An image recognition-based online identification method for machining precision of a numerical control machine tool

Abstract

The application belongs to the technical field of numerical control machine tool processing supervision, and specifically relates to a numerical control machine tool processing precision online identification method based on image recognition, which comprises workpiece image acquisition, image processing analysis, processing quality judgment, feedback adjustment and numerical control machine tool operation regulation and control evaluation; the application realizes real-time acquisition of workpiece images through an image high-precision acquisition module, pre-processes the workpiece images and extracts key feature information through an image processing analysis module, judges whether corresponding control signals are generated based on the key feature information through a machine tool control module, adjusts corresponding cutting parameters of the numerical control machine tool based on the control signals through a feedback adjustment module, realizes intelligent control of the processing process, improves the processing precision of the numerical control machine tool in real time, analyzes the operation regulation and control performance of the numerical control machine tool through a machine tool operation evaluation module, and checks and maintains the numerical control machine tool in time, so that the subsequent processing precision, processing stability and processing efficiency of the numerical control machine tool are ensured, and the intelligent degree is high.

Description

Technical Field

[0001] This invention relates to the field of CNC machine tool machining monitoring technology, specifically a method for online identification of CNC machine tool machining accuracy based on image recognition. Background Technology

[0002] Numerical control machine tools are automated machine tools equipped with a program control system. These machine tools can logically process programs with control codes or other symbolic instructions, decode them, represent them with coded numbers, input them into the numerical control device through an information carrier, and then control the machine tool's actions to achieve automated processing of workpieces.

[0003] Currently, in the machining process of CNC machine tools, it is difficult to reflect the actual machining status of the workpiece based on image recognition and automatically adjust the cutting parameters according to changes in machining quality. It is impossible to achieve intelligent control of the machining process, and it is also impossible to reasonably analyze the operation and control performance of CNC machine tools and provide accurate early warning feedback, which is not conducive to improving the machining accuracy of CNC machine tools.

[0004] To address the aforementioned technical shortcomings, a solution is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide an online identification method for machining accuracy of CNC machine tools based on image recognition. This method solves the problems of existing technologies, which are unable to reflect the actual machining state of the workpiece based on image recognition and automatically adjust cutting parameters according to changes in machining quality. Furthermore, these technologies cannot reasonably analyze the operation and control performance of CNC machine tools and provide accurate early warning feedback, which is not conducive to improving the machining accuracy of CNC machine tools and results in low intelligence.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for online identification of machining accuracy of CNC machine tools based on image recognition includes the following steps:

[0008] Step 1: Acquire workpiece images in real time during CNC machine tool processing using a high-precision image acquisition module, and send the acquired workpiece images to the image processing and analysis module via a processor;

[0009] Step 2: The image processing and analysis module preprocesses the received workpiece image, extracts key feature information using image recognition algorithms, and sends the extracted key feature information to the machine tool control module via the processor.

[0010] Step 3: The machine tool control module determines whether the current machining quality of the CNC machine tool meets the standard based on the received key feature information. If the machining quality does not meet the standard, it generates a corresponding control signal and sends the generated control signal to the feedback adjustment module via the processor.

[0011] Step 4: The feedback adjustment module receives the corresponding control signals and adjusts the corresponding cutting parameters of the CNC machine tool based on the received control signals to improve the machining accuracy of the CNC machine tool in real time.

[0012] Step 5: The machine tool operation evaluation module analyzes the operation and control performance of the CNC machine tool. Through analysis, it generates an operation and control qualified signal or an operation and control early warning signal. The operation and control qualified signal or the operation and control early warning signal is sent to the CNC machine tool monitoring terminal via the processor. When the CNC machine tool monitoring terminal receives the operation and control early warning signal, it issues an early warning.

[0013] Furthermore, in step five, the specific analysis process of the machine tool operation evaluation module is as follows:

[0014] During the machining process of a CNC machine tool, a detection period of duration T1 is set. The machine tool adjustment performance value and the machine tool machining stability value during the detection period are obtained. The machine tool adjustment performance value and the machine tool machining stability value are compared with the preset machine tool adjustment performance threshold and the preset machine tool machining stability threshold respectively. If the machine tool adjustment performance value or the machine tool machining stability value exceeds the corresponding preset threshold, an operation control early warning signal is generated.

[0015] Furthermore, if neither the machine tool adjustment performance value nor the machine tool machining stability value exceeds the corresponding preset threshold, the machine tool adjustment performance value and the machine tool machining stability value are weighted and summed to obtain the machine tool operation and adjustment detection value.

[0016] The machine tool operation and adjustment detection value is compared with the preset machine tool operation and adjustment detection threshold. If the machine tool operation and adjustment detection value exceeds the preset machine tool operation and adjustment detection threshold, an operation control early warning signal is generated; if the machine tool operation and adjustment detection value does not exceed the preset machine tool operation and adjustment detection threshold, an operation control qualified signal is generated.

[0017] Furthermore, the machine tool operation evaluation module is connected to the machine tool efficiency analysis module and the machine tool stability analysis module. The machine tool efficiency analysis module analyzes the adjustment efficiency performance of the CNC machine tool during the detection period, obtains the machine tool adjustment performance value through analysis, and sends the machine tool adjustment performance value to the machine tool operation evaluation module.

[0018] The machine tool stability analysis module analyzes the cutting stability of the CNC machine tool machining process during the detection period, obtains the machine tool machining stability value through analysis, and sends the machine tool machining stability value to the machine tool operation evaluation module.

[0019] Furthermore, the specific analysis process of the machine tool performance tuning analysis module is as follows:

[0020] When the machine tool control module generates the corresponding control signal, the time when the corresponding control signal is generated is marked as the first time, and the time when the feedback adjustment module completes the corresponding adjustment operation is marked as the second time. The interval between the first time and the corresponding second time is marked as the verification time.

[0021] The machine tool adjustment status value is obtained by averaging all inspection durations within the detection period, comparing the inspection durations with a preset inspection duration threshold, marking the percentage of inspection durations exceeding the preset threshold as inspection outlier values, and marking the largest inspection duration exceeding the preset threshold as an inspection timeout value. The machine tool adjustment status value is obtained by numerically calculating the adjustment time value, inspection outlier values, and inspection timeout values.

[0022] Furthermore, the specific analysis process of the machine tool stability analysis module is as follows:

[0023] During the machining process of CNC machine tools, the cutting stability value is obtained in real time through analysis. The cutting stability value is compared with the preset cutting stability threshold. If the cutting stability value exceeds the preset cutting stability threshold, the corresponding cutting stability threshold is marked as the cutting stability value.

[0024] The number of cutting stability values ​​during the detection period is obtained and the ratio of the number of cutting stability values ​​is calculated to obtain the stability status value. The average value of all cutting stability values ​​exceeding the preset cutting stability threshold is marked as the stability performance value, and the value of the cutting stability value with the largest value exceeding the preset cutting stability threshold is marked as the stability amplitude value. The machine tool machining stability value is obtained by numerically calculating the stability status value, stability performance value, and stability amplitude value.

[0025] Furthermore, the method for obtaining the cutting stability value is as follows:

[0026] Real-time data of all cutting parameters are collected, and the deviation values ​​between the real-time data of the corresponding cutting parameters and the corresponding standard data are marked as cutting parameter table values. Each cutting parameter is pre-set to correspond to a set of preset deviation influence values. The product of the cutting parameter table value of the corresponding cutting parameter and the corresponding preset deviation influence value is marked as the cutting parameter measurement value. The cutting parameter measurement values ​​of all cutting parameters are obtained and summed to obtain the cutting stability value.

[0027] Furthermore, the processor communicates with the machine tool processing management and evaluation module. The machine tool operation evaluation module sends the qualified operation control signal to the machine tool processing management and evaluation module through the processor. When the machine tool processing management and evaluation module receives the qualified operation control signal, it analyzes the processing management status of the CNC machine tool during the detection period. Through analysis, it determines whether a CNC machine tool management abnormal signal is generated. When a CNC machine tool management abnormal signal is generated, it is sent to the CNC machine tool monitoring terminal through the processor. When the CNC machine tool monitoring terminal receives the CNC machine tool management abnormal signal, it issues an early warning.

[0028] Furthermore, the specific analysis process of the machine tool processing management module is as follows:

[0029] The workpieces processed by CNC machine tools during the inspection period are subjected to quality inspection. Workpieces that do not meet the quality requirements are marked as scrapped workpieces. The ratio of the number of scrapped workpieces to the total number of workpieces processed by CNC machine tools during the inspection period is marked as the scrapped number percentage.

[0030] The system obtains the average processing time per workpiece processed by the CNC machine tool during the detection period and marks it as the machine tool efficiency value. The machine tool efficiency value and the scrap rate are compared with the preset machine tool efficiency threshold and the preset scrap rate threshold respectively. If the machine tool efficiency value or the scrap rate exceeds the corresponding preset threshold, a CNC machine tool management abnormal signal is generated.

[0031] Furthermore, if the machine tool efficiency value and the scrapped number do not exceed the corresponding preset threshold, then several monitoring points are set in the area where the CNC machine tool is located, and the real-time temperature, real-time dust concentration and real-time noise decibel value of the corresponding monitoring points are collected and marked as temperature detection value, dust detection value and noise detection value respectively.

[0032] The temperature, gray, and noise values ​​are numerically calculated to obtain the point inspection value. The percentage of monitoring points whose point inspection values ​​exceed the preset point inspection threshold is marked as the abnormal point condition value, and the average of the point inspection values ​​of all monitoring points is marked as the machine tool imaging value.

[0033] The abnormal point value and machine tool imaging value are compared with the preset abnormal point threshold and preset machine tool imaging threshold respectively. If the abnormal point value or machine tool imaging value exceeds the corresponding preset threshold, the CNC machine tool is judged to be in an abnormal state.

[0034] The total duration of the CNC machine tool in an abnormal state during the detection period is obtained and marked as the abnormal state value. The abnormal state value is compared with the preset abnormal state threshold. If the abnormal state value exceeds the preset abnormal state threshold, a CNC machine tool management abnormal signal is generated.

[0035] Compared with the prior art, the beneficial effects of the present invention are:

[0036] 1. In this invention, the image processing and analysis module preprocesses the workpiece image and extracts key feature information. The machine tool control module determines whether to generate a corresponding control signal based on the key feature information. The feedback adjustment module adjusts the corresponding cutting parameters of the CNC machine tool based on the control signal, thereby realizing intelligent control of the machining process. Furthermore, the machine tool operation evaluation module analyzes the operation and control performance of the CNC machine tool and performs timely inspection and maintenance, significantly improving the machining accuracy of the CNC machine tool.

[0037] 2. In this invention, the machine tool operation evaluation module sends the qualified operation control signal to the machine tool processing management evaluation module. When the machine tool processing management evaluation module receives the qualified operation control signal, it analyzes the processing management status of the CNC machine tool during the detection period. When an abnormal signal of CNC machine tool management is generated, it takes reasonable measures to ensure the processing efficiency and workpiece quality of the CNC machine tool while reducing the adverse effects on the environment of the area where the CNC machine tool is located. This significantly reduces the management difficulty for managers and has a high degree of intelligence. Attached Figure Description

[0038] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0039] Figure 1 This is a flowchart of the method in Embodiment 1 of the present invention;

[0040] Figure 2 This is a system block diagram of Embodiment 1 of the present invention;

[0041] Figure 3 This is a system block diagram of Embodiment 2 of the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0043] Example 1: As Figure 1-2 As shown, the present invention proposes an online identification method for CNC machine tool machining accuracy based on image recognition, comprising the following steps:

[0044] Step 1: Acquire workpiece images in real time during CNC machine tool processing using a high-precision image acquisition module, and send the acquired workpiece images to the image processing and analysis module via a processor;

[0045] Step 2: The image processing and analysis module preprocesses the received workpiece image (including grayscale conversion, edge detection, and other preprocessing operations), extracts key feature information (such as chip morphology, workpiece surface roughness, and other key features) using image recognition algorithms (such as deep learning algorithms), and sends the extracted key feature information to the machine tool control module via the processor.

[0046] Through online image acquisition and processing technology, real-time monitoring and feedback of the CNC machine tool machining process are realized, which greatly improves machining efficiency;

[0047] Step 3: The machine tool control module determines whether the current machining quality of the CNC machine tool meets the standard based on the received key feature information. If the machining quality does not meet the standard (such as abnormal chip shape, excessive workpiece surface roughness, etc.), the corresponding control signal is generated and sent to the feedback adjustment module through the processor.

[0048] Image recognition-based machining quality assessment methods can accurately reflect the actual machining status of workpieces and ensure the improvement of CNC machine tool machining accuracy;

[0049] Step 4: The feedback adjustment module receives the corresponding control signals and adjusts the corresponding cutting parameters of the CNC machine tool based on the received control signals (such as reducing the cutting speed and feed rate). It can automatically adjust the cutting parameters according to the changes in machining quality, realize intelligent control of the machining process, improve the machining accuracy of the CNC machine tool in real time, reduce material waste and rework costs caused by machining quality problems, and improve the economic benefits of enterprises.

[0050] Step 5: The machine tool operation evaluation module analyzes the operation and control performance of the CNC machine tool. Through analysis, it generates either a qualified operation control signal or a warning signal. This signal is then sent to the CNC machine tool monitoring terminal via a processor. Upon receiving the warning signal, the monitoring terminal issues an alert to remind management personnel to promptly investigate and analyze the cause, and to inspect and repair the CNC machine tool. This ensures the subsequent machining accuracy and stability of the CNC machine tool and improves its machining efficiency, demonstrating a high degree of intelligence. The specific analysis process of the machine tool operation evaluation module is as follows:

[0051] During the machining process of the CNC machine tool, a detection period of duration T1 is set, preferably four hours. The machine tool adjustment performance value and the machine tool machining stability value during the detection period are obtained. The machine tool adjustment performance value YX and the machine tool machining stability value QX are compared with the preset machine tool adjustment performance threshold and the preset machine tool machining stability threshold respectively. If the machine tool adjustment performance value YX or the machine tool machining stability value QX exceeds the corresponding preset threshold, it indicates that the operation effect of the CNC machine tool is poor during the detection period, and an operation control early warning signal is generated.

[0052] If neither the machine tool adjustment performance value YX nor the machine tool machining stability value QX exceeds the corresponding preset threshold, the machine tool operation and adjustment detection value WK is obtained by weighted summation of the machine tool adjustment performance value YX and the machine tool machining stability value QX using the formula WK=a1*YX+a2*QX; where a1 and a2 are preset weight coefficients with values ​​greater than zero, and the larger the value of the machine tool operation and adjustment detection value WK, the worse the overall operating effect of the CNC machine tool during the detection period;

[0053] The machine tool operation and adjustment test values ​​are compared with the preset machine tool operation and adjustment test thresholds. If the machine tool operation and adjustment test values ​​exceed the preset machine tool operation and adjustment test thresholds, it indicates that the overall operation performance of the CNC machine tool during the test period is poor, and the CNC machine tool needs to be inspected and maintained in a timely manner, thus generating an operation control early warning signal. If the machine tool operation and adjustment test values ​​do not exceed the preset machine tool operation and adjustment test thresholds, it indicates that the overall operation performance of the CNC machine tool during the test period is good, thus generating an operation control qualified signal.

[0054] It should be noted that the machine tool operation evaluation module communicates with the machine tool performance tuning analysis module and the machine tool stability analysis module. The machine tool performance tuning analysis module analyzes the adjustment efficiency performance of the CNC machine tool during the detection period, obtains the machine tool performance value through analysis, and sends the machine tool performance value to the machine tool operation evaluation module, providing data support for the analysis process of the machine tool operation evaluation module and ensuring the accuracy of its analysis results. The specific analysis process of the machine tool performance tuning analysis module is as follows:

[0055] When the machine tool control module generates the corresponding control signal, the time when the corresponding control signal is generated is marked as the first time, and the time when the feedback adjustment module completes the corresponding adjustment operation is marked as the second time. The interval between the first time and the corresponding second time is marked as the verification time. The larger the value of the verification time, the slower the adjustment efficiency for the corresponding control signal, which is less conducive to ensuring the machining accuracy of the CNC machine tool.

[0056] The system obtains all the effective inspection durations within the detection period and calculates their average to obtain the adjustment time table value. It also compares the effective inspection duration with the preset effective inspection duration threshold and marks the percentage of effective inspection durations exceeding the preset effective inspection duration threshold within the detection period as the effective inspection out-of-table value. The system also marks the maximum effective inspection duration exceeding the preset effective inspection duration threshold as the effective inspection timeout value.

[0057] The machine tool adjustment performance value YX is obtained by numerically calculating the adjustment time table value YS, the inspection error table value YL, and the inspection timeout value YP using the formula YX=wq2*YL+(wq1*YS+wq3*YP) / 2. Here, wq1, wq2, and wq3 are preset proportional coefficients, with wq2>wq1>wq3>0. Furthermore, the larger the machine tool adjustment performance value YX, the faster the machining adjustment efficiency of the CNC machine tool during the inspection period, which is more conducive to ensuring the machining accuracy and workpiece quality of the CNC machine tool.

[0058] Furthermore, the machine tool stability analysis module analyzes the cutting stability of the CNC machine tool machining process during the detection period. This analysis yields the machine tool machining stability value, which is then sent to the machine tool operation evaluation module. This provides data support for the machine tool operation evaluation module's analysis process, further ensuring the accuracy of its analysis results. The specific analysis process of the machine tool stability analysis module is as follows:

[0059] During the machining process of CNC machine tools, real-time data of all cutting parameters (such as cutting speed, feed rate, etc.) are collected. The deviation values ​​of the real-time data of the corresponding cutting parameters from the set standard data are marked as cutting parameter table values. Each cutting parameter is pre-set to correspond to a set of preset deviation influence values. The preset deviation influence values ​​are all positive numbers, and the greater the adverse effect of the deviation of the corresponding cutting parameter on the CNC machine tool, the greater the value of the preset deviation influence value.

[0060] The product of the cutting parameter table value of the corresponding cutting parameter and the corresponding preset deviation influence value is marked as the cutting parameter measurement value. The cutting parameter measurement values ​​of all cutting parameters are obtained and summed to obtain the cutting stability value. The cutting stability value is compared with the preset cutting stability threshold. If the cutting stability value exceeds the preset cutting stability threshold, the corresponding cutting stability threshold is marked as the cutting stability deviation value.

[0061] The number of cutting stability and anomaly values ​​during the detection period is obtained and the ratio of the number of cutting stability and analysis values ​​is calculated to obtain the stability and anomaly status value. The average value of all cutting stability and anomaly values ​​exceeding the preset cutting stability and analysis threshold is marked as the stability and anomaly performance value. The value of the cutting stability and anomaly value with the largest value exceeding the preset cutting stability and analysis threshold is marked as the stability and anomaly amplitude value.

[0062] The machine tool machining stability value QX is obtained by numerically calculating the stability value QN, stability performance value QK, and stability amplitude value QP using the formula QX=rg1*QN+(rg2*QK+rg3*QP) / 2. Here, rg1, rg2, and rg3 are preset proportional coefficients, where rg1>rg2>rg3>0. Furthermore, the larger the value of the machine tool machining stability value QX, the worse the control of the cutting parameters of the CNC machine tool is during the detection period, which is less conducive to ensuring the machining accuracy and workpiece quality of the CNC machine tool.

[0063] Example 2: Figure 3 As shown, the difference between this embodiment and Embodiment 1 is that the processor is connected to the machine tool processing management and evaluation module. The machine tool operation evaluation module sends the operation control qualified signal to the machine tool processing management and evaluation module through the processor. When the machine tool processing management and evaluation module receives the operation control qualified signal, it analyzes the processing management status of the CNC machine tool during the detection period and determines whether an abnormal signal for CNC machine tool management is generated through analysis.

[0064] When an abnormal signal is generated in CNC machine tool management, it is sent to the CNC machine tool monitoring terminal via a processor. Upon receiving the abnormal signal, the monitoring terminal issues an early warning, allowing management personnel to promptly investigate the cause and take appropriate measures. This ensures the machining efficiency and workpiece quality of the CNC machine tool, while minimizing adverse impacts on the environment surrounding the machine tool. The module boasts a high degree of intelligence and reduces management complexity. The specific analysis process of the machine tool processing management module is as follows:

[0065] The workpieces processed by CNC machine tools during the inspection period are subjected to quality inspection. Workpieces that do not meet the quality requirements are marked as scrapped workpieces. The ratio of the number of scrapped workpieces to the total number of workpieces processed by CNC machine tools during the inspection period is marked as the scrapped number percentage.

[0066] The system obtains the average processing time per workpiece processed by the CNC machine tool during the detection period and marks it as the machine tool efficiency value. The machine tool efficiency value and the scrap rate are compared with the preset machine tool efficiency threshold and the preset scrap rate threshold, respectively. If the machine tool efficiency value or the scrap rate exceeds the corresponding preset threshold, it indicates that there are problems with the processing quality and processing efficiency of the CNC machine tool and the management performance of the CNC machine tool is poor, and an abnormal signal for CNC machine tool management is generated.

[0067] If the machine tool efficiency value and the scrapped number do not exceed the corresponding preset threshold, then set up several monitoring points in the area where the CNC machine tool is located, collect the real-time temperature, real-time dust concentration and real-time noise decibel value of the corresponding monitoring points and mark them as temperature detection value, dust detection value and noise detection value respectively.

[0068] Through formula The temperature detection value SR, the gray detection value SK, and the noise detection value SL are numerically calculated to obtain the point inspection value SY; where hy1, hy2, and hy3 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the point inspection value SY, the greater the adverse impact of the CNC machine tool processing on the environment of the corresponding monitoring point at the corresponding moment.

[0069] The inspection value SY of the corresponding monitoring point is compared with the preset inspection threshold. The proportion of monitoring points whose inspection value SY exceeds the preset inspection threshold is marked as an abnormal point value. The average value of the inspection values ​​of all monitoring points is marked as the machine tool imaging value.

[0070] The abnormal point value and machine tool imaging value are compared with the preset abnormal point threshold and preset machine tool imaging threshold respectively. If the abnormal point value or machine tool imaging value exceeds the corresponding preset threshold, it indicates that the adverse effects of the CNC machine tool processing on the environment of the area where it is located are relatively large in general. Then the CNC machine tool is judged to be in an abnormal state.

[0071] The total duration of the CNC machine tool in an abnormal state during the detection period is obtained and marked as the abnormal state value. The abnormal state value is compared with the preset abnormal state threshold. If the abnormal state value exceeds the preset abnormal state threshold, it indicates that the management performance of the CNC machine tool during the detection period is poor, and a CNC machine tool management abnormal signal is generated.

[0072] The working principle of this invention is as follows: During use, a high-precision image acquisition module acquires workpiece images in real time during CNC machine tool processing. The image processing and analysis module preprocesses the workpiece images and extracts key feature information, enabling real-time monitoring and feedback of the CNC machine tool processing process. The machine tool control module determines whether the current processing quality of the CNC machine tool meets the standards based on the key feature information. If the processing quality does not meet the standards, a corresponding control signal is generated. The feedback adjustment module adjusts the corresponding cutting parameters of the CNC machine tool based on the control signal, automatically adjusting the cutting parameters according to changes in processing quality, realizing intelligent control of the processing process, and improving the processing accuracy of the CNC machine tool in real time. Furthermore, the machine tool operation evaluation module analyzes the operation and control performance of the CNC machine tool to generate a qualified operation and control signal or an early warning signal. When an early warning signal is generated, it reminds the management personnel to investigate and analyze the cause and inspect and repair the CNC machine tool, ensuring the subsequent processing accuracy and stability of the CNC machine tool and improving its processing efficiency. The invention has a high degree of intelligence.

[0073] The above formulas are all dimensionless numerical calculations. These formulas are derived from software simulations using collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to actual conditions. The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. The preferred embodiments do not describe all details exhaustively, nor do they limit the invention to specific implementations. Obviously, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for online identification of machining accuracy of CNC machine tools based on image recognition, characterized in that, Includes the following steps: Step 1: Acquire workpiece images in real time during CNC machine tool processing using a high-precision image acquisition module, and send the acquired workpiece images to the image processing and analysis module via a processor; Step 2: The image processing and analysis module preprocesses the received workpiece image, extracts key feature information using image recognition algorithms, and sends the extracted key feature information to the machine tool control module via the processor. Step 3: The machine tool control module determines whether the current machining quality of the CNC machine tool meets the standard based on the received key feature information. If the machining quality does not meet the standard, it generates a corresponding control signal and sends the generated control signal to the feedback adjustment module via the processor. Step 4: The feedback adjustment module receives the corresponding control signals and adjusts the corresponding cutting parameters of the CNC machine tool based on the received control signals to improve the machining accuracy of the CNC machine tool in real time. Step 5: The machine tool operation evaluation module analyzes the operation and control performance of the CNC machine tool. Through analysis, it generates an operation and control qualified signal or an operation and control early warning signal. The operation and control qualified signal or the operation and control early warning signal is sent to the CNC machine tool monitoring terminal via the processor. When the CNC machine tool monitoring terminal receives the operation and control early warning signal, it issues an early warning. In step five, the specific analysis process of the machine tool operation evaluation module is as follows: During the machining process of a CNC machine tool, a detection period of duration T1 is set. The machine tool adjustment performance value and the machine tool machining stability value during the detection period are obtained. The machine tool adjustment performance value and the machine tool machining stability value are compared with the preset machine tool adjustment performance threshold and the preset machine tool machining stability threshold respectively. If the machine tool adjustment performance value or the machine tool machining stability value exceeds the corresponding preset threshold, an operation control early warning signal is generated. If neither the machine tool adjustment performance value nor the machine tool machining stability value exceeds the corresponding preset threshold, the machine tool adjustment performance value and the machine tool machining stability value are weighted and summed to obtain the machine tool operation and adjustment detection value. The machine tool operation and adjustment detection value is compared with the preset machine tool operation and adjustment detection threshold. If the machine tool operation and adjustment detection value exceeds the preset machine tool operation and adjustment detection threshold, an operation control early warning signal is generated; if the machine tool operation and adjustment detection value does not exceed the preset machine tool operation and adjustment detection threshold, an operation control qualified signal is generated. The machine tool operation evaluation module is connected to the machine tool efficiency analysis module and the machine tool stability analysis module. The machine tool efficiency analysis module analyzes the adjustment efficiency performance of the CNC machine tool during the detection period, obtains the machine tool adjustment performance value through analysis, and sends the machine tool adjustment performance value to the machine tool operation evaluation module. The machine tool stability analysis module analyzes the cutting stability of the CNC machine tool machining process during the detection period, obtains the machine tool machining stability value through analysis, and sends the machine tool machining stability value to the machine tool operation evaluation module. The specific analysis process of the machine tool performance analysis module is as follows: When the machine tool control module generates the corresponding control signal, the time when the corresponding control signal is generated is marked as the first time, and the time when the feedback adjustment module completes the corresponding adjustment operation is marked as the second time. The interval between the first time and the corresponding second time is marked as the verification time. The machine tool adjustment status value is obtained by averaging all inspection durations within the detection period, comparing the inspection durations with a preset inspection duration threshold, marking the percentage of inspection durations exceeding the preset threshold as inspection outlier values, and marking the largest inspection duration exceeding the preset threshold as an inspection timeout value. The machine tool adjustment status value is obtained by numerically calculating the adjustment time value, inspection outlier values, and inspection timeout values.

2. The online identification method for machining accuracy of CNC machine tools based on image recognition according to claim 1, characterized in that, The specific analysis process of the machine tool stability analysis module is as follows: During the machining process of CNC machine tools, the cutting stability value is obtained in real time through analysis. The cutting stability value is compared with the preset cutting stability threshold. If the cutting stability value exceeds the preset cutting stability threshold, the corresponding cutting stability threshold is marked as the cutting stability value. The number of cutting stability values ​​during the detection period is obtained and the ratio of the number of cutting stability values ​​is calculated to obtain the stability status value. The average value of all cutting stability values ​​exceeding the preset cutting stability threshold is marked as the stability performance value, and the value of the cutting stability value with the largest value exceeding the preset cutting stability threshold is marked as the stability amplitude value. The machine tool machining stability value is obtained by numerically calculating the stability status value, stability performance value, and stability amplitude value.

3. The online identification method for machining accuracy of CNC machine tools based on image recognition according to claim 2, characterized in that, The method for obtaining the cutting stability value is as follows: Real-time data of all cutting parameters are collected, and the deviation values ​​between the real-time data of the corresponding cutting parameters and the corresponding standard data are marked as cutting parameter table values. Each cutting parameter is pre-set to correspond to a set of preset deviation influence values. The product of the cutting parameter table value of the corresponding cutting parameter and the corresponding preset deviation influence value is marked as the cutting parameter measurement value. The cutting parameter measurement values ​​of all cutting parameters are obtained and summed to obtain the cutting stability value.

4. The online identification method for machining accuracy of CNC machine tools based on image recognition according to claim 1, characterized in that, The processor communicates with the machine tool processing management and evaluation module. The machine tool operation evaluation module sends the qualified operation control signal to the machine tool processing management and evaluation module through the processor. When the machine tool processing management and evaluation module receives the qualified operation control signal, it analyzes the processing management status of the CNC machine tool during the detection period. Through analysis, it determines whether an abnormal CNC machine tool management signal has been generated. When an abnormal CNC machine tool management signal is generated, it is sent to the CNC machine tool monitoring terminal through the processor. When the CNC machine tool monitoring terminal receives the abnormal CNC machine tool management signal, it issues an early warning.

5. The online identification method for machining accuracy of CNC machine tools based on image recognition according to claim 4, characterized in that, The specific analysis process of the machine tool processing management module is as follows: The workpieces processed by CNC machine tools during the inspection period are subjected to quality inspection. Workpieces that do not meet the quality requirements are marked as scrapped workpieces. The ratio of the number of scrapped workpieces to the total number of workpieces processed by CNC machine tools during the inspection period is marked as the scrapped number percentage. The system obtains the average processing time per workpiece processed by the CNC machine tool during the detection period and marks it as the machine tool efficiency value. The machine tool efficiency value and the scrap rate are compared with the preset machine tool efficiency threshold and the preset scrap rate threshold respectively. If the machine tool efficiency value or the scrap rate exceeds the corresponding preset threshold, a CNC machine tool management abnormal signal is generated.

6. The online identification method for machining accuracy of CNC machine tools based on image recognition according to claim 5, characterized in that, If the machine tool efficiency value and the scrapped number do not exceed the corresponding preset threshold, then set up several monitoring points in the area where the CNC machine tool is located, collect the real-time temperature, real-time dust concentration and real-time noise decibel value of the corresponding monitoring points and mark them as temperature detection value, dust detection value and noise detection value respectively. The temperature, gray, and noise values ​​are numerically calculated to obtain the point inspection value. The percentage of monitoring points whose point inspection values ​​exceed the preset point inspection threshold is marked as the abnormal point condition value, and the average of the point inspection values ​​of all monitoring points is marked as the machine tool imaging value. If the abnormal point value or machine tool imaging value exceeds the corresponding preset threshold, the CNC machine tool is determined to be in an abnormal state. The total duration of the CNC machine tool being in an abnormal state during the detection period is obtained and marked as the abnormal time value. If the abnormal time value exceeds the preset abnormal time threshold, a CNC machine tool management abnormal signal is generated.

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