A method and device for monitoring machining status in a CNC system

Abstract

This invention discloses a machining status monitoring method and device for CNC systems, relating to the field of machining status monitoring technology. By retrieving tool images and performing grayscale processing and pixel statistics, the device can monitor and quickly reflect the tool's usage status (apparent tool value) in real time. By collecting and analyzing cutting force, cutting speed, amplitude, and temperature of various parts of the machine tool, influencing factors are calculated. The tool's apparent value and influencing factors are then comprehensively analyzed to obtain the tool status value, constructing a curve of the tool status value changing over time. Based on this, predictive monitoring analysis is performed to determine the tool's status, intelligently adjusting the tool replacement strategy, and realizing intelligent, scientific, and automated tool replacement. This allows for timely response to changes in tool status, ensuring the smooth progress of the machining process and maximizing product quality and production efficiency.

Description

Technical Field

[0001] This invention relates to the field of machining status monitoring technology, and more specifically, to a machining status monitoring method and device for CNC systems. Background Technology

[0002] Numerical Control System (CNC) is an automated system that uses computer programs to control machine tools and other manufacturing equipment to perform machining. It uses pre-programmed instructions to control the movement of tools and machine tools to achieve high-precision machining of complex parts. In CNC machining, the cutting tool is particularly important, as it directly determines the surface roughness, shape accuracy, and dimensional accuracy of the machined surface.

[0003] Currently, CNC machining tools are typically replaced after reaching a preset service life. However, this fixed-time tool replacement strategy has limitations. If the tool is replaced prematurely before its service life is fully exhausted within the preset period, it will lead to resource waste and increased costs. In particular, for high-cost tools or large machining equipment, excessively high replacement frequency will significantly increase production costs. Summary of the Invention

[0004] The main objective of this invention is to provide a machining status monitoring method for CNC systems, in order to overcome the problems mentioned in the background art.

[0005] To achieve the above objectives, according to one aspect of the present invention, a machining status monitoring method for a CNC system is provided, the method comprising the following steps:

[0006] Step 1: Collect and save machining information by communicating with the CNC system; the machining information includes tool image, cutting force, cutting speed, amplitude, and temperature of various parts of the machine tool;

[0007] Step 2: Perform real-time monitoring and analysis based on the tool graphic to obtain tool appearance values;

[0008] Step 3: Monitor and analyze the degree of influence of the cutting tool based on machining information to obtain the influencing factors;

[0009] Step 4: Conduct a comprehensive analysis based on the tool's apparent value and influencing factors to determine whether tool replacement is necessary; specifically:

[0010] The tool appearance value and influencing factor corresponding to each acquisition time are retrieved, and the tool appearance value is divided by the influencing factor to obtain the tool state value, denoted as ZFi. The tool state value is plotted as time on the x-axis and the tool state value on the y-axis to obtain the curve of the tool state value changing with time. The tangent line of the curve is drawn at the state point, and the tangent line expression is obtained by data fitting. The derivative of the tangent line expression is obtained to obtain the state derivative of each state point, denoted as Ui, where i = 1, 2, 3...I, I takes a positive integer value, I represents the total number of acquisition times, and i represents the sequence number of any acquisition time.

[0011] When the absolute value of the state derivative is greater than the upper limit of the change interval, a high tool wear is accumulated once; when the absolute value of the state derivative is in the change interval, a high tool wear is accumulated once; when the absolute value of the state derivative is greater than the lower limit of the change interval, a low tool wear is accumulated once. The accumulated number of high tool wear, medium tool wear, and low tool wear are counted and recorded as P1, P2, and P3, respectively.

[0012] The tool state value corresponding to the current acquisition moment is recorded as ZF, and it is substituted into the set formula along with the cumulative number of high wear P1, the cumulative number of tool wear P2, the cumulative number of tool low wear P3, and the state derivative Ui. The loss index Pn is calculated, where n1, n2, and n3 are set proportional coefficients, and n1 > n2 > n3 > 0;

[0013] When the wear index is greater than or equal to the set wear threshold, a tool change command is generated to control the tool retraction operation and replace the tool with a new one.

[0014] Furthermore, the specific process of real-time monitoring and analysis based on tool graphics is as follows:

[0015] 201: Retrieve the tool images corresponding to each acquisition time, identify the tool images using a photo recognition device, and perform grayscale processing on them; divide the grayscale processed cutting edge image into several squares, count the number of pixels in each square and record it as Gk, where k = 1, 2, 3...K, K is a positive integer, K represents the total number of squares, and k represents the sequence number of any one of the squares; take the center point of each square and calculate the shortest distance between it and the edge of the tool cutting edge and record it as Lk;

[0016] 202: Identify pixels and their corresponding horizontal and vertical gradients. Calculate the gradient magnitude of each pixel using the Sobel operator and compare and analyze them to obtain the sharpness value of the square.

[0017] 203: Substitute the number of pixels in the square Gk, the shortest distance Lk, and the sharpness value Tak into the set formula. The sharpness value Rb of each grid cell is calculated, where b1, b2, and b3 are set proportional coefficients, the values ​​of which are set by those skilled in the art according to actual needs. Grid cells with the same shortest distance are grouped together, and the sharpness values ​​of each grid cell in the group are averaged to obtain the sharpness mean. The sharpness mean of each group can be obtained from this. The groups are numbered according to the order of their shortest distance from largest to smallest.

[0018] 203: Using the label as the horizontal axis and the average sharpness as the vertical axis, a tool sharpness variation curve is obtained, and based on this, the tool sharpness average variation trend is analyzed to obtain the tool apparent value.

[0019] Furthermore, the specific steps for comparing and analyzing the gradient magnitudes corresponding to each pixel are as follows:

[0020] The gradient magnitude of each pixel within the grid is compared and analyzed with the set magnitude range to classify the pixels corresponding to the gradient magnitude into high-resolution, medium-resolution, and low-resolution pixels. The cumulative number of high-resolution, medium-resolution, and low-resolution pixels within the grid is counted and recorded as T1, T2, and T3, respectively. The gradient magnitudes corresponding to high-resolution, medium-resolution, and low-resolution pixels are summed to obtain high-resolution, medium-resolution, and low-resolution values, which are recorded as T4, T5, and T6, respectively.

[0021] Substitute T1, T2, T3, T4, T5, and T6 into the set formula. The clarity value Ta of the grid is obtained by calculation, where a1, a2, and a3 are set as proportional coefficients, and a1 > a2 > a3 > 0. Their values ​​can be set by those skilled in the art according to actual needs.

[0022] Furthermore, the specific steps for analyzing the trend of the average sharpness of cutting tools are as follows:

[0023] At each sharp point, draw tangent lines to the curve. Using the tangent line expression fitted by the data, differentiate the tangent line expression to obtain the tangent line derivative at each sharp point. Summate the tangent line derivatives greater than zero to obtain the increasing trend value, denoted as Z1. Count the number of tangent line derivatives equal to zero, denoted as Z2, and count the number of tangent line derivatives less than zero, denoted as Z3. Summate the tangent line derivatives less than zero and take the absolute value to obtain the decreasing trend, denoted as Z4. Use the established formula... The apparent value of the tool Zd is obtained by calculation, where d1, d2, d3, and d4 are set proportional coefficients, the values ​​of which can be set by those skilled in the art according to actual needs; thus, the apparent value of the tool corresponding to each acquisition time is recorded as Zdi.

[0024] Furthermore, the specific process for monitoring and analyzing the degree of influence of machining information on the cutting tool is as follows:

[0025] 501: Retrieve the cutting force, cutting speed, amplitude, and temperature of various parts of the machine tool at each acquisition time.

[0026] 502: Set a sensitivity coefficient for each part of the machine tool. Compare each part of the machine tool with all the set machine tool parts to match the corresponding sensitivity coefficient. Multiply the temperature of each part of the machine tool by the corresponding sensitivity coefficient to obtain the temperature sensitivity value, denoted as Mqi. Compare and analyze it to obtain the temperature sensitivity influence value, where q = 1, 2, 3...Q, Q takes a positive integer value, Q represents the total number of machine tool parts, and q represents the serial number of any part in the machine tool.

[0027] 503: The cutting force Fi, cutting speed Vi, amplitude Ai, and temperature-sensitive influence value Ygi are calculated using a set formula. The influence factor FYi is calculated, where h1, h2, h3, and h4 are the set proportional coefficients; thus, the influence factor corresponding to each acquisition time can be obtained.

[0028] Furthermore, the specific process of comparing and analyzing the temperature sensitivity values ​​is as follows:

[0029] The temperature-sensitive value is compared and analyzed with the set temperature-sensitive range to divide the machine tool parts corresponding to the temperature-sensitive value into high-impact parts, medium-impact parts and low-impact parts. The cumulative number of high-impact parts, medium-impact parts and low-impact parts in the machine tool is counted and recorded as Y1, Y2 and Y3 respectively.

[0030] Substitute the cumulative number of high-impact parts Y1, the cumulative number of medium-impact parts Y2, the cumulative number of low-impact parts Y3, and the temperature sensitivity value Mqi of each part of the machine tool into the set formula. The temperature-sensitive influence value Ygi is calculated, where g1, g2, and g3 are set proportional coefficients, and g1 > g2 > g3 > 0.

[0031] To achieve the above objectives, according to another aspect of the present invention, a machining status monitoring device for a CNC system is provided, the device comprising: a data acquisition module, a status monitoring module, and a machining control module;

[0032] The data acquisition module communicates with the CNC system to collect and save machining information, including tool image, cutting force, cutting speed, amplitude, and temperature of various parts of the machine tool.

[0033] The condition monitoring module performs real-time monitoring and analysis based on the tool graphic to obtain the tool appearance value. At the same time, it monitors and analyzes the degree of influence on the tool based on the machining information to obtain the influencing factor.

[0034] The machining control module performs a comprehensive analysis based on tool appearance and influencing factors to determine whether tool replacement is necessary; specifically:

[0035] The tool appearance value and influencing factor corresponding to each acquisition time are retrieved, and the tool appearance value is divided by the influencing factor to obtain the tool state value, denoted as ZFi. The tool state value is plotted as time on the x-axis and the tool state value on the y-axis to obtain the curve of the tool state value changing with time. The tangent line of the curve is drawn at the state point, and the tangent line expression is obtained by data fitting. The derivative of the tangent line expression is obtained to obtain the state derivative of each state point, denoted as Ui, where i = 1, 2, 3...I, I takes a positive integer value, I represents the total number of acquisition times, and i represents the sequence number of any acquisition time.

[0036] When the absolute value of the state derivative is greater than the upper limit of the change interval, a high tool wear is accumulated once; when the absolute value of the state derivative is in the change interval, a high tool wear is accumulated once; when the absolute value of the state derivative is greater than the lower limit of the change interval, a low tool wear is accumulated once. The accumulated number of high tool wear, medium tool wear, and low tool wear are counted and recorded as P1, P2, and P3, respectively.

[0037] The tool state value corresponding to the current acquisition moment is recorded as ZF, and it is substituted into the set formula along with the cumulative number of high wear P1, the cumulative number of tool wear P2, the cumulative number of tool low wear P3, and the state derivative Ui. The loss index Pn is calculated, where n1, n2, and n3 are set proportional coefficients, and n1 > n2 > n3 > 0;

[0038] When the wear index is greater than or equal to the set wear threshold, a tool change command is generated to control the tool retraction operation and replace the tool with a new one.

[0039] The beneficial effects of this invention are:

[0040] (1) By retrieving tool images and performing grayscale processing, pixel statistics and other operations, this invention can monitor and quickly respond to the tool's usage status (tool apparent value) in real time, providing detailed analysis and monitoring of the tool's usage status, realizing in-depth analysis and accurate evaluation of the tool's status, and providing important technical support and decision-making basis for tool management in industrial production;

[0041] (2) This invention collects and analyzes cutting force, cutting speed, amplitude and temperature of various parts of the machine tool to calculate the influencing factors, which can accurately reflect the actual influence of machine tool working conditions on the tool, realize real-time monitoring of the tool use environment, and make tool management more scientific and efficient.

[0042] (3) By constructing a curve of tool status values ​​changing over time, the present invention allows workers to understand the working status of the tool in real time, including the consumption of its service life. It can promptly detect abnormal changes or rapid consumption of the tool during use and perform predictive monitoring and analysis to determine the tool status, intelligently adjust the tool replacement strategy, avoid the waste of premature replacement, maximize the service life of the tool, and reduce the overall procurement and maintenance costs of the tool. It realizes the intelligent, scientific and automated tool replacement, can respond to changes in tool status in a timely manner, ensure the smooth progress of the processing process, and maximize product quality and production efficiency. Attached Figure Description

[0043] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0044] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0045] Figure 2 This is a schematic diagram of the device of the present invention. Detailed Implementation

[0046] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0047] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0048] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of the invention described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0049] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0050] According to embodiments of the present invention, such as Figure 1 As shown, a machining status monitoring method for a CNC system is provided, which includes the following steps:

[0051] Step 1: Establish a communication connection with the CNC system to collect and save machining information; the machining information includes tool image, cutting force, cutting speed, amplitude (amplitude of machine tool vibration), and temperature of various parts of the machine tool;

[0052] Step 2: Real-time monitoring and analysis of the tool's usage status based on tool images to obtain tool appearance values, specifically:

[0053] Retrieve tool images corresponding to each acquisition time; use an image recognition device to identify the tool images and perform grayscale processing; divide the grayscale processed cutting edge image into several squares, and count the number of pixels in each square, denoted as Gk, where k = 1, 2, 3...K, K is a positive integer, K represents the total number of squares, and k represents the sequence number of any one of the squares; take the center point of each square and calculate the shortest distance between it and the edge of the tool cutting edge, denoted as Lk; it should be noted that under normal circumstances, the closer the square is to the edge of the cutting edge, the more pixels the corresponding square should have, because the cutting edge is sharper; when the tool is worn and the cutting edge is less sharp, the number of pixels in the corresponding square close to the edge is less.

[0054] Identify pixels and their corresponding horizontal and vertical gradients. Calculate the gradient magnitude of each pixel using the Sobel operator. Note that a larger gradient magnitude indicates a sharper pixel, while a smaller gradient magnitude indicates a blurrier pixel. Compare the gradient magnitude of each pixel within a grid with a set range. A high-resolution pixel is accumulated when the gradient magnitude exceeds the upper limit of the range; a medium-resolution pixel is accumulated when the gradient magnitude is within the range; and a low-resolution pixel is accumulated when the gradient magnitude is below the lower limit. Count the cumulative number of high-resolution, medium-resolution, and low-resolution pixels within the grid and label them T1, T2, and T3, respectively. Sum the gradient magnitudes of the high-resolution, medium-resolution, and low-resolution pixels to obtain the high-resolution value, medium-resolution value, and low-resolution value, and label them T4, T5, and T6, respectively. Use the set formula... The sharpness value Ta of each grid is calculated, where a1, a2, and a3 are set as proportional coefficients, and a1 > a2 > a3 > 0. Their values ​​are set by those skilled in the art according to actual needs. Thus, the sharpness value of each grid is denoted as Tak.

[0055] The number of pixels Gk, the shortest distance Lk, and the sharpness value Tak of the square are calculated using a set formula. The sharpness value Rb of each grid cell is calculated, where b1, b2, and b3 are set scaling factors, the values ​​of which are determined by those skilled in the art according to actual needs. Grid cells with the same shortest distance are grouped together, and the sharpness values ​​of each grid cell within a group are averaged to obtain the sharpness mean. The sharpness mean of each group is thus obtained, and the groups are labeled according to their shortest distances from largest to smallest. A two-dimensional Cartesian coordinate system is constructed with the label as the x-axis and the sharpness mean as the y-axis. The sharpness mean is input into the coordinate axis according to its corresponding label, and the sharpness... The position of the mean value on the coordinate axis is marked as the sharp point. A smooth curve is used to connect the sharp points to obtain the tool sharpness variation curve. It should be noted that when the tool is under normal conditions (no wear or very little wear that does not affect processing and can be ignored), the mean sharpness value should increase accordingly. This is because the closer to the edge of the cutting edge, the sharper it should be. Conversely, if the cutting edge is worn and becomes dull, the squares closer to the edge of the cutting edge may display a lower mean sharpness value because the distribution of pixels around the edge is no longer obvious, and the clarity is reduced.

[0056] At each sharp point, draw tangent lines to the curve. Using the tangent line expression fitted by the data, differentiate the tangent line expression to obtain the tangent line derivative at each sharp point. Summate the tangent line derivatives greater than zero to obtain the increasing trend value, denoted as Z1. Count the number of tangent line derivatives equal to zero, denoted as Z2, and count the number of tangent line derivatives less than zero, denoted as Z3. Summate the tangent line derivatives less than zero and take the absolute value to obtain the decreasing trend, denoted as Z4. Use the established formula... The apparent value of the tool is calculated as Zd, where d1, d2, d3, and d4 are set proportional coefficients, the values ​​of which can be set by those skilled in the art according to actual needs. The apparent value of the tool corresponding to each acquisition moment is denoted as Zdi, where i = 1, 2, 3...I, where I is a positive integer, I represents the total number of acquisition moments, and i represents the sequence number of any one acquisition moment. As can be seen from the formula, the sharper the tool, the larger the apparent value of the tool.

[0057] By retrieving tool images and performing grayscale processing and pixel statistics, the tool's usage status (apparent value) can be monitored and reflected in real time. This provides detailed analysis and monitoring of tool usage status, enabling in-depth analysis and accurate evaluation of tool status, and providing important technical support and decision-making basis for tool management in industrial production.

[0058] Step 3: Real-time monitoring and analysis of the impact of machining information on tool usage is conducted to obtain influencing factors, specifically:

[0059] Retrieve the cutting force, cutting speed, amplitude, and temperature of each part of the machine tool at each acquisition time (divide the machine tool into several parts and collect the temperature of each part), and record them as Fi, Vi, Ai and Wqi respectively, where q=1, 2, 3...Q, Q takes the value of a positive integer, Q represents the total number of machine tool parts, and q represents the serial number of any part in the machine tool;

[0060] Each part of the machine tool is assigned a sensitivity coefficient. It's important to note that different parts of the machine tool are affected by temperature to varying degrees due to their different functions and operating conditions. Therefore, a specific sensitivity coefficient needs to be assigned to each part. These sensitivity coefficients reflect the degree to which each part affects the overall performance of the cutting tool when temperature changes. For example, the tool guideway is more sensitive to temperature changes because temperature changes directly affect the accuracy and stability of the cutting tool, thus affecting machining quality. Other parts of the machine tool, such as the housing or auxiliary structures, respond less to temperature changes, so their sensitivity coefficients are set to lower values. The temperature of each part is compared with all the set machine tool parts to match the corresponding sensitivity coefficient. The temperature of each part of the machine tool is multiplied by the corresponding sensitivity coefficient to obtain the temperature sensitivity value, which is recorded as Mqi. The temperature sensitivity value is compared with the set temperature sensitivity range. When the temperature sensitivity value is greater than the upper limit of the set temperature sensitivity range, a high-impact part is accumulated; when the temperature sensitivity value is within the set temperature sensitivity range, a medium-impact part is accumulated; when the temperature sensitivity value is greater than the upper limit of the set temperature sensitivity range, a low-impact part is accumulated. The cumulative number of high-impact parts, medium-impact parts and low-impact parts in the machine tool are counted respectively and recorded as Y1, Y2 and Y3. The set formula is then used. The temperature-sensitive influence value Ygi is calculated, where g1, g2, and g3 are set proportional coefficients, and g1 > g2 > g3 > 0;

[0061] The cutting force Fi, cutting speed Vi, amplitude Ai, and temperature-sensitive influence value Ygi are calculated using a set formula. The influence factor FYi is calculated, where h1, h2, h3, and h4 are set proportional coefficients. This yields the influence factor corresponding to each data collection moment. The formula shows that a larger temperature-sensitive influence value indicates a greater negative interference between machine tool temperature and tool accuracy, resulting in a larger influence factor. Similarly, higher cutting forces and speeds indicate more frequent tool use and larger cutting volumes, leading to greater negative interference with tool wear and heat, and thus a larger influence factor. Specifically, high cutting forces and speeds exacerbate tool wear, especially at the cutting edges, as high-speed cutting increases friction and heat on the tool surface, accelerating wear on the cutting edge. High cutting forces and speeds generate significant heat, which can cause deformation of the tool material or a decrease in surface hardness, affecting tool life and machining quality. Larger machine tool vibration amplitudes accelerate tool fatigue wear, particularly at the cutting edge, leading to premature wear and failure of the cutting edge, reducing tool life.

[0062] By collecting and analyzing cutting force, cutting speed, amplitude, and temperature of various parts of the machine tool, influencing factors are calculated, which can accurately reflect the actual impact of machine tool working conditions on the cutting tool. This enables real-time monitoring of the cutting tool's operating environment, making cutting tool management more scientific and efficient.

[0063] Step 4: Conduct a comprehensive analysis based on the tool's apparent value and influencing factors to determine whether tool replacement is necessary, thereby achieving intelligent, scientific, and automated tool replacement; specifically:

[0064] The tool apparent value Zdi and influence factor FYi at each acquisition time are retrieved, and the tool apparent value is divided by the influence factor to obtain the tool state value, denoted as ZFi. A two-dimensional rectangular coordinate system is constructed with time as the abscissa and tool state value as the ordinate. The tool state value is input into the coordinate axis according to its corresponding acquisition time, and the position of the state value in the coordinate axis is denoted as a state point. A smooth curve is used to connect the state points in sequence to obtain the curve of tool state value changing with time. Tangent lines are drawn to the curve at the state points, and the tangent line expression is obtained by data fitting. The derivative of the tangent line expression is obtained to obtain the state derivative of each state point, denoted as Ui. Since the tool state value will only decrease with the use of time, the state derivative is less than zero.

[0065] The absolute value of the state derivative is compared with the change range. When the absolute value of the state derivative is greater than the upper limit of the change range, a high tool wear is accumulated once; when the absolute value of the state derivative is within the change range, a high tool wear is accumulated once; when the absolute value of the state derivative is greater than the lower limit of the change range, a low tool wear is accumulated once. The cumulative number of high tool wear, medium tool wear, and low tool wear is counted and recorded as P1, P2, and P3, respectively.

[0066] The tool status value corresponding to the current acquisition moment is obtained and denoted as ZF. Then, a predefined formula is used... The wear index Pn is calculated, where n1, n2, and n3 are set proportional coefficients, and n1 > n2 > n3 > 0. The wear index is compared with the set wear threshold. When the wear index is greater than or equal to the set wear threshold, it indicates that the tool condition has accumulated to a level that has a significant negative impact on machining, resulting in a high risk that the machined parts will not meet the required accuracy. The tool needs to be replaced. A tool replacement command is then generated to control the tool path to retract (tool retraction operation) and replace the tool with a new one to ensure the smooth progress of the machining process and the stability of product quality.

[0067] By constructing a curve showing how tool status values ​​change over time, operators can monitor the tool's working status in real time, including the consumption of its lifespan. This allows for the timely detection of abnormal changes or rapid wear during tool use, and predictive monitoring and analysis to determine the tool's condition. Intelligent adjustments to tool replacement strategies avoid premature replacement waste, maximize tool lifespan, and reduce overall tool procurement and maintenance costs. This achieves intelligent, scientific, and automated tool replacement, enabling timely responses to changes in tool status, ensuring smooth machining processes, and maximizing product quality and production efficiency.

[0068] According to embodiments of the present invention, such as Figure 2 As shown, a machining status monitoring device for a CNC system is also provided. The device includes: a data acquisition module, a status monitoring module, and a machining control module.

[0069] The data acquisition module communicates with the CNC system to collect and save machining information, including tool image, cutting force, cutting speed, amplitude, and temperature of various parts of the machine tool.

[0070] The condition monitoring module performs real-time monitoring and analysis based on the tool graphic to obtain the tool appearance value. At the same time, it monitors and analyzes the degree of influence on the tool based on the machining information to obtain the influencing factor.

[0071] The machining control module performs a comprehensive analysis based on tool appearance and influencing factors to determine whether tool replacement is necessary; specifically:

[0072] The tool appearance value and influencing factor corresponding to each acquisition time are retrieved, and the tool appearance value is divided by the influencing factor to obtain the tool state value, denoted as ZFi. The tool state value is plotted as time on the x-axis and the tool state value on the y-axis to obtain the curve of the tool state value changing with time. The tangent line of the curve is drawn at the state point, and the tangent line expression is obtained by data fitting. The derivative of the tangent line expression is obtained to obtain the state derivative of each state point, denoted as Ui, where i = 1, 2, 3...I, I takes a positive integer value, I represents the total number of acquisition times, and i represents the sequence number of any acquisition time.

[0073] When the absolute value of the state derivative is greater than the upper limit of the change interval, a high tool wear is accumulated once; when the absolute value of the state derivative is in the change interval, a high tool wear is accumulated once; when the absolute value of the state derivative is greater than the lower limit of the change interval, a low tool wear is accumulated once. The accumulated number of high tool wear, medium tool wear, and low tool wear are counted and recorded as P1, P2, and P3, respectively.

[0074] The tool state value corresponding to the current acquisition moment is recorded as ZF, and it is substituted into the set formula along with the cumulative number of high wear P1, the cumulative number of tool wear P2, the cumulative number of tool low wear P3, and the state derivative Ui. The loss index Pn is calculated, where n1, n2, and n3 are set proportional coefficients, and n1 > n2 > n3 > 0;

[0075] When the wear index is greater than or equal to the set wear threshold, a tool change command is generated to control the tool retraction operation and replace the tool with a new one.

[0076] The above are merely embodiments of the present invention and are not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A machining status monitoring method for a CNC system, characterized in that, Includes the following steps: Step 1: Collect and save machining information by communicating with the CNC system; the machining information includes tool image, cutting force, cutting speed, amplitude, and temperature of various parts of the machine tool; Step 2: Perform real-time monitoring and analysis based on the tool graphic to obtain tool appearance values; Step 3: Monitor and analyze the degree of influence of the cutting tool based on machining information to obtain the influencing factors; Step 4: Conduct a comprehensive analysis based on the tool's apparent value and influencing factors to determine whether tool replacement is necessary; specifically: The tool appearance value and influencing factor corresponding to each acquisition time are retrieved and calculated using a formula to obtain the tool state value corresponding to each acquisition time. The tool state value is plotted as time on the horizontal axis and the tool state value on the vertical axis to obtain the curve of tool state value changing with time. Tangent lines are drawn to the curve at the state points, and the tangent line expression is obtained by data fitting. The state derivative of each state point is obtained by differentiating the tangent line expression. When the absolute value of the state derivative is greater than the upper limit of the change range, a high tool wear is accumulated once; when the absolute value of the state derivative is within the change range, a high tool wear is accumulated once; when the absolute value of the state derivative is greater than the lower limit of the change range, a low tool wear is accumulated once; the cumulative number of high tool wear, medium tool wear, and low tool wear is counted. Obtain the tool state value corresponding to the current acquisition time, and normalize it with the cumulative number of high wear, the cumulative number of tool wear, the cumulative number of tool low wear, and the state derivative, and take its value. Analyze the value to obtain the wear index. When the wear index is greater than or equal to the set wear threshold, a tool change command is generated to control the tool retraction operation and replace the tool with a new one.

2. The machining status monitoring method for a CNC system according to claim 1, characterized in that, The specific process of real-time monitoring and analysis based on tool graphics is as follows: 201: Retrieve the tool images corresponding to each acquisition time, use a photo recognition device to identify the tool images, and process them into grayscale; divide the grayscale processed cutting edge image into several squares, count the number of pixels in each square; take the center point of each square and calculate the shortest distance between it and the edge of the tool cutting edge. 202: Identify pixels and their corresponding horizontal and vertical gradients. Calculate the gradient magnitude of each pixel using the Sobel operator and compare and analyze them to obtain the sharpness value of the square. 203: Normalize the number of pixels, shortest distance, and sharpness value of each grid and take their values. Analyze the values ​​to obtain the sharpness value of each grid. Group the grids with the same shortest distance into one group and calculate the mean sharpness value of each grid in the group. The mean sharpness value of each group can be obtained from this. The groups are then numbered according to the order of their shortest distance from largest to smallest. 203: Using the label as the horizontal axis and the average sharpness as the vertical axis, a tool sharpness variation curve is obtained, and based on this, the tool sharpness average variation trend is analyzed to obtain the tool apparent value.

3. The machining status monitoring method for a CNC system according to claim 2, characterized in that, The specific steps for comparing and analyzing the gradient magnitudes corresponding to each pixel are as follows: The gradient magnitude of each pixel in the grid is compared and analyzed with the set magnitude range to classify the pixels corresponding to the gradient magnitude into high-resolution, medium-resolution, and low-resolution pixels. The cumulative number of high-resolution, medium-resolution, and low-resolution pixels in the grid is counted respectively, and the gradient magnitudes corresponding to high-resolution, medium-resolution, and low-resolution pixels are summed to obtain the high-resolution value, medium-resolution value, and low-resolution value. The clarity value of each grid cell is obtained by analyzing the cumulative number of high-definition pixels, the cumulative number of medium-definition pixels, the cumulative number of low-definition pixels, the high-definition value, the medium-definition value, and the low-definition value.

4. The machining status monitoring method for a CNC system according to claim 3, characterized in that, The specific steps for analyzing the trend of the average sharpness of cutting tools are as follows: At each sharp point, draw tangent lines to the curve. Using the tangent line expression fitted by the data, differentiate the tangent line expression to obtain the tangent line derivative at each sharp point. Summate the tangent line derivatives greater than zero to obtain the increasing trend value, denoted as Z1. Count the number of tangent line derivatives equal to zero, denoted as Z2, and count the number of tangent line derivatives less than zero, denoted as Z3. Summate the tangent line derivatives less than zero and take the absolute value to obtain the decreasing trend, denoted as Z4. Use the established formula... The apparent value of the tool Zd is obtained by calculation, where d1, d2, d3, and d4 are set proportional coefficients, the values ​​of which are set by those skilled in the art according to actual needs; thus, the apparent value of the tool corresponding to each acquisition time is denoted as Zdi, where i = 1, 2, 3...I, I represents the total number of acquisition times, and i represents the sequence number of any acquisition time.

5. The machining status monitoring method for a CNC system according to claim 1, characterized in that, The specific process for monitoring and analyzing the impact of machining information on the cutting tool is as follows: 501: Retrieve the cutting force, cutting speed, amplitude, and temperature of various parts of the machine tool at each acquisition time. 502: Set a sensitivity coefficient for each part of the machine tool, compare each part of the machine tool with all the set machine tool parts to match the corresponding sensitivity coefficient, multiply the temperature of each part of the machine tool by the corresponding sensitivity coefficient to obtain the temperature sensitivity value, and compare and analyze them to obtain the temperature sensitivity influence value. 503: Normalize the values ​​of cutting force, cutting speed, amplitude, and temperature sensitivity, and obtain the influencing factors through numerical analysis.

6. The machining status monitoring method for a CNC system according to claim 5, characterized in that, The specific process of comparing and analyzing the temperature sensitivity values ​​is as follows: The temperature-sensitive value is compared and analyzed with the set temperature-sensitive range to divide the machine tool parts corresponding to the temperature-sensitive value into high-impact parts, medium-impact parts and low-impact parts, and the cumulative number of high-impact parts, medium-impact parts and low-impact parts in the machine tool is counted respectively. The temperature-sensitive influence value is obtained by analyzing the cumulative number of high-impact parts, medium-impact parts, low-impact parts, and the temperature-sensitive values ​​of various parts of the machine tool.

7. A machining status monitoring device for a CNC system, characterized in that... A machining status monitoring method for a CNC system as described in any one of claims 1-6, the device comprising: a data acquisition module, a status monitoring module, and a machining control module; The data acquisition module communicates with the CNC system to collect and save machining information, including tool image, cutting force, cutting speed, amplitude, and temperature of various parts of the machine tool. The condition monitoring module performs real-time monitoring and analysis based on the tool graphic to obtain the tool appearance value. At the same time, it monitors and analyzes the degree of influence on the tool based on the machining information to obtain the influencing factor. The machining control module performs a comprehensive analysis based on tool appearance and influencing factors to determine whether tool replacement is necessary; specifically: The tool appearance value and influencing factor corresponding to each acquisition time are retrieved and calculated using a formula to obtain the tool state value corresponding to each acquisition time. The tool state value is plotted as time on the horizontal axis and the tool state value on the vertical axis to obtain the curve of tool state value changing with time. Tangent lines are drawn to the curve at the state points, and the tangent line expression is obtained by data fitting. The state derivative of each state point is obtained by differentiating the tangent line expression. When the absolute value of the state derivative is greater than the upper limit of the change range, a high tool wear is accumulated once; when the absolute value of the state derivative is within the change range, a high tool wear is accumulated once; when the absolute value of the state derivative is greater than the lower limit of the change range, a low tool wear is accumulated once; the cumulative number of high tool wear, medium tool wear, and low tool wear is counted. Obtain the tool state value corresponding to the current acquisition time, and normalize it with the cumulative number of high wear, the cumulative number of tool wear, the cumulative number of tool low wear, and the state derivative, and take its value. Analyze the value to obtain the wear index. When the wear index is greater than or equal to the set wear threshold, a tool change command is generated to control the tool retraction operation and replace the tool with a new one.

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