefficient after each iteration; obtaining the over-grinding credibility at the current time point based on similarity characteristics between the over-grinding coefficient at the current time point and the over-grinding coefficients obtained after all iterations; Grinding parameter feedback control module: feedback controls tool processing according to the over-grinding coefficient and the over-grinding credibility at the current time point.

2. A CNC tool processing control system according to claim 1, characterized in that: The method for obtaining the abnormal parameters of the tool grinding state includes: The product of the range and the mean of the surface temperature data in the time window is used as the heat accumulation coefficient corresponding to the time window; Taking the absolute average deviation of the noise decibel data within the time window as the noise anomaly coefficient corresponding to the time window; According to the heat accumulation coefficient and the noise abnormality coefficient of each time window, the tool grinding state abnormality parameter of each time window is obtained; the heat accumulation coefficient and the noise abnormality coefficient are both positively correlated with the tool grinding state abnormality parameter.

3. A CNC tool processing control system according to claim 2, characterized in that: The method for obtaining the tool grinding state abnormality parameter of each time window according to the heat accumulation coefficient and the noise abnormality coefficient of each time window includes: The weighted sum of the heat accumulation coefficient and the noise anomaly coefficient in each time window is normalized and used as the tool grinding state anomaly parameter in each time window.

4. A CNC tool processing control system according to claim 1, characterized in that: The method for obtaining the spindle state abnormality parameter includes: In each of the time windows, the product of the absolute value of the difference between the load data at the end point of the time domain and the midpoint of the time domain and the average value of the spindle temperature data is used as the first numerator; the sum of the absolute value of the difference between the load data at the starting point of the time domain and the midpoint of the time domain and a preset positive parameter dividing by zero is used as the first denominator; and the ratio of the first numerator to the first denominator is used as the spindle state abnormality parameter corresponding to the time window.

5. The processing control system of a CNC tool according to claim 1, characterized in that: The method for obtaining the over-grinding coefficient includes: The normalized results of the Pearson correlation coefficients between the abnormal tool grinding state parameters and the abnormal spindle state parameters in a preset number of the latest time windows are used as the over-grinding coefficients at the current time point.

6. A CNC tool processing control system according to claim 5, characterized in that: The method of iteratively adjusting the output power of the spindle motor to obtain the overgrinding coefficient after each iteration includes: The output power of the spindle motor in the last time window at the current time point is used as the iteration basic power; the product of the iteration basic power and each element in the preset adjustment ratio sequence is used as the iteration power of each iteration; Based on the iterative power, the tool processing is controlled, the processing data of the preset number of the time windows are acquired, and the over-grinding coefficient corresponding to the iterative power is obtained.

7. A machining control system for a CNC tool according to claim 6, characterized in that: The method for obtaining the over-grinding credibility at the current time point according to similar features between the over-grinding coefficient at the current time point and the over-grinding coefficients obtained after all iterations includes: The absolute value of the difference between the mean value of the over-grinding coefficient obtained after all iterations and the over-grinding coefficient at the current time point is negatively correlated and normalized to obtain the over-grinding credibility at the current time point.

8. A machining control system for a CNC tool according to claim 7, characterized in that: The method for feedback controlling tool processing according to the over-grinding coefficient and the over-grinding credibility at the current time point includes: When the over-grinding credibility is less than a preset credibility threshold, it is determined that tool processing does not need adjustment; When the over-grinding confidence is greater than or equal to a preset confidence threshold, it is determined that tool processing needs to be adjusted.

9. A machining control system for a CNC tool according to claim 8, characterized in that: When it is determined that the tool needs to be adjusted, the product of the over-grinding reliability and the over-grinding coefficient is used as the grinding degree value of the tool and input into the tool grinding machine for adjustment.

10. A machining control system for a CNC tool according to claim 8, characterized in that: After determining that the tool needs to be adjusted, the method further includes: The adjustment frequency of tool processing is counted, and when the adjustment frequency is greater than a preset frequency threshold, an abnormal warning signal is issued.

Images